Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial emotion recognition using multi-modal information

Facial emotion recognition will become vitally important in future multi-cultural visual communication systems, for emotion translation between cultures, which may be considered analogous to speech translation. However so far the recognition of facial emotions is mainly addressed by computer vision researchers, based on facial display. Also detection of vocal expressions of emotions can be found in research work done by acoustic researchers. Most of these research paradigms are devoted purely to visual or purely to auditory human emotion detection. However we found that it is very interesting to consider both these auditory and visual information together, for processing, since we hope this kind of multi-modal information processing will become a datum of information processing in future multimedia era. By several intensive subjective evaluation studies we found that human beings recognise anger, happiness, surprise and dislike by their visual appearance, compared to voice only detection. When the audio track of each emotion clip is dubbed with a different type of auditory emotional expression, still anger, happiness and surprise were video dominant. However the dislike emotion gave mixed responses to different speakers. In both studies we found that sadness and fear emotions were audio dominant. As a conclusion, we propose a method of facial emotion detection by using a hybrid approach, which uses multi-modal information for facial emotion recognition.
GCC2Verilog Compiler Toolset for Complete Translation of C Programming Language into Verilog HDL

Reconfigurable computing using a field‐programmable gate‐array (FPGA) device has become a promising solution in system design because of its power efficiency and design flexibility. To bring the benefit of FPGA to many application programmers, there has been intensive research about automatic translation from high‐level programming languages (HLL) such as C and C++ into hardware. However, the large gap of syntaxes and semantics between hardware and software programming makes the translation challenging. In this paper, we introduce a new approach for the translation by using the widely used GCC compiler. By simply adding a hardware description language (HDL) backend to the existing state‐of‐the‐art compiler, we could minimize an effort to implement the translator while supporting full features of HLL in the HLL‐to‐HDL translation and providing high performance. Our translator, called GCC2Verilog, was implemented as the GCC's cross compiler targeting at FPGAs instead of microprocessor architectures. Our experiment shows that we could achieve a speedup of up to 34 times and 17 times on average with 4‐port memory over PICO microprocessor execution in selected EEMBC benchmarks.
Joint TX-RX optimisation for MIMO-SDMA based on a null-space constraint

We address the problem of transmission pre- and post-filtering in multi-user MIMO communications using SDMA. In this set-up, multiple antennas are used at the base station and at the terminals. A double level of spatial multiplexing is used: the users are spatially multiplexed (SDMA) and each user receives spatially multiplexed bit streams (SDM). Multi-user interference is a potential performance limitation in this scheme. To avoid this, we propose a pre-filter with a null-space constraint that block-diagonalizes the channel matrix, resulting in zero multi-user interference. Each terminal then only has to eliminate its own inter-stream interference, which does not require information from the other users' channel. This scheme thus breaks down the optimisation schemes in independent, smaller per-user optimisation. It lends itself to numerous designs of which joint transmit-receive MMSE is discussed. The performance of this design scheme is assessed by simulations.
Texture Analysis in the Presence of Speckle Noise

We investigate the performance of selected texture models for the purpose of land use classification. The texture models are evaluated based on the resulting classification error rates. Three classes of texture models are evaluated: fractal models, lognormal random fields and grey level co-occurrence matrices. The effect of filtering and noise transformation is investigated. The lognormal random field model gives the best performance.
Stability and Controllability of Double Integrator Consensus Systems in Heterogeneous Networks

A consensus system is a set of communicating agents that agree on a variable of interest using only local communication. Some possible uses for consensus systems include smart grids, wireless sensor networks, social networks and various applications of unmanned aerial vehicles such as surveillance, mapping and environmental monitoring. If the agents have double integrator dynamics, then the consensus system is called a double integrator consensus system. In this thesis a double integrator consensus system in continuous time is studied. The agents are assumed to be communicating their position and velocity information along some possibly different, weighted directed communication networks that are modeled by weighted directed graphs. The mathematical model of the system has the form ẍ(t) = −Lxx(t) −βLẋẋ(t), (S1) where x : R+ → R and ẋ : R+ → R are the collected positions and velocities of the agents, Lx and Lẋ are the Laplacians of the communication graphs and β ∈ R+ \ {0} is a gain. A stability concept for such systems is derived in the thesis. The system (S1) is said to be consensus stable if all of its velocity differences approach zero in time, and asymptotically consensus stable, if all of its position differences tend to zero. The standard assumption made in the literature, that the communication networks are the same, i.e. homogeneous, for both information, is too strong in many cases. Thus, the algorithm is investigated under the assumption that the communication graphs may be different and possibly even disconnected. Necessary and sufficient conditions for consensus stability and asymptotic consensus stability are presented in the special case that the communication graphs are weighted and undirected. These conditions are subsequently relaxed to some cases of weighted directed graphs. Moreover, we consider the convergence rate of the system and the final convergence value. Following the stability analysis, it is shown how the autonomous consensus system (S1) can be transformed into a control system by introducing an external control input to a subset of the agents. The resulting system is given by ẍ(t) = −Axx(t) −A f ẋẋ(t) +Bu(t), (S2) where x : R+ → Rf and ẋ : R+ → Rf are the states of the controlled, “follower” agents, u : R+ → Rl is the control input
Microcell prediction in dense urban area

The use of microcell to increase the capacity of cellular mobile communication, especially in the dense urban areas, is an attractive possibility. A number of experimental and theoretical studies have been undertaken regarding propagation in the urban microcellular system. A microcell prediction model based on building thickness is developed to predict the statistical properties of the cell site. The model is validated based on several drive tests performed in different countries/cities with different transmitter heights, frequencies, cell-site parameters, and mobile environments. The first finding is that this empirical-based microcell model performed well in all these areas with a combination of various parameters. Second, the terrain does have a major impact on microcell prediction. This paper addresses the sufficient requirement of having only a two-dimensional (2-D) microcell prediction model. The impact of terrain contour on the microcell prediction is not included.
Comparison of a Mobile Technology Application With the Balance Error Scoring System

Context: The most widely used method for postural balance assessment relies on the subjective observations of a test administrator. Accelerometry has been shown to provide a valid and reliable method for assessment of balance, and recent advances in microelectromechanical systems have made the technology available in mobile electronic devices. Objective: To compare a mobile technology application with a commonly used subjective balance assessment. Setting: Biomechanics laboratory. Participants: Twenty-one nonathlete college-aged individuals (7 men, 14 women; mean age 23 ± 3 years) volunteered to participate. Subjects were excluded if they reported any preexisting condition that might affect postural balance. Results: A strong inverse correlation was found between the scores for the two balance assessment methods (r = -.767, p < .01). Conclusions: Advances in technology have provided an attractive means to objectively quantify postural balance with off-the-shelf mobile consumer electronic devices.
Signal Processing and General-Purpose Computing on GPUs

Graphics processing units (GPUs) are the massively parallel high-performance processors used for graphics accelerators in personal computers. These processors have been driven to very high levels of performance and low price-points by the need for real-time computer graphics in mass-market gaming. However, GPUs are no longer specialized for computer graphics. Over the last five years, they have evolved a general-purpose programmable architecture and supporting ecosystem that make possible their use for a wide range of nongraphics tasks, including many applications in signal processing (SP). Commercial, high-performance SP applications that use GPUs as accelerators for generalpurpose (GP) tasks (referred to as GPGPU processing) are still emerging, but many aspects of the architecture of GPUs and their wide availability make them interesting options for implementing and deploying such applications. In this article, we will review some SP applications of GPUs, summarize their key architectural features, and survey the options available for programming these devices.
Cool Infographics: Effective Communication with Data Visualization and Design

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Methodologies for trend detection.

Methodologies for detection of change in river flow data are briefly reviewed. After discussion of the issue of data for change detection, the basics of statistical testing for trend detection are presented, including such concepts as the null hypothesis, the test statistic, the significance level and the trend index. Test assumptions are thoroughly discussed. A review of practical tests (parametric, distribution-free i.e. non-parametric, and resampling: permutation and bootstrap approaches) applicable for different properties of data, is presented. The case where the records are not independent and are not normally distributed is also included. A few special problems, such as extremes and spatial data (with correlation between gauges), and detectability of change are also discussed. Assessment of a few relevant recent publications is offered.
Proof of the local REM conjecture for number partitioning. I: Constant energy scales

The number partitioning problem is a classic problem of combinatorial optimization in which a set of $n$ numbers is partitioned into two subsets such that the sum of the numbers in one subset is as close as possible to the sum of the numbers in the other set. When the $n$ numbers are i.i. d. variables drawn from some distribution, the partitioning problem turns out to be equivalent to a mean-field antiferromagnetic Ising spin glass. In the spin glass representation, it is natural to define energies -- corresponding to the costs of the partitions, and overlaps -- corresponding to the correlations between partitions. Although the energy levels of this model are {\em a priori} highly correlated, a surprising recent conjecture asserts that the energy spectrum of number partitioning is locally that of a random energy model (REM): the spacings between nearby energy levels are uncorrelated. In other words, the properly scaled energies converge to a Poisson process. The conjecture also asserts that the corresponding spin configurations are uncorrelated, indicating vanishing overlaps in the spin glass representation. In this paper, we prove these two claims, collectively known as the local REM conjecture.
Attitudes towards online feedback on writing: Why students mistrust the learning potential of models

Abstract This exploratory study sheds new light on students’ perceptions of online feedback types for a complex writing task, summary writing from spoken input in a foreign language (L2), and investigates how these correlate with their actual learning to write. Students tend to favour clear-cut, instructivist rather than constructivist feedback, and guided self-evaluation through model solutions in online learning environments. However, the former type is too limited to tackle all dimensions of advanced writing. Constructivist feedback, in the form of guided modelling, allows addressing the higher-order concerns involved in summary writing. In addition, it is widely acknowledged that activating the zone of proximal development (ZPD) through cognitive involvement is beneficial to learning. To investigate students’ learning from both types of feedback, a one-group pre-post-test intervention study was set up. Students attending a course on summary writing in L2 within a bachelor programme in Applied Languages (n=38) followed an individual online learning module containing both instructivist fill-the-gap exercises and model solutions with constructivist guiding questions for self-assessment. The students’ actual learning gain was measured through pre- and post-tests, and compared with their perceived learning gain, as expressed in self-evaluation. The comparison reveals a dichotomy between the students’ observed learning curve and an underestimation of their own progress. This dichotomy was found to originate in a mismatch between their expectations towards the online learning module and the characteristics of the constructivist feedback conveyed. This mismatch can be attributed to three key factors: (1) evaluation, (2) linguistic focus, and (3) learner motivation.
Language and Learning: The Home and School Years

Chapter One 2 Language Is Uniquely Human 2 Chapter Two: The Study and Structure of Language 35 The Study of Language 36 Chapter Three: Learning the First Language 76 Chapter Four: First Language Learners 105 Chapter Five: Learning More than One Language 151 Chapter Six: Successful Second Language Learners 184 Chapter Seven: Language Disorders in Monolingual and Bilingual Children 212 Chapter Eight: Learning to Use Language 243 Chapter Nine: Language and Cognitive Growth 263 Chapter Ten: Language in School 298 Chapter Eleven: Intercultural Communication 338 Chapter Twelve: Language and Learning is ALL about Inclusion 354 Glossary 386 References by Chapter 392 Bibliography 412
Econometrics and decision making: Effects of communication of results

Esta tesis incluye tres estudios que analizan como la informacion se presenta en varios contextos, como estos diferentes modos de presentacion influyen las percepciones de los tomadores de decisiones y como mejorar la comunicacion de la informacion para eliminar distorsiones. Capitulo 1 analiza una situacion donde expertos hacen inferencias utilizando diferentes presentaciones de un analisis de regresion, un metodo de estadistica ampliamente utilizado. Capitulo 2 introduce un modo de presentacion basado en experiencia y pone a prueba su eficacia a traves de multiples problemas probabilisticas. Capitulo 3 demuestra los efectos del modo de presentacion y el numero de opciones disponibles sobre las cantidades y la distribucion de las donaciones a las ONG y sus campanas. En general, los resultados sugieren que el modo de presentacion es un determinante importante de las percepciones y decisiones, y pueden ser reestructuradas para mejorar la precision de las inferencias.
Evaluating an Interactive Media Tutorial for Presentation Authoring

Abstract This paper describes the design, development, and testing of an interactive media tutorial for presentation authoring. Several agencies are calling for university graduates to possess skills with information technologies including the ability to communicate visually in a variety of contexts. Unfortunately, many students do not receive explicit instruction in this area. This project was aimed at solving a context-specific problem while at the same time documenting the design and development process and subsequent testing to glean "lessons learned" for future projects.
AUTOMATED HIGHWAY SYSTEMS: PLATOONS OF VEHICLES VIEWED AS A

Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) that makes use of communications to autonomously guide cooperative vehicles on an Automated Highway System (AHS). For the past decade, different architectures of automated vehicles have been proposed, but most of them did not or barely addressed the inter-vehicle communication problem. In this thesis, we address the collaborative driving problem by using a platoon of cars driven by more or less autonomous software agents interacting in a Multiagent System (MAS) environment: the automated highway. To achieve this, we propose a hierarchical driving agent architecture based on three layers (guidance layer, management layer and traffic control layer). This architecture can be used to develop centralized platoons, where the driving agent of the head vehicle coordinates other driving agents by applying strict rules, and decentralized platoons, where the platoon is considered as a team of driving agents with a similar degree of autonomy, trying to maintain a stable platoon.
On even spread of test cases in adaptive random testing

Random testing (RT) is a basic and simple software testing technique. However, it is controversial whether RT is an effective method to detect software failures. Based on the common observation that inputs revealing software failures tend to cluster into contiguous regions, adaptive random testing (ART) has been proposed to enhance the failure-detection capability of RT. The basic intuition of ART is to evenly spread random test cases over the whole input domain. This thesis investigates a number of topics about such an “even spread” intuition. It has been generally believed that how evenly an ART algorithm spreads test cases has an impact on how effectively it detects software failures, and an even distribution of test cases brings a good failure-detection capability. This thesis investigates some existing ART algorithms, and proposes several families of new ART algorithms. Some simulations are conducted and the experimental results show that these new algorithms significantly enhance the failure-detection capability of ART. Test case distributions of these new ART algorithms are also examined, and it is observed that there is a strong correlation between the evenness of test case distribution and the failure-detection capability of an ART algorithm. All previous studies have used the failure-detection capability to measure the effectiveness of ART. This thesis proposes two new measurements to evaluate the performance of ART, which are also correlated to the even spread of test cases. One measurement is the coverage achieved by ART. A series of experiments are conducted in
Adaptive Intrusion Detection Based on Machine Learning: Feature Extraction, Classifier Construction and Sequential Pattern Prediction

In recent years, intrusion detection has emerged as an important technique for network security. Due to the large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, to optimize the performance of intrusion detection systems (IDSs) becomes an important open problem. In this paper, a general framework of adaptive intrusion detection based on machine learning is presented. In the framework, three perspectives of challenging problems are explored, which include feature extraction, classifier construction and pattern prediction for sequential data. It is illustrated that the three perspectives of research challenges are mainly suitable for machine learning methods using unsupervised, supervised and reinforcement learning algorithms, respectively. Several recently developed machine learning algorithms, including a multi-class support vector machine with principal component analysis (PCA) for feature reduction and a reinforcement learning algorithm for sequential prediction, are applied and evaluated both on network-based traffic data and on host-based program behaviors. Experiments on the KDD99 intrusion detection data set and the system call data from University of New Mexico show very promising results for the machine learning approaches to adaptive intrusion detection. Some directions for future research works are also discussed.
Professional Multilinguals: Some Exploratory Considerations on Language and the Identities of Translators and Interpreters

The role played by language(s) in the formation and performance of identities is well known and researched, but the affective aspects of multilingualism (emotions, identity, attitudes, etc.) have received marginal attention in the fields of sociolinguistics, literary studies, and psychology. With multilingualism becoming a prominent feature in communicative situations of all kinds, the professional figures of translators and interpreters have become more and more conspicuous. The nature of the profession means that language and multilingual competence is one of its defining dimensions, leading translators and interpreters to be located at the point of contact of two or more cultures. Nevertheless, whereas translation is seen by laypeople as a routine activity performed by multilingual users, professionals' experiences and points of view as multilinguals are rarely considered. This paper seeks to address, at least partially, this gap in research. Professionals' written responses to an email interview regarding their relationship with language and their profession are analysed in view of findings in neurolinguistics, social psychology and Translation Studies, with the aim to offer some considerations on the applicability of traditional theories of language and identity to 'professional multilinguals'.