Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of the discrete Fourier transform. 1. Manipulating the powers of two

Since fast algorithms for the discrete Fourier transform (DFT) were first introduced thirty years ago, they have had a major impact on signal processing and are now a basic part of every electrical engineer's education. However, some of the options, and particularly the recent advances, are not as widely known as they deserve. Over two papers, the fast algorithms for the DFT are reviewed. The first paper considers why the DFT works and looks at the various fast algorithms for transforms whose orders are a power of two. The second paper will look at techniques for other orders, at adapting algorithms for purely real data, and at the problems for fixed-point noise.
CSTA K--12 Computer Science Standards: Revised 2011

ion Understand the notion of hierarchy and abstraction in computing including highlevel languages, translation, instruction sets, and logic circuits. Discuss the value of abstraction to manage problem complexity. Describe the concept of synchronization as an algorithm to divide and conquer large problems. Connections to other fields Examine connections between elements of mathematics and computer science including binary numbers, logic, sets, and functions. Describe how mathematical and statistical functions, sets, and logic are used in computation. Use abstraction to decompose a problem into sub-problems. Decompose a problem by defining new functions and classes. Demonstrate concurrency by separating processes into threads and dividing data into parallel streams. Provide examples of interdisciplinary applications of computational thinking. Describe how computation shares features with art and music by translating human intention into an artifact. Understand the connections between other fields and computer science. Participate in a simulation to act out the solution to a local issue. Make a list of issues to consider while addressing a larger problem. Level 1A Level 1B Level 2 Level 3A Level 3B Computational Thinking
A Study on Correlation-based Cross-Modal Information Retrieval

: [ Objective ] Summarize the fundamental strategies and core issues in Cross-Modal Information Retrieval (CMIR) based on correlation, and do research about the pros and cons of using partial least squares in feature subspace projection in order to improve retrieval effect. [ Methods ] Based on Wikipedia CMIR dataset, LDA and BOW models are used as a characteristic expression of text and image resources, cosine distance as the similarity measure, and the least squares method is used to learn subspace projection function replacing canonical correlation analysis method. [ Results ] Using comparative analysis of the influence of three features subspace projection methods named canonical correlation analysis, partial least squares regression, partial least squares correlation on CMIR results according to three retrieval evaluation indicators that are P@K, MAP and NDCG, and the results show that partial least squares correlation obtains the best results. [ Limitations ] In dealing with data, partial least squares method assumes a linear relationship between the data and an orthogonal relationship between the data base vectors, therefore the non-linear, non-orthogonal problem can not be solved. [ Conclusions ] Feature subspace projection learning by using partial least squares correlation is more consistent with original spatial information, and CMIR results are more stable.
Online Web news extraction via tag path feature weighted by text block density

Web news extraction is the basis and an open research problem of many “big data” and “big knowledge” applications. Presently, tag paths and text block density are two excellent features that can help to solve this problem. The tag path feature can distinguish well the content from the noise for the whole webpage, but it has difficulty in recognizing noise in the content block or the content in the noise block. The text block density feature can recognize well the high-density content block, but it is not robust enough. Aiming at the abovementioned problems, we propose a Web information extraction model, referred to as CEDP, which can effectively combine the tag path feature and the text block density feature. We design a tag path feature weighted by the text block density in order to utilize the merits of the two features above. In addition, we design a Web news extraction method via the weighted tag path feature, CEDP-NLTD. CEDP-NLTD is a fast, universal, non-training, online Web news extraction algorithm that is suitable for extracting heterogeneous Web news from the big data environment of the Web across various resources, styles, and languages. Experiments on public datasets such as CleanEval show that the CEDP-NLTD method achieves better performance than the state-of-the-art CETR, CETD, CEPR, and CEPF methods, and it achieves better performance than CEDP-TD, CEDP-CTD, and CEDP-DSum, which are respectively generated from CEDP by using one of the three block density features of CETD.
Design of a Vibrotactile Vest for Contour Perception

A vibrotactile array is a promising human computer interface which could display graphical information to users in a tactile form. This paper presents the design and testing of an image contour display system with a vibrotactile array. The tactile image display system is attached to the back of the user. It converts visual graphics into 2D tactile images and allows subjects to feel the contours of objects through vibration stimulus. The system consists of a USB camera, 48 (6×8) vibrating motors and an embedded control system. The image is captured by the camera and the 2D contour is extracted and transformed into vibrotactile stimuli using a temporal-spatial dynamic coding method. Preliminary experiments were carried out and the optimal parameters of the vibrating time and duration were explored. To evaluate the feasibility and robustness of this vibration mode, letters were also tactilely displayed and the recognition rate about the alphabet letter display was investigated. It was shown that under the condition of no pre-training for the subjects, the recognition rate was 82%. Such a recognition rate is higher than that of the scanning mode (47.5%) and the improved handwriting mode (76.8%). The results indicated that the proposed method was efficient in conveying the contour information to the visually impaired by means of vibrations.
The methodology of a multi-model project examining how facebook infrastructures social relations

ABSTRACT It is the purpose of this paper to make explicit the methodology (the theory of the methods) by which we conducted research for an Economic and Social Research Council-funded research project on the relationship of values to value. Specifically, we wanted to study the imperative of Facebook to monetize social relationships, what happens when one of our significant forms of communication is driven by the search for profit, by the logic of capital. We therefore wanted to ‘get inside’ and understand what capital's new lines of flight, informationally driven models of economic expansion, do to social relations. Taking up the challenge to develop methods appropriate to the challenges of ‘big data', we applied four different methods to investigate the interface that is Facebook: we designed custom software tools, generated an online survey, developed data visualizations, and conducted interviews with participants to discuss their understandings of our analysis. We used Lefebvre's [(2004). Rhythmnanalysis: Space, time and everyday life. London: Continuum] rhythmanalysis and Kember and Zylinska's [(2012). Life after new media: Mediation as a vital process. Cambridge, MA: MIT Press] ideas about ‘lifeness’ to inform our methodology. This paper reports on a research process that was not entirely straightforward. We were thwarted in a variety of ways, especially by challenge to use software to study software and had to develop our project in unanticipated directions, but we also found much more than we initially imagined possible. As so few academic researchers are able to study Facebook through its own tools (as Tufekci [(2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In ICWSM ‘14: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (pp. 505–514)] notes how, unsurprisingly, at the 2013 ICWSM only about 5% of papers were about Facebook and nearly all of these were co-authored with Facebook data scientists), we hope that our methodology is useful for other researchers seeking to develop less conventional research on Facebook.
Learning in the Presence of Unawareness

Abstract : The standard assumption in game theory and decision theory is that the game/decision problem is, in a sense, completely understood. For example, decision problems are typically described in terms of states and outcomes, where acts are taken to be functions from states to outcomes. It is typically assumed that a decision maker (DM) knows the state space, the outcome space, and the set of feasible acts. But this is far from clear in practice. In a complex decision problem, agents may be unaware of many relevant features, and thus unaware of possible states, outcomes, and feasible acts.
Hierarchical analytical and simulation modelling of human-machine systems with interference

The article considers the principles of building the analytical and simulation model of the human operator and the industrial control system hardware and software. E-networks as the extension of Petri nets are used as the mathematical apparatus. This approach allows simulating complex parallel distributed processes in human-machine systems. The structural and hierarchical approach is used as the building method for the mathematical model of the human operator. The upper level of the human operator is represented by the logical dynamic model of decision making based on E-networks. The lower level reflects psychophysiological characteristics of the human-operator.
Feature methodologies for heterogeneous object realization.

Heterogeneous objects are composed of different constituent materials. They are sometime known as functionally gradient materials (FGM). They have the ability to exhibit continuously varying composition and/or microstructure, thus producing a gradation in their properties. Such material gradation can be tailored to achieve multiple functionalities and to satisfy conflicting design requirements. These properties in general cannot be achieved by using one single material. For example, a prosthesis using the graded interface in the orthopedic implant is shown in Figure 1. Conventional methods of fixing an artificial bone and joining the prosthesis to the bone include total close contact of the prosthesis to the bone. However, this causes pain to the patient during weight bearing because there is micromotion of the prosthesis within the bone, and subsequently the prosthesis may even loosen in the bone. A more effective method for adhering a prosthesis to the bone is to coat it with a porous metal because new bone grows into the pores after the implantation. A graded layer of hydroxyapatite (HAp) is coated on the porous metal. It bonds to the bone physicochemically, thereby increasing the adhesion strength and the rate of binding to the bone. Therefore, porous metal with a HAp coating remedies the drawbacks of cementless prosthesis. It prevents pain to the patient caused by micromotion while walking or loosening of a prosthesis fixed without the bone cement.
Segmenting Chinese Words from Food Safety Emergencies

: [ Objective ] This paper examines the automatic word segmentation models, which plays key roles to build databases for food safety administration. We used the statistical learning method based on conditional random field to segment words from food safety emergencies. [ Methods ] First, we analyzed the length of target words and conducted multiple experiments on the selection and template of word features for the automatic segmentation methods. Second, we identified the impacts of different features and templates to the segmentation results. [ Results ] We found that selecting more features might not yield better results due to the characteristics interference. About 46.62% of the phrases from the corpus of food safety emergencies only contained two or three words. The first words before and after the current word of the features template pose more effects to the results. [ Conclusions ] We have identified the optimal feature and template for the automatic segmentation of words and the F score reaches 92.88% with the 5Tag features.
Information Seeking Behavior of the Off-Campus Students at the University of Botswana: A Case of Two Satellite Centers

ABSTRACT The focus of this study is on the information-seeking behavior of the off-campus students in 2 of the 7 satellite centers of the Centre for Continuing Education, the outreach arm of the University of Botswana. Although the university deeply committed itself to the project, the university library could not afford to establish branch libraries in all the off-campus centers. With the nearest university branch libraries located some 160 km and 200 km away from the centers of study and only branch offices of the public library available, the findings revealed that the library and information needs of the students were not significantly met. As printed sources were revealed as their preferred choice of information format, e-mail and the Internet sources were not given much priority, even though there was evidence that suggests the students were adequately equipped through the teaching of information skills. Questionnaire and interview tools were used.
Coaching the transition to flexible learning: re-thinking instructional design

This paper reviews the experiences of an educational development project devised to support teaching staff responsible for curriculum redesign during the transition to a successful technology-facilitated off-campus study programme. The approach adopted a just-in-time (JIT) coaching model rather than a just-in-case (JIC) training model for educational support. Structured and semi-structured discussions with individuals and the entire staff cohort were used to catalyse and sustain the redesign and rethinking process. Repeatedly collecting and responding to student feedback within any given course enabled a rapid and focussed approach to redesign and redevelopment. Up to three or four iterations of course development informed in this way were required to finalise course transformation. Team and peer-group approaches to course redevelopment may help diminish the initially high level of dependency on external educational development support.
Evaluation of Applicability of SWAT-CUP Program for Hydrologic Parameter Calibration in Hardware Watershed

The purpose of this study was to calibrate the hydrologic parameters of SWAT model and analyze the daily runoff for the study watershed using SWAT-CUP. The Hardware watershed is located in Virginia, USA. The watershed area is 356.15 km 2 , and the land use accounts for 73.4 % of forest and 23.2 % of pasture. Input data for the SWAT model were obtained from the digital elevation map, landuse map, soil map and others. Water flow data from 1990 to 1994 was used for calibration and from 1997 to 2005 was for validation. The SUFI-2 module of the SWAT-CUP program was used to calibrate the hydrologic parameters. The parameters were calibrated for the highly sensitive parameters presented in previous studies. The P-factor, R-factor, R 2 , Nash-Sutcliffe efficiency (NS), and average flow were used for the goodness-of-fit measures. The applicability of the model was evaluated by sequentially increasing the number of applied parameters from 4 to 11. In this study, 10-parameter set was accepted for calibration in consideration of goodness-of-fit measures. For the calibration period, P-factor was 0.85, R-factor was 1.76, R 2 was 0.51 and NS was 0.49. The model was validated using the adjusted ranges of selected parameters. For the validation period, P-factor was 0.78, R-factor was 1.60, R 2 was 0.60 and NS was 0.57.
Automatically detected acoustic landmarks for assessing natural emotion from speech

This Master thesis focuses on the field of emotion recognition from speech, for which the interest has grown considerably during the past ten years with more than 100 papers per year since 2004. Today, sophisticated and relevant methods to extract features from speech permitting to analyse emotions are available. Although these methods give convincing results on acted emotions, such emotions are not really natural and used techniques are thus not applicable in the real world. Current techniques mainly focus on acoustic speech features, e.g., pitch, energy, voice quality, and lead to poor performances when they are applied on spontaneous and natural recording of speech. Thus, the present challenge for this field resides in the search for relevant techniques permitting to a robust emotion recognition from spontaneous speech recordings. A possible solution would consist in exploiting linguistic features that are being used for the emotion recognition task. However, to enabling future non-human interaction partners, the use of linguistic features has to be done in an automatic way, as it is the case for the analysis of the acoustic features. Until now, only few studies focused on emotion recognition through linguistic features. Some used automatic speech recognition (ASR) systems output to extract features rather than manual annotation of the data. Results show that experiments based on ASR output lead to lower performances than experiments based on manual annotation. This comes from the fact that ASR is likely to introduce inaccuracies in the transcriptions, because the emotion introduces significant variabilities. This Master thesis investigates new techniques for linguistic features extraction from speech with the aim to achieve emotion recognition without using ASR systems. Thus, instead of using word transcription from ASR systems, our technique exploits acoustic landmarks, i.e., events correlated with changes in speech production and perception, which are automatically detectable in the speech signal. We compared emotion recognition performance of manually transcribed words and automatically detected acoustic landmarks on the same corpus (SEMAINE), using state-of-the-art methods for features extraction, e.g., Bag of words and n-grams. Results show that considering simple landmarks as voiced/unvoiced segments of speech already leads to better performance than using manually transcribed words. When considering vowels/consonant landmarks or the p-center landmark alone, i.e., rhythmic events, no improvement can be observed. Whereas best scores are obtained on the fusion of voiced/unvoiced landmarks with p-centers. Although the difference between scores obtained on words and those from the fusion of voiced/unvoiced landmarks and p-centers is not significant, this work shows the interest of working with acoustic landmarks. Indeed, by automatically extracting linguistic features through the acoustic signal, we are able to overcome the need of working with ASR systems, while improving scores.
Clustering Analysis of Attitudes of Prospective Computer Programmers towards Programming

This study aim to determine the clustering tendencies of attitude variables of the students studying at computer programming department towards computer programming and inspect whether factors such as gender, grade and type of education (daytime and evening education) have an influence on the clusters obtained from the analysis. The study is conducted in fall semester of 2013-2014 academic year. The sample of the study consists of 947 students from 12 universities in different regions of Turkey. “Attitude Scale toward Computer Programming” is used as a data collecting tool. Clustering tendencies of attitude variables are determined by hierarchical clustering analysis. Independent t-test is used to determine whether gender, grade and type of education influence these clusters. Attitude variables of the students are collected under six clusters named “To become prominent in programming”, “Need for programming and importance of it”, “Interest for programming and success rate”, “Failure in programming”, “Indifference in programming” and “Unimportance of programming”. It has been seen that gender factor has a significant effect on “Interest for programming and success rate”, “Failure in programming”, and “Indifference in programming” clusters and, grade factor has significant effect on “Interest for programming and success rate”, “Failure in programming”, “Indifference in programming” and “Unimportance of programming” statistically. Type of education factor has no statistically significant effect on these clusters.
The Use of Artificial Neural Network and Support Vector Classification for Recovery Factor Prediction

Oil and gas recovery factor is a key parameter for oil industry, and it can be effectively predicted by appropriate data mining algorithms. In this paper, three regression algorithms and three classification algorithms have been applied to forecast recovery factor of oil and gas. The three regression algorithms are the support vector regression (SVR), the artificial neural network (ANN), and the multiple regression analysis (MRA), while the three classification algorithms are the support vector classification (SVC), the naive Bayesian (NBAY), and the Bayesian successive discrimination (BAYSD). The purpose of this paper is to demonstrate how to select proper algorithms in three algorithms (SVR, ANN, MRA) for recovery factor regression and/or three algorithms (SVC, NBAY, BAYSD) for recovery factor classification. In general, when all these six algorithms are used to solve a real-world problem, they often produce different solution accuracies. Toward this issue, it has been proposed that a) when an algorithm is applied to a real-world problem, its solution accuracy is expressed with the total mean absolute relative residual for all samples, R(%), and b) result availability of a given algorithm application is applicable if R(%)<10, and inapplicable if R(%)≥10. A case study of recovery factor in 39 global oilfields has been used to validate the proposed approaches. This case study consists of two problems: regression and classification. For the regression problem, only ANN is applicable since its R(%) value is 5.89, whereas SVR and MRA are inapplicable because their R(%) values are 68.9 and 38.4, respectively. For the classification problem, only SVC is applicable since its R(%) value is 0, whereas NBAY and BAYSD are inapplicable because their R(%) values are 24.7 and 34.5, respectively. From this case study, it is concluded that the preferable algorithm is ANN for recovery factor regression, while the preferable algorithm is SVC for recovery factor classification.
A REVIEW ON RESOURCE ALLOCATION MECHANISM IN CLOUD ENVIORNMENT

Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Cloud computing is a hot topic all over the world nowadays, through which customers can access information and computer power via a web browser. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Cloud computing means saving and accessing the data over the internet instead of local storage. In this paper, we have provided a short review on the types, models and architecture of the cloud environment.