Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying generic object-oriented programming skills for OOP course using Delphi technique: Work in progress

: One of the largest challenges facing educators teaching object-oriented courses is deciding how to evaluate each students programming skill. This research focus on how we addressed this challenge in object oriented programming course by proposing a new assessment method to assess students’ object-oriented programming skills. The process begins by identifying generic object-oriented skills that students should acquired. In this paper we discuss the approach taken in the process of identifying the object-oriented skills, i.e. Delphi technique. Delphi technique is a structured multi-step process that uses a group of experts to achieve a consensus opinion. We present the methodology of three Delphi processes to identify object-oriented programming skills. The identified skills will be used to guide both the coverage of student learning assessments and can be used by instructors to identify what topics merit emphasis.
Inflation targeting as a framework for monetary policy

Inflation distorts prices, erodes savings, discourages investment, stimulates capital flight, inhibits growth, and makes economic planning a nightmare. During the past decade, several advanced economies have taken a new approach to the age-old problem of controlling inflation through monetary policy known as "inflation targeting. " This pamphlet explains the requirements of putting the new policy in place, the experience of the countries that have tried it, and whether it has applicability to developing countries.
Two Strategies on Border Labeling applied to the Estimation of Ejection Fraction

As we have already presented in previous conferences, our algorithm uses SPECT data to automatically recover the three-dimensional shape of the human's left ventricle. The reconstruction of both the internal (endocardium) and external (epicardium) surfaces, allows us to present a synthetic cardiac cycle for an actual patient and derive its associated ejection fraction estimation. In this paper, we complement our MLC filter methodology with another paradigm, the Discreet Contour Deformation Model. A comparison between ejection fraction values is presented, regarding both methods.
A Framework for Requirements Engineering Using System Dynamics

In many systems engineering activities the elicitation of requirements is regarded as a central activity for the efficient and effective functioning of the intended system. In recent years, the field of Requirements Engineering has received much attention and many research and practical approaches have been proposed. In this paper we present a Requirements Engineering framework that is motivated by the System Dynamics paradigm. The framework consists of four key activities: ontology modelling, goal modelling, process modelling and scenarios generation. It is our premise that the synergy between these four activities results in a robust way of working that provides requirements stakeholders with a systematic approach to articulating, defining, debating, and agreeing on the set of desirable functional and non-functional properties of the intended system. The approach is demonstrated with examples from a very large application and claims substantiated from experiences from this project.
Controlled Probing - A system for targeted floating car data collection

Since vehicle to infrastructure communication will become state of the art in a few years, data from mobility and environment sensors in the vehicle could produce valuable information for traffic management and vehicle safety applications. The massive deployment of such data sources could however lead to high data traffic that would unnecessarily strain both the communication network, processing and storage resources. In this paper we present Controlled Probing, a system solution for floating car data collection which allows to select exactly the location, the time period and the type of sensor data to be collected. We address the system performance with respect to the accuracy and timeliness of the received probes.
An augmented Lagrangian SQP method for solving some special class of nonlinear semi-definite programming problems

In this paper, we consider a special class of nonlinear semi-definite programming problems that represents the fixed order H2/H¥ synthesis problem. An augmented Lagrangian sequential quadratic programming method combined with a trust region globalization strategy is described, taking advantage of the problem structure and using inexact computations. Some numerical examples that illustrate the performance of the method are given.
Metrics for symbol clustering from a pseudoergodic information source

We discuss a set of metrics, which aim to facilitate the formation of symbol groups from a pseudoergodic information source. An optimal codification can then be applied on the symbols (such as Huffman Codes by S. JR Pierce (1980)) for aero memory sources where it tends to the theoretical limit of compression limited by the entropy. These metrics can be used as a fitness measure of the individuals in the Vasconcelos genetic algorithm as an alternative to exhaustive search.
Game suspension boundary detection by reading two clocks for broadcast basketball video

This paper presents a novel algorithm for finding suspension boundaries (starting and end time points of event) of broadcast basketball videos, which employs the superimposed features extracted from the game clock and the shot clock to infer the suspension boundary. The main challenge of suspension boundary detection in the proposed way is to read (localize and recognize) the game clock and the shot clock. The difficulties of reading the two clocks from basketball videos lie in that the two clocks are independent from each other and are paused and reset frequently. This paper proposes two new techniques against the difficulties. The first new technique is a new set of functions of instantiating the second-periodicity method to identify the pixels inside second-regions without knowing the transit frames of two clocks. The second technique is a procedure of finding the proper digit-sequence from the second place of the shot clock. This technique solves the difficulty caused by the frequent pauses and the resets of two clocks. With the information extracted from the game clock and the shot clock this paper infers the start and the end time points of suspension clips. Experimental results show that the proposed algorithm can find much more accurate boundary points of suspensions at a much lower computing cost than the existing algorithms can.
Research into Practice: Meeting the Literacy Needs of Students with ADHD in the Middle School Classroom

used by teachers and parents alike (Armstrong, 1999). It has been estimated that 7.5% of the school-aged population will be diagnosed with ADHD, although the American Psychiatric Association (2000) has suggested that the figure is more likely 3% to 5%. Regardless of the figures used, ADHD affects a considerable number of students being served in our schools today. While the prevalence of ADHD is indeed sig nificant, it is particularly alarming that many teachers, parents, and other professionals work ing with children do not truly understand the disorder itself. Further, many individuals who are charged with the responsibility of working with these students on a day-to-day basis are unfamiliar with specific strategies that have proven to be effective in working with students with ADHD. This column will provide criteria used to identify students with ADHD as well as specific strategies that have been effective in helping maximize the learning opportunities of these students.
Learning 3D Articulation and Deformation using 2D Images

With the rise of Augmented Reality, Virtual Reality and 3D printing, methods for acquiring 3D models from the real world are more important then ever. One approach to generate 3D models is by modifying an existing template 3D mesh to fit the pose and shape of similar objects in images. To model the pose of an highly articulated and deformable object, it is essential to understand how an object class can articulate and deform. In this paper we propose to learn a class model of articulation and deformation from a set of annotated Internet images. To do so, we incorporate the idea of local stiffness, which specifies the amount of distortion allowed for a local region. Our system jointly learns the stiffness as it deforms a template 3D mesh to fit the pose of the objects in images. We show that this seemingly complex task can be solved with a sequence of convex optimization programs. We demonstrate our approach on two highly articulated and deformable animals, cats and horses. Our approach obtains significantly more realistic deformations compared to other related approaches.
Learning While Doing : A Knowledge CompilationApproach to Learning in External

Integrated, intelligent agents must be able to adapt their behavior in dynamic environments. A number of systems that interact in the world use knowledge organized hierarchically. For these systems, an obvious approach to learning is to compile the steps leading to a particular action, allowing an agent to become increasingly responsive to the environment with experience. This work presents an analysis of the requirements for such knowledge compilation in external domains. These requirements underspecify the exact nature of compilation, and a diverse group of architectures are considered for the addition of knowledge compilation. Although an architecture can meet the general requirements, its functional constraints can also present additional problems for realizing compilation. Analyzing these problems and the contributing functional constraints leads to the identiication of solution alternatives. A methodology for exploring the characteristics of diierent alternatives is introduced, including qualitative and quantitative evaluation criteria. These will guide both implementation decisions and the empirical comparison of implemented alternatives. Preliminary results suggest that several of the presented approaches are able to compile knowledge robustly for at least a simple task. Further, the developing methodology appears to facilitate meaningful comparison of the approaches. The proposed future work involves extending the approaches to more complex tasks and more relevant domains, in order to assess the generality of the diierent alternatives. Finally, evaluation and the possible contributions of the overall work are outlined by considering the versatility , eeciency, scalability and generality of the individual approaches to knowledge compilation to be developed and compared in the proposed thesis.
Computer Assisted Pronunciation Training: Targeting Second Language Vowel Perception Improves Pronunciation.

This paper first provides an overview of factors that constrain ultimate attainment in adult second language (L2) pronunciation, finding that first language influence and the quantity and quality of L2 phonetic input account for much of the variation in the degree of foreign accent found across adult L2 learners. The author then evaluates current approaches to computer assisted pronunciation training (CAPT), concluding that they are not well grounded in a current understanding of L2 accent. Finally, the author reports on a study in which twenty-two Mandarin speakers were trained to better discriminate ten Canadian English vowels. Using a specially designed computer application, learners were randomly presented with recordings of the target vowels in monosyllabic frames, produced by twenty native speakers. The learners responded by clicking on one of ten salient graphical images representing each vowel category and were given both visual and auditory feedback as to the accuracy of their selections. Pre- and post-tests of the learners’ English vowel pronunciation indicated that their vowel intelligibility significantly improved as a result of training, not only in the training context, but also in an untrained context. In a third context, vowel intelligibility did not improve.
Simultaneous Separation and Segmentation in Layered Music

In many pieces of music, the composer signals how individual sonic elements (samples, loops, the trumpet section) should be grouped by introducing sources or groups in a layered manner. We propose to discover and leverage the layering structure and use it for both structural segmentation and source separation. We use reconstruction error from non-negative matrix factorization (NMF) to guide structure discovery. Reconstruction error spikes at moments of signiﬁcant sonic change. This guides segmentation and also lets us group basis sets for NMF. The number of sources, the types of sources, and when the sources are active are not known in advance. The only information is a speciﬁc type of layering structure. There is no separate training phase to learn a good basis set. No prior seeding of the NMF matrices is required. Unlike standard approaches to NMF there is no need for a post-processor to partition the learned basis functions by group. Source groups are learned automatically from the data. We evaluate our method on mixtures consisting of looping source groups. This separation approach outperforms a standard clustering NMF source separation approach on such mixtures. We ﬁnd our segmentation approach is competitive with state-of-the-art segmentation methods on this dataset.
A Study of using Syntactic and Semantic Structures for Concept Segmentation and Labeling

This paper presents an empirical study on using syntactic and semantic information for Concept Segmentation and Labeling (CSL), a well-known component in spoken language understanding. Our approach is based on reranking N -best outputs from a state-of-the-art CSL parser. We perform extensive experimentation by comparing different tree-based kernels with a variety of representations of the available linguistic information, including semantic concepts, words, POS tags, shallow and full syntax, and discourse trees. The results show that the structured representation with the semantic concepts yields significant improvement over the base CSL parser, much larger compared to learning with an explicit feature vector representation. We also show that shallow syntax helps improve the results and that discourse relations can be partially beneficial.
Exceedingly biocompatible and thin-layered reduced graphene oxide nanosheets using an eco-friendly mushroom extract strategy

Purpose A simple, one-pot strategy was used to synthesize reduced graphene oxide (RGO) nanosheets by utilizing an easily available over-the-counter medicinal and edible mushroom, Ganoderma lucidum. Methods The mushroom was boiled in hot water to liberate the polysaccharides, the extract of which was then used directly for the reduction of graphene oxide. The abundance of polysaccharides present in the mushroom serves as a good reducing agent. The proposed strategy evades the use of harmful and expensive chemicals and avoids the typical tedious reaction methods. Results More importantly, the mushroom extract can be easily separated from the product without generating any residual byproducts and can be reused at least three times with good conversion efficiency (75%). It was readily dispersible in water without the need of ultrasonication or any surfactants; whereas 5 minutes of ultrasonication with various solvents produced RGO which was stable for the tested period of 1 year. Based on electrochemical measurements, the followed method did not jeopardize RGO’s electrical conductivity. Moreover, the obtained RGO was highly biocompatible to not only colon (HT-29) and brain (U87MG) cancer cells, but was also viable towards normal cells (MRC-5). Conclusion Besides being eco-friendly, this mushroom based approach is easily scalable and demonstrates remarkable RGO stability and biocompatibility, even without any form of functionalization.
Hot Gas Structure in the Elliptical Galaxy NGC 4472

We present X-ray spectroscopic and morphological analyses using Chandra ACIS and ROSAT observations of the giant elliptical galaxy NGC 4472 in the Virgo Cluster. We discuss previously unobserved X-ray structures within the extended galactic corona. In the inner 2' of the galaxy, we find X-ray holes or cavities with radii of ~2 kpc, corresponding to the position of radio lobes. These holes were produced during a period of nuclear activity that began 1.2 × 107 yr ago and may be ongoing. We also find an asymmetrical edge in the galaxy X-ray emission 3' (14 kpc) northeast of the core and an ~8' tail (36 kpc) extending southwest of the galaxy. These two features probably result from the interaction of NGC 4472 gas with the Virgo gas, which produces compression in the direction of NGC 4472's infall and an extended tail from ram pressure stripping. Assuming that the tail is in pressure equilibrium with the surrounding gas, we compute its angle to our line of sight and estimate that its true extent exceeds 100 kpc. Finally, in addition to emission from the nucleus (first detected by Soldatenkov, Vikhlinin, and Pavlinsky), we detect two small extended sources within 10'' of the nucleus of the galaxy, both of which have luminosities of ~7 × 1038 ergs s-1.
Lewy body cortical involvement may not always predict dementia in Parkinson’s disease

Background: The presence of Lewy bodies (LB) in the neocortex and limbic system in patients with Parkinson’s disease (PD) is commonly thought to be linked with cognitive impairment. The authors present here a series of patients with diagnosis of PD in life and no significant cognitive impairment who, at necropsy, satisfied the current neuropathological criteria for dementia with Lewy bodies (DLB). Methods: Two hundred and seventy six brains with PD pathology were examined at the Queen Square Brain Bank in London between 1993 and 1999. The neuropathological diagnosis was PD, but 117 patients also had sufficient LB involvement above the brain stem to satisfy the current neuropathological criteria for DLB (50 patients had a neuropathological picture consistent with the limbic category of DLB and 67 with neocortical DLB). Forty eight cases were excluded who developed early cognitive impairment together with motor features of parkinsonism, 12 cases for lack of detailed clinical history, and 19 cases with coexistent features of advanced Alzheimer’s disease changes. Thirty eight patients (13.8% of the total with PD pathology and 32.5 % of the total with DLB pathology) were found where there was no or very late cognitive impairment reported in the clinical records. Results: Selected cases were 24 men and 14 women, with a mean (SD) age at onset of parkinsonian symptoms of 60.1 (10.1) years and a mean disease duration of 15.3 (5.5) years. At some time during the evolution of the disease 21 patients developed different degrees of cognitive impairment (after a mean disease duration of 12.2 (4.8) years). Clinical diagnosis at death was PD in 10 cases and PD with dementia in 11. In the remaining 17 patients no history of cognitive impairment was ever recorded in life and all of them had a clinical diagnosis of PD at death; in this subgroup, nine patients later revealed a neuropathological picture consistent with limbic (or transitional) category of DLB and eight with neocortical DLB. Interestingly, in all these patients the parkinsonian features including the response to dopaminergic drugs were indistinguishable from classic brain stem PD. Conclusions: The authors demonstrate that the classic pathology of DLB can commonly be seen outside the generally accepted clinical spectrum for DLB and that important factors other than the absolute number of LB in the neocortex and limbic system influence the development of cognitive impairment in PD. Furthermore, the pathology of PD may be indistinguishable from that reported in DLB, suggesting that the two clinicopathological syndromes may be attributable to the same biological abnormality.
Scaffold: Quantum Programming Language

Abstract : For quantum computing to become potentially viable however, research must also make inroads regarding the implementation, compilation, and architectural issues that lie between high-level mathematical algorithm expressions and low-level physical implementations. In particular, we need tools and analysis techniques that can--for a given algorithm and potential physical implementation technology--answer questions like: how much would it cost (i.e., how much resource in terms of qubits, gates, time are required? ) to implement the algorithm in this technology? What is its performance potential? Is it scalable? Are there more algorithms that offer such speedups over classical computers? Our work is building a language and toolflow to answer such questions. This document describes the Scaffold programming language, its design goals, and related tools. Scaffold is a programming language for expressing quantum algorithms. A quantum algorithm can consist of a wide variety of components (including classical and quantum routines) which will be defined using different coding techniques. As a quantum programming language (QPL), Scaffold was formulated to make it easy to express an algorithm with so many disparate components in a clean and efficient manner. It is from this notion of putting things together that Scaffold derives its name.
Statistical Transformation of Language and Pronunciation Models for Spontaneous Speech Recognition

We propose a novel approach based on a statistical transformation framework for language and pronunciation modeling of spontaneous speech. Since it is not practical to train a spoken-style model using numerous spoken transcripts, the proposed approach generates a spoken-style model by transforming an orthographic model trained with document archives such as the minutes of meetings and the proceedings of lectures. The transformation is based on a statistical model estimated using a small amount of a parallel corpus, which consists of faithful transcripts aligned with their orthographic documents. Patterns of transformation, such as substitution, deletion, and insertion of words, are extracted with their word and part-of-speech (POS) contexts, and transformation probabilities are estimated based on occurrence statistics in a parallel aligned corpus. For pronunciation modeling, subword-based mapping between baseforms and surface forms is extracted with their occurrence counts, then a set of rewrite rules with their probabilities are derived as a transformation model. Spoken-style language and pronunciation (surface forms) models can be predicted by applying these transformation patterns to a document-style language model and baseforms in a lexicon, respectively. The transformed models significantly reduced perplexity and word error rates (WERs) in a task of transcribing congressional meetings, even though the domains and topics were different from the parallel corpus. This result demonstrates the generality and portability of the proposed framework.