Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditory discrimination problems in developmental dyslexia.

The phonemic discrimination of subjects with developmental dyslexia was investigated in the present study. Two adult developmental phonological dyslexics are first reported. Both were good at reading real words but had difficulty reading and spelling novel stimuli. Further testing revealed a perceptual discrimination problem that was restricted to a narrow range of phonemes in both subjects. In order to test the generality of this finding, 20 further developmental dyslexics were tested on their nonword reading skill and phonemic discrimination ability. There was a significant association between the two variables-subjects poor at phonemic discrimination were also very likely to be poor at nonword reading. It is suggested that phonemic discrimination problems at an early age may disrupt the normal acquisition of alphabetic processing skills for reading and spelling. Remedial implications of the findings are discussed.
Scaling Multi-Armed Bandit Algorithms

The Multi-Armed Bandit (MAB) is a fundamental model capturing the dilemma between exploration and exploitation in sequential decision making. At every time step, the decision maker selects a set of arms and observes a reward from each of the chosen arms. In this paper, we present a variant of the problem, which we call the Scaling MAB (S-MAB): The goal of the decision maker is not only to maximize the cumulative rewards, i.e., choosing the arms with the highest expected reward, but also to decide how many arms to select so that, in expectation, the cost of selecting arms does not exceed the rewards. This problem is relevant to many real-world applications, e.g., online advertising, financial investments or data stream monitoring. We propose an extension of Thompson Sampling, which has strong theoretical guarantees and is reported to perform well in practice. Our extension dynamically controls the number of arms to draw. Furthermore, we combine the proposed method with ADWIN, a state-of-the-art change detector, to deal with non-static environments. We illustrate the benefits of our contribution via a real-world use case on predictive maintenance.
ILP Based Power-Aware Test Time Reduction Using On-Chip Clocking in NoC Based SoC

Network-on-chip (NoC) based system-on-chips (SoC) has been a promising paradigm of core-based systems. It is difficult and challenging to test the individual Intellectual property IP cores of SoC with the constraints of test time and test power. By reusing the on-chip communication network of NoC for the testing of different cores in SoC, the test time and test cost can be reduced effectively. In this paper, we have proposed a power-aware test scheduling by reusing existing on-chip communication network. On-chip test clock frequencies are used for power efficient test scheduling. In this paper, an integer linear programming (ILP) model is proposed. This model assigns different frequencies to the NoC cores in such a way that it reduces the test time without crossing the power budget. Experimental results on the ITC’02 benchmark SoCs show that the proposed ILP method gives up to 50% reduction in test time compared to the existing method.
A new genetic algorithm for motor parameter estimation

The genetic algorithms are being increasingly used in motor parameter estimation because they are based on simple models while offering high accuracy results, stable solutions and high convergence speeds. The proposed algorithm uses the binary tournament parent selection method, a dynamic population table with an elitist strategy and a special 6-point crossover operator. It has been tested against data from three different induction motors. The agreement between the theoretical and calculated values of the parameters is very good and superior to results existing in the literature.
Peripheral Representations: from Perception to Visual Search

Author(s): Deza, Arturo | Advisor(s): Eckstein, Miguel P. | Abstract: The human visual field is composed of a high acuity region at the center of gaze called the fovea, andits complement, the visual periphery. Not much is known about the computations and representationsof the visual periphery, as most of the focus in the field of human (and machine) vision is geared towardsfoveal vision. Thus, the focus of this thesis will be on understanding the computations performed bythe human visual system in the visual periphery. In doing so, I will begin by modelling the perceptionof clutter and how it changes as a function of the behavioural task and point of fixation, developinga collection of foveated clutter models that enhance non-foveated models. I will then propose a newmetamer model that renders how the information is distorted in the visual field, and what this tells usabout the computations done in the visual periphery. Finally, I will conclude with the design of twohybrid man-machine collaborative visual search systems that try to overcome the limitations in humanvisual search imposed by the visual periphery and observer inefficiencies in terminating exploration.
Nonlinear subunit models of neuronal receptive fields in the early visual pathway

Our visual system is sensitive to boundaries defined by differences in cues such as luminance (first-order cue), as well as texture, contrast, or motion (second-order cues). Gradients in these cues can be utilized to perform tasks such as figure-ground segregation and 3D shape perception. A significant fraction of neurons in the early visual cortex of cats and monkeys have been shown to be selective to both first- and second-order boundaries. These neurons are thought to be the neural correlate for perceptual encoding of such boundaries. They are selective for the same boundary orientation irrespective of the cue (first- or second-order) that defines it (“form cue-invariance”), which makes these neurons powerful candidates for the task of segmentation. However, the neural circuitry that gives rise to this selectivity for the early stages of visual processing remains unclear. To address this question, I perform neurophysiological recordings at the early stages of the visual pathway in cats, and then build biologically inspired neural circuit models that can account for visual response properties of neurons at subcortical as well as early cortical stages. In Chapter 2, I use multi-electrode recordings to demonstrate the presence of a significant fraction of neurons in cat Area 18 with nonlinear receptive fields like those of subcortical Y-type cells. These neurons have receptive field properties intermediate between subcortical Y cells and cortical orientation selective cue-invariant neurons. These are strong candidates for building cue-invariant orientation-selective neurons. Furthermore I present a novel neural circuit model that pools such Y-like neurons in an unbalanced “push-pull” manner, to generate orientation-selective cue-invariant receptive fields.In Chapter 3, I estimate biologically constrained neural network models of cat LGN receptive fields using recent machine learning methods (deep learning). The receptive fields are modeled as arising from a two-stage convolutional neural network model. The first stage, corresponding to retinal bipolar cell subunits, is modeled as a convolutional filter layer, and the second stage is modeled as a pooling layer. These two layers are separated by an intermediate parametric nonlinearity. I train such a neural network model for each recorded LGN neuron, using its spiking responses to naturalistic texture stimuli. These models are not only better in comparison to the standard linear-nonlinear models at predicting response to arbitrary stimuli, but they also recover biologically interpretable subunit models.In chapter 4, I evaluate the integration of ON- and OFF-pathway inputs by individual neurons in early cortical areas of the cat (Area 17 and Area 18). In this study, I model receptive fields of cortical simple cells as a linear weighted sum of rectified inputs from model ON- and OFF-center LGN afferents, with the weights estimated using a regression framework. The estimated models reveal significant asymmetries in spatiotemporal integration of ON and OFF signals within…
A study of K-12 teachers’ TPACK on the technology acceptance of E-schoolbag

ABSTRACT E-Schoolbag is an integrated platform presented on the portable digital devices for educational purposes. Successful and sustainable integration of e-Schoolbag into the classroom often demands teachers to be equipped with essential knowledge, skills, and willingness to employ it on a voluntary basis. The purpose of this study was to examine if teacher’s TPACK abilities could predict their inclination to use e-Schoolbag. A total of 1185 teachers from 45 primary and secondary schools in China participated in this study. A structural equation model was established to examine if and how the TPACK framework might affect TAM. Results showed that the teachers’ TPACK level had a large impact on their acceptance of e-Schoolbag through perceived usefulness (PU) and ease of use (EOU). Specifically, the technological knowledge (TK) component was more significantly correlated with the e-Schoolbag acceptance than Pedagogical knowledge (PK) and content knowledge (CK). The path coefficient value further showed that TPACK affected the EOU more than the PU.
RC4c : A Secured Way to View Data Transmission in Wireless Communication Networks

In spite of several crypto analytical criticisms on RC4 data encryption algorithm, its simplicity, speed has made it one of the popular encryption techniques employ in wireless communication networks. However, in recent time many experts no longer consider RC4 secure against attacks. This is due to some vulnerability detected through repetition of keys over a period of time. This exposes the weakness of the exclusive OR operator on which the RC4 technique is anchored. In recent time, another variant of RC4 was developed called RC42s. This encryption algorithm innovatively solves the problem of mutual exclusive of XOR operator in RC4; however, the experimental result shows the improvement rate of 68% over RC4. That is, 32% of encrypted messages are susceptible to hacking whenever there is key collision. In this paper, a novel method is developed to counter the negative effect of the mutual exclusiveness of the functional operator on RC4 and RC42s. The new technique called RC4c uses 2s complement and shifting operation to give a perfect and secured data encryption technique for wireless network. Performance analysis on speed and effect of mutual exclusiveness of XOR on RC4, RC42s and RC4c security was done. It was discovered that RC4c not only completely nullifies the weakness introduced by mutual exclusiveness of XOR in RC4 and RC42s but maintains the simplicity and throughput of the RC4 and RC42s.
Natural computation, much ado about nothing

The information coding of the environment by the brain is subject to intense debate at the level of its biological implementation (functional properties) as well as at the level of its reliability, efficiency and precision. This study is based on in-vivo intracellular recordings of responses of primary visual cortex neurons to stimuli of various complexity including natural scene animated by eye-movements. We show that functional selectivity are generated by cortical recurrence notably by the excitation/inhibition balance expressed through a large diversity of combinatory of synaptic input functional selectivity. In natural condition with eye-movement, the observed code is highly reliable, binary and temporal and cortical computation is removing input redundancies. Nonlinearity or gain controls, which biphasic temporal profile sculpts the cortical responses, increase the temporal precision and reproducibility of the code. We propose in discussion, a sketch of a statistical model of adaptation and self-organisation consisting in the maximisation of the mutual-information between the system and its environment. This model, based on a decomposition of entropy into microscopic ordered redundancy, is sufficient to account for some defining properties of living systems such as reliability, diversity/specialisation, and optimal or efficient coding.
Expert Systems With Applications

Stock market prediction is one of the most challenging problems which has been distressing both re- searchers and ﬁnancial analysts for more than half a century. To tackle this problem, two completely opposite approaches, namely technical and fundamental analysis, emerged. Technical analysis bases its predictions on mathematical indicators constructed on the stocks price, while fundamental analysis ex-ploits the information retrieved from news, proﬁtability, and macroeconomic factors. The competition between these schools of thought has led to many interesting achievements, however, to date, no sat-isfactory solution has been found. Our work aims to combine both technical and fundamental analysis through the application of data science and machine learning techniques. In this paper, the stock market prediction problem is mapped in a classiﬁcation task of time series data. Indicators of technical analysis and the sentiment of news articles are both exploited as input. The outcome is a robust predictive model able to forecast the trend of a portfolio composed by the twenty most capitalized companies listed in the NASDAQ100 index. As a proof of real effectiveness of our approach, we exploit the predictions to run a high frequency trading simulation reaching more than 80% of annualized return. This project represents a step forward to combine technical and fundamental analysis and provides a starting point for developing new trading strategies.
IT-ITeS sector's export performance — A comparative study using regression

Exports in IT-ITeS industry is one of the significant contributors in the total service exports of India. Firm characteristics, ownership structure and industry characteristics significantly impact the export performance of Indian service firms. Two models, namely, pooled OLS regression and two-way Random Effect Model (REM) are employed to identify the difference between export the performance of the IT-ITeS industry and other service industries. It was found that the earnings through the export of IT-ITeS industries, is higher than that observed in other service industries. Also, the model is used to identify the effect of firm characteristics on export earnings. Furthermore, it was found that export behavior varies across different ownership groups.
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories

While word embeddings are now a de facto standard representation of words in most NLP tasks, recently the attention has been shifting towards vector representations which capture the different meanings, i.e., senses, of words. In this paper we explore the capabilities of a bidirectional LSTM model to learn representations of word senses from semantically annotated corpora. We show that the utilization of an architecture that is aware of word order, like an LSTM, enables us to create better representations. We assess our proposed model on various standard benchmarks for evaluating semantic representations, reaching state-of-the-art performance on the SemEval-2014 word-to-sense similarity task. We release the code and the resulting word and sense embeddings at http://lcl.uniroma1. it/LSTMEmbed.
Characterization of Hyperbrain Networks During Joint Piano Playing Using a Complex Dynamics Framework

Social interaction is essential for human life, but we have little understanding of the neural mechanisms supporting it. Recent research has shown correlated activity between the brains of individuals (Goldstein et al. 2018; Müller et al. 2018; Dikker et al. 2017; Toppi et al. 2016) using the novel technique of Electroencephalography Hyperscanning, which allows us to record multiple persons’ electrical brain activity at the same time. Interpretation of this data, however, is still unclear: does common activity reflect social interaction or is it just a by-product of shared perception? Furthermore, there is no unifying framework on how to analyze these novel data. Although we did not find evidence for synchronous brain activity between pianists playing duets using a complex dynamics framework, we were able to differentiate music pieces with ambiguous leadership roles from those with clear leadership roles using multivariate statistical approaches (graph theory). Furthermore, ambiguous leadership network characteristics correlated with participants’ perceptions of the quality of their performances. This thesis also contributes to this field by expanding previously proposed frameworks (Duan et al. 2015) to include a complex dynamics approach and thoroughly discussing issues in hyperbrain analysis. By standardizing the protocols, interpretations, and data analysis approaches of data from EEG hyperscanning, we can better elucidate what this synchrony means, effectively helping us move the field of single-person social neuroscience towards a two person neuroscience (Dumas 2011; Schilbach et al. 2013). This has profound implications at several levels, including the quantification of high level social constructs, such as empathy or joint attention, to clinical research, where these statistics can be used as diagnosis tools for the socially impaired brain.
Empirical Linguistic Study of Sentence Embeddings

The purpose of the research is to answer the question whether linguistic information is retained in vector representations of sentences. We introduce a method of analysing the content of sentence embeddings based on universal probing tasks, along with the classification datasets for two contrasting languages. We perform a series of probing and downstream experiments with different types of sentence embeddings, followed by a thorough analysis of the experimental results. Aside from dependency parser-based embeddings, linguistic information is retained best in the recently proposed LASER sentence embeddings.
Evaluation Of Local Disparity Estimation For View Interpolation

In this paper, we discuss the disparity estimation problem which is the most important challenge to generate an intermediate image for view synthesis. The main problem in estimating the disparity is to find the correspondence among the input images. The quality of the synthesized novel view is dependent on the accuracy of disparity estimation. Once we find the exact disparities among the input images, a simple low complexity interpolation is carried out to generate the new view. We offer a view of a comparative study between different state-of-the-art disparity estimation algorithms showing the advantages, disadvantages, and the peak signal to noise ratio (PSNR) of the reconstructed view using each one. ـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ * Egyptian Armed Forces ** Modern Technical Institute, Cairo, Egypt *** College of Engineering, Cairo University, Cairo, Egypt Proceedings of the 7 ICEENG Conference, 25-27 May, 2010 EE201 2
Towards Fine-grained Text Sentiment Transfer

In this paper, we focus on the task of fine-grained text sentiment transfer (FGST). This task aims to revise an input sequence to satisfy a given sentiment intensity, while preserving the original semantic content. Different from the conventional sentiment transfer task that only reverses the sentiment polarity (positive/negative) of text, the FTST task requires more nuanced and fine-grained control of sentiment. To remedy this, we propose a novel Seq2SentiSeq model. Specifically, the numeric sentiment intensity value is incorporated into the decoder via a Gaussian kernel layer to finely control the sentiment intensity of the output. Moreover, to tackle the problem of lacking parallel data, we propose a cycle reinforcement learning algorithm to guide the model training. In this framework, the elaborately designed rewards can balance both sentiment transformation and content preservation, while not requiring any ground truth output. Experimental results show that our approach can outperform existing methods by a large margin in both automatic evaluation and human evaluation.
HVDC Systems Voltage Profiles Under Different Conditions for Interconnected power systems

High voltage direct current (HVDC) electric power transmission systems are preferred, in some situations, to the common alternating current AC transmission systems as a means for bulk transmission of electrical power. Modern form of HVDC transmission uses, extensively, modern technology. Some AC electric power systems, which are required to be connected together, may not be synchronized. Therefore, they cannot be connected even if the distance between them is short. Also, in some circumstances, it is physically impossible, or highly impracticable, to connect them, even if they are synchronized. In such conditions, HVDC systems are considered to be the most effective method for connecting these systems.
The first Met Office Unified Model/JULES Regional Atmosphere and Land configuration, RAL1

Abstract. In this paper we define the first "Regional Atmosphere and Land" (RAL) science configuration for kilometre scale modelling using the UM and JULES. "RAL1" defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against and it is the intention that from this point forward significant changes to the system will be documented in literature. This is reproducing the process used for global configurations of the UM which was first documented as a science configuration in 2011. While it is our goal to have a single defined configuration of the model that performs effectively in all regions, this has not yet been possible. Currently we define two sub-releases, one for mid-latitudes (RAL1-M) and one for tropical regions (RAL1-T). The differences between RAL1-M and RAL1-T are documented and where appropriate, we define how the model configuration relates to the corresponding configuration of the global forecasting model.
A Large-Scale Corpus for Conversation Disentanglement

Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89% of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.
Generating Responses with a Specific Emotion in Dialog

It is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction. After a careful investigation of real-life conversation data, we found that there are at least two ways to express emotions with language. One is to describe emotional states by explicitly using strong emotional words; another is to increase the intensity of the emotional experiences by implicitly combining neutral words in distinct ways. We propose an emotional dialogue system (EmoDS) that can generate the meaningful responses with a coherent structure for a post, and meanwhile express the desired emotion explicitly or implicitly within a unified framework. Experimental results showed EmoDS performed better than the baselines in BLEU, diversity and the quality of emotional expression.