Miracle-dz/UniFAR-FTUs huggingface dataset:
Educational Data Mining: A Case Study Perspectives from Primary to University Education in Australia

— At present there is an increasing emphasis on both data mining and educational systems, making educational data mining a novel emerging field of research. Educational data mining (EDM) is an attractive interdisciplinary research domain that deals with the development of methods to utilise data originating in an educational context. EDM uses computational methodologies to evaluate educational data in order to study educational questions. The first part of this paper introduces EDM, describes the different types of educational data environments, diverse phases of EDM, the applications and goals of EDM, and some of the most promising future lines of research. Using EDM, the second part of this paper tracks students in Australia from primary school Year 1 through to successful completion of high school, and, thereafter, enrolment in university. The paper makes an assessment of the role of student gender on successive rates of educational completion in Australia. Implications for future lines of enquiry are discussed.
Gravity based online calibration for monolithic triaxial accelerometers' gain and offset drift

In most industry process, periodic sensor calibrations are required to assure sensors working properly. Real-time, online calibration processing for accelerometer is usually needed in some special situation. The paper presents a new and simple method for monolithic triaxial accelerometer gain and offset drift calibration. The procedure uses the fact that the modulus of the inertial mass of the triaxial accelerometer equals 1g under static conditions. The paper consider a state estimate problem where the measure equation is linear but the sensor has a time-varying gain and offset In our study, the input noises of the accelerometers existed in signals are also considered in the calibration procedure.
A comparison of several nonlinear filters for re-entry vehicle tracking

This paper compares the performance of several non-linear filters for the real-time estimation of the trajectory of a re-entry vehicle from its radar observations. In particular, it examines the effect of using two different coordinate systems on the relative accuracy of an Extended Kalman Filter. Other filters considered are Iterative-Sequential Filters, Single-Stage Iteration Filters and Second Order Filters. It is shown that a Range-Direction-Cosine Extended Kalman filter which uses the measurement coordinate system has less bias and less RMS error than a Cartesian Extended Kalman Filter which uses the Cartesian coordinate system. This is due to the fact that the observations are linear in the Range-Direction-Cosine coordinate system, but nonlinear in the Cartesian coordinate system. It is further shown that the performance of the Cartesian Iterative-Sequential Filter which successively relinearizes the observations around their latest estimates approaches that of a Range-Direction-Cosine Extended Kalman Filter. The use of a single-stage iteration to reduce the dynamic nonlinearity improves the accuracy of both the Extended Kalman filters, but the improvement over the Range-Direction-Cosine Extended Kalman Filter is very small indicating that the dynamic nonlinearity is less significant than the measurement nonlinearity in Re-entry Vehicle Tracking under the assumed data rates and measurement accuracies. The comparison amongst the nonlinear filters is carried out using ten sets of observations on two trajectories based on actual flight test data. The numerical results demonstrate that for RV tracking at normal data rates and normal measurement accuracies, the Range-Direction-Cosine Extended Kalman Filter performs better than other non-linear filters in terms of the trade-off between estimation accuracy, computation time and the radar energy.
Cloud Computing: An Overview

Cloud made it easy for an organization to increase its capability without actually adding new infrastructure, new software or updating existing technology; as it is Internet based system for providing services to the end users on pay per usage basis. Cloud computing reduces cost of computation & storage to a large extend and also improves productivity. From few days cloud has grown from a promising business application to fastest growing IT industries. Cloud offers services such as storage, computation & applications etc for different types of markets such as health care, net banking, several government organizations and other financial applications. Now many popular educational institutes and enterprises are also getting their applications and data shifted to the cloud. In this paper we summarize different classifications and service models of the cloud. In further sections major characteristics and working of cloud is discussed. Next section discusses the general implementation requirements for cloud computing. Also its comparison with the Grid computing is mentioned in next part. Like other online applications cloud computing security also has several downsides. In the last sections major benefits and downsides of cloud computing has been discussed.
Enhancement Approachof Object Constraint Language Generation

OCL is the most prevalent language to document system constraints that are annotated in UML. Writing OCL specifications is not an easy task due to the complexity of the OCL syntax. Therefore, an approach to help and assist developers to write OCL specifications is needed. There are two approaches to do so: First, creating an OCL specifications by a tool called COPACABANA. Second, an MDA-based approach to help developers in writing OCL specification by another tool called NL2OCLviaSBVR that generates OCL specification automatically. This study presents another MDA-based approach called En2OCL, and its objective is twofold. 1- to improve the precison of the existing works. 2- to present a benchmark of these approaches. The benchmark shows that the accuracy of COPACABANA, NL2OCLviaSBVR, and En2OCL are 69.23, 84.64, and 88.40 respectively.
Satellite remote sensing of wild rice

Wild rice (Zizania aquatica) is a primary staple for Native Americans in the northern United States and there is a strong need to timely map and monitor its production on American Indian Reservations. This paper describes a methodology for the detection and classification of wild rice using satellite remote sensing. Landsat-7 data were used to map and estimate wild rice crop areas for the Leech Lake American Indian Reservation in Minnesota. A 14-band dataset was created using bands 1-5 and 7 from Landsat-7 images for July and October 1999. Two additional bands of image texture were also created from the 15-metre panchromatic channel of Landsat-7 for each time period and added to the dataset. Since wild rice grows in standing water, a data transform was developed to identify wet vegetation in the dataset. The data transform was created from the multi-temporal dataset and based on a minimum noise fraction transformation, image textural analysis, and hierarchical data masking. A lake and water mask was created and applied to the image to isolate wild rice beds from the wet vegetation transform. Training data for wild rice were collected using a GPS mounted to a laptop computer containing the image data. A spectral matched filtering algorithm was then applied to the image transform using ground collected data to classify wild rice within the data mask. Final classified data were verified using wild rice data collected in the field and from aerial photographs. We estimated nearly 2000 hectares of wild rice available for harvest for 1999 within the boundaries of the Leech Lake American Indian Reservation. This is over a 65% decrease in crop area of wild rice from 1994 (5750 hectares). We were able to verify the 1999 crop loss and subsequent insurance claim for the Leech Lake American Indian Reservation. Such mapping with Landsat-7 provides a more accurate wild rice database than can be routinely updated with repeat Landsat-7 coverage.
Brain-Machine Interfaces: The Perception-Action Closed Loop: A Two-Learner System

A brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception. In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor outputs to control a variety of practical devices for motor-disabled people. Feedback from the prosthetic device, conveyed to the user either via normal sensory pathways or directly through brain stimulation, establishes a closed control loop. An important aspect of a BMI is the capability to distinguish between different patterns of brain activity, with each being associated with a particular intention or mental task. Hence, adaptation is a key component of a BMI because, on the one hand, users must learn to modulate their neural activity to generate distinct brain patterns, while, on the other hand, machine-learning techniques need to discover the individual brain patterns characterizing the mental tasks executed by the user.
Design of Almost Perfect Complementary Sequence Pairs

Sequences with very low out-of-phase auto-correlation and cross-correlation function are widely used for synchronization in mobile communication and multimedia systems where reliable data transmissions are required. For optimum detection, synchronization sequences usually have out-of-phase correlation values that are very low or zero. However sequences with ideal correlation function are very rare. Thus, this paper presents a new algorithm to generate almost perfect complementary sequence pairs with ideal bipolar correlation. Using the auto-correlation properties of these synchronization sequences, we can obtain a correlation circuit whose output contains two peak values equal in magnitude and opposite in polarity at zero and middle shifts. This correlation circuit can be used to double-check the synchronization timing and thus reduce the synchronization search time.
Intermediate Language Translation and Evaluation for Binary Code Software Weakness Analysis

The use of third-party libraries has become a natural phenomenon as more programs are built on a large scale. The problem with using third-party libraries is that, in most cases, we only have access to the binary code. And due to the lack of access to source code, software weakness analysis is very difficult. In this paper, we propose a method to translate binary code to Smart Intermediate Language (SIL) for software weakness analysis
Sistema per la traduzione in Lingua Italiana dei Segni: Blue Sign Translator / Wireless Sign System

L’obiettivo di questo studio è la realizzazione di un sistema multimediale per la traduzione in Lingua Italiana di Segni che sia sempre disponile, anche in tutte quelle situazioni in cui non sia possibile la presenza di un interprete. Tale sistema rappresenterebbe un notevole ausilio all’integrazione sociale del non udente. Dall’analisi delle effettive necessità dei sordi, è emerso che essi preferiscono comunicare attraverso la lingua dei segni poiché questa viene percepita come la propria lingua madre e, di fatto, permette una immediatezza di comunicazione altrimenti difficilmente ottenibile. Gli sviluppi attuali delle tecnologie informatiche e l’incremento della capacità elaborativa dei dispositivi portabili (cellulari, PDA, ecc.) consentono di poter verosimilmente pensare al progetto di ausili tecnologici in grado di effettuare la traduzione automatica in Lingua dei Segni. Il progetto attualmente sta procedendo grazie al contributo della Fondazione Monte dei Paschi e attualmente il team che vi sta lavorando è composto dal Prof. Roberto Giorgi, con ruolo di coordinatore, dal Dr. Srjdan Balovic, dal Prof. Sandro Bartolini, dall’Ing. Paolo Bennati e dall’Ing. Marco Guerrini. L’obiettivo è quello di proporre al non udente un “dispositivo-traduttore” che potrebbe avere molteplici impieghi anche in ambito didattico (es.: insegnamento della lingua dei segni agli udenti). Il progetto è svolto in un ambito di ricerca e si prefigge l’analisi delle problematiche, la progettazione e la prototipazione del sistema in oggetto affinché questo possa essere realizzato efficientemente come prodotto commerciale. Il nostro gruppo è aperto sia alla collaborazione con associazioni e con altri gruppi di ricerca, e considera essenziale la condivisione dei propri risultati con gli altri tramite pubblicazioni e meeting. Infine, è nostra intenzione stimolare l’interesse dell’industria alla fase realizzativa nel momento in cui la fase di studio e di prototipazione avrà raggiunto i risultati sperati.
Path-compatible pruned convolutional (PCPC) codes: a new scheme for unequal error protection

Path pruning and its application to convolutional codes for unequal error protection (UEP), which result in path-compatible pruned convolutional (PCPC) codes, together with a cascaded implementation are proposed in this paper. Necessary and sufficient conditions are derived for a sub-class of PCPC codes whose decoding can be conducted via only a single decoder for the mother code.
Sign Language Translator Application Using OpenCV

This research focuses on the development of sign language translator application using OpenCV Android based, this application is based on the difference in color. The author also utilizes Support Machine Learning to predict the label. Results of the research showed that the coordinates of the fingertip search methods can be used to recognize a hand gesture to the conditions contained open arms while to figure gesture with the hand clenched using search methods Hu Moments value. Fingertip methods more resilient in gesture recognition with a higher success rate is 95% on the distance variation is 35 cm and 55 cm and variations of light intensity of approximately 90 lux and 100 lux and light green background plain condition compared with the Hu Moments method with the same parameters and the percentage of success of 40%. While the background of outdoor environment applications still can not be used with a success rate of only 6 managed and the rest failed.
Least squares approach to data-aided frequency estimation in frequency-selective fading channels

A new data-aided frequency estimator for frequency-selective fading channels is introduced. This estimator is developed based on a least squares (LS) error criterion. The proposed method can estimate frequency offsets without the need for channel information. It is shown through simulation that the proposed LS method can be preferred to the existing techniques in mobile communications.
The theory and application of privacy-preserving computation

Abstract : Privacy is a growing concern in the digital world as more information becomes digital every day. Often the implications of how this information could be exploited for nefarious purposes are not explored until after the fact. The public is becoming more concerned about this. This dissertation introduces a new paradigm for tackling the problem, namely, transferable multiparty computation (T-MPC). T-MPC builds upon existing multiparty computation work yet allows some additional exibility in the set of participants. T-MPC is orders of magnitude more efficient for certain applications. This greatly increases the scalability of the sizes of networks supported for privacy-preserving computation.
Privacy in location-based applications; going beyond k-anonymity, cloaking and anonymizers

An obvious requirement for evaluating spatial queries in Location Based Services (LBS) is that the location of the query point needs to be shared with the location server responding to user queries. Spatial data such as points of interest are indexed at this potentially untrusted server (host) and queries are evaluated by navigating the underlying index structure used to partition the data. However, a user’s location is highly sensitive information that once compromised, can expose him to various threats such as stalking and inference about his health problems or political/religious affiliations. Such growing concerns about users’ location privacy in LBS is considered to be the biggest impediment to the explosive growth and popularity of location-based services. The anonymity and cloaking-based approaches proposed to address this problem cannot provide stringent privacy guarantees without incurring costly computation and communication overhead. Furthermore, they require a trusted intermediate anonymizer to protect user locations during query processing. In this dissertation, we identify the key challenges of enabling privacy in location-based services using an untrusted server model. We propose three solutions to the location privacy problem. Our first solution employs a space transformation scheme to privately evaluate location queries in a space unknown to the untrusted server. The novel one-way transformation developed allows fast computation of location queries in the transformed space while respecting user privacy. We develop our second solution based on the theory of Private Information Retrieval to achieve yet stronger levels of privacy. This strong measure of privacy comes with more computational cost. Finally, we propose a more fundamental technique that enables oblivious traversal of tree-structured spatial indexes for query processing. With this technique, the original spatial index is replaced with an encrypted spatial index that is hosted at the server. While preserving user privacy, this technique allows a wide range of spatial queries to be efficiently evaluated over the encrypted index.
Neural fuzzy digital filtering: Properties

The paper describes the structure of the neural fuzzy filtering; giving an approach of this kind of filters called NFDF. This filters have an adaptive fuzzy inference mechanism in order to deduce the filter answers to select the best parameter values into the knowledge base (KB), actualizing the filter weights to give a good enough answers in natural linguistic sense; this require that all of the states bound into NFDF error functional, also considering the Nyquist criterion. A conventional filter can't classifies and deduce its responses into levels, the difference with the NFDF is that it characterizes the variables of a reference system and the set of membership functions using levels of response into the KB describing the classification of the filter using its probabilistic properties with respect to the rules set decisions, performing the NFDF. The paper also describes illustratively the neural net architecture into the filter mechanism. The results expressed in formal sense by the definitions related in the papers included into the paper references. Finally, the paper shows schematically the NFDF operation applying the first order ARMA model as reference system using the Matlab© software.
Proving Partial Correctness Beyond Programs

Partial correctness is perhaps the most important functional property of algo-rithmic programs. It can be broadly stated as: on all terminating executions, a given relation holds between a program's inputs and outputs. It has been formalised in several logics, from, e.g, Hoare logics [1] to temporal logics [2]. Partial correctness is also a relevant property for any class of specification that has a notion of terminating execution. For example, communication protocols have both nonterminating executions (all messages are forever lost and resent) and terminating executions (all messages sent are properly received). Here, partial correctness may, for instance, require that on all terminating executions , the set of messages corresponding to the transmission of a given file are received and the reception of every message is acknowledged. How can one naturally specify such generic partial-correctness properties, and how can one verify them in a maximally trustworthy manner? One possibility would be to use Hoare logics, but that solution is not optimal because Hoare logics intrinsically require programs (as their deduction rules focus on how program-instructions modify logical predicates), and we are targeting systems specified in formalisms that are not programs but more abstract models, e.g., one more naturally specifies communication protocols in some version of state-transition systems. Another possibility is to state partial-correctness properties in temporal logic and to use a model checker to prove the temporal formula on a state-transition system specification. This is a better solution, since it stays at a model-abstraction level; however, as we are aiming at trustworthy verification, this is not satisfactory as even in case of a successful verification, one's trust in the result is limited as one does not get independently-checkable verification certificates. Moreover, model checkers are limited to essentially finite-state systems (perhaps up to some data abstraction), a limitation we want to avoid.
Expert Perspectives on Using Mainstream Mobile Technology for School-Age Children Who Require Augmentative and Alternative Communication (AAC): A Policy Delphi Study.

Despite legislation in the U.S.A requiring the use of assistive technology in special education, there remains an underutilization of technology-based speech intervention for young students who require augmentative and alternative communication (AAC). The purpose of this Policy Delphi study was to address three guiding research questions that relate to the feasibility of using mainstream mobile technology, facilitative actions, and stakeholder roles for implementation and utilization of AAC in elementary school settings. Data were collected in two rounds of questionnaires given to experts in special education, assistive technology and speech and language pathology, with experience in AAC. Round 1 included 19 participants, 14 of whom also completed the Round 2 questionnaire. The results indicated that a very strong case can be made that mainstream mobile devices have several advantages over traditional AAC systems, not only in their affordability, but also transparency and social acceptance by providing an ideal medium for inclusion in mainstream settings. A challenge that confronts AAC innovations is the tendency to focus on the technology instead of pedagogical, social and therapeutic goals. Until a perfect AAC system becomes available for mainstream mobile devices that meet individuals’ communicative, educational and physical needs and personal preferences, it is apparent that multimodality will continue to be the model. The utilization of mainstream mobile technology for AAC necessitates certain facilitative actions and stakeholder responsibilities. Team collaboration is essential in supporting AAC use and, when applicable, facilitating the inclusion and mainstreaming of students who use AAC in the general education setting.
Analysis of the cluster size in coordinated multipoint transmission

In this work we consider the optimization of the cell number to configure the different clusters in a coordinated multipoint transmission downlink environment where block diagonalization is employed to remove interference among users inside each cluster. Two possible schemes for the power allocation to each user's transmission are compared, namely uniform power and waterfilling,
Information characteristics as constraints to innovation

The characteristics of information inhibit organizational learning cycles which are the processes necessary to develop intelligent organizations. One major thesis is that information is subject to limited appropriability since its exchange is irreversible, its performance is uncertain, and its use does not limit further use. Inappropriability encourages the retention of asymmetric information. Unfortunately, the organizational learning cycle also generates asymmetric information. Asymmetric information facilitates opportunistic behavior which reduces the incentive to innovate, although the disincentives may be mitigated by interpersonal trust. Effective intelligent organizations need (1) to design organizational and information systems based on information characteristics and (2) to develop interpersonal trust. <<ETX>>