Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-evolving Artificial Immune System via Developing T and B Cell for Permutation Flow-shop Scheduling Problems

— Artificial Immune System is applied as a Heuristic Algorithm for decades. Nevertheless, many of these applications took advantage of the benefit of this algorithm but seldom proposed approaches for enhancing the efficiency. In this paper, a Self-evolving Artificial Immune System is proposed via developing the T and B cell in Immune System and built a self-evolving mechanism for the complexities of different problems. In this research, it focuses on enhancing the efficiency of Clonal selection which is responsible for producing Affinities to resist the invading of Antigens. T and B cell are the main mechanisms for Clonal Selection to produce different combinations of Antibodies. Therefore, the development of T and B cell will influence the efficiency of Clonal Selection for searching better solution. Furthermore, for better cooperation of the two cells, a co-evolutional strategy is applied to coordinate for more effective productions of Antibodies. This work finally adopts Flow-shop scheduling instances in OR-library to validate the proposed algorithm.
A Type-Passing Approach for the Implementation of Parametric Methods in Java

Parametric methods are recognized as a very useful tool for reusing code and augmenting the expressiveness of an object-oriented language providing parametric classes. This paper focuses on their implementation techniques for Java. Existing proposals for extending Java with parametric polymorphism are outlined, showing and discussing the basic implementation techniques: type-erasure, code-expansion and type-passing. Up until now, only type-erasure and code-expansion have been exploited for the implementation of parametric methods, but they suffer from some problems. Basically, type-erasure is unable to support parametric types at run-time, while code-expansion generally leads to significant increase of both disk and memory overhead. The main goal of this paper is to study a type-passing approach aimed at tackling these issues. This is based on the management of method descriptors, which are objects passed to the method bodies at invocation-time, and which carry information on the instantiation of the method’s type parameters. Run-time efficiency is guaranteed thanks to a special treatment of descriptors deferring their creation at load-time. Dynamic dispatching of method calls is supported by exploiting a data structure called the Virtual Parametric Methods Table, resembling Virtual Methods Tables of C++. The approach here presented turns out to be a significant alternative to the existing proposals for implementing generics in Java, supporting parametric types at run-time without unnecessary code footprint.
Neuralyzer: Flexible Expiration Times for the Revocation of Online Data

Once data is released to the Internet, there is little hope to successfully delete it, as it may have been duplicated, reposted, and archived in multiple places. This poses a significant threat to users' privacy and their right to permanently erase their very own data. One approach to control the implications on privacy is to assign a lifetime value to the published data and ensure that the data is no longer accessible after this point in time. However, such an approach suffers from the inability to successfully predict the right time when the data should vanish. Consequently, the author of the data can only estimate the correct time, which unfortunately can cause the premature or belated deletion of data. This paper tackles the problem of prefixed lifetimes in data deletion from a different angle and argues that alternative approaches are a desideratum for research. In our approach, we consider different criteria when data should be deleted, such as keeping data available as long as there is sufficient interest for it or untimely delete it in cases of excessive accesses. To assist the self-destruction of data, we propose a protocol and develop a prototype, called Neuralyzer, which leverages the caching mechanisms of the Domain Name System (DNS) to ensure the successful deletion of data. Our experimental results demonstrate that our approach can completely delete published data while at the same time achieving flexible expiration times varying from few days to several months depending on the users' interest.
Perceived Group Discrimination and Problem Behavior : The Moderating Role of Traditional Cultural Values and Familial Relationships in Mexican American Adolescents

i ABSTRACT A theme in the life experiences of ethnic minority adolescents is the perception of discrimination and its concomitant challenges. Although existing literature has examined the perception of discrimination in adolescents, little research has examined how the cultural and familial setting may heighten or alleviate the impact of perceived discrimination on psychological outcomes in Latino youth. The current study investigated how traditional cultural values and parent-adolescent relationships prospectively interact with perceptions of group based discrimination to influence Latino adolescent mental health, adjustment, and risky behaviors. Data used from the Parents and Youth Study included 194 Mexican American (MA) adolescents. Adolescents reported on their perceptions of group discrimination, endorsement of traditional Mexican cultural values, and parent-child relationships in the 7 th grade (Time 1). The study also used indices of externalizing (mother report), internalizing, substance use and risky sexual behavior (adolescent report) in 10 th grade (Time 2). The findings demonstrated that traditional Mexican cultural values, particularly familism, moderated the relationship between perceived group discrimination and adolescent sexual behavior. Additionally, a better overall relationship with mother and father buffered the detrimental effects of perceived group discrimination on risky sexual behavior. The current work discusses future directions of how the context of culture and family may shape an adolescent " s response to perceived discrimination and the well-being of minorities. ii DEDICATION I dedicate this dissertation to my family. All my efforts are reflective of your love and care. Los quiero mucho y que Dios los bendiga siempre. iii ACKNOWLEDGMENTS I would first and foremost like to thank my mentors Delia Saenz and Virginia Kwan for guiding me through the dissertation, graduate school and life. The other two members of my committee, Nancy Gonzales and Christian Geiser, were also extremely helpful and their feedback is invaluable. I would also like to thank my family and friends for continually being a support system for me. Lastly, I am absolutely grateful to my Lord and Savior, Jesus Christ, for all the wonderful blessings in my life.
Logics for hybrid systems

Hybrid systems are heterogenous dynamical systems characterized by interacting continuous and discrete dynamics. Such mathematical models have proved fruitful in a great diversity of engineering applications, including air-traffic control, automated manufacturing, and chemical process control. The high-profile and safety-critical nature of the application areas has fostered a large and growing body of work on formal methods for hybrid systems: mathematical logics, computational models and methods, and computer-aided reasoning tools supporting the formal specification and verification of performance requirements for hybrid systems, and the design and synthesis of control programs for hybrid systems that are provably correct with respect to formal specifications. This paper offers synthetic overview of, and original contributions to, the use of logics and formal methods in the analysis of hybrid systems.
The evolution of a lexicon and meaning in robotic agents through self-organization

This paper discusses interdisciplinary experiments, combining robotics and evolutionary computational linguistics. The goal of the experiments is to investigate if robotic agents can originate a language, in particular a lexicon. In the experiments two robots engage in a series of so-called language games. Starting from the assumption that the robots know how to communicate and are able to detect some sensory information from the environment, the agents ground conceptual meaning and develop a lexicon. The experiments show that the robots are able to form a shared communication system. The paper investigates the influence of using non-linguistic information in the formation of the lexicon, which takes the form of pointing (1) to indicate the topic of the language game, and (2) to give feedback on the outcome of the game.
Sexual violence: The sacred witness of the Church 1

The trauma of sexual violence is pandemic. Such trauma contributes to numerous physical and psychological symptoms that affect individuals throughout their lifespan. Survivors are most inclined to turn to their faith and their religious community for support. Given the prevalence of sexual violence and the severity of its symptoms, it is critical that the Church serve as a sacred witness to facilitate the recovery process.
The effect of language-focused professional development on the knowledge and behaviour of preschool practitioners

The purpose of this project was to investigate the effectiveness of a language-focused professional development programme on the knowledge and behaviour of preschool practitioners (sometimes called early years practitioners) in the UK. In Study 1 we determined whether the training received by practitioners is effective in improving their knowledge of how to support children’s language and communicative development. In Study 2 we tested whether trained practitioners, and practitioners from centres with embedded Language Champions, were able to implement the techniques they had been taught. For this, we video-recorded practitioners interacting, one to one, with 2- and 3–4-year-old children in their centres. We conclude that (1) practitioners retain the knowledge they have been taught, both about how children learn and about how to promote this learning, and that (2), in some respects, this knowledge translates well into practice; practitioners in centres with embedded Language Champions and trained practitioners used language-enriching behaviours when interacting with children more often than did untrained practitioners. We discuss how the translation of some techniques into overt behaviour could be made more effective.
Level-of-detail representation of bidirectional texture functions for real-time rendering

This paper presents a new technique for rendering bidirectional texture functions (BTFs) at different levels of detail (LODs). Our method first decomposes each BTF image into multiple subbands with a Laplacian pyramid. Each vector of Laplacian coefficients of a texel at the same level is regarded as a Laplacian bidirectional reflectance distribution function (BRDF). These vectors are then further compressed by applying principal components analysis (PCA). At the rendering stage, the LOD parameter for each pixel is calculated according to the distance from the viewpoint to the surface. Our rendering algorithm uses this parameter to determine how many levels of BTF Laplacian pyramid are required for rendering. Under the same sampling resolution, a BTF gradually transits to a BRDF as the camera moves away from the surface. Our method precomputes this transition and uses it for multiresolution BTF rendering. Our Laplacian pyramid representation allows real-time anti-aliased rendering of BTFs using graphics hardware. In addition to provide visually satisfactory multiresolution rendering for BTFs, our method has a comparable compression rate to the available single-resolution BTF compression techniques.
HST WFPC2 Observations of the Peculiar Main Sequence of the Double Star Cluster NGC 2011 in the Large Magellanic Cloud

We report the serendipitous discovery of a peculiar main sequence in archived Hubble Space Telescope Wide Field Planetary Camera 2 (WFPC2) observations of the young star cluster NGC 2011 in the Large Magellanic Cloud. The bright part of this main sequence exhibits a prominent double, forklike feature, as if it consists of twin main sequences, one of them being redder. The color-magnitude diagram, constructed from the stars found in the only available WFPC2 field of the cluster, is used to distinguish the stars according to their membership to each of these sequences and to study their spatial distribution. We find that there are two well-distinguished populations in the sense that the redder main sequence is dominated by stars that belong to the main body of the cluster, while the stars of the bluer main sequence belong to the surrounding region. Providing that NGC 2011 is a verified binary cluster, with the second companion unfortunately not observed, and taking into account the general region where this cluster is located, we discuss the possible scenarios from both star formation and an early dynamical evolution point of view that might explain this unique discovery.
On the Interplay between Readability, Summarization, and MTranslatability

NLP can be used in several ways to help people read texts that are inaccessible to them in some way. Machine translation can be used to translate texts from a language one do not know, and summarization can be used to shorten texts in order to make them faster and/or easier to read. Both these techniques do in a sense improve readability. In this paper we describe a small initial study of the interplay between readability, summarization, and MTranslatability. We applied an extractive summarizer to a set of news stories, and investigated the effect on readability and machine translation (MT). Extractive summarizations are created by extracting the most important sentences from a text; no re-writing of sentences takes place. Smith and Jönsson (2011a) showed that by using extractive summarization, readability was improved according to several readability measures. Their study was performed on Swedish text, and showed an effect on several genres including news. MTranslatability is the translatability of a text by an MT system, i.e., how easy a text is for an MT system to translate (Bernth and Gdaniec, 2001). This notion has mostly been exploited in connection to rule-based MT systems, for instance by improving MT output through re-writing the source according to some controlled language rule set (see e.g Roturier (2004)). MTranslatability is also related to the notion of confidence estimation (CE), to estimate the quality of machine translated sentences (Specia et al., 2009). Many features used for CE are also similar to those used in many readability formulas, such as sentence length. CE differs from the notion of MTranslatability, however, in that in CE the estimation is done after the translation phase, whereas MTranslatability is related to assessing whether a sentence or text is easy or hard to translate before it is sent to the MT system.
Vector based hysteresis current control scheme for vector controlled induction motor drives

Simple and novel vector based hysteresis current control schemes are presented for vector controlled induction motor drives. The proposed algorithm combines the principles of direct torque control (DTC) and field oriented control (FOC). The proposed algorithm generates the reference currents as per the principle of vector control algorithm. The, the vector based hysteresis comparators integrated with look-up tables are proposed for vector controlled induction motor drive. The proposed lookup tables based vector control gives good dynamic response with reduced harmonic distortion. This paper presents 6-sector and 12-sector based vector control algorithms. To validate the proposed methods numerical simulation studies have been carried out and compared with the existing algorithms. The simulation results show the effectiveness of the proposed technique.
NGF and proNGF Regulate Functionally Distinct mRNAs in PC12 Cells: An Early Gene Expression Profiling

The biological activities of NGF and of its precursor proNGF are quite distinct, due to different receptor binding profiles, but little is known about how proNGF regulates gene expression. Whether proNGF is a purely pro-apoptotic molecule and/or simply a “less potent NGF” is still a matter of debate. We performed experiments to address this question, by verifying whether a proNGF specific transcriptional signature, distinct from that of NGF, could be identified. To this aim, we studied gene expression regulation by proNGF and NGF in PC12 cells incubated for 1 and 4 hours with recombinant NGF and proNGF, in its wild-type or in a furin-cleavage resistant form. mRNA expression profiles were analyzed by whole genome microarrays at early time points, in order to identify specific profiles of NGF and proNGF. Clear differences between the mRNA profiles modulated by the three neurotrophin forms were identified. NGF and proNGF modulate remarkably distinct mRNA expression patterns, with the gene expression profile regulated by NGF being significantly more complex than that by proNGF, both in terms of the total number of differentially expressed mRNAs and of the gene families involved. Moreover, while the total number of genes modulated by NGF increases dramatically with time, that by proNGFs is unchanged or reduced. We identified a subset of regulated genes that could be ascribed to a “pure proNGF” signalling, distinct from the “pure NGF” one. We also conclude that the composition of mixed NGF and proNGF samples, when the two proteins coexist, influences the profile of gene expression. Based on this comparison of the gene expression profiles regulated by NGF and its proNGF precursor, we conclude that the two proteins activate largely distinct transcriptional programs and that the ratio of NGF to proNGF in vivo can profoundly influence the pattern of regulated mRNAs.
Protein secondary structure prediction using a fully complex-valued relaxation network

Knowledge of the various protein functions is essential to understand the manifestation of diseases and develop suitable drugs to alleviate them. As proteins form conformational patterns like α-helix and β-strands that eventually fold up into 3-D structure, prediction of the secondary structure of proteins is an important intermediate step in understanding the final structure of proteins that are vital for performing biological functions. Thus, there is a need to predict the secondary structure of proteins accurately and efficiently. Recent studies in machine learning have shown that complex-valued neural networks have better decision making ability than real-valued networks. Therefore, we use a Fully Complex-valued Relaxation Network (FCRN) classifier to predict the secondary structure of proteins in this paper. FCRN classifier is a single hidden layer neural network classifier with nonlinear input, hidden and output layers. The neurons in the input layer convert the real-valued input features to the Complex domain using a circular transformation. The neurons in the hidden layer employ a fully complex-valued sech activation function and those in the output layer employ the fully complex-valued exp activation function. For constant random input parameters, FCRN estimates the output weights corresponding to the minimum energy point of a logarithmic error function that represents both the magnitude and phase error explicitly. The prediction performance of FCRN is compared against the best results available in the literature for this problem. Our results show that FCRN presents higher or comparable prediction accuracy than other classifiers available in the literature.
The Impacts of Attitudes and Engagement on Electronic Word of Mouth (eWOM) of Mobile Sensor Computing Applications

As one of the latest revolutions in networking technology, social networks allow users to keep connected and exchange information. Driven by the rapid wireless technology development and diffusion of mobile devices, social networks experienced a tremendous change based on mobile sensor computing. More and more mobile sensor network applications have appeared with the emergence of a huge amount of users. Therefore, an in-depth discussion on the human–computer interaction (HCI) issues of mobile sensor computing is required. The target of this study is to extend the discussions on HCI by examining the relationships of users’ compound attitudes (i.e., affective attitudes, cognitive attitude), engagement and electronic word of mouth (eWOM) behaviors in the context of mobile sensor computing. A conceptual model is developed, based on which, 313 valid questionnaires are collected. The research discusses the level of impact on the eWOM of mobile sensor computing by considering user-technology issues, including the compound attitude and engagement, which can bring valuable discussions on the HCI of mobile sensor computing in further study. Besides, we find that user engagement plays a mediating role between the user’s compound attitudes and eWOM. The research result can also help the mobile sensor computing industry to develop effective strategies and build strong consumer user—product (brand) relationships.
A Data-Driven Approach to A Priori SNR Estimation

The a priori signal-to-noise ratio (SNR) plays an important role in many speech enhancement algorithms. In this paper, we present a data-driven approach to a priori SNR estimation. It may be used with a wide range of speech enhancement techniques, such as, e.g., the minimum mean square error (MMSE) (log) spectral amplitude estimator, the super Gaussian joint maximum a posteriori (JMAP) estimator, or the Wiener filter. The proposed SNR estimator employs two trained artificial neural networks, one for speech presence, one for speech absence. The classical decision-directed a priori SNR estimator by Ephraim and Malah is broken down into its two additive components, which now represent the two input signals to the neural networks. Both output nodes are combined to represent the new a priori SNR estimate. As an alternative to the neural networks, also simple lookup tables are investigated. Employment of these data-driven nonlinear a priori SNR estimators reduces speech distortion, particularly in speech onset, while retaining a high level of noise attenuation in speech absence.
A Note on the $L_1$ Consistency of Variable Kernel Estimates

1 . Introduction . Most consistent nonparametric density estimates have a built-in smoothing parameter . Numerous schemes have been proposed (see, e.g ., references found in Rudemo, 1982 ; or Devroye and Penrod, 1984) for selecting the smoothing parameter as a function of the data only (a process called automatization), and for introducing locally adaptable smoothing parameters . In this note, we give conditions which insure that estimators of the form
Towards Mathematical Expression Understanding

Mathematical expressions are an important means of scientific communication, used not only for numerical calculation and theorem proving but also for clarifying concept definitions and ensuring formal operations are not ambiguous. In this paper, we address the problem of understanding the meaning of mathematical expressions. In our approach, we formulate the problem as a task of translation from Presentation MathML to Content MathML expressions. We propose a machine learning-based approach that combines automatic extraction of fragment rules and statistical machine translation. Experimental results showed the potential of our approach for understanding the meaning of mathematical expressions.
Towards the Unsupervised Acquisition of Implicit Semantic Roles

This paper describes a novel approach to find evidence for implicit semantic roles. Our data-driven models generalize over large amounts of explicit annotations only, in order to acquire information about implicit roles. We establish a generic background knowledge base of probablistic predicate-role co-occurrences in an unsupervised manner, and estimate thresholds which trigger the prediction of a missing role. Our approach outperforms the stateof-the-art in terms of recognition rate and offers a more flexible alternative to rulebased solutions which rely on costly, language and domain-specific lexica.