Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Document Clustering Techniques for Personalized Peer Assessment in Exploratory Courses

Peer review has been proposed as a complement to project-based learning in courses covering a wide and heterogeneous syllabus. By reviewing peers’ projects, students can explore other subjects thoroughly apart from their own project topic. This objective relies however in a proper distribution of the works to review, which is a complex and time-consuming task. Beyond simple topic selection, students may report different types of works, which influence their peers’ assessment; for example, works focused on a project development approach versus in-depth literature researches. Introducing detailed metadata is time-consuming (thus users are typically reluctant) and, even more important, prone to error. In this paper we explore the potential of text mining and natural language processing technologies for automatic classification of texts, in order to facilitate the adaptation and diversification of the works assigned to the students for review, in the context of a course on Artificial Intelligence.
Down-regulation of p63 is required for epidermal UV-B-induced apoptosis.

In the epidermis, p53 plays an important role in UV-B protection that led us to examine the role, if any, that p63, a p53 homologue highly expressed in the basal layer of the epidermis, might play in the epidermal UV-B response. One p63 isoform, deltaNp63alpha, decreased dramatically in normal keratinocytes or newborn epidermis at both the protein and RNA levels after UV-B irradiation. In an attempt to further investigate the significance of the UV-B-induced decrease of this p63 isoform as well as further delineate the function of p63 in the epidermis, we generated transgenic mice that constitutively express deltaNp63alpha in the mouse epidermis using the loricrin promoter (ML.deltaNp63alpha). The ML.deltaNp63alpha mouse epidermis developed normally, with no overt phenotype and an unaltered proliferation rate. When challenged by UV-B exposure, the ML.deltaNp63alpha mice exhibited a 40-45% decrease in the number of apoptotic cells in the epidermis as compared with nontransgenic littermates. These results suggest that aberrant expression of deltaNp63alpha altered the UV-B-induced apoptotic pathway in the transgenic epidermis, proving that down-regulation of deltaNp63alpha in response to UV-B is important to epidermal apoptosis. The forced overexpression of deltaNp63alpha may act via a dominant negative effect on the endogenous p53 transcriptional activity required for UV-B-induced apoptosis.
Digital Image Forgery Detection using Wavelet Decomposition and Edge Detection

In the presented work, a copy-paste or cut-paste detection algorithm is discussed. The algorithm is based on wavelet decomposition of the input image and then extracting the edges so that the cut-paste or copy- paste region is detected by examining the edge pixels in wavelet domain. The scheme consists of: image acquisition, gray scale conversion, wavelet decomposition using haar wavelet, edge detection using sobel filter and then analysis of the region falling under the straight edge chain. The cut-paste or copy-paste region is normally in rectangular or square edge region. The edge pixels are analyse for their chain code so that the straight line could be extracted from the pixel tracing. This gives the possible region of image forgery. Further, the input image and forged images are compared with respect to different properties like entropy, power energy, standard deviation etc and re discussed in the algorithm.
Sensorimotor learning and control in autism spectrum disorders: The role of sensorimotor integration.

The aim of the current thesis was to examine the role of sensorimotor integration during sensorimotor learning and control processes in autism spectrum disorders. Autistic participants were matched (IQ, age, gender) with control participants across three experimental chapters (chapters three-five) within the contexts of motor learning, imitation and observational practice. An additional control experiment (chapter two), which examined observational practice, was also completed in order to determine suitable data collection and analysis techniques. In Chapter Two it was confirmed that atypical biological kinematics properties are coded during observational practice via underlying sensorimotor processes, rather than spatial encoding of peak velocity via processes associated with stimulus- response compatibility. In Chapter Three it was observed that autistic participants can successfully form new internal action models, but their movements are characterised by increased variability in the spatial position of peak acceleration. In Chapter Four, it was shown that autism participants were able improve their imitation of atypical biological kinematics when presented in a fixed trial-order. Suggesting that in part imitation difficulties in autism may be related to differences in sensorimotor processing and integration. In Chapter Five it was observed that individuals with autism, like typically developed controls, can code atypical biological kinematics via observational practice. There are however potential differences in the processing of reafference when updating an existing internal action model. The findings of the current thesis will be summarised and critically evaluated with regards to the current literature. Theoretical implications will be considered, and potential future directions and research applications will be discussed.
Group recommendations: approaches and evaluation

Recommender systems have been an active research topic for the past decade. However, most of previous studies have focused on recommendations to a single user. Recently, as group recommender systems have emerged as an expedient field, several interesting approaches have been proposed. However, despite all of these advances, the current generation of group recommendation approaches still needs further improvements to make more effective recommendations. In this paper, we discuss the limitations of existing group recommendation approaches and present possible developments that could lead to provide better group recommendations. We perform extensive experiments with different group recommendation approaches. The results show that the performance of that group recommendation approaches is limited either by the group type or group size and no single approach consistently performs better than the other approaches. The unavailability of real-life data sets uncovers the doubts for the accuracy of evaluation; the lack of standard terminology/procedure for evaluation also could lead to poor evaluation.
Committee Decisions on Monetary Policy: Evidence from Historical Records of the Federal Open Market Committee

In many countries, monetary policy decisions are made by committees. In the United States, these decisions are made by the Federal Reserve's Federal Open Market Committee (FOMC), which consists of the seven members of the Board of Governors and the presidents of the twelve district banks. This book examines the process by which the preferences of the FOMC's individual members are translated into collective policy choices. This focus on the aggregation of individual preferences into group decisions is unique and provides an important perspective on the evolution of monetary policy choices. To study decision making by the FOMC, the authors have used both formal voting records and detailed transcripts and summaries of deliberations contained in the committee's Memoranda of Discussion and FOMC Transcripts. The latter sources have been used to construct data sets describing individual committee members' policy preferences for the 1970-1978 and 1987-1996 periods when the FOMC was chaired by Arthur Burns and Alan Greenspan, respectively. These data are used to estimate monetary policy reaction functions for individual Committee members and to explore the role of majoritarian pressures, pressures for consensus, and the power of the chairman in collective decision making. The rich anecdotal evidence found in the Memoranda of Discussion and FOMC Transcripts inspires the narrative approach taken in two chapters, on the influence of political pressure on FOMC deliberations and on the relevance of the time inconsistency problem for the rise of inflation in the 1970s.
Neurophysiological mechanisms involved in language learning in adults

Little is known about the brain mechanisms involved in word learning during infancy and in second language acquisition and about the way these new words become stable representations that sustain language processing. In several studies we have adopted the human simulation perspective, studying the effects of brain-lesions and combining different neuroimaging techniques such as event-related potentials and functional magnetic resonance imaging in order to examine the language learning (LL) process. In the present article, we review this evidence focusing on how different brain signatures relate to (i) the extraction of words from speech, (ii) the discovery of their embedded grammatical structure, and (iii) how meaning derived from verbal contexts can inform us about the cognitive mechanisms underlying the learning process. We compile these findings and frame them into an integrative neurophysiological model that tries to delineate the major neural networks that might be involved in the initial stages of LL. Finally, we propose that LL simulations can help us to understand natural language processing and how the recovery from language disorders in infants and adults can be accomplished.
Photonic band gaps of three-dimensional face-centred cubic lattices

We show that the photonic analogue of the Korringa-Kohn-Rostoker method is a viable alternative to the plane-wave method for analysing the spectrum of electromagnetic waves in a three-dimensional periodic dielectric lattice. Firstly, in the case of a fcc lattice of homogeneous dielectric spheres, we reproduce the main feature of the spectrum obtained by the plane-wave method, namely that for a sufficiently high dielectric contrast a full gap opens in the spectrum between the eighth and ninth bands if the dielectric constant of the spheres is lower than the dielectric constant of the background medium. If , no gap is found in the spectrum. The maximal value of the relative band-gap width approaches 14% in the close-packed case and decreases monotonically as the filling fraction decreases. The lowest dielectric contrast for which a full gap opens in the spectrum is determined to be 8.13. Eventually, in the case of a fcc lattice of coated spheres, we demonstrate that a suitable coating can enhance gap widths by as much as 50%.
Embedding cryptographically secure matrix transformation in structured Compressive Sensing

In the context of secure communication using Compressive Sensing (CS) embedded Cryptography, the measurement matrix can be used as a secret key if it fulfills some requirements. This secret measurement matrix shall be generated at each communication entity using pre-shared information. For this reason, several measurement matrix generation mechanisms have been developed. In this paper we propose a mechanism for the design and the embedding of a cryptographically secure matrix transformation in structured CS. For this purpose, existing deterministic constructions of structured matrices, which are appropriate for specific applications, are used as input of the proposed mechanism. At the transmitter side, a secret matrix is generated using the designed secure matrix transformation. This matrix possesses the requirements of the corresponding CS-based application as well as the requirements of a cryptographic key. At the receiver side, the same secure matrix transformations are embedded into the CS reconstruction algorithm, so that the structure of the original non-secure measurement matrix can be reused during the recovery/decryption of the original signal. The proposed mechanism considers the fact that the Spark, the Null Space Property (NSP) order and the Restricted Isometry Property (RIP) order of CS measurement matrices, possess invariance under elementary column and row transformation of this matrices.
Task Scheduling for Maximizing Performance and Reliability Considering Fault Recovery in Heterogeneous Distributed Systems

Machine and network failures worsen the results of executing applications on system. Therefore, the reliability of applications on system is an important issue. The recovery of failed machines may increase processing time. This work studies the expected makespan to schedule tasks in heterogeneous distributed systems. A task may be replicated many times to reduce the expected execution time. A two-phase algorithm is proposed. The first phase uses a linear program formulation and a rounding procedure to obtain a favorable allotment to minimize the expected makespan. The second phase applies a scheduling method that is based on the expected executed time and the communication time. During execution, two strategies are considered. In the first strategy, no replication of a task on a set of processors can be stopped. In the second strategy, once a replication of a task has been completed, the other replications of the task are immediately aborted. A comparison reveals that the proposed algorithm significantly outperformed previously proposed algorithms in terms of schedule length ratio, reliability and speedup.
Feature Extraction of Epilepsy Seizure UsingNeural Network

The neural network classifies, extract Features to identify the EEGs according to the percentage distribution of energy features. An area of great interest is the development of device that incorporate algorithms capable of detecting early onset of seizures or even predicting those hours before they occur. This lead time will allow for new types of interventional treatment. In the near future patient’s seizure may be detected and treated well before any physical harm. Epilepsy is one of the most common neurological disorders with a widespread 0.6-0.8% of the world’s population. One-third of the patients achieve sufficient seizure control from medicine and other 8-10% benefit from respective surgery. For the remaining 25% of patients, no sufficient treatment is currently available. There are number of researchers present in literature and still going on regarding automated detection of epileptic seizures. The work proposed by A Subasi [1] in 2005 decomposition of EEG signal applied to artificial neural network and the work proposed by D Najumnissa [2], the features are considered and modified using neural network. Feature extraction is most important for their classification of healthy and unhealthy subjects. Here we are using pattern recognition for feature extraction using some parameters.
Nighttime Home Blood Pressure and the Risk of Hypertensive Target Organ Damage

In ambulatory blood pressure (BP) monitoring, nighttime BP has a superior ability to predict hypertensive target organ damage than awake BP. We evaluated whether nighttime BP, assessed by a home BP monitor, was associated with hypertensive target organ damage. We measured clinic BP, out-of-clinic BP including nighttime home BP, and the urinary albumin:creatinine ratio (UACR) in 854 patients who had cardiovascular risk factors. Nighttime home BP was measured at 2:00, 3:00, and 4:00 AM, in addition to clinic, awake ambulatory, nighttime ambulatory, and awake home BP. Nighttime home systolic BP (SBP) was slightly higher than nighttime ambulatory SBP (difference, 2.6 mm Hg; P<0.001). Clinic (r=0.186), awake ambulatory (r=0.173), nighttime ambulatory (r=0.194), awake home (r=0.298), and nighttime home (r=0.311) SBPs were all associated with log-transformed UACR (all P<0.001). The correlation coefficient for the relationship between nighttime home SBP and log-transformed UACR was significantly greater than that for the relationship between nighttime ambulatory SBP and log-transformed UACR (P<0.001). The goodness of fit of the association between SBP and UACR was improved by adding nighttime home SBP to the other SBPs (P<0.001). Nighttime home diastolic BP also improved the goodness-of-fit of the association between diastolic BP and UACR (P=0.001). Similar findings were observed for the left ventricular mass index in the subgroup (N=594). In conclusion, nighttime home BP is slightly different from (but comparable to) nighttime ambulatory BP. The addition of nighttime home BP to other BP measures improves the association of BP with hypertensive target organ damage.
Probability distribution of speech signal spectral envelope

We propose a model for the generation of speech signals based on the stochastic properties of the speech signal. It is shown that the speech signal is the multiplication of a Gaussian random process (RP) by a slowly time-varying Rayleigh RP. This assumption is justified since it results in a spherically invariant random process (SIRP) with a Gaussian distribution in short intervals and a Laplacian distribution for long intervals. This result is justified by studying the probability distribution function (PDF) of the estimated power spectrum density (PSD) of the speech signal using linear predictive coding (LPC) for several segmentation lengths. Our experiments show that the PDF of the estimated PSD is well approximated by a Rayleigh distribution around the formant frequencies and by a Gaussian distribution in frequencies far from the formant frequencies.
Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning

Recently developed deep learning techniques have significantly improved the accuracy of various speech and image recognition systems. In this paper we adapt some of these techniques for protein secondary structure prediction. We first train a series of deep neural networks to predict eight-class secondary structure labels given a protein's amino acid sequence information and find that using recent methods for regularization, such as dropout and weight-norm constraining, leads to measurable gains in accuracy. We then adapt recent convolutional neural network architectures--Inception, ReSNet, and DenseNet with Batch Normalization--to the problem of protein structure prediction. These convolutional architectures make heavy use of multi-scale filter layers that simultaneously compute features on several scales, and use residual connections to prevent underfitting. Using a carefully modified version of these architectures, we achieve state-of-the-art performance of 70.0% per amino acid accuracy on the public CB513 benchmark dataset. Finally, we explore additions from sequence-to-sequence learning, altering the model to make its predictions conditioned on both the protein's amino acid sequence and its past secondary structure labels. We introduce a new method of ensembling such a conditional model with our convolutional model, an approach which reaches 70.6% Q8 accuracy on CB513. We argue that these results can be further refined for larger boosts in prediction accuracy through more sophisticated attempts to control overfitting of conditional models. We aim to release the code for these experiments as part of the TensorFlow repository.
Physical-layer security: practical aspects of channel coding and cryptography

ACKNOWLEDGEMENTS Let me start at the beginning and thank my parents. They provided a glorious childhood for me and my siblings in a small town in northern Utah. They are extremely diligent and humble, and they raised their family to appreciate traditional Christian values. No matter where my career leads, if I can only duplicate their success with their children, then I will have no regrets. Thank you Mom and Dad. Next, I want to say thank you to my Ph. Steve hired me into his group at a time when most faculty probably wouldn't have been willing to take on new students. So Steve, thank you for letting me join the group, for guiding me in my research, for opening up the world to me, for teaching me to write, for letting me teach your class on occasion, and for involving me with Whisper Communications. I will always be grateful for the many diverse experiences that made up my Ph.D., and you were the driving force behind nearly all of them. Also, a very sincere thank you to the members of my Ph.D. dissertation committee: I appreciate the time you spent serving on my committee and the guidance you provided one-on-one to me. In that same vein, I should thank the other faculty at you João for helping me discover a meaningful problem and publish it within three months. Thank you also for picking me, my family, and our eight suitcases up from the airport, for helping us find a place to live, for taking us to the beach, for feeding us pancakes, and for entertaining us with your family symphony. iv During the last two years of my Ph.D., I started working with some of the best people I know in a very cool start-up company called Whisper Communications. Thank you Steve, Jeff, Cenk, and Demijan. I learned a lot from each one of you, and will always call you my friends. Gerhard. Thanks for reading my bills for me and telling me how to pay them (Fausto), helping me find contact solution (also Fausto), taking me to Bola em Jogo for futebol, teaching me about Portugal, and, of course, solving research problems with me. Although I appear to be the last of the McLaughlins for now, I still assert that our research group was very active and productive. Some of my best friends from my years at …
Inflammatory and Coagulation Biomarkers and Mortality in Patients with HIV Infection

Background In the Strategies for Management of Anti-Retroviral Therapy trial, all-cause mortality was higher for participants randomized to intermittent, CD4-guided antiretroviral treatment (ART) (drug conservation [DC]) than continuous ART (viral suppression [VS]). We hypothesized that increased HIV-RNA levels following ART interruption induced activation of tissue factor pathways, thrombosis, and fibrinolysis. Methods and Findings Stored samples were used to measure six biomarkers: high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), amyloid A, amyloid P, D-dimer, and prothrombin fragment 1+2. Two studies were conducted: (1) a nested case–control study for studying biomarker associations with mortality, and (2) a study to compare DC and VS participants for biomarker changes. For (1), markers were determined at study entry and before death (latest level) for 85 deaths and for two controls (n = 170) matched on country, age, sex, and date of randomization. Odds ratios (ORs) were estimated with logistic regression. For each biomarker, each of the three upper quartiles was compared to the lowest quartile. For (2), the biomarkers were assessed for 249 DC and 250 VS participants at study entry and 1 mo following randomization. Higher levels of hsCRP, IL-6, and D-dimer at study entry were significantly associated with an increased risk of all-cause mortality. Unadjusted ORs (highest versus lowest quartile) were 2.0 (95% confidence interval [CI], 1.0–4.1; p = 0.05), 8.3 (95% CI, 3.3–20.8; p < 0.0001), and 12.4 (95% CI, 4.2–37.0; p < 0.0001), respectively. Associations were significant after adjustment, when the DC and VS groups were analyzed separately, and when latest levels were assessed. IL-6 and D-dimer increased at 1 mo by 30% and 16% in the DC group and by 0% and 5% in the VS group (p < 0.0001 for treatment difference for both biomarkers); increases in the DC group were related to HIV-RNA levels at 1 mo (p < 0.0001). In an expanded case–control analysis (four controls per case), the OR (DC/VS) for mortality was reduced from 1.8 (95% CI, 1.1–3.1; p = 0.02) to 1.5 (95% CI, 0.8–2.8) and 1.4 (95% CI, 0.8–2.5) after adjustment for latest levels of IL-6 and D-dimer, respectively. Conclusions IL-6 and D-dimer were strongly related to all-cause mortality. Interrupting ART may further increase the risk of death by raising IL-6 and D-dimer levels. Therapies that reduce the inflammatory response to HIV and decrease IL-6 and D-dimer levels may warrant investigation. Trial Registration: ClinicalTrials.gov (NCT00027352).