Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and analysis of an adaptive compressive sensing architecture for epileptic seizure detection

Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as a representative signal carrying valuable information pertaining to the current brain state. In this work, we investigate the stability of time domain EEG features while varying the channel conditions. We identify the feature sets that would provide the most robust EEG classification accuracy. Moreover, an embedded Compressive Sensing (CS)-based EEG encoding system whose complexity is adapted to the channel condition is proposed. We also propose a framework called Classification Accuracy-Compression Ratio-Signal to Noise Ratio (CA-CR-SNR) that adapts compression ratio according to the channel condition. Simulation results show that selecting appropriate EEG feature combinations can relatively overcome the impact of bad channel conditions; however, this simple solution is still inadequate. The proposed adaptive algorithm reconfigures the compression ratio based on a channel feedback signal to further improve the classification accuracy.
Mapping and Characterizing Global-Scale Human Settlements Using HPC

Spatiotemporal analytics derived from large-scale analysis of satellite imagery is a key component in the models used for global population mapping, risk analysis, and critical infrastructure assessment. Making informed national-level decisions based on image derived analytics require processing petabytes of high resolution satellite image data. The computational process often involves extracting, representing, and classifying visual data patterns to detect different types of man-made structures [1]. Computing numerical feature descriptors from raw image data is an expensive but key step in the computational process. The scale of the computational process and the need to repeat the process at time stipulated intervals can easily saturate conventional CPUs. As a feasible solution, we have built a computational system based on GPGPU architecture for deriving big image data analytics [7]. In this study, we demonstrate an efficient GPU implementation for multi-scale feature extraction and exploit multi-GPU architecture for large-scale image analysis for characterizing human settlements.
Text segmentation in natural scenes using Toggle-Mapping

We offer, in this paper, a new method to segment text in natural scenes. This method is based on the use of a morphological operator: the Toggle Mapping. The efficiency of the method is illustrated and the method is compared, according to various criteria, with common methods issued from the state of the art. This comparison shows that our method gives better results and is faster than the state of the art methods. Our method reduces also the number of segmented regions. This can lead to time saving in a complete scheme (executing time of multiple processing steps usually depends on the number of regions) and proves that our algorithm is more relevant.
Adverse drug events in ambulatory care.

BACKGROUND Adverse events related to drugs occur frequently among inpatients, and many of these events are preventable. However, few data are available on adverse drug events among outpatients. We conducted a study to determine the rates, types, severity, and preventability of such events among outpatients and to identify preventive strategies. METHODS We performed a prospective cohort study, including a survey of patients and a chart review, at four adult primary care practices in Boston (two hospital-based and two community-based), involving a total of 1202 outpatients who received at least one prescription during a four-week period. Prescriptions were computerized at two of the practices and handwritten at the other two. RESULTS Of the 661 patients who responded to the survey (response rate, 55 percent), 162 had adverse drug events (25 percent; 95 percent confidence interval, 20 to 29 percent), with a total of 181 events (27 per 100 patients). Twenty-four of the events (13 percent) were serious, 51 (28 percent) were ameliorable, and 20 (11 percent) were preventable. Of the 51 ameliorable events, 32 (63 percent) were attributed to the physician's failure to respond to medication-related symptoms and 19 (37 percent) to the patient's failure to inform the physician of the symptoms. The medication classes most frequently involved in adverse drug events were selective serotonin-reuptake inhibitors (10 percent), beta-blockers (9 percent), angiotensin-converting-enzyme inhibitors (8 percent), and nonsteroidal antiinflammatory agents (8 percent). On multivariate analysis, only the number of medications taken was significantly associated with adverse events. CONCLUSIONS Adverse events related to drugs are common in primary care, and many are preventable or ameliorable. Monitoring for and acting on symptoms are important. Improving communication between outpatients and providers may help prevent adverse events related to drugs.
Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha −1 . These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.
Efficient runtime support for embedded MPSoCs

Recently, many software runtime systems have been proposed that allow developers to efficiently map applications to contemporary consumer electronic devices and high-performance academic processing platforms. Most of these runtime systems employ advanced scheduling techniques for automatic task assignment to all available processing elements. However, they focus on a particular environment and architecture, and it is not easy to port them to reconfigurable embedded MPSoCs. As a consequence, in the embedded community, researchers implement hardwired application-specific task schedulers, which can not be used by other embedded MPSoCs. To address this problem, in this paper we propose a lightweight runtime software framework for reconfigurable shared-memory MPSoCs, that integrate a master embedded processor connected to slave cores. Similarly to many of the aforementioned advanced runtime systems, we adopt a task-based programming model that uses simple, pragma-based annotations of the application software, in order to dynamically resolve task dependencies. Our runtime system supports heterogeneity in the hardware resources, and is also low-overhead to account for possible limitations in their processing capabilities and available on-chip memory. To evaluate our proposal, we have prototyped an MPSoC with seven slaves to a Xilinx ML605 FPGA board. We run three micro-benchmarks that achieve a performance speedup of 3.8x, 7x and 5.8x, and energy consumption of 27%, 14% and 18% respectively, compared to a single-core baseline system with no runtime support.
The Importance of Self-Esteem

The second chapter focuses on the two major functions of self-esteem, especially as they occur in relation to positive psychology, self-control or regulation, and positive emotions. One important function is self-protection, which concerns maintaining a sense of self and identity. In this sense, self-esteem is seen as buffering us from stress in everyday life, helping us deal with disappointment, and bouncing back from failure. The other major self-esteem function concerns enhancement or the expansion of the self and its abilities. In this case, it is shown how healthy self-esteem plays a pivotal role in helping us move beyond our comfort zone, take risks to reach past current limits, see new possibilities, and explore different personal, career, and interpersonal dimensions of life. This material also includes examining the three major theories of self-esteem and the nature of positive emotions.
Throughput optimized error-free transmission using optimum combination of AMC and ARQ based on imperfect CSI

A new scheme for error-free transmission is presented which uses optimized combination of adaptive modulation and coding (AMC) and automatic repeat request (ARQ). Using conventional error correction codes with finite length, a limited value of packet error rate (PER) is inevitable. More powerful codes with more extra parity bits results in lower PER, however, at the cost of more transmit power per bit which is limited by average power constraint. Remained erroneous packets may be detected by error detection codes and be substituted by retransmission request in ARQ. However, when error correction capability is weak, throughput decreases due to increase of required retransmissions. In this article a method for optimum combination of error correction codes in AMC and error detection codes in ARQ is provided. In practice, due to estimation error and time variant channels, noisy and outdated channel state information (imperfect CSI) is available which restricts providing exact instantaneous PER constraint. To satisfy instantaneous PER constraint based on imperfect CSI, a novel technique is designed in this paper. Numerical evaluations indicate the efficiency of the proposed design.
Investigation of the Properties of the HEAF Estimator Using Simulation Experiments and MPEG-encoded Video Traces

It was discovered that some LAN traffic exhibits self-similar rather than Poisson behaviour and there is ongoing research towards finding and improving suitable estimators which may help to characterize various types of network traffic. Such characterization can be potentially applied for control purposes such as traffic shaping, load balancing, etc. The Hurst exponent is used to measure the intensity of long-range dependence (LRD) in the network traffic. Despite having several existing estimators, LRD analysis is still impeded by the difficulty of actual identification of its intensity. This paper continues work on estimating the Hurst exponent from the autocorrelation function, a so-called HEAF estimator introduced earlier by the authors. It also compares HEAF with a few existing estimators such as wavelet, Higuchi, aggregated variance time (V/T) and rescaled-range (R/S). The simulation studies show that HEAF can be used to capture the LRD in the network traffic if true process is fGn and FARIMA
The Temperley–Lieb algebra and its generalizations in the Potts and XXZ models

We discuss generalizations of the Temperley–Lieb algebra in the Potts and XXZ models. These can be used to describe the addition of integrable boundary terms of different types. We use the Temperley–Lieb algebra and its one-boundary, two-boundary, and periodic extensions to classify different integrable boundary terms in the two-, three-, and four-state Potts models. The representations always lie at critical points where the algebras becomes non-semisimple and possess indecomposable representations. In the one-boundary case we show how to use representation theory to extract the Potts spectrum from an XXZ model with particular boundary terms and hence obtain the finite size scaling of the Potts models. In the two-boundary case we find that the Potts spectrum can be obtained by combining several XXZ models with different boundary terms. As in the Temperley–Lieb case, there is a direct correspondence between representations of the lattice algebra and those in the continuum conformal field theory.
Hacking the Visual Arts edTPA: Undergoing Scientific Management, Performativity, and Standardization of Language Games in Performance Assessment

In this article, we investigate the Visual Arts Educative Teacher Performance Assessment (edTPA) while integrating aspects of philosopher Jean-François Lyotard’s thought in order to bring to light implicit assumptions made by the test. We expose disconnections between a mission for art education to create teachers as inquirers and the type of teacher education sought and assessed by the edTPA. We concentrate on examining three areas of the test that create implicit directives for artist-educators to accomplish as they learn to become state-certified teachers; and we suggest ways to hack—or successfully perform the tasks—while simultaneously understanding the structural bias of the exam. Finally, we suggest, following Elliot Eisner, that visual arts education as a discourse must assert its commitment to assessment without measurement and move beyond performance assessments as major criteria for teacher education.
Cooperative multisensor system for real-time face detection and tracking in uncontrolled conditions

The presented work describes an innovative architecture for multi-sensor distributed video surveillance applications. The aim of the system is to track moving objects in outdoor environments with a cooperative strategy exploiting two video cameras. The system also exhibits the capacity of focusing its attention on the faces of detected pedestrians collecting snapshot frames of face images, by segmenting and tracking them over time at different resolution. The system is designed to employ two video cameras in a cooperative client/server structure: the first camera monitors the entire area of interest and detects the moving objects using change detection techniques. The detected objects are tracked over time and their position is indicated on a map representing the monitored area. The objects’ coordinates are sent to the server sensor in order to point its zooming optics towards the moving object. The second camera tracks the objects at high resolution. As well as the client camera, this sensor is calibrated and the position of the object detected on the image plane reference system is translated in its coordinates referred to the same area map. In the map common reference system, data fusion techniques are applied to achieve a more precise and robust estimation of the objects’ track and to perform face detection and tracking. The work novelties and strength reside in the cooperative multi-sensor approach, in the high resolution long distance tracking and in the automatic collection of biometric data such as a person face clip for recognition purposes.
Dynamic and Transparent Data Tiering for In-Memory Databases in Mixed Workload Environments

Current in-memory databases clearly outperform their diskbased counterparts. In parallel, recent PCIe-connected NAND flash devices provide significantly lower access latencies than traditional disks allowing to re-introduce classical memory paging as a cost-efficient alternative to storing all data in main memory. This is further eased by new, dedicated APIs which bypass the operating system, optimizing the way data is managed and transferred between a DRAM caching layer and NAND flash. In this paper, we will present a new approach for in-memory databases that leverages such an API to improve data management without jeopardizing the original performance superiority of in-memory databases. The approach exploits data relevance and places less relevant data onto a NAND flash device. For real-world data access skews, the approach is able to efficiently evict a substantial share of the data stored in memory while suffering a performance loss of less than 30%.
THE NATURE OF VOCOIDS ASSOCIATED WITH SYLLABIC CONSONANTS IN TASHLHIYT

Tashlhiyt Berber has entered the phonological folklore as an unusual language in which any consonant can be syllabic, many words consisting entirely of consonants. I shall argue for an alternative analysis, according to which the epenthetic vowels which frequently accompany syllabic consonants are the phonetic realizations of syllable nuclei. Where no epenthetic vowel is evident, it can be regarded as hidden by the following consonant, according to a gestural overlap model. On this view, Tashlhiyt syllable structure is a quite unmarked CV(C(C)), and syllabification is unproblematic.
Feed allowance and maternal backfat levels during gestation influence maternal cortisol levels, milk fat composition and offspring growth

Abstract The fetal and early postnatal environment can have a long-term influence on offspring growth. Using a pig model, we investigated the effects of maternal body condition (thin or fat) and maternal gestation feeding level (restricted, control or high) on maternal stress, milk composition, litter size, piglet birth weight and pre-weaning growth. A total of sixty-eight thin (backfat depth about 8 mm) and seventy-two fat (backfat depth about 12 mm) gilts were selected at about 22 weeks. This backfat difference was then accentuated nutritionally up to service at about 32 weeks. During gestation, individual gilts from within each group were randomly allocated to a gestation diet at the following feed allowances: 1·8 kg/d (restricted); 2·5 kg/d (control) and 3·5 kg/d (high) until day 90 of gestation. During gestation restricted gilts had higher levels of cortisol than high and control fed animals. Piglets born to fat gilts had higher average daily gain during the lactation period and higher weaning weights at day 28 than piglets born to thin gilts. Gilts on a high feed level had heavier piglets than those provided with restricted and control allocations. Fat gilts had less saturated fat in their milk at day 21 of lactation and higher unsaturated fat levels. No differences were found in the n-6:n-3 PUFA ratio in the milk between thin and fat gilts. In conclusion, maternal body condition influenced the daily weight gain of offspring up to weaning (day 28) and milk fat composition. Furthermore, maternal feed level during gestation alters maternal cortisol levels and milk fat composition.
On watermarking in frequency domain

A wavelet-based image watermarking scheme is proposed, based on insertion of 'logo' image as watermark in midfrequency domain. This new approach provides flexibility in determining the pixel to be watermarked and increases the data hiding capacity. It is easy to implement watermark embedding algorithm as well as the corresponding detection algorithm. The watermarking algorithm is tested under different attacks such as median filtering, image cropping and image compression. It is also robust. The experimental results prove that the method is more tamper proof and less perceptible for any type of images other than well known private methods in frequency domain. In the proposed approach, an original image is decomposed into wavelet coefficients then watermark is embedded through algorithm. The wavelet transform filters can be used as security key for the extraction of inserted watermark. The proposed watermark extraction technique is independent of the original image. The watermark embedded image is produced by taking the inverse 2-D discrete wavelet transform of the altered wavelet decomposition. Here we have given the relation between the area of the channel in which we insert the watermark and the area affected in original image.
On the aggregate SNR of amplified relaying channels

This paper considers the aggregate signal to noise ratio of amplified relaying channels, where the term aggregate refers to the inclusion of propagated noise terms generated as relaying terminals amplify both the information and noise portions of received signals. This consideration is motivated by recent findings for wireless relaying networks indicating that the performance of amplified relaying can approach and in some cases exceed the performance of decoded relaying. Expressions for the aggregate signal to noise ratio are developed for general amplified relaying channels with a given set of source, destination, and relaying terminals, link connectivity link attenuation, transmit power, and receiver noise. These expressions provide a method for analyzing the impact of varying the link connectivity or power allocation for a given set of terminals, and support the comparison of amplified relaying channels with decoded relaying channels.
Computationally Feasible Logo Recognition using Deep Learning

This thesis explores the visual task of logo recognition using deep learning with the special constraint that it should be computationally feasible. Using a single-step approach, I fine tune pre-trained convolutional neural networks to minimize the training costs. The final model, that is trained in only a few hours on a CPU, has an accuracy of 88.7% on the validation set.
Collaborative Access Control Model for Online Social Networks

Online Social Network (OSN) has become a popular and de facto portal for digital information sharing. Preserving information privacy is indispensable in such applications, as the shared information would be sensitive. The issue becomes more challenging due to participation of multiple parties on the shared resources or data. In this paper, we propose an effective access control technique to allow or disallow the shared resources considering authorization requirements of all the multiple parties. A logical representation of the proposed access control technique is prepared to analyse the privacy risk.
Parallel Approximation Algorithms

Many problems of great practical importance are hard to solve computationally, at least if exact solutions are required. We survey a number of (NPor P-complete) problems for which fast parallel approximation algorithms are known: The O-l knapsack problem, binpacking, the minimal makeshift problem, the list scheduling problem, greedy scheduling, and the high density subgraph problem. Algorithms for these problems are presented highlighting the underlying techniques and principles, and several types of parallel approximation schemes axe exhibited. *The author was partly supported by NSF grant DCR-8351757.
Usable Biometrics

The term biometrics is derived from the Greek bio (life) and metric (measure). Biometrics, as a means of identification, can be traced back to both ancient Egypt1 and China. Pioneering work on modern biometrics technologies dates back to the late 1960s, when researchers began looking at voice patterns, fingerprints, and hand geometry as a means for establishing unique individual identity. By the mid-1980s, the biometrics industry was established and the first systems went live in controlled environments. In recent years, with the heightened security concerns gripping much of the world and the rising amount of fraud in the commercial sector, biometrics technologies have received a great deal of attention. However, the application of biometrics spans all industries.