Miracle-dz/UniFAR-FTUs huggingface dataset:
Can psychology provide a coherent account of human behavior ? A proposed multiexplanation-model theory

Human behavior cannot be understood by using only models of explanation utilized in the natural sciences. Multiple models of explanation, which are not consistent with, or reducible to each other, are required and are in fact used in psychology to explain human actions. This situation, called "Multiexplanation," could cause a problem of developing a justified correspondence between psychological phenomena and multiple models of explanation. Unless this problem is solved, the explanatory capability of a psychological theory seems inconsistent and ad hoc. A solution suggesting "correspondence guidelines" between phenomena and available models of explanation and "organization guidelines" for constructing a coherent psychological theory is offered. It contributes to the development of a "multiexplanation-model theory" (or a "multimodel theory" for brevity) which employs different models of explanation needed for proposing accounts of psychological phenomena. Many scientists and philosophers believe that science is defined by its methodology. They have devoted much time and effort to develop a unified methodology for all scientific disciplines such as the natural sciences, social sciences and even humanities. Since one of the most important aspects of scientific methodology is the appropriate explanation model or model of explanation, that is, the procedure or scheme by which scientists provide us a particular account of the phenomenon under investigation, they have made heroic attempts to develop a general set of models of explanation to encompass all scientific disciplines. A model of explanation is different from an explanation. Consider the following two examples. Suppose that a visitor to a museum of science asks (a) why does this ball fall about 4.9 m. in 1 sec? and (b) why this hungry rat in this operant box presses bar Bl three times more than bar B21 In answer to question (a), the visitor is told that (1) there is a law of nature called the law of Free Falling Bodies: 8= 1/2 gt2, where S signifies distance, t signifies time and g denotes the gravitational factor, and (2) that if one plugs 1 sec. into this formula, then one derives that S = 1/2 g or 4.9 m. That is, one is provided with an explanation to question (a),
Mathematical Models for Pedestrian Motion

Subject of this work is the mathematical modelling of pedestrians, whose motion can be described in terms of ordinary and partial differential equations. We introduce the most interesting models and their output. Models that are continuous in space (social force models) are as well considered as spatially discrete models (cellular automaton models). We analyze in particular a one-dimensional, continuous in space model, for which we introduce some extensions. We investigate existence and uniqueness for local solutions of this extended model. In addition we present our numerical results. We investigate also the case that the number of pedestrians tends to infinity. Furthermore we analyze for the continuous in space models under which conditions motion around homogeneous flow is stable. In addition we present a numerical simulation for a two dimensional social force model and give an outlook about the work which is still open.
A Block-Based Linear MMSE Noise Reduction with a High Temporal Resolution Modeling of the Speech Excitation

A comprehensive linear minimum mean squared error (LMMSE) approach for parametric speech enhancement is developed. The proposed algorithms aim at joint LMMSE estimation of signal power spectra and phase spectra, as well as exploitation of correlation between spectral components. The major cause of this interfrequency correlation is shown to be the prominent temporal power localization in the excitation of voiced speech. LMMSE estimators in time domain and frequency domain are first formulated. To obtain the joint estimator, we model the spectral signal covariance matrix as a full covariance matrix instead of a diagonal covariance matrix as is the case in the Wiener filter derived under the quasi-stationarity assumption. To accomplish this, we decompose the signal covariance matrix into a synthesis filter matrix and an excitation matrix. The synthesis filter matrix is built from estimates of the all-pole model coefficients, and the excitation matrix is built from estimates of the instantaneous power of the excitation sequence. A decision-directed power spectral subtraction method and a modified multipulse linear predictive coding (MPLPC) method are used in these estimations, respectively. The spectral domain formulation of the LMMSE estimator reveals important insight in interfrequency correlations. This is exploited to significantly reduce computational complexity of the estimator. For resource-limited applications such as hearing aids, the performance-to-complexity trade-off can be conveniently adjusted by tuning the number of spectral components to be included in the estimate of each component. Experiments show that the proposed algorithm is able to reduce more noise than a number of other approaches selected from the state of the art. The proposed algorithm improves the segmental SNR of the noisy signal by 13 dB for the white noise case with an input SNR of 0 dB.
Identity theft reports of adolescents: An exploration of the experiences of school counselors

Purpose The purpose of this paper was to explore the identity theft victimization experiences of high school students, as well as the predictors of it being reported to school counselors. Design/methodology/approach In July and August 2014, an online survey was sent to every registered member of the American School Counselor’s Association (ASCA). School counselors were questioned about their experiences in regarding online victimization of their students. Findings Results of this analysis revealed extremely interesting predictors of school counselor demographics related to the number of reports filed by students. Research limitations/implications There was a small response rate because the survey was sent to all registered members of the ASCA, instead of a specific targeted group (which would have been more appropriate). Practical implications The results indicate a need for further resources and training dedicated to school counselors to manage identity theft victimization of students. Originality/value As far as the authors are aware, no other study of this kind has previously been performed. In addition, there is little known about identity theft victimization of adolescents.
Audio-based guitar tablature transcription using multipitch analysis and playability constraints

This paper proposes a method of guitar tablature transcription from audio signals. Multipitch estimation and fingering configuration estimation are essential for transcribing tablatures. Conventional multipitch estimation methods, including latent harmonic allocation (LHA), often estimate combinations of pitches that people cannot play due to inherent physical constraints. Unplayable combinations of pitches are eliminated by filtering the results of LHA with three constraints. We first enumerate playable fingering configurations, and use them to suppress any undesirable combination of pitches. The optimal fingering configuration in each time frame is optimized to satisfy the need for temporal continuity by using dynamic programming. We use synthesized guitar sounds from MIDI data (ground truth) for evaluation. Experiments with them demonstrate the improvement of multipitch estimation by 5.9 points on average in F-measure and the transcribed tablatures are playable.
Results on the quantitative mu-calculus qMu

The mu-calculus is a powerful tool for specifying and verifying transition systems, including those with both demonic and angelic choice; its quantitative generalisation qMu extends that to probabilistic choice. We show that for a finite-state system the logical interpretation of qMu, via fixed-points in a domain of real-valued functions into [0,1], is equivalent to an operational interpretation given as a turn-based gambling game between two players. The logical interpretation provides direct access to axioms, laws and meta-theorems. The operational, game- based interpretation aids the intuition and continues in the more general context to provide a surprisingly practical specification tool. A corollary of our proofs is an extension of Everett's singly-nested games result in the finite turn-based case: we prove well-definedness of the minimax value, and existence of fixed memoriless strategies, for all qMu games/formulae, of arbitrary (including alternating) nesting structure.
Mining the Active Proteome of Arabidopsis thaliana

Assigning functions to the >30,000 proteins encoded by the Arabidopsis genome is a challenging task of the Arabidopsis Functional Genomics Network. Although genome-wide technologies like proteomics and transcriptomics have generated a wealth of information that significantly accelerated gene annotation, protein activities are poorly predicted by transcript or protein levels as protein activities are post-translationally regulated. To directly display protein activities in Arabidopsis proteomes, we developed and applied activity-based protein profiling (ABPP). ABPP is based on the use of small molecule probes that react with the catalytic residues of distinct protein classes in an activity-dependent manner. Labeled proteins are separated and detected from proteins gels and purified and identified by mass spectrometry. Using probes of six different chemotypes we have displayed activities of 76 Arabidopsis proteins. These proteins represent over 10 different protein classes that contain over 250 Arabidopsis proteins, including cysteine, serine, and metalloproteases, lipases, acyltransferases, and the proteasome. We have developed methods for identification of in vivo labeled proteins using click chemistry and for in vivo imaging with fluorescent probes. In vivo labeling has revealed additional protein activities and unexpected subcellular activities of the proteasome. Labeling of extracts displayed several differential activities, e.g., of the proteasome during immune response and methylesterases during infection. These studies illustrate the power of ABPP to display the functional proteome and testify to a successful interdisciplinary collaboration involving chemical biology, organic chemistry, and proteomics.
The combination of the functionalities of feedback circuits is determinant for the number and size of attractors of molecular networks

Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits (motifs). Here, we analyze the behavior of pathways containing non-hierarchical and non-unidirectional interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the precise number and size of the attractors. We show that the different sets of expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the structures producing the epistasis results have remarkably similar sets of combinations of functionalities, that combined with other network properties could greatly improve epistasis analysis.
Language Resource Addition: Dictionary or Corpus?

In this paper, we investigate the relative effect of two strategies of language resource additions to the word segmentation problem and part-of-speech tagging problem in Japanese. The first strategy is adding entries to the dictionary and the second is adding annotated sentences to the training corpus. The experimental results showed that the annotated sentence addition to the training corpus is better than the entries addition to the dictionary. And the annotated sentence addition is efficient especially when we add new words with contexts of three real occurrences as partially annotated sentences. According to this knowledge, we executed annotation on the invention disclosure texts and observed word segmentation accuracy.
To err is human : correlating fMRI decoding and behavioral errors to probe the neural representation of natural scene categories

New multivariate methods for the analysis of functional magnetic resonance imaging (fMRI) data have enabled us to decode neural representations of visual information with unprecedented fidelity. But how do we know if humans make use of the information that we decode from the fMRI data for their behavioral response? In this chapter we propose a method for correlating the errors from fMRI decoding with the errors made by subjects in a behavioral task. High correlations suggest that subjects use information that is closely related to the content of the fMRI signal to make their behavioral response. We demonstrate the viability of this method using the example of natural scene categorization. Humans are extremely efficient at categorizing natural scenes (such as forests, highways, or beaches), despite the fact that different classes of natural scenes often share similar image statistics. Where and how does this happen in the brain? By applying multi-voxel pattern analysis to fMRI data recorded while subjects viewed natural scenes we found that the primary visual cortex (V1), the parahippocampal place area (PPA), retrosplenial cortex (RSC), and the lateral occipital complex (LOC) all contain information that distinguishes among natural scene categories. Correlation of the decoding errors with errors made by the subjects in a behavioral experiment revealed that only the information about scene categories contained in the PPA, RSC, and LOC is directly related to behavior, but not the information in area V1. A match between behavioral performance and accuracy of decoding scene categories from the PPA and RSC for two manipulations of the stimuli (scene inversion and quality of category exemplar) underscores the central role of these two areas in natural scene categorization.
Adaptive power optimization for space-time coded MIMO systems

An optimized power allocation scheme is proposed to compensate for the diversity loss and decrease the error propagation due to the group interference suppression and cancellation (G-ISC) algorithm in multilayered space-time trellis codes (ML-STTC). In the proposed scheme, with high probabilities, the signals in the first group can be correctly detected and their interference to other groups can be cancelled perfectly, which in turn facilitates the detection of the signals in other groups. Simulation results reveal the optimized scheme is adaptive to signal-to-noise ratio (SNR) and can achieve significant performance improvement without adding much delay and complexity.
Image Classification Based on Fuzzy Support Vector Machine

As a basic two-class classifier, support vector machine (SVM) has been proved to perform well in image classification, which is one of the most common tasks of image processing. However, for the n-class problem in image classification, SVM treats it as n two-class problems, in this way, unclassifiable regions exist. In this paper, we introduce fuzzy support vector machine (FSVM) and define a membership function to classify images which are unclassifiable using conventional SVM. For the input vector of SVM and FSVM, we use combined image feature histogram. Being compared with the conventional SVM, FSVM shows the same result as SVM for the images in the classifiable regions, and for those in the unclassifiable regions, FSVM generates better result than SVM.
Dynamic Changes in Subgraph Preference Profiles of Crucial Transcription Factors

Transcription factors with a large number of target genes—transcription hub(s), or THub(s)—are usually crucial components of the regulatory system of a cell, and the different patterns through which they transfer the transcriptional signal to downstream cascades are of great interest. By profiling normalized abundances (AN) of basic regulatory patterns of individual THubs in the yeast Saccharomyces cerevisiae transcriptional regulation network under five different cellular states and environmental conditions, we have investigated their preferences for different basic regulatory patterns. Subgraph-normalized abundances downstream of individual THubs often differ significantly from that of the network as a whole, and conversely, certain over-represented subgraphs are not preferred by any THub. The THub preferences changed substantially when the cellular or environmental conditions changed. This switching of regulatory pattern preferences suggests that a change in conditions does not only elicit a change in response by the regulatory network, but also a change in the mechanisms by which the response is mediated. The THub subgraph preference profile thus provides a novel tool for description of the structure and organization between the large-scale exponents and local regulatory patterns.
Collaborative Localization for Wireless Sensor Networks

In this paper, we present a self sufficient and distributed localization algorithm for wireless sensor networks that does not require any external infrastructure support to estimate sensor node coordinates. Our algorithm uses measured inter-node distances and localizes the entire network in a local coordinate system upto a global translation, rotation and reflection. Using TOSSIM simulations, we show that our approach consumes only 0.1% of battery energy and exhibits smaller localization errors as compared to traditional approaches. The main contribution of this work is a multi-phase approach that allows the resource constrained sensor nodes to collaborate with each other and solve the localization problem for the entire network.
Intrusion Detection System for Power-Aware OLSR

Optimized Link State Routing (OLSR) is a standard proactive routing protocol for Wireless Sensor Network (WSN). OLSR uses two kinds of the control messages: Hello and Topology Control (TC). As these messages are un-authenticated, OLSR is prone to several attacks namely, black hole, wormhole, Gray hole etc. This paper is focused at Sleep Deprivation Torture Attack on OLSR. Sleep deprivation attack is one of the most interesting attack in layer 2 where the attacker tries to use a low energy node until all its energy is exhausted and the node goes into permanent sleep. This attack is also possible in routing level. In OLSR low energy node declare their status through willingness property of HELLO message. Using this information an attacker node can choose that low energy node deliberately and forward all traffic through that node. This leads to low energy node in a permanent sleep mode. In this paper we propose a specification based Intrusion Detection System (IDS) for that type of attack. The performance of the propose algorithm is studied by Network Simulator (NS2) and effectiveness of the propose scheme, along with a comparison with existing techniques is demonstrated.
Losing Kapwa: Colonial Legacies and the Filipino American Family

Largely due to their colonial past and neocolonial present, Filipinos have been receiving certain messages for generations regarding their heritage and the Western culture, and have been facing constant tensions and struggles between the Filipino and the Western, and the individual and the collective. This constant push-pull between Filipino and Western cultural values shape and are reflected in Filipinos’ daily lives, interactions with each other, and interactions with the rest of the world. To this end, this article discusses a wide range of factors pertinent to Filipino Americans—from immigration, family structure, and values, to parenting, gender roles, and intergenerational issues—to convey that these factors need to be understood in the context of colonialism, its legacies, and its contemporary implications when conceptualizing Filipinos Americans’ experiences. We also provide a discussion of Filipino values that go “beyond the surface” as we talk about the core Filipino value of kapwa, and how ethnic identity, acculturation, and cultural conflicts among and between Filipinos need to be conceptualized through kapwa. Then, we discuss how colonialism and its most insidious legacy—colonial mentality—may influence kapwa, acculturation, ethnic identity, and Filipino American families. We end with some recommendations for researchers and service providers, with the hope that a historically informed and sociopolitically contextualized understanding of Filipino Americans will be helpful to those who work with this population.
Fault detection in an Ethernet network using anomaly signature matching

In an Ethernet network, a common type of failure is the temporary of extended loss of bandwidth, or soft failure as it is referred to in the literature. Though the causes of soft failures vary, to the network user such failures are perceived as noticeably degraded or anomalous performance.This work uses anomaly detection as a means to signal performance degradations that are indicative of network soft failures. Detection is done via a signature matching mechanism, call a fault feature vector, which will detect the occurrence of a fault by looking for anomaly conditions particular to the fault. In a two-year study of the Carnegie Mellon University Computer Science Network the fault feature vector mechanism proved effective in detecting faults and discriminating between faults types. This mechanism was also effective at abstracting large amounts of network data to only those events which warranted operator attention; in this two-year study, over 32 million monitored data points were reduced to under a two hundred event matchings.
A group-based inference approach to customized marketing on the Web integrating clustering and association rules techniques

Because of the intelligent computing specialty of the World Wide Web, extensive customized marketing can be executed at much lower cost and has become an emerging research issue. Therefore, the first purpose of this paper is to propose a system framework to serve as a foundation for developing a customized marketing system on the Web according to the discussions on data sources, data categories, and inference foundations. Most previous studies used induction-learning techniques to perform individual-based inference for customized marketing. However, it not only costs more to learn the personal preferences, but also some difficulties occur from using induction-learning techniques. The second purpose of this paper is to solve these problems. A group-based approach that integrates clustering and association rules is proposed. We conducted a field study to collect data to demonstrate the proposed group-based inference approach and evaluate its performance. The results reveal that this integrated approach can learn both more detailed and precise rules.