Miracle-dz/UniFAR-FTUs huggingface dataset:
A Carbon-Aware Incentive Mechanism for Greening Colocation Data Centers

The massive energy consumption of data centers worldwide has resulted in a large carbon footprint, raising serious concerns to sustainable IT initiatives and attracting a great amount of research attention. Nonetheless, the current efforts to date, despite encouraging, have been primarily centered around owner-operated data centers (e.g., Google data center), leaving out another major segment of data center industry—colocation data centers—much less explored. As a major hindrance to carbon efficiency desired by the operator, colocation suffers from “split incentive”: tenants may not be willing to manage their servers for carbon efficiency. In this paper, we aim at minimizing the carbon footprint of geo-distributed colocation data centers, while ensuring that the operator's cost meets a long-term budget constraint. We overcome the “split incentive” hurdle by devising a novel online carbon-aware incentive mechanism, called <inline-formula><tex-math notation="LaTeX">$\mathsf {GreenColo}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">GreenColo</mml:mi></mml:math><inline-graphic xlink:href="ren-ieq1-2767043.gif"/></alternatives></inline-formula>, in which tenants voluntarily bid for energy reduction at self-determined prices and will receive financial rewards if their bids are accepted at runtime. Using trace based simulation we show that <inline-formula><tex-math notation="LaTeX">$\mathsf {GreenColo}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">GreenColo</mml:mi></mml:math><inline-graphic xlink:href="ren-ieq2-2767043.gif"/></alternatives></inline-formula> results in a carbon footprint fairly close (23 versus 18 percent) to the optimal offline solution with future information, while being able to satisfy the colocation operator's long-term budget constraint. We demonstrate the effectiveness of <inline-formula><tex-math notation="LaTeX">$\mathsf {GreenColo}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">GreenColo</mml:mi></mml:math><inline-graphic xlink:href="ren-ieq3-2767043.gif"/></alternatives></inline-formula> in practical scenarios via both simulation studies and scaled-down prototype experiments. Our results show that <inline-formula><tex-math notation="LaTeX">$\mathsf {GreenColo}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">GreenColo</mml:mi></mml:math><inline-graphic xlink:href="ren-ieq4-2767043.gif"/></alternatives></inline-formula> can reduce the carbon footprint by up to 24 percent without incurring any additional cost for the colocation operator (compared to the no-incentive baseline case), while tenants receive financial rewards for “free” without violating service level agreement.
ProfCOMP integrated author system/simulation package

The authors present ProfCOMP, a software package to develop and execute lessons in a wide range of subjects. It was designed in such a way that a professor without previous knowledge of computer science can create lessons, including text, graphics, and animations, make interrogations, and, depending on the responses, decide on the course of the lesson. ProfCOMP also includes the possibility of performing a digital simulation of systems represented by ordinary differential equations. Examples are presented. <<ETX>>
Structured deep neural networks for speech recognition

Deep neural networks (DNNs) and deep learning approaches yield state-of-the-art performance in a range of machine learning tasks, including automatic speech recognition. The multi-layer transformations and activation functions in DNNs, or related network variations, allow complex and difficult data to be well modelled. However, the highly distributed representations associated with these models make it hard to interpret the parameters. The whole neural network is commonly treated a “black box”. The behaviours of activation functions and the meanings of network parameters are rarely controlled in the standard DNN training. Though a sensible performance can be achieved, the lack of interpretations to network structures and parameters causes better regularisation and adaptation on DNN models challenging. In regularisation, parameters have to be regularised universally and indiscriminately. For instance, the widely used L2 regularisation encourages all parameters to be zeros. In adaptation, it requires to re-estimate a large number of independent parameters. Adaptation schemes in this framework cannot be effectively performed when there are limited adaptation data. This thesis investigates structured deep neural networks. Special structures are explicitly designed, and they are imposed with desired interpretation to improve DNN regularisation and adaptation. For regularisation, parameters can be separately regularised based on their functions. For adaptation, parameters can be adapted in groups or partially adapted according to their roles in the network topology. Three forms of structured DNNs are proposed in this thesis. The contributions of these models are presented as follows. The first contribution of this thesis is the multi-basis adaptive neural network. This form of structured DNN introduces a set of parallel sub-networks with restricted
THE EFFECTS OF TASK COMPLEXITY ALONG RESOURCE-DIRECTING AND RESOURCE-DISPERSING FACTORS ON EFL LEARNERS’ WRITTEN PERFORMANCE

The present study investigated the effects of task complexity along resource-directing and resource dispersing factors namely pre-task planning time and number of elements on EFL learners' written task production in terms of accuracy. To this end, 30 participants performed decision-making task. Using T-test as the statistical means of analysis, the results of the study revealed the significant differences of cognitive load of task complexity on EFL learners’ performance in terms of accuracy. This study carries some implications for foreign language acquisition (FLA) researchers, language teachers, and syllabus designers. Findings may also add to the present literature in FLA theory, language testing, syllabus design, and material development. Therefore, providing insights on designing and implementation of tasks in EFL classroom settings.
Self-limitation, dynamic and flexible approaches for particle swarm optimisation

Swarm Intelligence (SI) is one of the prominent techniques employed to solve optimisation problems. It has been applied to problems pertaining to engineering, schedule, planning, networking and design. However, this technique has two main limitations. First, the SI technique may not be suitable for the online applications, as it does not have the same aspects of limitations as an online platform. Second, setting the parameter for SI techniques to produce the most promising outcome is challenging. Therefore, this research has been conducted to overcome these two limitations. Based on the literature, Particle Swarm Optimisation (PSO) was selected as the main SI for this research, due to its proven performances, abilities and simplicity. Five new techniques were created based on the PSO technique in order to address the two limitations. The first two techniques focused on the first limitation, while the other three techniques focused on the latter. Three main experiments (benchmark problems, engineering problems, path planning problems) were designed to assess the capabilities and performances of these five new techniques. These new techniques were also compared against several other well-established SI techniques such as the Genetic Algorithm (GA), Differential Equation (DE) and Cuckoo Search Algorithm (CSA). Potential Field (PF), Probabilistic Road Map (PRM), Rapidly-explore Random Tree (RRT) and Dijkstra’s Algorithm (DA) were also included in the path planning problem in order to compare these new techniques’ performances against Classical methods of path planning. Results showed all five introduced techniques managed to outperform or at least perform as good as well-established techniques in all three experiments.
Psychological and Physiological Acoustics : Paper 1 pPPb 4 Models for evaluating navigational techniques for higher-order ambisonics

Models are presented that predict perceived source localization and spectral coloration for the purpose of evaluating navigational techniques for higher-order ambisonics. Previous evaluations typically rely on binaural localization models, which conflate the effects of the navigational technique with those of the adopted ambisonics-to-binaural rendering approach. Moreover, studies on navigation-induced coloration have been largely qualitative. The presented models are applied directly to translated ambisonics impulse responses (i.e., before rendering to binaural) and are validated through listening experiments. Localization is predicted using an extension to a precedence-effect-based localization model. Coloration is predicted using a linear combination of spectral energies and notch-depths in a difference-spectrum between the test and reference signals. For two interpolation-based navigational techniques and a range of translation distances, localization and coloration are also measured subjectively through binaural-synthesis-based listening tests, wherein subjects judge source position for a spatialized sample of speech and rate the induced coloration in pink noise relative to reference signals. The proposed localization model is shown to predict the data with comparable accuracy to that of a binaural localization model and the coloration metrics used are shown to best predict perceived coloration compared to alternative sets of metrics.
Novel Machine Learning Methods Based on Information Theory

Machine learning is the area of artificial intelligence and computer science that studies algorithms that can learn from data, make predictions, and produce behaviors based on examples. This thesis develops new methods of machine learning based on information theory (IT) and information theoretic learning (ITL): (1) On the one hand, IT is used for feature selection. Specifically, two new algorithms are developed. The first one takes into account the cost (computational, economic, etc.) of each feature —besides its relevance—. The second one makes use of the concept of ensemble, quite common for classification scenarios, but very little explored in the literature of feature selection. (2) On the other hand, IT and ITL concepts can be employed as an alternative error function, thus allowing the exploration of another not very well studied field in the literature: the local modeling approach. Specifically, a new algorithm for classification is developed. This algorithm is based on the combination of neural networks by means of local modeling and techniques based on ITL.
Class-based quality of service over air interfaces in 4G mobile networks

We present a framework for quality of service provisioning over the air interfaces in future wireless networks, including 3G enhancement and 4G mobile networks. The framework is based on the paradigm of service classes, wherein each class can exhibit a characteristic behavior in terms of resource allocation over the air interface. Using this QoS framework, future wireless network operators can define their own sets of service classes, choose the preferred way of implementing the QoS behavior of these classes, and offer class-based pricing schemes. The user application can choose the service class that best suits its expectations in terms of QoS and cost of access. A class-based bandwidth scheduling scheme is described as a mechanism to implement this QoS framework over CDMA air interfaces. This scheme incorporates the paradigm of service classes, in conjunction with fair resource allocation and air interface congestion resilience, while allocating air interface bandwidth to mobile users.
Judging rapid resolutions: Insight into the information requirements for accurate categorisation using exposure duration and resolution

Humans are capable of making incredibly rapid visual categorisations. The speed of these non-analytic categorisations is the result of processing broad, global properties of an image, rather than by using specific features. It is unknown just how much (or little) information is actually required for rapid categorisations. We systematically reduced the amount of information in hundreds of photographs of birds, faces, and paintings, and asked people to decide whether two simultaneously presented images were from the same category, or two different categories. We flashed these images on screen for either 50, 250, 500, or 2500 milliseconds. This novel experimental design is the first to combine these two traditionally separate techniques of investigating human categorisation decisions. We found that people's accuracy in categorising images depended both on image resolution and exposure duration, but that exposure duration only affected accuracy at higher resolutions. Interestingly, categorisation accuracy peaked at 500ms, with no significant change beyond that exposure duration, suggesting that there may be a hard limit for accurate image categorisation. We conclude that the novel combination of resolution and exposure duration is a promising way to investigate human categorisation, and our findings provide a promising avenue for future research into the minimum exposure duration requirements of categorisation. Keywords: categorisation, non-analytic, analytic, resolution, exposure duration
Using syntax features and document discourse for relation extraction on PharmGKB and CTD

We present an approach to the extraction of relations between pharmacogenomics entities like drugs, genes and diseases which is based on syntax and on discourse. Particularly, discourse has not been studied widely for improving Text Mining. We learn syntactic features semi-automatically from lean document-level annotation. We show how a simple Maximum Entropy based machine learning approach helps to estimate the relevance of candidate relations based on dependency-based features found in the syntactic path connecting the involved entities. Maximum Entropy based relevance estimation of candidate pairs conditioned on syntactic features improves relation ranking by 68% relative increase measured by AUCiP/R and by 60% for TAP-k (k=10). We also show that automatically recognizing document-level discourse characteristics to expand and filter acronyms improves term recognition and interaction detection by 12% relative, measured by AUCiP/R and by TAP-k (k=10). Our pilot study uses PharmGKB and CTD as resources.
A Theoretical Integration of User Satisfaction and Technology Acceptance

In general, perceptions of information systems (IS) success have been investigated within two primary research streams--the user satisfaction literature and the technology acceptance literature. These two approaches have been developed in parallel and have not been reconciled or integrated. This paper develops an integrated research model that distinguishes beliefs and attitudes about the system (i.e., object-based beliefs and attitudes) from beliefs and attitudes about using the system (i.e., behavioral beliefs and attitudes) to build the theoretical logic that links the user satisfaction and technology acceptance literature. The model is then tested using a sample of 465 users from seven different organizations who completed a survey regarding their use of data warehousing software. The proposed model was supported, providing preliminary evidence that the two perspectives can and should be integrated. The integrated model helps build the bridge from design and implementation decisions to system characteristics (a core strength of the user satisfaction literature) to the prediction of usage (a core strength of the technology acceptance literature).
Classification of objects in residential monitoring systems

This paper presents a real-time object classification method for distinguishing between humans, pets and other objects in residential security systems. Specifically, we propose using an ensemble of radial basis function (RBF) networks on gradient images extracted from the scene. A specific advantage of using an ensemble is its ability to cope with the inherent variability in the image formation and data acquisition. process. Experimental results for two different ensembles of networks are presented with average cross validation performances of 91% and 95% respectively.
Haptic induced motor learning and the extension of its benefits to stroke patients

HAPTIC INDUCED MOTOR LEARNING AND THE EXTENSION OF ITS BENEFITS TO STROKE PATIENTS by Adrienne M. Coppa In this research, the Haptic Master robotic arm and virtual environments are used to induce motor learning in subjects with no known musculoskeletal or neurological disorders. It is found in this research that both perception and performance of the subject are increased through the haptic and visual feedback delivered through the Haptic Master. These system benefits may be extended to enhance therapies for patients with loss of motor skills due to neurological disease or brain injury. Force and visual feedback were manipulated within virtual environment scenarios to facilitate learning. In one force feedback condition, the subject is required to maneuver a sphere through a haptic maze or linear channel. In the second feedback condition, the subject's movement was stopped when the sphere came in contact with the haptic walls. To resume movement, the force vector had to be redirected towards the optimal trajectory. To analyze the efficiency of the various scenarios, the area between the optimal and actual trajectories was used as a measure of learning. The results from this research demonstrated that within more complex environments one type of force feedback was more successful in facilitating motor learning. In a simpler environment, two out of three subjects experienced a higher degree of motor learning with the same type of force feedback. Learning is not enhanced with the presence of visual feedback. Also, in nearly all studied cases, the primary limitation to learning is shoulder and attention fatigue brought on by the experimentation. HAPTIC INDUCED MOTOR LEARNING AND THE EXTENSION OF ITS BENEFITS TO STROKE PATIENTS by Adrienne M. Coppa A Thesis Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Master of Science in Biomedical Engineering Department of Biomedical Engineering
Human Detection Using Surf And Sift Feature Extraction Methods In Different Color Spaces

Local feature matching has become a commonly used method to compare images. For tracking and human detection, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. two different types of image feature algorithms, Scale Invariant Feature Transform (SIFT) and the more recent Speeded Up Robust Features (SURF), have been used to compare the images. In this paper, we propose the use of a rich set of feature descriptors based on SIFT and SURF in the different color spaces.
Self-regulated Mobile Learning and Assessment: An Evaluation of Assessment Interfaces

Assessment for learning has an important role to play in self-regulated learning but the assessment interface can impact learner motivation and performance. Learners are able to assess their knowledge of learning content and, through repeated assessment and high-quality feedback, close the gap between their current performance and the performance goals of the assessment. Technology, specifically, mobile devices, provide learners with access to these assessments regardless of time or location. The widespread use and acceptance of mobile devices allow users in remote areas, where there is a lack of infrastructure and suitably qualified teachers, access to learning material and content. The challenge is to ensure the assessments are presented in such a way so that self-regulated learning is supported. In this study, two different multiple-choice assessment interfaces are presented to support the learning of secondary school mathematics and science content in South Africa using a Mxit application called MathWars. The two assessments methods were evaluated to determine how self-regulated learning principles are supported. The results of the study identify key findings with regards to learner motivation to retake assessments and the importance of identifying performance goals and access to solutions.
Design of Flood Warning System Based IoT and Water Characteristics

Indonesia is one of the tropical country with high rainfall. It causes a flood disaster that with the loss of property and life. Currently handling flood disasters in developing countries are still using many conventional ways. By using conventional way the flood early warning system is still not able to reach the wider community so that information too late of impending disaster received by the public. Besides that the speed of information is needed in the handling of disasters. Therefore a rapid flood warning system that can reach a wide area such as the internet is necessary to minimize the effects of disasters. IoT (Internet of Thing) is smart technology which has capability to send data in real time, connected to the smart phone, sensor and web service. This ability can be used for smart application to control and manage an early warning system. In this research we also use IoT technology to monitor and control the early detection system of flood disaster. By using IoT technology, sensor data can be sent in real time from sensor to smartphone through internet network. The sensor data contains the water characteristics data on the dam that will be processed in the data base. The processing data of water characteristic will be a decision of hazard level. That decision will be sent to users as notification into the user smartphone application. This research will be useful for deeply research on disaster management system and flood early detection system in Indonesia.
Investigating Dictionary Skills and Word Knowledge of Iraqi EFL Students

The purpose of this paper is to identify the types of dictionary skills and the word knowledge of EFL Iraqi students. The subjects were twenty-six Iraqi EFL 5th grade (science stream) secondary students in the Iraqi school in Kuala Lumpur, Malaysia. The data was gathered via a questionnaire comprising 27- items about their use of the dictionary. The data was analyzed using descriptive statistics where frequency counts were tabulated and converted into percentages. The findings revealed that students used dictionaries most frequently to find the meanings of words, or to obtain a translation, and also to check the spelling, and pronunciation. In addition, students mostly preferred electronic bilingual dictionaries to paper dictionaries. The results indicate a very limited use of the dictionary as a tool for language learning, and it is therefore recommended that more attention be directed to training students in effective dictionary skills to enhance their vocabulary learning.
The di/dt capability of thyristors

This paper describes an experimental investigation of the di/dt failure mechanism of thyristors. The location of the initial turn-on region and the spread of the "on" region were observed on a specially designed thyristor having many monitoring electrodes. The turn-on process was studied for triggering by gate, by breakover, and by dv/dt. In many cases it was found that turn-on occurred at almost the same region, whether it was triggered by breakover or by dv/dt. This area coincided with the final holding position in the turn-off process. The di/dt capability of the thyristor was measured. It was found that the capabilities were almost the same for the three triggering methods. The destruction temperature in the di/dt test was estimated from the area of the burn-out spots and the energy dissipation.