Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimising Visual Layout for Training and Learning Technologies

The layout and arraying of information in electronic aids used for training can affect viewer comprehension and impressions. This paper explains existing layout guidance, and defines an integrated design model for applying these recommendations. To test the efficacy of this model, experiments were conducted under Murdoch University Human Research Ethics Committee (HREC) approval 2010/012. In these experiments two similar presentations were created, which contained the same content. However, one of these presentations applied the integrated design model to shape the positioning of the visual content, and a variant was developed that flipped the layout, so it did not conform to this design approach. The experimental results demonstrated that developing layouts that bias the important visual material to the top and left positively influenced viewer impressions. These results will have design implications for predominantly text-based material (e.g. presentations, webpages, e-learning systems); particularly when the content is being delivered to people who typically read from left to right and top to bottom.
Making the Connection

Abstract This study explored the use of the Web-based virtual environment, Adobe Connect Pro, in a postgraduate online teacher education programme at the University of Waikato. It applied the tenets of Moore’s Theory of Transactional Distance (Moore, 1997) in examining the efficacy of using the virtual classroom to promote quality dialogue and explored how both internal and external structural elements related to the purpose and use of the classroom affected the sense of learner autonomy. The study provides an illustration of the complexity of the relationship that exists between the elements of Moore’s theory, and how the implementation of an external structuring technology such as the virtual classroom, can have both positive impacts (dialogue creation) and negative impacts (diminished sense of learner autonomy). It also suggests that, although Moore’s theory provides a useful conceptual “lens” through which to analyse online learning practices, its tenets may need revisiting to reflect the move toward the use of synchronous communication tools in online distance learning.
Bayesian Inference of Nonstationary Precipitation Intensity-Duration-Frequency Curves for Infrastructure Design

PURPOSE: The purpose of this document is to demonstrate the application of Bayesian Markov Chain Monte Carlo (MCMC) simulation as a formal probabilistic-based means by which to develop local precipitation Intensity-Duration-Frequency (IDF) curves using historical rainfall time series data collected for a given surface network station, including the treatment of a nonstationary climate condition. This objective will be accomplished by independently revisiting parts of an example originally profiled by Cheng and AghaKouchak (2014). This Technical Note will conclude with a brief discussion of some potential opportunities for future U.S. Army Corps of Engineers (USACE) research and development directed at extreme rainfall frequency analysis.
Pilot symbol-assisted coherent multistage interference canceller using recursive channel estimation for DS-CDMA mobile radio

A pilot symbol-assisted coherent multistage interference canceller using recursive channel estimation is proposed for DS-CDMA mobile radio cellular system. Since the channel variation caused by fading is recursively estimated at each interference cancelling stage, the accuracy of channel estimation is improved successively. Computer simulation results show that the required E/sub b//N/sub 0/ at the average BER of 3/spl times/10/sup -2/ is improved by /spl sim/3.5 dB compared to the matched filter receiver for 10 users and two paths with equal power, and where f/sub d/T=10/sup -3/ (f/sub d/: fading maximum Doppler frequency, T: data symbol duration).
AWARE-WS : a multipurpose science dashboard

AWARE is a mobile instrumentation framework for Android devices that focuses on users, researchers and application developers. In this paper we introduce AWARE’s web services (AWARE-WS), a multipurpose dashboard for science. AWARE-WS allows researchers to manage and interact with longitudinal, largescale and distributed user studies that focus on human behavior. Additionally, developers can manage and share their own plugins or sensors for extending the AWARE framework. AWARE-WS was designed and implemented taking into account AWARE’s secure, private, scalable, and interoperable data exchange requirements and has been thoroughly evaluated for its performance, scalability and usability. We stress-tested AWARE-WS with an increasing amount of generated data streams (e.g., sensor data), from 1000 to 10 Million devices, to improve the overall system’s performance. Using pagination and on-demand data loading, we achieved a reasonable average loading time of 1,9 seconds.
Type Safety for Distributed Concurrent Objects and Runtime Upgrades

In today’s society, we are becoming increasingly dependent on the reliability and availability of complex software systems distributed across networks such as the Internet. To construct such systems, object-oriented approaches to software development are well suited for encapsulating data and functions into software units that are integrated to form large systems. This support for modular program development combined with the encapsulation property of objects makes object-orientation the leading paradigm for development of distributed systems, where qualities such as limited inter-dependencies between software components, flexibility with regards to changes, and protection of components’ integrity are important. Distributed systems are often designed to run continuously, so modifications and extensions of existing software components, as well as additions of new ones, must happen while the overall system is running and available. For critical systems where faults and inaccessibility can have dramatic implications, it is necessary to ensure that system modifications are applied in a safe manner without compromising system availability, and that system crashes do not occur during or after such updates. It is therefore important to develop formal methods that support the development and maintenance of modular non-terminating systems. This thesis studies type safety of object-oriented distributed systems and dynamic class constructs. Based on the Creol language, the thesis addresses type safety for languages with concurrent objects, multiple inheritance and asynchronous method calls as the communication primitive. Moreover, we investigate an asynchronous upgrade model that captures the distributed nature of systems and study the integration of classical object-oriented features with runtime upgrade mechanisms.
Physics- and process-based bipolar transistor modeling for integrated circuit design

Many applications require circuits to be operated close to the performance limits of current silicon (production) processes to meet the required circuit specifications for, e.g., high speed, low noise, and low power consumption. Therefore, the circuits must be carefully optimized by selecting the individual transistor configurations. As a consequence, model parameters for a large variety of configurations (100 or more) are often requested. Unfortunately, most present design tools and modeling methods do not support an efficient generation of the respective parameter sets for bipolar compact models. This paper describes an approach that is physics and process based; facilitates an extremely fast generation of consistent model parameter sets, even during the initial phase of process development; and reduces parameter extraction efforts significantly. This allows one to quickly explore various process options in advance and to align process development with circuit product requirements. The approach is supported by a computer-aided-design tool named TRADICA, which can be combined with circuit simulators allowing the emitter size and number of emitter, base, and collector contacts to be the only model parameters visible to designers. Related modeling and parameter extraction issues are also discussed because these areas are often unknown and tend to be underestimated by circuit designers and process developers but have a significant impact on the flexibility, capability, and accuracy of circuit design.
License plate recognition using DTCNNs

Automatic license plate recognition requires a series of complex image processing steps. For practical use, the amount of data to be processed must be minimized at early stage. This paper shows that the computationally most intensive steps can be realized by discrete time cellular neural networks (DTCNNs). Moreover, high-level operations like 'finding the license plate in the image' and 'finding the characters on the plate' need only a small number of DTCNNs. Real-life tests show that the DTCNNs are capable of correctly identifying more than 85% out of all license plates while leaving only 0.5% of the original information to be inspected for actual recognition.
The development of an instrument for assessing studentsʼ perceptions of biology teachersʼ instructional use of diagrams

Science teaching involves using scientific diagrams to explain important concepts, to provide visual images, or to motivate students. However, teachers often wonder if their use of diagrams is effective in helping students learn science. This study aimed to help science teachers evaluate how students perceive their use of diagrams during instruction. Subsequently, we adapted an instrument to measure studentsʼ perceptions of science teachersʼ instructional use of diagrams based on Tuan et al.ʼs (2000) Student Perceptions of Teachersʼ Knowledge (SPOTK) questionnaire. The adapted instrument initially had four categories ‐ teacherʼs instructional practice in using diagrams; teacherʼs use of multiple forms of scientific representations; teacherʼs use of diagrams in assessment practices; and studentsʼ understanding of and competence in using scientific diagrams. The instrument was administered to 215 Australian high school biology students in Years 9-10. Following factor analysis, 20 items remained in the final instrument and three scales were extracted ‐ Instruction with Diagrams, Assessment with Diagrams, and Studentsʼ Diagrammatic Competency. The reliability of the total instrument Studentsʼ Perceptions of Teachersʼ Use of Biology Diagrams was 0.91 and the reliability of each category ranged from 0.65 to 0.90. This instrument is specifically related to the diagrammatic usage in biology lessons and, hopefully, with further research can be generalised to other science lessons. Future research will investigate the relationship between teachersʼ instruction with diagrams and studentsʼ understanding of them.
Distributed Sparse Class-Imbalance Learning and Its Applications

In the present work, the study on class imbalance problems in a <italic>distributed</italic> setting exploiting sparsity structure in the data has been carried out. We formulate the class-imbalance learning problem as a cost-sensitive learning problem with <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="maurya-ieq1-2688372.gif"/></alternatives></inline-formula> regularization. The cost-sensitive loss function is a cost-weighted smooth hinge loss. The resultant optimization problem is minimized within the <italic>Distributed Alternating Direction Method of Multiplier</italic> (DADMM) framework. We partition the data matrix across samples. This operation splits the original problem into a distributed <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="maurya-ieq2-2688372.gif"/></alternatives></inline-formula> regularized smooth loss minimization and a <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="maurya-ieq3-2688372.gif"/></alternatives></inline-formula> regularized squared loss minimization. <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="maurya-ieq4-2688372.gif"/></alternatives></inline-formula> regularized subproblem is solved via Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and random coordinate descent method in parallel at multiple processing nodes using <italic>MPI</italic> whereas <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="maurya-ieq5-2688372.gif"/></alternatives></inline-formula> regularized problem is just a simple soft-thresholding operation. We show, empirically, that the distributed solution approximates the centralized solution on many benchmark data sets. The centralized solution is obtained via Cost-Sensitive Stochastic Coordinate Descent (CSSCD). Empirical results on small and large-scale benchmark datasets show some promising avenues to further investigate the real-world applications of the proposed algorithms such as anomaly detection, class-imbalance learning, etc. To the best of our knowledge, ours is the first work to study class-imbalance in a <italic>distributed</italic> environment on large-scale <italic>sparse</italic> data.
Bimodal speech recognition using lip movement measured by optical flow analysis

This paper proposes bimodal speech recognition using lip movements measured by optical-flow analysis. The optical flow is defined as the distribution of apparent velocities of brightness pattern movements. Since the optical flow can be computed without extracting the speaker’s lip contours and location, robust visual features can be obtained on lip movements. Our method calculates two visual features in each frame: variances of horizontal and vertical components of flow velocities. Since these features represent movement of the speaker’s mouth, they are especially useful for estimating pause/silence periods in noise-corrupted speech. The visual features are combined with acoustic features in the framework of HMM-based recognition. Phoneme HMMs are trained using the combined features extracted from clean speech data. In recognizing noise-corrupted speech, the observation probability of visual features are weighted. Experiments have been carried out using audio-visual data by 11 male speakers uttering connected digits. The following improvements of word accuracy over the audio-only recognition scheme were achieved by combining visual information only for silence HMM; 5% at SNR=5dB and 12% at SNR=10dB.
Characterization of circRNA–miRNA–mRNA networks regulating oxygen utilization in type II alveolar epithelial cells of Tibetan pigs

Understanding the signaling pathway regulatory mechanisms in type II alveolar epithelial (ATII) cells, the progenitor cells responsible for proliferating and regenerating type I alveolar epithelial (ATI) and ATII cells, in Tibetan pigs is beneficial for exploring methods of preventing and repairing cellular damage during hypoxia. We simulated a hypoxic environment (2% O2) for culture ATII cells of Tibetan pigs and Landrace pigs, with cells cultured under normoxic conditions (21% O2) as a control group, and performed integrated analysis of circular RNA (circRNA)–microRNA (miRNA)–messenger RNA (mRNA) regulatory axes by whole-transcriptome sequencing. Functional enrichment analysis indicated that the source genes of the differential expressed circRNAs (DEcircRNAs) were primarily involved in cell proliferation, cellular processes, and cell killing. A series of DEcircRNAs were derived from inhibitors of apoptosis proteins and led to a key autonomous effect as modulators of cell repair in Tibetan pigs under hypoxia. The significant higher expression of COL5A1 in TL groups may inhibited apoptosis of ATII cells in Tibetan pigs under lower oxygen concentration, and may lead their better survive in the hypoxia environment. In addition, a competing endogenous RNA (ceRNA) network of functional interactions was constructed that included novel_circ_000898-ssc-miR-199a-5p-CAV1 and novel_circ_000898-ssc-miR-378-BMP2, based on the node genes ssc-miR-199a-5p and ssc-miR-378, which may regulate multiple miRNAs and mRNAs that mediate endoplasmic reticulum (ER) stress-induced apoptosis and inflammation and attenuate hypoxia-induced injury in ATII cells under hypoxic conditions. These results broaden our knowledge of circRNAs, miRNAs, and mRNAs associated with hypoxia and provide new insights into the hypoxic response of ATII cells in Tibetan pigs.
A survey of force control of robot manipulators using soft computing techniques

One of the most important and fundamental tasks of the robot manipulators is position/force control. It is necessary to realize the desired force to perform sophisticated tasks such as grinding, deburring, wiping, or assembling of objects with the robot. In this survey paper, we mainly discuss on existing several force control systems for robot manipulators by applying soft computing techniques, such as neural networks, fuzzy reasoning, and fuzzy-neural networks. We also point out some open problems for future work.
A text-based implementation model for reusable aspect models

Model-Driven Engineering utilizes modeling to provide abstraction techniques in order to effectively manage the complexity inherent in software systems development for a problem domain. Further, it emphasizes the use of models as primary development artifacts, and encourages researchers and professionals to develop ways that can lead to obtaining executable systems from models. Reusable Aspect Models is a multi-view modeling approach, which combines existing aspect-oriented approaches to modeling class, sequence and state diagrams, into a single approach. These models can serve as an effective design notation to provide abstraction for a given domain. Moreover, these models may be used as input to a model-driven engineering process to obtaining an executable system, by transforming them into aspect-oriented code. However, when investigating different ways of transforming models into code, an important technical issue is to determine a formal (and semantically equivalent) computer-understandable, text-based implementation of the graphical model. Here, a text-based model for Reusable Aspect Models has been presented. To make sure that the text-based model captures all relevant concepts of the notation, a conceptual reference model for Reusable Aspect Models has been presented first, and later implemented through the use of XML schemas.
Explaining the PENTA model: a reply to Arvaniti and Ladd*

This paper presents an overview of the Parallel Encoding and Target Approximation (PENTA) model of speech prosody, in response to an extensive critique by Arvaniti & Ladd (2009). PENTA is a framework for conceptually and computationally linking communicative meanings to fine-grained prosodic details, based on an articulatory-functional view of speech. Target Approximation simulates the articulatory realisation of underlying pitch targets – the prosodic primitives in the framework. Parallel Encoding provides an operational scheme that enables simultaneous encoding of multiple communicative functions. We also outline how PENTA can be computationally tested with a set of software tools. With the help of one of the tools, we offer a PENTA-based hypothetical account of the Greek intonational patterns reported by Arvaniti & Ladd, showing how it is possible to predict the prosodic shapes of an utterance based on the lexical and postlexical meanings it conveys.
Estimation of snow water equivalent using passive microwave radiation data

The principal objective of this study was to analyze, through regression modelling, the degree to which passive microwave radiation can be used to interpret snow water equivalent. The methodology reflects the importance of isolating phenomena other than snow water equivalent which may influence the microwave signal. These factors include: snow wetness; depth hoar; complex mountainous terrain; dense forest cover; and atmospheric precipitable water. It is shown that for a non-forested, non-mountainous terrain, the snow water equivalent of a pack with no depth hoar and no melting snow can be estimated with 95 percent confidence within plus or minus 44 mm. This error doubles when there is depth hoar present, and the relationship between snow water equivalent and brightness temperature reverses, as the scattering is greatest in shallow snowpacks with large depth hoar crystals. Significant results are obtained for melting snow conditions, despite theory indicating the contrary, by using only the night-time satellite passes. Dense forest cover impacts the microwave signal but appears not to completely mask the influence of the underlying snowpack. Lastly, it is difficult to collect ground data in regions of complex mountainous terrain which accurately represent snow conditions over a large area. However, some information can be obtained using high elevation SNOTEL sites from the Rocky Mountains.
Malware Detection and Prevention

The use of web applications has increased steadily over recent years and they are now essential in many areas such as the financial sector, e-commerce, e-government, social media networks, medical data, e-business, academic activities, e-banking, e-shopping, and e-mail. Furthermore, web applications enable users to interact with back-end data servers to insert, delete and modify data content by making a web site their own space. However, malicious software (malware) based attacks commonly target and exploit vulnerabilities in Web applications, often with devastating consequences to businesses and individuals. Therefore, detecting and preventing malware in Web applications has become a significant and timely issue. Although there are many malware detection and prevention approaches, the existing approaches suffer from many various problems, such as low detection accuracy, high-false alarm rates and the inability to deal with new malware attacks. The aim of this thesis is to detect and prevent both known and new unknown malware attacks with improved detection accuracy and reduced false-alarm rates. Through a comprehensive survey of crime toolkits and described attack vectors targeting web applications. A new feature selection approach is proposed to identify the key features needed to build intrusion detection systems. In this thesis a new framework is proposed for efficient malware detection and prevention that overcomes existing limitations of intrusion detection systems. The proposed approach links the attack with a suitable response technique. We propose an approach for detecting anomalies based on multi-stage rules. The central idea is to apply sequential multi stage rules that correctly capture the behavior of abnormal and normal activities. A new approach for detect and prevent SQLIA at the runtime. The proposed approach applies negative taint to identify untrusted data at the database layer. We have demonstrated the robustness of our proposed techniques for malware detection and prevention by testing them on a large dataset that contains a diverse many of intrusions and recent malware samples collected over the period of 2011-2012.