Miracle-dz/UniFAR-FTUs huggingface dataset:
Creativity-Supporting Learning Environment---CSLE

Despite much public discussion about the importance of creativity and innovation-friendly teaching in Finnish higher education, the impact of the general opinion on actual teaching practices has been limited. In the Finnish computer science education the teaching mostly follows a pattern of lectures, fixed exercise sets, and exams. With this article we want to open a discussion about possibilities of enhancing the learning environment by focusing on creative problem solving. We will present results from two research experiments in which we aimed to provide computer science students with a practically oriented learning environment with an explicit intention of supporting the creative work of students. There exists a vast amount of scientific theory about creativity, yet it is unclear on how to turn that theory into practice. Thus, our main interest was to find ways of applying creativity theory in practice in the context of computer science education. Our research experiments consist of a practically oriented computer science course, where LEGO®Mindstorms robots were used as the platform for the student work. Methodological tools used in this study included content analysis of student products, observations from our learning sessions and semi-structured interviews with the students. The course was organised two times: the first time was in spring 2009 and the second in spring 2010. The total number of attending students was 72. In this article we argue that our approach of providing a creativity-supporting practical computer science course was a success. We gained a lot of ideas on how to support creativity, the students were clearly motivated, and they began to learn a new kind of experimental working style. The robotics kit seemed to work well both as a trigger for motivation and as a platform to support experimental learning, enhancing students’ creativity and working style. In our opinion these findings are of great importance, and give promising practical ideas for the support of creativity in higher computer science education.
Civil society under strain : counter-terrorism policy, civil society, and aid post-9/11

1) Introduction 2) Resistance, Compliance, Alliance, and Self-Regulation: Nonprofit Sector Responses to Counter-Terrorism Law and Policy in the United States 3) An Exceptional Response? : Security and Civil Society in Spanish Policy After 11th March 2004 4) Uzbekistan: Osama or the Georges - Shifts in Perception of Threat in Uzbekistan and the Impact on Civil Society 5) Abandoning the Human Security Agenda? Counting the Cost in Iraq and Afghanistan? 6) Kenya 7) Afghanistan: "Civil Society with Guns is not Civil Society" 8) Conclusion.
Cybercrime detection techniques based on support vector machines

This paper presents the cybercrime detection model by using support vector machines (SVMs) to classify social network (Facebook) dataset. We try to compare between three kinds of classification algorithms such as: SVMs, AdaBoostM1, and NaiveBayes in order to find a high percentage of classification accuracy. Finally, we conclude SVMs as the best classification algorithm, which uses different breeds of kernel functions in order to improve the classification accuracy on Facebook dataset. Besides, we are using the Weka 3.7. 4 software to evaluate classifiers on Facebook dataset.
X-ray emission from GPS and CSS sources

Many X-ray observations of GigaHertz Peaked Spectrum and Compact Steep Spectrum sources have been made with Chandra X-ray Observatory and XMM-Newton Observatory over the last few years. The X-ray spectra contribute the important information to the total energy distribution of the compact radio sources. In addition the spatial resolution of Chandra allows for studies of the X-ray morphology of these sources on arcsec scales and provide a direct view of their environments. This paper gives a review of the current status of the X-ray observations and their contribution to our understanding of the nature of these compact radio sources. It also describes primary physical processes that lead to the observed X-ray emission and summarize X-ray emission properties expected from interactions between an expanding radio source and the intergalactic environment.
Partial Activation of SA- and JA-Defensive Pathways in Strawberry upon Colletotrichum acutatum Interaction

Understanding the nature of pathogen host interaction may help improve strawberry (Fragaria × ananassa) cultivars. Plant resistance to pathogenic agents usually operates through a complex network of defense mechanisms mediated by a diverse array of signaling molecules. In strawberry, resistance to a variety of pathogens has been reported to be mostly polygenic and quantitatively inherited, making it difficult to associate molecular markers with disease resistance genes. Colletotrichum acutatum spp. is a major strawberry pathogen, and completely resistant cultivars have not been reported. Moreover, strawberry defense network components and mechanisms remain largely unknown and poorly understood. Assessment of the strawberry response to C. acutatum included a global transcript analysis, and acidic hormones SA and JA measurements were analyzed after challenge with the pathogen. Induction of transcripts corresponding to the SA and JA signaling pathways and key genes controlling major steps within these defense pathways was detected. Accordingly, SA and JA accumulated in strawberry after infection. Contrastingly, induction of several important SA, JA, and oxidative stress-responsive defense genes, including FaPR1-1, FaLOX2, FaJAR1, FaPDF1, and FaGST1, was not detected, which suggests that specific branches in these defense pathways (those leading to FaPR1-2, FaPR2-1, FaPR2-2, FaAOS, FaPR5, and FaPR10) were activated. Our results reveal that specific aspects in SA and JA dependent signaling pathways are activated in strawberry upon interaction with C. acutatum. Certain described defense-associated transcripts related to these two known signaling pathways do not increase in abundance following infection. This finding suggests new insight into a specific putative molecular strategy for defense against this pathogen.
Iterative Channel Estimation Using LSE and Sparse Message Passing for MmWave MIMO Systems

We propose an iterative channel estimation algorithm based on the least square estimation (LSE) and sparse message passing (SMP) algorithm for the millimeter wave (mmWave) MIMO systems. The channel coefficients of the mmWave MIMO are approximately modeled as a Bernoulli–Gaussian distribution and the channel matrix is sparse with only a few nonzero entries. By leveraging the advantage of sparseness, we propose an algorithm that iteratively detects the exact locations and values of nonzero entries of the sparse channel matrix. At each iteration, the locations are detected by the SMP, and values are estimated with the LSE. We also analyze the Cramér–Rao Lower Bound (CLRB), and show that the proposed algorithm is a minimum variance unbiased estimator under the assumption that we have the partial priori knowledge of the channel. Furthermore, we employ the Gaussian approximation for message densities under density evolution to simplify the analysis of the algorithm, which provides a simple method to predict the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm has much better performance than the existing sparse estimators, especially when the channel is sparse. In addition, our proposed algorithm converges to the CRLB of the genie-aided estimation of sparse channels with only five turbo iterations.
Bayes risk weighted vector quantization with posterior estimation for image compression and classification

Classification and compression play important roles in communicating digital information. Their combination is useful in many applications, including the detection of abnormalities in compressed medical images. In view of the similarities of compression and low-level classification, it is not surprising that there are many similar methods for their design. Because some of these methods are useful for designing vector quantizers, it seems natural that vector quantization (VQ) is explored for the combined goal. We investigate several VQ-based algorithms that seek to minimize both the distortion of compressed images and errors in classifying their pixel blocks. These algorithms are investigated with both full search and tree-structured codes. We emphasize a nonparametric technique that minimizes both error measures simultaneously by incorporating a Bayes risk component into the distortion measure used for the design and encoding. We introduce a tree-structured posterior estimator to produce the class posterior probabilities required for the Bayes risk computation in this design. For two different image sources, we demonstrate that this system provides superior classification while maintaining compression close or superior to that of several other VQ-based designs, including Kohonen's (1992) "learning vector quantizer" and a sequential quantizer/classifier design.
Journal of Modern Applied Statistical Methods a Fano-huffman Based Statistical Coding Method a Fano-huffman Based Statistical Coding Method

Statistical coding techniques have been used for lossless statistical data compression, applying methods such as Ordinary, Shannon, Fano, Enhanced Fano, Huffman and Shannon-Fano-Elias coding methods. A new and improved coding method is presented, the Fano-Huffman Based Statistical Coding Method. It holds the advantages of both the Fano and Huffman coding methods. It is more easily applicable than the Huffman coding methods and it is more optimal than Fano coding method. The optimality with respect to the other methods is realized on the basis of
A Survey on Effective Video Retrieval using Image Fuzzy Ontology

In this paper a fuzzy basedapproach is used forEffective retrieval of video.Initially a video ispartitioned into framesand then we use fuzzylogic method in order to retrieve a video.Content based Video Retrieval (CBVR) involves the process for retrieving a set of alike video shots from a large database, with similar content as that of the query video shot. Two significant cues in this context are shape (of the moving object) and motion kinematics (movement pattern), which describe the lowlevel content of a video shot. This finds application in content analysis, video on demand, duplicate detection and incident analysis
The Egyptian labor market revisited

This book offers a ground-breaking research methodology applied to an analysis of labor issues in Egypt. This volume is a sequel to a 1998 publication by the Economic Research Forum (ERF). Its true significance lies in the contributors' reliance on fresh data and solid analytical techniques used to examine a wide spectrum of pertinent issues concerning the labor market in Egypt. The range of topics includes labor supply, employment and unemployment, youth labor market school-to-work transition, internal and international migration, earnings and inequality, and gender and education. The papers in this volume are the very first research available based on data collected in the Egypt Labor Market Panel Survey of 2006, a follow-up to the Egypt Labor Market Survey of 1998. The panel design used for collecting data is state-of-the-art methodology in the labor field, and has never before been implemented in Egypt on this scale.
Insights to Variable Block Size Motion Estimation by Design Space Exploration

Numerous different implementations for H.264/AVC variable block size motion estimation have been proposed in the recent years to make this computationally challenging task more feasible for mobile devices with video encoding support. The variable block size motion estimation problem defined by the standard is complex and multidimensional, offering a wide variety of possibilities for efficient implementation. One of the most popular implementation architectures are systolic arrays. In this paper we look at the full-search variable block size motion estimation problem on 1D systolic arrays from a high level by modeling the system with a software tool that enables design space exploration and cycle-accurate simulation. Our design space exploration tool has provided many interesting insights to the VBSME problem that give directions for making efficient designs.
Learning the Optimal Control of Coordinated Eye and Head Movements

Various optimality principles have been proposed to explain the characteristics of coordinated eye and head movements during visual orienting behavior. At the same time, researchers have suggested several neural models to underly the generation of saccades, but these do not include online learning as a mechanism of optimization. Here, we suggest an open-loop neural controller with a local adaptation mechanism that minimizes a proposed cost function. Simulations show that the characteristics of coordinated eye and head movements generated by this model match the experimental data in many aspects, including the relationship between amplitude, duration and peak velocity in head-restrained and the relative contribution of eye and head to the total gaze shift in head-free conditions. Our model is a first step towards bringing together an optimality principle and an incremental local learning mechanism into a unified control scheme for coordinated eye and head movements.
A Hybrid Method for Eyes Detection in Facial Images

This paper proposes a hybrid method for eyes local- ization in facial images. The novelty is in combining techniques that utilise colour, edge and illumination cues to improve accuracy. The method is based on the observation that eye regions have dark colour, high density of edges and low illumination as compared to other parts of face. The first step in the method is to extract connected regions from facial images using colour, edge density and illumination cues separately. Some of the regions are then removed by applying rules that are based on the general geometry and shape of eyes. The remaining connected regions obtained through these three cues are then combined in a systematic way to enhance the identification of the candidate regions for the eyes. The geometry and shape based rules are then applied again to further remove the false eye regions. The proposed method was tested using images from the PICS facial images database. The proposed method has 93.7% and 87% accuracies for initial blobs extraction and final eye detection respectively.
Econometric methods for evaluating the cost-effectiveness of health care interventions using observational data

This thesis explores the use of observational microdata in cost-effectiveness analysis. The application of econometric methods adjusting for selection bias is first reviewed and critically appraised in the economic evaluation literature using a structured template. Limitations of identified studies include lack of good quality evidence regarding the performance of different analytical approaches; inadequate assessment of the sensitivity of their results to violations of fundamental assumptions or variations to crucial estimator parameters; failure to combine the cost and effectiveness outcomes in a summary measure; and no consideration of stochastic uncertainty for the purpose of evaluating cost-effectiveness. Data from the Birthplace national cohort study are used in an attempt to address these limitations in the context of an empirical comparison of estimators relying on regression, matching, as well as the propensity score. It is argued that although these methods cannot address the potential impact of unobservable confounding, a novel approach to bias-corrected matching, combining entropy balancing with seemingly unrelated regression, still has the potential to offer important advantages in terms of analytical robustness. The net economic benefit is proposed as a straightforward way to exploit the strengths of rigorous econometric methodology in the development of reliable and informative cost-effectiveness analyses.
Special Topic: Calcium Signaling Membrane Protein Reconstitution for Functional and Structural Studies

Membrane proteins are involved in various critical biological processes, and studying membrane proteins represents a major challenge in protein biochemistry. As shown by both structural and functional studies, the membrane environment plays an essential role for membrane proteins. In vitro studies are reliant on the successful reconstitution of membrane proteins. This review describes the interaction between detergents and lipids that aids the understanding of the reconstitution processes. Then the techniques of detergent removal and a few useful techniques to refine the formed proteoliposomes are reviewed. Finally the applications of reconstitution techniques to study membrane proteins involved in Ca 2+ signaling are summarized. Citation: Wang LG, Tonggu LG. Membrane protein reconstitution for functional and structural studies. Membrane proteins are essential for cellular life. They are involved in various critical biological processes, such as ion and solute transport (ion channels and transporter proteins), energy conversion (proteins for respiration and ATP synthesis), and cell signaling (e.g., G protein-coupled receptors and receptor kinases). Consequently about a quarter of the genes in most genomes encodes membrane proteins. Membrane proteins account for 70% of all known pharmacological targets and 50% of potential new drug targets due to their intimate involvement in a wide variety of diseases, including diabetes, cancer and neurological disorders [2]. As shown by both structural and functional studies, the membrane environment plays an essential role for membrane proteins [35]. It is better to study membrane proteins in their native environment (i.e., cell membranes). However, the result can be difficult to interpret due to the complexity of the native membranes and the interferences with other membrane constituents or other reactions. Therefore, recon-stitution of purified membrane proteins into liposomes (proteoliposomes) has been developed and served as a powerful tool for elucidating both functional and structural aspects of membrane proteins. The reconstitution of membrane proteins into liposomes makes it possible to accurately control the factors (e.g., lipid composition, and interacting proteins) that may affect the functions of the target proteins. This approach has been employed successfully to study the function of various ion channels and receptors, such as sodium channels [6], potassium channels [7,8], calcium channels [9], and IP 3 Rs [10] assessed by measuring the movement of radioactive ions/calcium ions, or the change of membrane potentials. Studies of membrane protein functions in reconstituted liposomes started in 1970s by the pioneering work of Rack-er and colleagues [1113]. In the past four decades, a large number of …
Accuracy of an UWB localization system based on a CMOS chip

In this paper, we analyze the achievable location accuracy of a high-precision localization system using time-difference-of-arrival measurements of ultra wide-band (UWB) pulses. This system is under development at our institute and based on a low-power CMOS chip. Similar commercially available localization systems achieve location accuracies (location error standard deviation) down to 15cm under line-of-sight conditions in a working range of 20m indoors (30cm in a 100m range outdoors). We review the fundamental limits of the ranging accuracy of any localization system satisfying the regulatory constraints for UWB emissions. Next, we incorporate realistic characteristics of the UWB radio channel and the CMOS receiver into the analysis. We show that the ranging accuracy is heavily influenced by the available signal-to-noise ratio, i.e., the distance between the antennas, and the multi-path nature of the UWB radio channel. The effect of sampling time jitter of the receiver clock is shown to be negligible. With our proposed system design a ranging accuracy down to 1cm in a working range of 40m can be achieved, which outperforms the accuracy of existing systems.
Week one FLT-PET response predicts complete remission to R-CHOP and survival in DLBCL

Despite improved survival in the Rituximab (R) era, a considerable number of patients with diffuse large B-cell lymphoma (DLBCL) ultimately die from the disease. Functional imaging using [18F]fluorodeoxyglucose-PET is suggested for assessment of residual viable tumor very early during treatment but is compromised by non-specific tracer retention in inflammatory lesions. The PET tracer [18F]fluorodeoxythymidine (FLT) as surrogate marker of tumor proliferation may overcome this limitation. We present results of a prospective clinical study testing FLT-PET as superior and early predictor of response to chemotherapy and outcome in DLBCL. 54 patients underwent FLT-PET prior to and one week after the start of R-CHOP chemotherapy. Repetitive FLT-PET imaging was readily implemented into the diagnostic work-up. Our data demonstrate that the reduction of FLT standard uptake valuemean (SUVmean) and SUVmax one week after chemotherapy was significantly higher in patients achieving complete response (CR, n=48; non-CR, n=6; p<0.006). Martingale-residual and Cox proportional hazard analyses showed a significant monotonous decrease of mortality risk with increasing change in SUV. Consistent with these results, early FLT-PET response showed relevant discriminative ability in predicting CR. In conclusion, very early FLT-PET in the course of R-CHOP chemotherapy is feasible and enables identification of patients at risk for treatment failure.
Evaluation of the Energy Consumption Introduced by a Trust Management Scheme on Mobile Ad-hoc Networks

Nodes in mobile ad-hoc networks communicate each other using the wireless transmission. Many protocols have been proposed allowing the establishment of multi-hop paths to connect source and destination nodes. The absence of a physical link between the nodes and the multi-hop routing lead to a lack of security. Different typologies of attack effective against MANETs have been investigated thoroughly, and many solutions have been proposed to take the required countermeasures. Many of them are based on the cryptography, protecting data with digital signatures and hash chains, but they are not useful when a fair node is compromised later in time. Using a Trust Management Scheme can help in these situations, allowing to evaluate dynamically if a node is trustworthy or not. The computation of a trust value requires the monitoring of the interactions between nodes, therefore it has an effect on the energy consumption, which is an important issue of ad-hoc networks. Energy availability is limited for the nodes in a MANET, so the security measures that they adopt must not excessively increase the consumption.
Learning Location from Vibration

Identifying the “semantic” location of a phone — for example, determining if it is resting on a desk, in a pocket, or on a chair — is a growing area of interest as smartphones become more prevalent. Taking advantage of the accelerometers and vibrators common in these devices, we use a machine learning approach to classify semantic location of a phone, even when it is at rest on a solid surface. An analysis of features extracted from time-series acceleration data and an ensuing classification process reveals the effectiveness of the approach. This analysis culminates in an implementation of a decision tree that produces 78% accuracy for a two-class demonstration.