Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiences in Using a Smartphone as a Virtual Reality Interaction Device

Input devices such as Nintendo Wiimotes are often used to select and manipulate virtual objects. While simple to use and easily available, these devices have some limitations. When used with common large displays such as televisions, they support only indirect manipulation. These devices also require the user to learn and remember which buttons map to which functions. We propose overcoming these limitations by using a smartphone as an interaction device. Smartphones, like Wiimotes, are readily available and easy to operate. Unlike the Wiimote, the smartphone has a touchscreen that can display the selected object, allowing the user to directly manipulate the object. Further, the touchscreen can customize the interface and provide buttons with clearly labeled functions specific to the object. We report on the lessons learned in integrating and using a smartphone as the interaction device for two applications. The first is a mixed reality game focused on general object selection and pose manipulation. We used this game in a pilot study evaluating usability. The second is an adaptation of an existing virtual reality application. This application demonstrated the ease of adaptation as well as improvements from using a smartphone.
ALIGNING GOAL AND PROCESS MODELS

Business Process Modeling (BPM) notations such as BPMN are used to effectively conceptualize and communicate important process characteristics to relevant stakeholders. BPMN gives an overview of the entire process of the particular organization but does not contain the notion of business goals and non-functional requirements. On the other hand Agent-Oriented conceptual modeling(AOCM) notations like i* are highly effective in representing requirements from an intentional stance and answering questions such as what goals exist, how key actors depend on each other and what alternatives must be considered but does not have the process flow. In this paper we illustrate, through a case study a methodology that would help all stakeholders to identify, at multiple levels of abstraction, the goals and their corresponding activities in a business environment
A VLSI array architecture for the on-line training of recurrent neural networks

The authors show how to derive systematically a VLSI systolic array for the real time recurrent learning (RTRL) algorithm of D. Zipser and R.J. Williams (1989). The main goal is to match the RTRL algorithm to the ring architecture proposed by J.N. Hwang et al. (1990) that was shown to be generally applicable to many neural network models. Although this task was a straightforward extension for the retrieving phase, skillful reformulation of the learning phase was necessary. The control complexity and memory requirements of the processing elements remain reasonably low. <<ETX>>
On velocity estimation using position measurements

Velocity measurement is often required in control system design of mechanical machines including robot manipulators. However, precise velocity measurement/estimation remains a challenging task, especially for very slow motions. Several methods have been published to estimate velocity from position measurements, typically the output of an incremental optical encoder. In the present work, a simple and efficient method is proposed to estimate velocity from an incremental encoder pulse train. Based on the physical characteristics of incremental encoders, the proposed method allows the user to select the estimation precision and tune the time delay, and yet requires less calculation than other methods. The proposed method is evaluated experimentally using a direct drive robot arm. An external laser dynamic calibrator is used to obtain accurate reference velocity measurements for comparison. Experimental results confirm the advantages of the proposed method.
Two Methods for Linearity Improvement in Digitally Controlled Delay Elements: Current Starved Type

Current starved delay elements (CSDEs) are among the popular architectures to manipulate rising or falling edges of signals in order to meet timing requirements. The digitally controllable generations of these topologies are now monotonic and reasonably power efficient, but they lack linearity in full range. Inherently, this subject may not seem problematic because by setting the dimensions of the design elements the desired delay can be acquired. However, in case that a chain of incremental delays are required we tend to employ more linear designs. In this paper two improvements in linearity are examined for two known CS designs. Both of the topologies are in 0.18um technology and meet appropriate design parameters like power, area and monotonic response.
TRADITIONAL AND ENHANCED FIELD LISTING FOR PROBABILITY SAMPLING

NORC, in common with most major field survey organizations, carries out its own listing for area samples. Trained listers are sent out to each segment to do the listing, following a strict protocol. The listers walk around each selected block, starting at the northwest corner and continuing around the block clockwise, writing down the address or description of every HU they find. These lists are of good quality, but are expensive to produce. Costs include training, travel, room and board, and listing time. In this paper we will refer to this listing process as traditional listing.
The Nature of Tags in a Knowledge Organization System of Primary Visual Resources

ABSTRACT The study examined user-generated tags to digitized primary visual archival resources to identify the nature of tags as image description. A collection of 3,191 tags from a digital portal (NINES) was used for the study. The tags were analyzed in terms of the attributes of primary archival visual resources users described. The tags were also compared to metadata to detect their relationship with metadata. The findings help understand users' tagging behavior and resource interpretation in primary and historical visual resources in humanities domains. The results of this study demonstrate the value of tags in identifying and determining user points of view and tags' potential to represent and connect to metadata description and to support discovery of relevant resources.
A Secure Error-Resilient Lossless Source Coding Scheme Based on Punctured Turbo Codes

In this paper, we develop a new error-resistant secure lossless source coding scheme for discrete sources. Recent results indicate that the same turbo principle that provides sub-optimal strategy for channel coding, can be used to obtain efficient source coding schemes. We extend the source turbo-coding idea to include security for transmission of the compressed data and also to ensure the lossless recovery of information at the receiver. Compression and security are achieved by adapting the random puncturing strategy to the statistics of the source. Lossless compression is guaranteed by finely puncturing the encoded data of a parallel-concatenated turbo code while verifying the integrity of the source information at the source encoder. Simulation results show that the proposed scheme can obtain a compression rate close to the source entropy. For the same block length, this scheme yields better compression rates in comparison with the Lempel-Ziv universal source coding. The proposed scheme has a robust and error-resistant performance over noisy channels for E b / N 0 >5 dB.
Optimizing Timeliness and Cost in Geo-Distributed Streaming Analytics

Rapid data streams are generated continuously from diverse sources including users, devices, and sensors located around the globe. This results in the need for efficient geo-distributed streaming analytics to extract timely information. A typical geo-distributed analytics service uses a hub-and-spoke model, comprising multiple edges connected by a wide-area-network (WAN) to a central data warehouse. In this paper, we focus on the widely used primitive of windowed grouped aggregation, and examine the question of how much computation should be performed at the edges versus the center. We develop algorithms to optimize two key metrics: WAN traffic and staleness(delay in getting results). We present a family of optimal offline algorithms that jointly minimize these metrics, and we use these to guide our design of practical online algorithms based on the insight that windowed grouped aggregation can be modeled as a caching problem where the cache size varies over time. We evaluate our algorithms through an implementation in Apache Storm deployed on PlanetLab. Using workloads derived from anonymized traces of a popular analytics service from a large commercial CDN, our experiments show that our online algorithms achieve near-optimal traffic and staleness for a variety of system configurations, stream arrival rates, and queries.
SemEval-2015 Task 12: Aspect Based Sentiment Analysis

SemEval-2015 Task 12, a continuation of SemEval-2014 Task 4, aimed to foster research beyond sentenceor text-level sentiment classification towards Aspect Based Sentiment Analysis. The goal is to identify opinions expressed about specific entities (e.g., laptops) and their aspects (e.g., price). The task provided manually annotated reviews in three domains (restaurants, laptops and hotels), and a common evaluation procedure. It attracted 93 submissions from 16 teams.
The Development of a Low-cost MEMS IMU/GPS Navigation System for Land Vehicles Using Auxiliary Velocity Updates in the Body Frame

Cost and space constraints are currently driving manufacturers of vehicles to investigate and develop next generation of low cost and small size navigation and guidance systems to meet the fast growing location services market demands. Advances in Micro-Electro- Mechanical Systems (MEMS) technology have shown promising light towards the development of such systems. MEMS are integrated micro devices or systems combining electrical and mechanical components whose size ranges from micrometers to millimeters. MEMS is an enabling technology and the MEMS industry has a projected 10-20% annual growth rate to reach 200 billion US$ market by 2005. Advances in MEMS technology combined with the miniaturization of electronics, have made it possible to produce chip-based inertial sensor for use in measuring angular velocity and acceleration. These chips are small, lightweight, consumes very little power, and extremely reliable. It has therefore found a wide spectrum of applications in the automotive and other industrial applications. MEMS technology, therefore, can be used to develop car navigation systems that are inexpensive, small, and consume low power (microwatt). However, due to the lightweight and fabrication process, MEMS sensors have large bias instability and noise, which consequently affect the obtained accuracy from MEMS-based IMUs. Introducing auxiliary velocity update in the body frame, (e.g. non-holonomic constraint and odometer signal) is an option to solve the problem. This paper describes the development of a MEMS IMU/GPS navigation system by the Mobile Multi-Sensor Systems (MMSS) Research Group at the University of Calgary. The development objective was to develop a fully integrated system with price range of US $100 – 200 using low-end (surface micromachined) MEMS inertial sensors. The system’s accuracy performance will be investigated by using land vehicle test and through the contributions of the auxiliary velocity updates in the body frame.
Voice activity robust detection of noisy speech in Toeplitz

A Toeplitz de-noising method using the maximum eigenvalue is proposed for the voice activity detection at low SNR scenarios. This method uses the self-correlation sequence of speech bandwidth spectrum to construct a new symmetric Toeplitz matrix and to compute the largest eigenvalue, and the double decision thresholds in the largest eigenvalue are applied in the decision framewok. Simulation results show that the presented algorithm is more effective in distinguishing speech from noise and has better robustness under various noisy environments. Compared with novel method of recurrence rate analysis, this algorithm shows lower wrong decision rate. The algorithm is of low computational complexity and is simple in real-time realization． DOI: http://dx.doi. org/10.11591/telkomnika.v13i1. 6902
Image segmentation using superpixel ensembles

Recently there has been an increasing interest in image segmentation due to the needs of locating objects with high segmentation accuracy as required by many computer vision and image processing tasks. While image segmentation remains a research challenge, ‘superpixel’ as the perceptual meaningful grouping of pixels has become a popular concept and a number of superpixel-based image segmentation algorithms have been proposed. The goal of this thesis is to examine the state-of-the-art superpixel algorithms and introduce new methods for achieving better image segmentation outcome. To improve the accuracy of superpixel-based segmentation, we propose a colour covariance matrix-based segmentation algorithm (CCM). This algorithm employs a novel colour covariance descriptor and a corresponding similarity measure method. Moreover, based on the CCM algorithm, we propose a multi-layer bipartite graph model (MBG-CCM) and a low-rank representation technique based algorithm (LRR-CCM). In MBG-CCM, different superpixel descriptors are fused by a multi-layer bipartite graph, and in LRR-CCM, the similarities of the covariance descriptors of the superpixel are measured by the subspace structure. Besides, we develop a new oversegmentation, called superpixel association, and propose a novel segmentation algorithm (SHST) which is able to generate hierarchical segmentation from superpixel associations. In addition to those unsupervised segmentation algorithms, we also explore the algorithms for supervised segmentation. We propose a model for semantic segmentation, named ‘generalized puzzle game’, by which the segmentation information contained in the superpixels can be integrated into the supervised segmentation.
Preliminary Analysis of Factors Motivating Software Crackers

Software piracy is a problem in the software industry. Software authors lose revenue when pirated software rather than legally purchased software is used. Policy developers invest time and money in restricting software piracy. However, much of the published research literature focuses on end-user piracy. This research explores why crackers invest their skill in this activity instead of more lucrative or legal employment. This study goes directly to the initial software pirates to determine what motivates them. The study applies an exploratory framework developed from the psychology literature in an anonymous online survey. The study finds the challenge of removing the copy protection from software as the strongest motivation. Desire for social participation and higher social status, while rewarding, were considered unnecessary for crackers to continue their actions.
A Framework for an Efficient Knowledge Mining Technique of Web Page Reorganisation using Splay Tree

Background/Objectives: Web Usage Mining (WUM) is one of the categories of web mining that identifies user patterns of web data, with the help of knowledge acquires from web logs. Methods/Statistical Analysis: The structure of the web site has to be reorganised to suit the user requirements to facilitate the user for the required pages with less page access delay. The Splay trees are efficient balanced trees when total running time is the measure of interest. Findings: The motive of mining is to find users’ access models automatically and quickly from the vast Web log data, like frequently accessed pages and time spent on those pages. Web usage mining consist of three phases namely Data pre-processing, Pattern discovery and Pattern analysis. Pre-processing tasks are used to translate unprocessed log files which are composed from web server into structured log file data. Pre-processed log file data are used for further process of web usage mining. This paper present the pre-processing technique and an approach for re-organisation of website based on the access frequency of web pages using splay tree structure. Application/Improvements: The nodes of the splay tree can be added with the information about priority of recently accessed web pages to reduce the page access delay.
Recognition of Infrastructure Events Using Principal Component Analysis

Recognition of Infrastructure Events Using Principal Component Analysis Lane David Broadbent School of Technology, BYU Master of Science Information Technology systems generate system log messages to allow for the monitoring of the system. In increasingly large and complex systems the volume of log data can overwhelm the analysts tasked with monitoring these systems. A system was developed that utilizes Principal Component Analysis to assist the analyst in the characterization of system health and events. Once trained, the system was able to accurately identify a state of heavy load on a device with a low false positive rate. The system was also able to accurately identify an error condition when trained on a single event. The method employed is able to assist in the real time monitoring of large complex systems, increasing the efficiency of trained analysts.
Systematic testing of multithreaded Java programs

Concurrent programs are becoming common, while testing techniques that can adequately test such programs are not widely available. Due to the nondeterministic nature of concurrent programs, program errors resulting from unintended timing dependencies can be extremely difficult to track down. We have designed, proved correct, and implemented a testing algorithm called ExitBlock that systematically and deterministically finds such errors. ExitBlock executes a program or a portion of a program on a given input multiple times, enumerating meaningful schedules in order to cover all program behaviors. Other systematic testers exist for programs whose concurrent elements are processes that run in separate memory spaces and explicitly declare what memory they will be sharing. ExitBlock extends previous approaches to multithreaded programs in which all of memory is potentially shared. A key challenge is to minimize the number of schedules executed while still guaranteeing to cover all behaviors. Our approach relies on the fact that for a program following a mutual-exclusion locking discipline, enumerating possible orders of the synchronized regions of the program covers all possible behaviors of the program. This thesis presents ExitBlock and its correctness proof, describes its implementation in the Rivet virtual machine, and demonstrates its ability to detect errors in actual programs. Thesis Supervisor: John Chapin Title: Assistant Professor of Electrical Engineering and Computer Science
Narrative, Machinima and Cognitive Realism: Constructing an Authentic Real World Learning Experience for Law Students

In Australian law schools didactic pedagogies such as lectures devoted to the transmission of theory and knowledge to a largely passive audience still predominate. However, curriculum design embedding authentic learning pedagogies has been shown to be supportive of student learning. The challenge in adopting such curriculum design is how to offer situated cognitive learning opportunities on a large scale to increasingly diverse cohorts. This paper is a case study of a blended learning approach introduced into a second year undergraduate law unit to teach negotiation theory and practice to a large cohort of students studying in full-time, part-time, and distance external modes. This innovation was situated in an authentic simulated learning environment created through careful design of a high-fidelity scenario, utilising a narrative depicted by Second Life machinima video and simulated documentation. This approach resulted in greater student engagement and improved learning outcomes.
Pageranked Conceptual Search for Android domain

As the web is rapidly growing, there is a need to cater the user’s interests and needs from their behavior to extract the relevant information from the contents of the web. Web mining is used to categorize users and pages by analyzing the user’s behavior, the contents of the pages and order of the URLs accessed. It has become an increasingly important task. There is also a sharp increase in the requirements for location-based services using location information, due to the development of wireless internet and mobile systems. Web structure mining approach has been user in our study. Concept mining and user profile updation approaches have been used. The concept of weighted page ranked algorithm is used to display the final results. Keywords—Web search, Personalised Search, page ranking, GPS, URL
Virtual Machine Support of Runtime Structural Reection

Increasing trends towards adaptive, distributed, generative and pervasive software have made object-oriented dynamically typed languages become increasingly popular. These languages oer dynamic software evolution by means of reection, facilitating the development of dynamic systems. Unfortunately, this dynamism commonly imposes a runtime performance penalty. In this paper, we describe how to extend a production JIT-compiler virtual machine to support runtime object-oriented structural reection oered by many dynamic languages. Our approach improves runtime performance of dynamic languages running on statically-typed virtual machines. At the same time, existing statically-typed languages are still supported by the virtual machine. We have extended the . Net platform with runtime structural reection adding prototype-based object-oriented semantics to the statically typed class-based model of . Net, supporting both kinds of programming languages. The assessment of runtime performance and memory consumption has revealed that a direct support of structural reection in a production JIT-based virtual machine designed for stati
Connectionist Principles in Theories of Speech Production

This chapter focuses on connectionist modeling in language production, highlighting how core principles of connectionism provide coverage for empirical observations about representation and selection at the phonological, lexical, and sentence levels. The first section focuses on the connectionist principles of localist representations and spreading activation. It discusses how these two principles have motivated classic models of speech production and shows how they cover results of the picture-word interference paradigm, the mixed error effect, and aphasic naming errors. The second section focuses on how newer connectionist models incorporate the principles of learning and distributed representations through discussion of syntactic priming, cumulative semantic interference, sequencing errors, phonological blends, and code-switching.
Recent advances and open challenges in hybrid brain-computer interfacing: a technological review of non-invasive human research

Context. In recent years, hybrid brain-computer interfaces (hBCIs) have proven to be a promising path towards practical brain-computer interfacing. These hybrid interfaces capitalize on the concurrent recording of various physiological signals, or of the elicitation of more than one mental process, to increase the number of possible input commands and achieve more ﬂexible and robust systems. Although hBCIs have previously been reviewed in some articles, a more recent, and complete survey of the literature is missing to lay the foundations for further research. Objective. This work aims at systematically reviewing recent articles on the topic of non-invasive hBCIs, to comprehensively identify the current trends, limitations and challenges that these studies report. Methods. Three major databases covering the ﬁelds of science and engineering were queried. From these and others sources, 55 journal articles from 2008 to November 2014 were selected and analyzed. Results. Twenty-two items were investigated to o.