Miracle-dz/UniFAR-FTUs huggingface dataset:
Frustrated Achievers: Winners, Losers, and Subjective Well Being in New Market Economies

To date the literature on subjective well-being has focused on the developed economies. We provide empirical evidence from two emerging market countries, Peru and Russia. Our results - and in particular a strong negative skew in the assessments of the respondents with the greatest income gains - support the importance of relative rather than absolute income differences. Among other factors, we attribute our results to shifts in reference norms and to macroeconomic volatility. Relative differences seem to matter more for those in the middle of the distribution than for the very wealthy or the very poor. Our respondents were more critical in assessing their progress vis-a-vis others in their country versus those in their community. The large and consistent gap we find between objective income trends and the subjective assessments of the upwardly mobile may have implications for the future economic and political behaviour of a group that is critical to the sustainability of market policies.
The Logics and Politics of Post-WWII Migration to Western Europe: The Organized Nativist Backlash

1. Introduction: immigration and state sovereignty 2. The origins and trajectory of post-WWII immigration 3. The organized nativist backlash: the surge of anti-immigrant groups 4. Immigration and state sovereignty: implications of the British and German cases 5. The logics and politics of a European immigration policy regime 6. The domestic legacies of postwar immigration: citizenship, monoculturalism, and the Keynesian welfare state 7. The logics and politics of immigrant political incorporation.
Direction of Movement Is Encoded in the Human Primary Motor Cortex

The present study investigated how direction of hand movement, which is a well-described parameter in cerebral organization of motor control, is incorporated in the somatotopic representation of the manual effector system in the human primary motor cortex (M1). Using functional magnetic resonance imaging (fMRI) and a manual step-tracking task we found that activation patterns related to movement in different directions were spatially disjoint within the representation area of the hand on M1. Foci of activation related to specific movement directions were segregated within the M1 hand area; activation related to direction 0° (right) was located most laterally/superficially, whereas directions 180° (left) and 270° (down) elicited activation more medially within the hand area. Activation related to direction 90° was located between the other directions. Moreover, by investigating differences between activations related to movement along the horizontal (0°+180°) and vertical (90°+270°) axis, we found that activation related to the horizontal axis was located more anterolaterally/dorsally in M1 than for the vertical axis, supporting that activations related to individual movement directions are direction- and not muscle related. Our results of spatially segregated direction-related activations in M1 are in accordance with findings of recent fMRI studies on neural encoding of direction in human M1. Our results thus provide further evidence for a direct link between direction as an organizational principle in sensorimotor transformation and movement execution coded by effector representations in M1.
A Hybrid Dimensionality Reduction Method Based on Support Vector Machine and Independent Component Analysis

—This paper presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) and data independence (unsupervised criterion). Classification accuracy is used as a metric to evaluate the performance of the method. By minimizing the structural risk, projection originated from the decision boundaries directly improves classification performance from a supervised perspective. From an unsupervised perspective, projection can also be obtained based on maximum independence among features (or attributes) in data to indirectly achieve better classification accuracy over more intrinsic representation of the data. Orthogonality interrelates the two sets of projections such that minimum redundancy exists between the projections, leading to more effective dimensionality reduction. Experimental results show that the proposed hybrid dimensionality reduction method that satisfies both criteria simultaneously provides higher classification performance, especially for noisy data sets, in relatively lower dimensional space than various existing methods.
On Uncertainties in Cross‐Correlation Lags and the Reality of Wavelength‐dependent Continuum Lags in Active Galactic Nuclei

We describe a model‐independent method of assessing the uncertainties in cross‐correlation lags determined from the light curves of active galactic nuclei (AGNs) and use this method to investigate the reality of lags between UV and optical continuum variations in well‐studied AGNs. Our results confirm the existence of such lags in NGC 7469. We find that the continuum variations at 1825, 4845, and 6962 Å follow those at 1315 Å by 0.22+0.12-0.13,1.25+0.48-0.35, and 1.84+0.93-0.94 days, respectively, based on the centroids of the cross‐correlation functions; the error intervals quoted correspond to 68% confidence levels, and each of these lags is greater than zero at no less than 97% confidence. We do not find statistically significant interband continuum lags in NGC 5548, NGC 3783, or Fairall 9. Wavelength‐dependent continuum lags may be marginally detected in the case of NGC 4151. However, on the basis of theoretical considerations, wavelength‐dependent continuum lags in sources other than NGC 7469 are not expected to have been detectable in previous experiments. We also confirm the existence of a statistically significant lag between X‐ray and UV continuum variations in the blazar PKS 2155−304.
The rise & fall of #NoBackDoor on Twitter: The apple vs. FBI case

In addition to using social media to connect with others worldwide, many people nowadays get their news about different national or international events such as natural disasters, crises, political elections, conflicts etc. via social media. This evolution in the usage of social media has not only led to the generation of massive amounts of data but also various information consumption behaviors. In this study, we developed a framework that can be used to monitor/understand, analyze, and visualize in real time how people consume information and react to events. Following the case study of Apple, Inc. vs. FBI, we tracked the usage of the #NoBackDoor on Twitter in real time and were able to understand what people are thinking about the case and who are the actors involved in this network. The framework can be applied to study other events and provide a deeper understanding of how public sentiments evolve during an event, whether it is a crisis or major news event.
A Collaborative Filtering Method Based on the Forgetting Curve

Collaborative filtering (CF) is one of the most successful approaches for making personalized recommendations. This paper aim sat the issue of tracking the drifting of the user's interests, and proposes a novel collaborative filtering recommendation method based on Ebbinghaus Forgetting Curve. The new method learns and tracks the user's interests by defining the user's interests as the short-term interest and the long-term interest, and by defining the weight function based on the time-window as well. In order to produce high quality recommendations, both the data weight based on the time-window and the data weight based on the item-similarity are used. Furthermore, the paper finds a special power function curve is much more fit to the forgetting curve through a mathematical analysis tool. Comparative experiments with the standard data show that the proposed method providing dramatically better quality recommendations.
Pervasive Malware Propagation Mechanism and Mitigation Techniques

i.e. malicious code/softwares poses prevalent threat to businesses and network across distributed systems. Like it is said in order to catch criminals, we have to think like a criminal, likewise in order to catch cyber criminals/terrorists, we have to think like a cyber-criminal. Malware campaigns have been the driving engines for cyber-warfare being used by cyber criminals & black hat hackers to target organizations, various governments, and financial institutions for leverage & selfish profits, since early decade. In the recent trends of past years, the sophistication of malware campaigns have grown more complex to perform targeted successful attacks and bypass the prevailing & evolving defense mechanisms out there. Our approach is motivated by the factor that malwares breed on the vulnerability of the software applications running across the web. Idea behind pervasive malware propagation mechanism is to provide insight towards various exploitable scenarios based on vulnerabilities and software coding flaws in the software system, its architecture and over the network. Understanding the control flow structure of malware propagation into the system & over the network provides greater insight into how vulnerabilities are being exploited , how target surface identification is being carried out by the attackers, how exactly the exploits are being delivered using the payloads & what mechanism is being used to maintain the access to the exploited victim over the network. Eventually some suggestions as precautionary mitigation mechanisms to stop the malware propagation.
Quantitative comparison of the OCT imaging depth at 1300 nm and 1600 nm

One of the present challenges in optical coherence tomography (OCT) is the visualization of deeper structural morphology in biological tissues. Owing to a reduced scattering, a larger imaging depth can be achieved by using longer wavelengths. In this work, we analyze the OCT imaging depth at wavelengths around 1300 nm and 1600 nm by comparing the scattering coefficient and OCT imaging depth for a range of Intralipid concentrations at constant water content. We observe an enhanced OCT imaging depth for 1600 nm compared to 1300 nm for Intralipid concentrations larger than 4 vol.%. For higher Intralipid concentrations, the imaging depth enhancement reaches 30%. The ratio of scattering coefficients at the two wavelengths is constant over a large range of scattering coefficients and corresponds to a scattering power of 2.8 ± 0.1. Based on our results we expect for biological tissues an increase of the OCT imaging depth at 1600 nm compared to 1300 nm for samples with high scattering power and low water content.
Powerwall Interactions

Analyzing large data sets on ordinary computer screens can be very cumbersome as size and resolution of desktop screens are rather limited. As a potential solution, ultra-high resolution powerwalls extend the researcher’s work space and facilitate the analysis of large data sets. However, the interaction with powerwalls requires new interface technologies beyond keyboard and mouse. This paper contributes an interaction concept which allows several users to collaboratively interact with powerwalls. The interaction concept uses gesture recognition and table-top techniques to control visualizations on a powerwall. To test our concept in practice we have developed a framework that connects devices, in particular table-top computers, gesture recognition devices and a powerwall with a server. This server renders visualizations on the powerwall. We brieﬂy discuss results of a prototypical implementation in an eye-tracking data analysis scenario.
Robust Controller design for active vibration control

The noise and vibration cancellation is important for precise control, especially for aeronautics, astronautics and advanced manufacture. A low order HR, robust controller is designed for the active vibration control by using the magnetostrictive actuator. XY-centering algorithm is employed to design the low order HR, robust controller. The numerical simulation solution shows the vibration is reduced more than 20dB using the designed HR, robust controller, which order is lower than the order of generalized actuator model. The real-time experimental solution is also given to show that the designed controller is efficient.
Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning

Hierarchical feature discovery using non-spiking convolutional neural networks (CNNs) has attracted much recent interest in machine learning and computer vision. However, it is still not well understood how to create a biologically plausible network of brain-like, spiking neurons with multi-layer, unsupervised learning. This paper explores a novel bio-inspired spiking CNN that is trained in a greedy, layer-wise fashion. The proposed network consists of a spiking convolutional-pooling layer followed by a feature discovery layer extracting independent visual features. Kernels for the convolutional layer are trained using local learning. The learning is implemented using a sparse, spiking auto-encoder representing primary visual features. The feature discovery layer extracts independent features by probabilistic, leaky integrate-and-fire (LIF) neurons that are sparsely active in response to stimuli. The layer of the probabilistic, LIF neurons implicitly provides lateral inhibitions to extract sparse and independent features. Experimental results show that the convolutional layer is stack-admissible, enabling it to support a multi-layer learning. The visual features obtained from the proposed probabilistic LIF neurons in the feature discovery layer are utilized for training a classifier. Classification results contribute to the independent and informative visual features extracted in a hierarchy of convolutional and feature discovery layers. The proposed model is evaluated on the MNIST digit dataset using clean and noisy images. The recognition performance for clean images is above 98%. The performance loss for recognizing the noisy images is in the range 0.1% to 8.5% depending on noise types and densities. This level of performance loss indicates that the network is robust to additive noise.
Implementing a Code Generator for Fast Matrix Multiplication in OpenCL on the GPU

This paper presents results of an implementation of code generator for fast general matrix multiply (GEMM) kernels. When a set of parameters is given, the code generator produces the corresponding GEMM kernel written in OpenCL. The produced kernels are optimized for high-performance implementation on GPUs from AMD. Access latencies to GPU global memory is the main drawback for high performance. This study shows that storing matrix data in a block-major layout increases the performance and stability of GEMM kernels. On the Tahiti GPU (Radeon HD 7970), our DGEMM (double-precision GEMM) and SGEMM (single-precisionGEMM) kernels achieve the performance up to 848 GFlop/s (90% of the peak) and 2646 GFlop/s (70%), respectively.
Predicting Pedestrian Counts in Crowded Scenes With Rich and High-Dimensional Features

Estimating the number of pedestrians in surveillance images and videos has important applications in intelligent transportation systems. This problem is particularly challenging when the scenes are densely crowded, in which the techniques of tracking a single pedestrian has limited effectiveness. Alternative approaches employ statistical learning algorithms to infer pedestrian counts directly from visual features computed on images or scenes. In this paper, we describe a system for predicting pedestrian counts that significantly extends the utility of those ideas. Our approach incorporates a richer set of features for statistical modeling. While these features give rise to regression problems in a high-dimensional space, we leverage learning techniques to reduce dimensionality while still attaining high accuracy for predicting the number of pedestrians. Empirical results have validated our strategy. Specifically, our system outperforms state-of-the-art methods on standard benchmark tasks by a large margin.
A Decision-Making Model Based on Persuasive Consistency Applied to MMORPGs

RPG is a game genre that focuses the cooperative interaction among players, as well as socialization and the improvement of communication skills. As these actions form the very context of these games, RPGs have an inherent level of decision-making that naturally occurs at all times throughout the course of the game. Those reasons led the idea to take advantage of the RPG genre to design a model that is built in a Petri Net and that combines concepts taken from the Game Theory and from the Theory of Persuasion for easing the decision-making process in, particularly, MMORPG environments, as they have all the benefits of the RPG genre and additionally, involves a massive number of players in a kind of plot, thus, reinforcing the needing of interaction and of taking decisions.
Motion Estimation Architecture Using Efficient Adder-Compressors for HDTV Video Coding

This paper presents real time HDTV (High Definition Television) architecture for Motion Estimation (ME) using efficient adder compressors. The architecture is based on the Quarter Sub-sampled Diamond Search algorithm (QSDS) with Dynamic Iteration Control (DIC) algorithm. The main characteristic of the proposed architecture is the large amount of Processing Units (PUs) that are used to calculate the SAD (Sum of Absolute Difference) metric. The internal structures of the PUs are composed by a large number of addition operations to calculate the SADs. In this paper, efficient 42 and 8-2 adder compressors are used in the PUs architecture to achieve the performance to work with HDTV (High Definition Television) videos in real time at 30 frames per second. These adder compressors enable the simultaneous addition of 4 and 8 operands respectively. The PUs, using adder compressors, were applied to the ME architecture. The implemented architecture was described in VHDL and synthesized to FPGA and, with Leonardo Spectrum tool, to the TSMC 0. 18µm CMOS standard cell technology. Synthesis results indicate that the new QSDS-DIC architecture reach the best performance result and enable gains of 12% in terms of processing rate. The architecture can reach real time for full HDTV (1920x1080 pixels) in the worst case processing 65 frames per second, and it can process 269 HDTV frames per second in the average case.
Fuzzy SVM for content-based image retrieval: a pseudo-label support vector machine framework

Conventional relevance feedback in content-based image retrieval (CBIR) systems uses only the labeled images for learning. Image labeling, however, is a time-consuming task and users are often unwilling to label too many images during the feedback process. This gives rise to the small sample problem where learning from a small number of training samples restricts the retrieval performance. To address this problem, we propose a technique based on the concept of pseudo-labeling in order to enlarge the training data set. As the name implies, a pseudo-labeled image is an image not labeled explicitly by the users, but estimated using a fuzzy rule. Therefore, it contains a certain degree of uncertainty or fuzziness in its class information. Fuzzy support vector machine (FSVM), an extended version of SVM, takes into account the fuzzy nature of some training samples during its training. In order to exploit the advantages of pseudo-labeling, active learning and the structure of FSVM, we develop a unified framework called pseudo-label fuzzy support vector machine (PLFSVM) to perform content-based image retrieval. Experimental results based on a database of 10,000 images demonstrate the effectiveness of the proposed method
SDM Techniques Based on TGSOM and its Application in R&D Performance Evaluation

Research and development activities (R&D) as the core competitiveness in the high-tech enterprises play an extremely important role and far-reaching significance. In order to measure the R&D performance of the high-tech enterprises scientifically and accurately, this paper introduces the tree-structured growing self-organizing maps (TGSOM) network into the spatial data mining (SDM) to be used in spatial clustering. This method not only can make up the limitation of the more data limit and biggish computation in the common spatial data mining, but also overcome the restriction of traditional maps (SOFM) that must appoint in advance. The performance measurement of 110 high-tech enterprises in Hebei Province shows that the results given by this method are reliable.
An analysis of the dairy industry: Regional impacts and rational price formation

ii CHAPTER 1: ECONOMIC CONTRIBUTION OF THE DAIRY INDUSTRY TO THE COLORADO ECONOMY . 1 REFERENCES . 26 CHAPTER 2: BRINGING TRANSPARENCY TO CLASS III MILK FUTURES: EVIDENCE OF RATIONAL PRICE FORMATION . 28 REFERENCES . 56
Stability of qt measurements in the PTB database depending on the selected lead

Prolonged QT interval is considered as an index of propensity to dangerous ventricular tachyarrhythmias. The Physionet/CinC challenge 2006 encourages teams to automatically measure the QT interval of lead Il's first representative beat in every record of the PTB database. A "median self-centering approach" based on manual entries to the challenge is used for evaluation of algorithms. We participated in the challenge using a single-lead wavelet based delineator. The selection of the representative beat was performed using information from several beats and the available leads. A score of 19.22 ms was obtained. Additionally the stability of the delineation in the different leads is studied. Comparing the QT measurement stability in 10 consecutive beats, we found significant inter-lead differences. The most stable QT measurements were found in precordial leads V2-V4, while frontal leads are those with greatest measurement variance. Leads with more stable QT were also those with maximum Twave amplitude.
Switching Strategies to Mitigate HIV Mutation

HIV mutates rapidly and may develop resistance to specific drug therapies. There is no general agreement on how to optimally schedule the available treatments for mitigating the effects of mutations. With a switched positive linear system, we examined different control strategies applied to a higher order nonlinear mutation model. Simulation results suggest that model predictive control could outperform the common clinical treatment recommendations. This brief is a step forward to develop further tools for helping the practitioners to find the optimal treatment schedule.
Evaluating Adaptive Authoring of Adaptive Hypermedia

Now-a-days a large amount of research exists into the design and implementation of adaptive systems, but still, a lot less targets the complex task of authoring of such systems, or their evaluation. In order to tackle these problems, we have looked into the causes of the complexity. Manual annotation has proven to be a bottleneck of authoring of adaptive hypermedia. Therefore, all means for supporting this authoring process by reusing automatically generated metadata should be sought. Previously, we have proposed the integration of the generic Adaptive Hypermedia authoring environment, MOT, into a semantic desktop environment, indexed by Beagle++. A prototype was built based upon this integration approach. This paper describes an evaluation of both the approach in general, and the prototype in particular.