Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Bias Against Interracial Couples Among White and Black Respondents, Relative to Multiracial Respondents

Interracial couples are an understudied but increasingly common social group in the United States. We used direct and indirect measures to examine explicit and implicit biases (respectively) against interracial couples among samples of (a) predominantly White (non-Black; N = 1,217), (b) Black (N = 293), and (c) multiracial (N = 284) respondents recruited from the United States. Results provide evidence of implicit and explicit bias against Black–White interracial couples among respondents in the predominantly White sample and the Black sample. There was no evidence of such biases among self-identified multiracial respondents; in fact, they self-reported favoritism for interracial couples. Consistent with psychological theory and our preregistered hypotheses, we found that personal experience with interracial romance and self-reported contact with interracial couples tended to predict lower levels of bias against interracial couples. This research exposes a robust bias against a growing social group (interracial couples) among predominantly White respondents and Black respondents relative to respondents who identify as multiracial.
Robust visual tracking with occlusion detection using compressive sensing

In this paper, tracking problem is considered as a sparse approximation of target by templates created during video process. In addition, some trivial templates are used to avoid the effects of noise and illumination changes. Each candidate is sparsely represented by the template set. This goal is achieved by solving an l1- regularized least-square equation. To find tracking result, a candidate with the minimum reconstruction error was adopted. Then, tracking was continued in particle filter framework. Two ideas were used to improve the algorithm performance. Firstly, the dictionary set was adaptively updated according to appearance changes. Secondly, using the area around the target, occlusion was diagnosed and subsequently the template set was updated. This technique prevented the occluded part of the target getting into the template set. The proposed approach shows a better performance than other previous tracker against full occlusion problem.
Combining Coagulation/MIEX with Biological Activated Carbon Treatment to Control Organic Fouling in the Microfiltration of Secondary Effluent

Coagulation, magnetic ion exchange resin (MIEX) and biological activated carbon (BAC) were examined at lab scale as standalone, and sequential pre-treatments for controlling the organic fouling of a microfiltration membrane by biologically treated secondary effluent (BTSE) using a multi-cycle approach. MIEX gave slightly greater enhancement in flux than coagulation due to greater removal of high molecular weight (MW) humic substances, although it was unable to remove high MW biopolymers. BAC treatment was considerably more effective for improving the flux than coagulation or MIEX. This was due to the biodegradation of biopolymers and/or their adsorption by the biofilm, and adsorption of humic substances by the activated carbon, as indicated by size exclusion chromatography. Coagulation or MIEX followed by BAC treatment further reduced the problematic foulants and significantly improved the flux performance. The unified membrane fouling index showed that the reduction of membrane fouling by standalone BAC treatment was 42%. This improved to 65%, 70%, and 93% for alum, ferric chloride and MIEX pre-treatment, respectively, when followed by BAC treatment. This study showed the potential of sequential MIEX and BAC pre-treatment for controlling organic fouling and thus enhancing the performance of microfiltration in the reclamation of BTSE.
Keynote-Dependent HMM Based Musical Chord Recognition Method

Chord sequences and chord boundary is the mid-level performance of the music signal compactness and robustness. Automatic chord recognition is very attractive to researchers in the field of music information retrieval. To improve accuracy of musical chord recognition algorithm, in this study, the importance of keynote was fully considered. According to the music theory, 24 keynotes were defined. For each keynote, a Hidden Markov model was established, which is called keynote-dependent HMM. And then, a recognition algorithm of music chord based on keynote-dependent HMM was proposed. In this algorithm, use MIDI music corpus to train the keynote-dependent HMM, and to improve the recognition rate and facilitate the calculation, a 6-dimensional vector of tonal centroid is used as the feature vector. The experimental results showed that the proposed keynote-dependent HMM had better recognition effect than that of keynote-independent model.
Vowel Identity between Note Labels Confuses Pitch Identification in Non-Absolute Pitch Possessors

The simplest and likeliest assumption concerning the cognitive bases of absolute pitch (AP) is that at its origin there is a particularly skilled function which matches the height of the perceived pitch to the verbal label of the musical tone. Since there is no difference in sound frequency resolution between AP and non-AP (NAP) musicians, the hypothesis of the present study is that the failure of NAP musicians in pitch identification relies mainly in an inability to retrieve the correct verbal label to be assigned to the perceived musical note. The primary hypothesis is that, when asked to identify tones, NAP musicians confuse the verbal labels to be attached to the stimulus on the basis of their phonetic content. Data from two AP tests are reported, in which subjects had to respond in the presence or in the absence of visually presented verbal note labels (fixed Do solmization). Results show that NAP musicians confuse more frequently notes having a similar vowel in the note label. They tend to confuse e.g. a 261 Hz tone (Do) more often with Sol than, e.g., with La. As a second goal, we wondered whether this effect is lateralized, i.e. whether one hemisphere is more responsible than the other in the confusion of notes with similar labels. This question was addressed by observing pitch identification during dichotic listening. Results showed that there is a right hemispheric disadvantage, in NAP but not AP musicians, in the retrieval of the verbal label to be assigned to the perceived pitch. The present results indicate that absolute pitch has strong verbal bases, at least from a cognitive point of view.
Using Lexical Knowlege to Evaluate the Novelty of Rules Mined from Text

We present a novel application of WordNet to estimating the interestingness of rules discovered by data-mining methods. We estimate the novelty of text-mined rules using semantic distance measures based on WordNet. In our experiments, we found that the the automatic scoring of rules based on our novelty measure correlates with human judgments about as well as human judgments correlate with each other.
The Beehive Cluster System

In this writeup, we present the system architecture of Beehive, a cluster system we are developing at Georgia Tech for supporting interactive applications and compute-intensive servers. The system provides a shared memory programming environment on a cluster of workstations interconnected by a high speed interconnect. The principal design features of Beehive include: a global address space across the cluster (Section 5), a configurable access granularity (Section 6), flexibility in supporting spatial and temporal notions of synchronization and consistency (Section 7), and multithreading (Section 4). The fundamental design principle is to use only commodity hardware and software components as the basis, and build the shared memory system architecture entirely in software. The mechanisms for shared memory parallel programming are made available to the application programmer via library calls. We consciously target application domains which are expected to be ideally suited for cluster parallel computing in designing the system architecture of Beehive. In particular, we base our design on our understanding of the requirements of interactive applications such as virtual environments, our work in the storage architecture of database servers [9, 10], as well as our experience in parallel computing for scientific domains [24, 30, 32]. Figure 1 pictorially depicts the current organization of the Beehive cluster. Each box of the cluster can be a uniprocessor or an SMP. We do not address heterogeneity in the processor architectures in our current design. The interconnect can be realized out of any commodity network switch so long as they have the right latency properties for shared memory style communication. The requirements from the operating system to support the Beehive system architecture are: a network file system , and the ability tospecify a virtual address range during memory allocation – a feature which is easily implementable in most Unix operating systems (e.g. using the mmapsystem call). In addition to these two requirements, athread-awareoperating system would be a plus for supporting the multithreading features of Beehive.
Enhancing TCP Performance Through Intelligent Activation / Deactivation

Improvements of TCP performance in wireless networks have been tossed around for several years. One mechanism that has received considerable attention is the use of error recovery techniques thereby compensating for the wireless loss characteristics. However, surprisingly, to our knowledge no study in error avoidance has been carried out. This paper proposed the use of an error avoidance scheme for improving the performance of TCP in wireless environments by simply avoiding transmitting data under poor radio coverage conditions. The decision to transmit or not is taken by comparing the signal to noise ratio (SNR). In effect, the transmissions are suspended when the SNR is decreasing and goes below a given threshold, and resume transmission when the SNR goes back above the SNR threshold. This can be realized without modifying TCP by using a thin layer between the network and transport layers. The result showed that the TCP sender could obtain significantly better performance, improvements up to 15 % on TCP throughput by using this technique.
Content-based image authentication by feature point clustering and matching

Digital multimedia makes fabricating and copying much easier than ever before. Therefore, it demands efficient and automatic techniques to identify and verify the content of digital multimedia. Image authentication is such a technique to automatically identify whether the query image is a fabrication or a simple copy of the original one. In this paper, we propose a perceptual image authentication technique based on clustering and matching of feature points of images. Feature points are first extracted from images with the k-largest local total variations and clustered using Fuzzy C-means clustering algorithm. Then, feature points in the query image and the anchor image are matched into pairs in zigzag ordering along the diagonals of the images cluster by cluster. In the mean time, the outliers of feature points are removed. Then, the system decisions about the authenticity of images are determined by the majority vote of whether three types of distance between matched feature point pairs are larger than their respective thresholds. The three types of distance include the following: (i) histogram-weighted distance, which is proposed in this paper; (ii) the normalized Euclidean distance; and (iii) the Hausdorff distance. The geometric transform between the query image and the anchor image is estimated, and the query image is registered. The possible tampered image blocks are detected, and the percentage of the tampered area is roughly estimated. The experimental results show the effectiveness and robustness of the proposed image authentication system. Copyright © 2012 John Wiley & Sons, Ltd.
Optimization of rotation-invariant feature detection method for pedestrian recognition

In the technology of Advanced Safety Vehicle(ASV), it is important elements to detect pedestrians. There is a technique which used image features and a classifier for pedestrian detection. However, pedestrians have pose variations, such as a rotation. That's why we cannot always get the same features. In order to solve this problem, we can use a way which used the rotation-invariant HOG, but this method needs very high costs because it's not optimized for pedestrian detection. Therefore, we improved calculating method of the rotation-invariant HOG for pedestrian detection and compared this proposed method with the conventional method. As a result, recognition rate was turned from 53.75% to 71.7%.
Decision Making is Caused by Information Processing and Emotion: A Synthesis of Two Approaches to Explain the Phenomenon of Confirmation Bias

In social psychology there are two dominant models of reasoning, one that is based on information processing and another based on emotion. This paper argues that there are problems with viewing decision making exclusively in these terms, which is what has been done to date in the field of social psychology. While cognitive psychologists and behavioural economists have accounted for the internal and external cognitive processes, generally they have not accounted for the role of culture in one’s decision making; this is one of the reasons why participants are said to be ‘irrational’ if they fail to act in a specific way. This paper synthesises two perspectives one that is concerned with information processing and another which is concerned with emotional reactions in order to explain the phenomenon of confirmation bias. Areas of agreement and disagreement will be identified, before recommendations are made for future research. It is hoped that this can serve as an argument for a more pluralistic framework that also acknowledges the role of ecological factors.
Oracles and queries that are sufficient for exact learning (extended abstract)

We show that the class of all circuits is exactly learnable in randomized expected polynomial-time using subset and superset queries. This is a consequence of the following result which we consider to be of independent interest: circuits are exactly learnable in randomized expected polynomial-time with equivalence queries and the aid of an <italic>NP</italic>-oracle. We also show that circuits are exactly learnable in deterministic polynomial-time with equivalence queries and a &Sgr;<subscrpt>3</subscrpt><supscrpt>p</supscrpt>-oracle. The hypothesis class for the above learning algorithms is the class of circuits of larger—but polynomially related—size. Also, the algorithms can be adapted to learn the class of DNF formulas with hypothesis class consisting of depth-3 Λ-V-Λ formulas (by the work of Angluin, this is optimal in the sense that the hypothesis class cannot be reduced to depth-2 DNF formulas. We also investigate the power of an <italic>NP</italic>-oracle in the context of learning with <italic>membership</italic> queries. We show that there are deterministic learning algorithms that use membership queries and an <italic>NP</italic>-oracle to learn: monotone boolean functions in time polynomial in the DNF size and CNF size of the target formula; and the class of <italic>O</italic>(log<italic>n</italic>)-DNF<inline-equation> <f> ∩</f> </inline-equation><italic>O</italic>(log<italic>n</italic>)-CNF formulas in time polynomial in <italic>n</italic>. Finally, we show that, with an <italic>NP</italic>-oracle and membership queries, there is a randomized polynomial-time algorithm that learns any class that is learnable from membership queries with unlimited computational power.
Fast Visualization of 2 D Dynamical Systems by the use of Virtual Ink Droplets

This paper presents a new visualization technique for flow fields in 2D. It utilizes a physical model of smearing ink over a sheet of paper as an intuitive metaphor for the representation of a dynamical system. This technique is capable of producing images that are comparable to those generated with line integral convolution (LIC), which is a well-known and established visualization technique for planar vector fields. Similar to oriented line integral convolution (OLIC), an extension to LIC, the virtual ink droplet method is capable of visualizing not only direction and velocity of flow (as LIC does), but also the orientation of vectors. The main advantage of the new method is, that animation sequences, which intuitively represent the dynamics induced by the underlying dynamical system, can be computed much more efficiently than by the use of LIC or OLIC. A speed-up of about 200 is usually achieved when virtual ink droplets are used instead.
Autonomia de professores

Durante as quase trezentas paginas de “ La autonomia del professorado” , o espanhol Jose Contreras Domingo, da Universitat de Barcelona , nos apresenta uma definicao de autonomia docente como qualidade do fenomeno educativo, nao apenas como qualidade profissional. O autor teoriza que a autonomia consiste na consciencia sobre a docencia, sobre o fazer e sobre o ser professor, mas ainda, sobre o sentido do ensino e da educacao na sociedade. Esclarece que a producao dos saberes pertinentes a docencia nao permite segregar “elaboracao e aplicacao”, “teoria e pratica”, mas obriga reuni-las e revela-las diretamente no contexto humano e social onde o fenomeno educativo acontece.
Multimodal emotion recognition with automatic peak frame selection

In this paper we present an effective framework for multimodal emotion recognition based on a novel approach for automatic peak frame selection from audio-visual video sequences. Given a video with an emotional expression, peak frames are the ones at which the emotion is at its apex. The objective of peak frame selection is to make the training process for the automatic emotion recognition system easier by summarizing the expressed emotion over a video sequence. The main steps of the proposed framework consists of extraction of video and audio features based on peak frame selection, unimodal classification and decision level fusion of audio and visual results. We evaluated the performance of our approach on eNTERFACE'05 audio-visual database containing six basic emotional classes. Experimental results demonstrate the effectiveness and superiority of the proposed system over other methods in the literature.
Source-Level Estimation of Energy Consumption and Execution Time of Embedded Software

Energy optimization of embedded software is of primary importance. Nevertheless, there is lack of accurate and usable methodologies and tools to estimate software performance (execution time, energy) and to allow a significant exploration of design alternatives. Current approaches use either instruction-level simulation (accurate but slow), or static-time source characterization (flexible but data-independent). This paper proposes a hybrid approach taking advantage of the strengths of both the above approaches. We present a fully automatic method for estimating the execution time and power consumption of a C program - run on a given architecture on given input data - based on statistically-accurate models for the architecture and for the compiler. Validation results against an ARM energy-enabled instruction-level simulator show an average absolute relative errors of 8.5%.
E-business framework for small and medium enterprises: A critical review

Small and medium enterprises (SMEs) are often described as the engine of growth for a country's economy due to their significant economic contributions. At the same time, ebusiness offers an opportunity for SMEs to significantly further their growth as it has been identified as a tool that could improve an enterprise's operational efficiency and competitiveness in the global market. Despite the promised potentials of e-business, its adoption among SMEs has been lower compared to that of larger enterprises. This study offers a multifaceted overview of the existing research by initially summarizing and then critically analyzing a range of research frameworks and evaluating the extent to which they take into account the nature of SMEs and their ability to provide a practical tool to transform SMEs. Our analysis reveals three groups of issues that may affect e-business use in SMEs: 1) Factors that drive and hinder adoption of ebusiness among SMEs; 2) Profiling of SMEs based on their capacity or readiness to adopt e-business; and 3) Implementation models that are inherently centered on specific technologies or ebusiness applications. Our overall analysis shows that the existing e-business frameworks, when used independently, inadequately explain the adoption behavior of the diverse SMEs and insufficiently provide a practical tool for SMEs in implementing e-business. Accordingly, this study suggests preliminary ideas towards an integrated e-business for SMEs framework.
A Sensitivity Analysis for the Pricing of European Call Options in a Binary Tree Model

The European call option prices have well-known formulae in the CoxRoss-Rubinstein model [2], depending on the volatility of the underlying asset. Nevertheless it is hard to give a precise estimate of this volatility. S. Muzzioli and C. Toricelli [6] handle this problem by using possibility distributions. In the first part of our paper we make some critical comments on their work. In the second part we present an alternative solution to the problem by performing a sensitivity analysis for the pricing of the option. This method is very general in the sense that it can be applied if one describes the uncertainty in the volatility by confidence intervals as well as if one describes it by fuzzy numbers. The conclusion is that the price of the option is not necessarily a strictly increasing function of the volatility.
No Planner is an Island: Lessons from the Nextcut Process Planner

My experience with "practical planning problems" is mainly through the process planning domain. I will describe some of the aspects of this domain that set it apart from the simulated toy-domains, and address their implications for the "typical" AI planning algorithms. The work itself is described in the references [8, 6, 7, 4, 5]. The paper is organized into three parts. In the first part, I describe the process planning domain in general terms and explain its interesting characteristics from AI planning view point. In the second part, I provide a brief overview of the NExT-COT process planning system. In the third part, I attempt to answer the questions raised in the symposium CFP in teams of our process planning work. It is possible to read Parts I and HI to get a high level picture, and then get details from Part H. "
The Digital Exhibition and Keyimage Ontology

The Age of Image predates and is currently contemporaneous with the Information Age. In our times the explosive expansion of Web 2.0 Social Space, typified by the phenomena of De.licio. us, Flickr, MySpace, YouTube…, and the concomitant emergence of folksonomy, present interesting challenges in the management of this information. One key process by which to accomplish this in Social Space, is the wedding of folksonomy (of the people) with ontology (of the machine). Such a wedding must necessarily be conducted in the shared physicality of the word, of language. In this respect, WordNet together with OWL, play the role of matchmaker. But the same Social Space also provides an opportunity for natural folksonomical tagging by digiFoto (key)image. The research harness for experimental keyimage tagging consists of Flickr as the main (digiFoto image) Social Space testbed and De.licio. us as the auxillary outreach secondary Social Space. Protege Editor with OWL-DL provides the support for the bridge from keyimage to the formal ontology. The primary end user application domain is the keyimage tagging of paintings in an online art gallery.