nils-herrmann/clean_scirepeval huggingface dataset:
Face, Age and Gender Recognition using Local Descriptors

This thesis focuses on the area of face processing and aims at designing a reliable framework to facilitate face, age, and gender recognition. A Bag-of-Words framework has been optimized for the task of face recognition by evaluating different feature descriptors and different bag-of-words configurations. More specifically, we choose a compact set of features (e.g., descriptors, window locations, window sizes, dictionary sizes, etc.) in order to produce the highest possible rate of accuracy. Experiments on a challenging dataset shows that our framework achieves a better level of accuracy when compared to other popular approaches such as dimension reduction techniques, edge detection operators, and texture and shape feature extractors. The second contribution of this thesis is the proposition of a general framework for age and gender classification. Although the vast majority of the existing solutions focus on a single visual descriptor that often only encodes a certain characteristic of the image regions, this thesis aims at integrating multiple feature types. For this purpose, feature selection is employed to obtain more accurate and robust facial descriptors. Once descriptors have been computed, a compact set of features is chosen, which facilitates facial image processing for age and gender analysis. In addition to this, a new color descriptor (CLR-LBP) is proposed and the results obtained is shown to be comparable to those of other pre-existing color descriptors. The experimental results indicates that our age and gender framework outperforms other proposed methods when examined on two challenging databases, where face objects are present with different expressions and levels of illumination. This achievement demonstrates the effectiveness of our proposed solution and allows us to achieve a higher accuracy over the existing state-of-the-art methods.
A Proof Procedure for Data Dependencies

A class of dependencies, tuple and equality generating dependencies, is defined, and the chase process is generalized to deal with these dependenetes. For total dependencies the chase is an exponential ttme decision procedure for the implication problem, and in some restricted cases it can be modified to run m polynomial Ume. For nontotal dependencies the chase is only a proof procedure. However, several cases for which it is a decision procedure are shown. It is also shown that equality is redundant for deciding implication of tuple-generating dependencies, and is "almost redundant" for dec~ding implication of equality-generating dependencies.
A systems approach to appraisal mechanisms in emotion

While artificial neural networks are regularly employed in modeling the perception of facial and vocal emotion expression as well as in automatic expression decoding by artificial agents, this approach is yet to be extended to the modeling of emotion elicitation and differentiation. In part, this may be due to the dominance of discrete and dimensional emotion models, which have not encouraged computational modeling. This situation has changed with the advent of appraisal theories of emotion and a number of attempts to develop rule-based models can be found in the literature. However, most of these models operate at a high level of conceptual abstraction and rarely include the underlying neural architecture. In this contribution, an appraisal-based emotion theory, the Component Process Model (CPM), is described that seems particularly suited to modeling with the help of artificial neural network approaches. This is due to its high degree of specificity in postulating underlying mechanisms including efferent physiological and behavioral manifestations as well as to the possibility of linking the theoretical assumptions to underlying neural architectures and dynamic processes. This paper provides a brief overview of the model, suggests constraints imposed by neural circuits, and provides examples on how the temporal unfolding of emotion can be conceptualized and experimentally tested. In addition, it is shown that the specific characteristics of emotion episodes can be profitably explored with the help of non-linear dynamic systems theory.
Why do humans reason? Arguments for an argumentative theory.

Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given the exceptional dependence of humans on communication and their vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. Poor performance in standard reasoning tasks is explained by the lack of argumentative context. When the same problems are placed in a proper argumentative setting, people turn out to be skilled arguers. Skilled arguers, however, are not after the truth but after arguments supporting their views. This explains the notorious confirmation bias. This bias is apparent not only when people are actually arguing, but also when they are reasoning proactively from the perspective of having to defend their opinions. Reasoning so motivated can distort evaluations and attitudes and allow erroneous beliefs to persist. Proactively used reasoning also favors decisions that are easy to justify but not necessarily better. In all these instances traditionally described as failures or flaws, reasoning does exactly what can be expected of an argumentative device: Look for arguments that support a given conclusion, and, ceteris paribus, favor conclusions for which arguments can be found.
RFID and GPS integrated navigation system for the visually impaired

This paper describes an RFID and GPS integrated navigation system, Smart-Robot (SR) for the visually impaired. The SR uses RFID and GPS based localization while operating indoor and outdoor respectively. The portable terminal unit is an embedded system equipped with an RFID reader, GPS, and analog compass as input devices to obtain location and orientation. The SR can guide the user to a predefined destination, or create a new route on-the-fly for later use. While in navigation mode, the SR reaches the destination by avoiding obstacles using ultrasonic and infrared sensor inputs. The SR also provides user feedback through a speaker, and vibrating motors on the glove. The SR prototype has been successfully implemented and is operational.
Generating Sequences With Recurrent Neural Networks

This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.
Towards a Framework to Detect Multi-stage Advanced Persistent Threats Attacks

Detecting and defending against Multi-Stage Advanced Persistent Threats (APT) Attacks is a challenge for mechanisms that are static in its nature and are based on blacklisting and malware signature techniques. Blacklists and malware signatures are designed to detect known attacks. But multi-stage attacks are dynamic, conducted in parallel and use several attack paths and can be conducted in multi-year campaigns, in order to reach the desired effect. In this paper the design principles of a framework are presented that model Multi-Stage Attacks in a way that both describes the attack methods as well as the anticipated effects of attacks. The foundation to model behaviors is by the combination of the Intrusion Kill-Chain attack model and defense patterns (i.e. a hypothesis based approach of known patterns). The implementation of the framework is made by using Apache Hadoop with a logic layer that supports the evaluation of a hypothesis.
An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data.
Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders.

Functional near-infrared spectroscopy (fNIRS) is an emerging functional neuroimaging technology offering a relatively non-invasive, safe, portable, and low-cost method of indirect and direct monitoring of brain activity. Most exciting is its potential to allow more ecologically valid investigations that can translate laboratory work into more realistic everyday settings and clinical environments. Our aim is to acquaint clinicians and researchers with the unique and beneficial characteristics of fNIRS by reviewing its relative merits and limitations vis-à-vis other brain-imaging technologies such as functional magnetic resonance imaging (fMRI). We review cross-validation work between fMRI and fNIRS, and discuss possible reservations about its deployment in clinical research and practice. Finally, because there is no comprehensive review of applications of fNIRS to brain disorders, we also review findings from the few studies utilizing fNIRS to investigate neurocognitive processes associated with neurological (Alzheimer's disease, Parkinson's disease, epilepsy, traumatic brain injury) and psychiatric disorders (schizophrenia, mood disorders, anxiety disorders).
In Vitro and In Vivo Anticancer Activity of Root Extracts of Sansevieria liberica Gerome and Labroy (Agavaceae)

Sansevieria liberica Gerome and Labroy (Agavaceae) is a perennial plant widely distributed in tropical Africa. Preparations of the plant are commonly used across Nigeria for the treatment of inflammatory conditions. Based on the fact that herbal medicine is a strong component of integrative medicine, this study was conducted to evaluate the anticancer activity of root extracts of Sansevieria liberica.  Sulforhodamine B (SRB) in vitro cytotoxicity assay, Sarcoma-180 (S-180) ascites and solid tumor, and L1210 lymphoid leukemia in vivo models were used in this study.  SL-A002 (IC50 23 µg/mL with HeLa), SL-A003 (IC50 22 µg/mL with HCT-116), and SL-A004 (IC50 23 and 18 µg/mL with A549 and THP-1, resp.) demonstrated significant activity in the SRB cytotoxicity assay. Potency was highest with the following pairs of extract : cancer cell line: SL-A002 : HeLa (IC50 23 µg/mL), SL-A003 : HCT-116 (IC50 22 µg/mL), and SL-A004 : THP-1 (IC50 18 µg/mL). SL-A002 demonstrated significant dose-dependent antitumor activity in the Sarcoma-180 (S-180) ascites model with peak effect produced at the dose of 120 mg/kg (i.p.) with inhibition of 89.36% compared to 97.96% for 5-FU (20 mg/kg i.p.). The inhibition of tumor growth by SL-A002 in the S-180 solid tumor model was 47.40% compared to a value of 50.18% for 5-FU. SL-A002 was also significantly active in the L1210 lymphoid leukemia model with 158.33% increase in mean survival time, the same value for 5-FU.  The hydroethanolic extract of Sansevieria liberica, SL-A002, possesses significant anticancer activity to warrant further extensive study to identify, isolate, and characterize the specific bioactive molecules responsible for the observed antitumor activity and the precise mechanism(s) of action.
Joint precision optimization and high level synthesis for approximate computing

Approximate computing has been recognized as an effective low power technique for applications with intrinsic error tolerance, such as image processing and machine learning. Existing efforts on this front are mostly focused on approximate circuit design, approximate logic synthesis or processor architecture approximation techniques. This work aims at how to make good use of approximate circuits at system and block level. In particular, approximation aware scheduling, functional unit allocation and binding algorithms are developed for data intensive applications. Simple yet credible error models, which are essential for precision control in the optimizations, are investigated. The algorithms are further extended to include bitwidth optimization in fixed point computations. Experimental results, including those from Verilog simulations, indicate that the proposed techniques facilitate desired energy savings under latency and accuracy constraints.
Anthropometric and physiological predispositions for elite soccer.

This review is focused on anthropometric and physiological characteristics of soccer players with a view to establishing their roles within talent detection, identification and development programmes. Top-class soccer players have to adapt to the physical demands of the game, which are multifactorial. Players may not need to have an extraordinary capacity within any of the areas of physical performance but must possess a reasonably high level within all areas. This explains why there are marked individual differences in anthropometric and physiological characteristics among top players. Various measurements have been used to evaluate specific aspects of the physical performance of both youth and adult soccer players. The positional role of a player is related to his or her physiological capacity. Thus, midfield players and full-backs have the highest maximal oxygen intakes ( > 60 ml x kg(-1) x min(-1)) and perform best in intermittent exercise tests. On the other hand, midfield players tend to have the lowest muscle strength. Although these distinctions are evident in adult and elite youth players, their existence must be interpreted circumspectly in talent identification and development programmes. A range of relevant anthropometric and physiological factors can be considered which are subject to strong genetic influences (e.g. stature and maximal oxygen intake) or are largely environmentally determined and susceptible to training effects. Consequently, fitness profiling can generate a useful database against which talented groups may be compared. No single method allows for a representative assessment of a player's physical capabilities for soccer. We conclude that anthropometric and physiological criteria do have a role as part of a holistic monitoring of talented young players.
Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation

Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.
A Mining and Visualizing System for Large-Scale Chinese Technical Standards

A technical standard is an established norm or requirement in regard to technical systems. It is usually a formal document that establishes uniform engineering or technical criteria, methods, processes and practices. Every year, more than 10K standards are released by standardization organizations, such as International Standardization Organization (ISO), American National Standards Institute (ANSI), and China National Institute of Standardization (CNIS). Thereinto, China publishes more than 4K national and industrial standards per year. Confronted with the large amount of standards, how to manage and make full use of these standards efficiently and effectively has become a major issue to be addressed. In this paper, we introduce a mining and visualizing system for large-scale Chinese technical standards from spatial and temporal perspectives. Specifically, we propose an s-index metric to measure the contribution of a drafting unit. Moreover, we develop a multiple dimensional mining and visualizing analysis system for standardization experts and users to explore regional differences and temporal trends of standards in China. Our system has run on 95K Chinese standards spanning from 2001 to 2016 and provided online service. The extensive experiments and case study demonstrate the effectiveness of our proposed system.
Electrocardiographic patch devices and contemporary wireless cardiac monitoring

Cardiac electrophysiologic derangements often coexist with disorders of the circulatory system. Capturing and diagnosing arrhythmias and conduction system disease may lead to a change in diagnosis, clinical management and patient outcomes. Standard 12-lead electrocardiogram (ECG), Holter monitors and event recorders have served as useful diagnostic tools over the last few decades. However, their shortcomings are only recently being addressed by emerging technologies. With advances in device miniaturization and wireless technologies, and changing consumer expectations, wearable "on-body" ECG patch devices have evolved to meet contemporary needs. These devices are unobtrusive and easy to use, leading to increased device wear time and diagnostic yield. While becoming the standard for detecting arrhythmias and conduction system disorders in the outpatient setting where continuous ECG monitoring in the short to medium term (days to weeks) is indicated, these cardiac devices and related digital mobile health technologies are reshaping the clinician-patient interface with important implications for future healthcare delivery.
A Hybrid Technique for Medical Image Segmentation

Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.
Opposite Effects of Δ-9-Tetrahydrocannabinol and Cannabidiol on Human Brain Function and Psychopathology

Δ-9-tetrahydrocannabinol (Δ-9-THC) and Cannabidiol (CBD), the two main ingredients of the Cannabis sativa plant have distinct symptomatic and behavioral effects. We used functional magnetic resonance imaging (fMRI) in healthy volunteers to examine whether Δ-9-THC and CBD had opposite effects on regional brain function. We then assessed whether pretreatment with CBD can prevent the acute psychotic symptoms induced by Δ-9-THC. Fifteen healthy men with minimal earlier exposure to cannabis were scanned while performing a verbal memory task, a response inhibition task, a sensory processing task, and when viewing fearful faces. Subjects were scanned on three occasions, each preceded by oral administration of Δ-9-THC, CBD, or placebo. BOLD responses were measured using fMRI. In a second experiment, six healthy volunteers were administered Δ-9-THC intravenously on two occasions, after placebo or CBD pretreatment to examine whether CBD could block the psychotic symptoms induced by Δ-9-THC. Δ-9-THC and CBD had opposite effects on activation relative to placebo in the striatum during verbal recall, in the hippocampus during the response inhibition task, in the amygdala when subjects viewed fearful faces, in the superior temporal cortex when subjects listened to speech, and in the occipital cortex during visual processing. In the second experiment, pretreatment with CBD prevented the acute induction of psychotic symptoms by Δ-9-tetrahydrocannabinol. Δ-9-THC and CBD can have opposite effects on regional brain function, which may underlie their different symptomatic and behavioral effects, and CBD's ability to block the psychotogenic effects of Δ-9-THC.
Review of position-sensorless operation of brushless permanent-magnet machines

The operation of a brushless permanent-magnet machine requires rotor-position information, which is used to control the frequency and phase angle of the machine's winding currents. Sensorless techniques for estimating rotor position from measurements of voltage and current have been the subject of intensive research. This paper reviews the state of the art in these sensorless techniques, which are broadly classified into three types: motional electromotive force, inductance, and flux linkage.