nils-herrmann/clean_scirepeval huggingface dataset:
Bi-objective Scheduling with cooperating Heuristics for Embedded Real-Time Systems

2018 Revised Feb 14, 2018  2018 This paper proposes Makespan and Reliability based approach, a static sheduling strategy for distributed real time embedded systems that aims to optimize the Makespan and the reliability of an application. This scheduling problem is NP-hard and we rely on a heuristic algorithm to obtain efficiently approximate solutions. Two contributions have to be outlined: First, a hierarchical cooperation between heuristics ensuring to treat alternatively the objectives and second, an Adapatation Module allowing to improve solution exploration by extending the search space. It results a set of compromising solutions offering the designer the possibility to make choices in line with his (her) needs. The method was tested and experimental results are provided.
Advanced IDD receiver for PDMA uplink system

The anticipated 1000 fold increase in traffic demand over the next decade and the explosion of new services and applications, such as the internet of things (IoT), pose great challenges for the current 4G mobile network. Pattern division multiple access (PDMA), as a novel non-orthogonal multiple access scheme, has been proposed recently to address these challenges. The performance of PDMA uplink system mainly depends on the receiver algorithm. The conventional message pass algorithm (MPA) cannot fully exploit the benefits of the coded PDMA system. In this paper, we devise an advanced receiver via iterative detection and decoding (IDD) algorithm which fully exploits coding potentials and diversity gains of the PDMA. The simulation results show that IDD based advanced receiver obtains a performance improvement about 1.6 dB at block error rate (BLER) of 0.01 for 300% overload compared with the MPA based approach.
LCL filter startup diagnostics for an Active Front End converter

Reliability of Active Front End (AFE) converter can be improved if converter faults can be identified before startup. Startup diagnostics of the LCL filter can be an useful tool to accomplish this. An algorithm based on Fast Fourier Transform (FFT) of the filter step response is shown to be able to detect variation of filter components. This is extended to consider conditions of short circuit and open circuit of filter components. This method can identify failed components in a LCL filter before operating the converter, which otherwise may lead to undesirable operation. Analytical expressions are derived for the frequency spectrum of the LCL filter during step excitation using continuous Fourier Transform and Discrete Fourier Transform (DFT). A finite state machine Finite State Machine (FSM) based algorithm is use to sequence the startup diagnostics before commencing normal operation of the power converter. It is shown that the additional computational resource required to perform the diagnostic algorithm is small when compared with the overall inverter control program. The diagnostic functions can be readily implemented in advanced digital controllers that are available today. The spectral analysis is supported by simulations and experimental results which validate the proposed method.
MODELING OF SPEECH SYNTHESIS USING INTELLIGENT SYSTEM BASED ON PROLOG

In this work, we present our Expert System for the automatic reading of a text written in Standard Arabic based on logic programming. To accomplish this, we have gone through three stages: Firstly, the creation of a knowledge base that contains different rules which determine the pronunciation of Arabic words and a set of the exceptions words; Secondly, the creation of a sound database that contains specific sounds (phonemes, diphones) to that language, spoken by a female speaker; Finally, the implementation of the system itself that will ensure the transformation of the written text into a spoken text. This transformation is performed by two manners; linguistic processing for converting graphemes phonemes "produce a phonetic text of the written text" and another acoustic treatment for the generation of the sound signal "play a sound corresponding to the resulting phonetic chain of the transcription step». This last step is achieved using a logic programming language which is less used nowadays, but represents in our opinion the cornerstone of logic programming languages, especially for natural language processing;
Project Selection by Using a Fuzzy TOPSIS Technique

Selection of a project among a set of possible alternatives is a difficult task that the decision maker (DM) has to face. In this paper, by using a fuzzy TOPSIS technique we propose a new method for a project selection problem. After reviewing four common methods of comparing investment alternatives (net present value, rate of return, benefit cost analysis and payback period) we use them as criteria in a TOPSIS technique. First we calculate the weight of each criterion by a pairwise comparison and then we utilize the improved TOPSIS assessment for the project selection. Fuzzy Theory, Pairwise Comparison, Project Selection, TOPSIS Technique.
Relating Chronic Pelvic Pain and Endometriosis to Signs of Sensitization and Myofascial Pain and Dysfunction.

Chronic pelvic pain is a frustrating symptom for patients with endometriosis and is frequently refractory to hormonal and surgical management. While these therapies target ectopic endometrial lesions, they do not directly address pain due to central sensitization of the nervous system and myofascial dysfunction, which can continue to generate pain from myofascial trigger points even after traditional treatments are optimized. This article provides a background for understanding how endometriosis facilitates remodeling of neural networks, contributing to sensitization and generation of myofascial trigger points. A framework for evaluating such sensitization and myofascial trigger points in a clinical setting is presented. Treatments that specifically address myofascial pain secondary to spontaneously painful myofascial trigger points and their putative mechanisms of action are also reviewed, including physical therapy, dry needling, anesthetic injections, and botulinum toxin injections.
Modeling Password Guessing with Neural Networks

Passwords still dominate the authentication space, but they are vulnerable to many different attacks; in recent years, guessing attacks in particular have notably caused a few high-profile information leaks. Password strength checkers, which attempt to guard against guessing attacks by enforcing heuristics like length and character variety, only model resistance to these brute-force attacks by proxy. In this paper, we reproduce the work of Melicher et. al , designing a strength checker that directly bases its ratings on resistance to guessing attacks. We provide a qualitative comparison of the ratings output by this system to those of a popular heuristic one, zxcvbn.
Smart-wire: A 0.5V 44uW 0°C to 100°C power-line energy harvesting sensor node

A 44 μW self-powered power line monitoring sensor node is implemented in 65 nm CMOS. A 450 kHz 30 kbps BPSK-modulated transceiver allows for 1.5-meter node-to-node powerline communication at 10E-6 BER. The node has a 3.354 ENOB 50 kSps SAR ADC for current measurement and a 440 Sps time-to-digital converter capable of measuring temperature from 0-100 °C in 1.12 °C steps. All components operate at a nominal supply voltage of 0.5 V, and are powered by dedicated regulators enabling fine-grained power management.
Elevated testosterone levels and social ranks in female rock hyrax

In mammals, males maintain significantly higher testosterone (T, 'the male hormone') levels than females throughout the year and are typically dominant over females. Our study of the cooperatively breeding rock hyrax (Procavia capensis) demonstrated a distinct exception. In most hyrax social groups that we studied, adult females were at the highest social rank within the group and showed higher than or equal T levels to males. To our knowledge, this is the first reported instance of adult female mammals demonstrating higher T levels than adult males. However, although T levels significantly correlated with rank in males, in females such correlations were not detected, suggesting a more complex interplay between behavior and endocrine factors in this species.
Laparoscopic Omohyoid Muscle Transection Surgery: A Novel Procedure Against Omohyoid Muscle Syndrome.

In OMS patients , the most important pathology change is the loosen of IT tendon sheath.[3] After that, the OH becomes short and fibrosis because of the disuse atrophy. When the patient swallows, the OH cannot be extended, the IT moved laterally and superiorly. The posterior clavicle margin of OH replace IT as a new origin of force, When the patient swallow, the shorten OH like a string, form an X‐shaped tent to elevate the SCM in the lateral neck during upward movement of the hyoid bone. The elevated SCM formed the mass in the neck.
Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation.

Functional near infrared spectroscopy (fNIRS) is a rapidly developing neuroimaging modality for exploring cortical brain behaviour. Despite recent advances, the quality of fNIRS experimentation may be compromised in several ways: firstly, by altering the optical properties of the tissues encountered in the path of light; secondly, through adulteration of the recovered biological signals (noise) and finally, by modulating neural activity. Currently, there is no systematic way to guide the researcher regarding these factors when planning fNIRS studies. Conclusions extracted from fNIRS data will only be robust if appropriate methodology and analysis in accordance with the research question under investigation are employed. In order to address these issues and facilitate the quality control process, a taxonomy of factors influencing fNIRS data have been established. For each factor, a detailed description is provided and previous solutions are reviewed. Finally, a series of evidence-based recommendations are made with the aim of improving consistency and quality of fNIRS research.
Breaking the Hierarchy: Distributed Control and Economic Optimality in Microgrids

Modeled after the hierarchical control architecture of power transmission systems, a layering of primary, secondary, and tertiary control has become the standard operation paradigm for islanded microgrids. Despite this superficial similarity, the control objectives in microgrids across these three layers are varied and ambitious, and they must be achieved while allowing for robust plug-and-play operation and maximal flexibility, without hierarchical decision making and time-scale separations. In this paper, we explore control strategies for these three layers and illuminate some possibly unexpected connections and dependencies among them. Building from a first-principle analysis of decentralized primary droop control, we study centralized, decentralized, and distributed architectures for secondary frequency regulation. We find that averaging-based distributed controllers using communication among the generation units offer the best combination of flexibility and performance. We further leverage these results to study constrained ac economic dispatch in a tertiary control layer. Surprisingly, we show that the minimizers of the economic dispatch problem are in one-to-one correspondence with the set of steady states reachable by droop control. In other words, the adoption of droop control is necessary and sufficient to achieve economic optimization. This equivalence results in simple guidelines to select the droop coefficients, which include the known criteria for power sharing. We illustrate the performance and robustness of our designs through simulations.
An Information Security Technique Using DES-RSA Hybrid and LSB

An Information Security Technique Using DES-RSA Hybrid and LSB 1 Sandeep Singh, 2 Aman Singh 1 Department of Computer Science Engineering, Lovely Professional University, Phagwara, Punjab, India 2 Assistant Professor, Department of Computer Science Engineering, Lovely Professional University, Phagwara, Punjab, India ______________________________________________________________________________________  Security plays a vital role while exchanging large amount of information from source to destination. Currently technology is increasing very rapidly from security point of view. Each and every person wants his communication must be confidential and protected from the access of the illegal users over internet. Cryptography and Steganography plays a very important role as security tools. Cryptography is the art of converting the readable information into an unreadable form. Steganography is a tool used to hide information inside a media files such as images, audio and video etc. In this paper a combined technique of both cryptography and steganography is proposed for the better security of the data. The message is initially encrypted with DES and the keys of DES are encrypted with RSA then the hybrid of both DES-RSA is embedded inside an image with help of LSB image steganography. Results of the technique provide a stronger security. The encryption time is also faster than the previous techniques as well as brute force attack to this technique is almost not possible.
Steps to take before intelligent user interfaces become real

Intelligent user interfaces have been proposed as a means to overcome some of the problems that directmanipulation interfaces cannot handle, such as: information overflow problems; providing help on how to use complex systems; or real-time cognitive overload problems. Intelligent user interfaces are also being proposed as a means to make systems individualised or personalised, thereby increasing the systems flexibility and appeal. Unfortunately, there are a number of problems not yet solved that prevent us from creating good intelligent user interface applications: there is a need for methods for how to develop them; there are demands on better usability principles for them; we need a better understanding of the possible ways the interface can utilise intelligence to improve the interaction; and finally, we need to design better tools that will enable an intelligent system to survive the life-cycle of a system (including updates of the database, system support, etc.). We define these problems further and start to outline their solutions.
Pre-trained Deep Convolutional Neural Networks for Face Recognition

Faculty of Mathematics and Natural Sciences Master of Science Pre-trained Deep Convolutional Neural Networks for Face Recognition by Siebert Looije S2209276 Pre-training of models is important because of the unavailability of datasets and models that are becoming more complex. We investigate two aspects of pre-training using face recognition tasks. The first is the use of models that are pre-trained on face datasets and non-face datasets. We will evaluate five pre-trained models based on their results with freezing multiple layers and on robustness. The second aspect is to investigate universal features in pre-trained deep models. This is done by evaluating the performance using only the first few layers for pre-training. This is also investigated by swapping the first layers of the models. We show that models pre-trained on face datasets achieve better results and are more robust in three face recognition tasks than models pre-trained on non-face datasets. The results with pre-training and swapping only the first layers show a significant difference between models that are pre-trained on face datasets and non-face datasets. From this, we conclude that it is important which dataset is used for pre-training the models and used for testing in face recognition. We also conclude that the first few layers of pre-trained models affect performance on face recognition.
Ongoing adaptive evolution of ASPM, a brain size determinant in Homo sapiens.

The gene ASPM (abnormal spindle-like microcephaly associated) is a specific regulator of brain size, and its evolution in the lineage leading to Homo sapiens was driven by strong positive selection. Here, we show that one genetic variant of ASPM in humans arose merely about 5800 years ago and has since swept to high frequency under strong positive selection. These findings, especially the remarkably young age of the positively selected variant, suggest that the human brain is still undergoing rapid adaptive evolution.
Patterns of Entry and Correction in Large Vocabulary Continuous Speech Recognition System

A study was conducted to evaluate user performance andsatisfaction in completion of a set of text creation tasks usingthree commercially available continuous speech recognition systems.The study also compared user performance on similar tasks usingkeyboard input. One part of the study (Initial Use) involved 24users who enrolled, received training and carried out practicetasks, and then completed a set of transcription and compositiontasks in a single session. In a parallel effort (Extended Use),four researchers used speech recognition to carry out real worktasks over 10 sessions with each of the three speech recognitionsoftware products. This paper presents results from the Initial Usephase of the study along with some preliminary results from theExtended Use phase. We present details of the kinds of usabilityand system design problems likely in current systems and severalcommon patterns of error correction that we found.
Utility of ultrasound and MRI in prenatal diagnosis of placenta accreta: a pilot study.

The purpose of this study was to evaluate transabdominal pelvic ultrasound and MRI for the prenatal diagnosis of placenta accreta.



A historical cohort pilot study was performed at our institution to identify women at risk of placenta accreta who had undergone both prenatal ultrasound and MRI. Findings at ultrasound and MRI were compared with the final diagnosis, which was established with clinical findings at delivery and pathologic examination of specimens. Volume measurements were made of low-signal-intensity intraplacental bands on T2-weighted MR images. Risk factors for placental insufficiency were recorded.



Thirteen patients at risk of placenta accreta underwent both sonography and MRI. Nine of these patients had abnormal placentation. With ultrasound, abnormal placentation was correctly identified in six of nine patients (67%) and the absence of accreta in two of four patients (50%). With MRI, abnormal placentation was correctly identified in seven of nine patients (78%) and the absence of accreta in three of four patients (75%). The volumes of low-signal-intensity bands were significantly different in the patients with abnormal placentation and those without placenta accreta (p = 0.047), and band volumes were significantly different among patients with accreta, increta, and percreta (p < 0.0005).



The accuracy of MRI may improve if volumes of low-signal-intensity bands are calculated, MRI is performed before 30 weeks' gestation, and risk factors for placental insufficiency are recognized.
A Low-Power Variable-Gain Amplifier With Improved Linearity: Analysis and Design

A low-power variable-gain amplifier (VGA) based on transconductance (gm)-ratioed amplification is analyzed and designed with improved linearity. The VGA has the merits of continuous gain tuning, low power consumption and small chip area. However, the linearity performance of the gm-ratioed amplifier is usually poor. We analyze distortion in gm-ratioed amplifiers and propose to improve the output linearity by applying load degeneration technique. It is found that theoretically the output linearity can be improved by 8.5 dB at the same power consumption. We also analyze gain, bandwidth and noise performance of the gm-ratioed amplifiers. Two VGAs based on gm-ratioed amplification are designed and fabricated in a 0.18-μm CMOS process-one with load degeneration only and the other with both input and load degeneration. The VGA with load degeneration only achieves gain of -20 to 41 dB, bandwidth of 121 to 211 MHz, and input and output P1dB up to - 17 dBm and 0.65 dBm, respectively. The VGA with both input and load degeneration achieves gain of -37 to 28 dB, bandwidth of 76 to 809 MHz, and input and output P1dB up to - 2.63 dBm and 2.29 dBm, respectively. The two VGAs consume a similar amount of power, which is about 3 to 5 mW from a 1.8-V supply. For the same bias condition, the proposed load degeneration improves the output linearity by more than 15 dB.
Citation analysis and mapping of nanoscience and nanotechnology: identifying the scope and interdisciplinarity of research

Diversification and fragmentation of scientific exploration brings an increasing need for integration, for example through interdisciplinary research. The field of nanoscience and nanotechnology appears to exhibit strong interdisciplinary characteristics. Our objective was to explore the structure of the field and ascertain how different research areas within this field reflect interdisciplinarity through citation patterns. The complex relations between the citing and cited articles were examined through schematic visualization. Examination of WOS categories assigned to journals shows the scatter of nano studies across a wide range of research topics. We identified four distinctive groups of categories each showing some detectable shared characteristics. Three alternative measures of similarity were employed to delineate these groups. These distinct groups enabled us to assess interdisciplinarity within the groups and relationships between the groups. Some measurable levels of interdisciplinarity exist in all groups. However, one of the groups indicated that certain categories of both citing as well as cited articles aggregate mostly in the framework of physics, chemistry, and materials. This may suggest that the nanosciences show characteristics of a distinct discipline. The similarity in citing articles is most evident inside the respective groups, though, some subgroups within larger groups are also related to each other through the similarity of cited articles.
Memory Attention Networks for Skeleton-based Action Recognition

Skeleton-based action recognition task is entangled with complex spatio-temporal variations of skeleton joints, and remains challenging for Recurrent Neural Networks (RNNs). In this work, we propose a temporal-then-spatial recalibration scheme to alleviate such complex variations, resulting in an end-to-end Memory Attention Networks (MANs) which consist of a Temporal Attention Recalibration Module (TARM) and a Spatio-Temporal Convolution Module (STCM). Specifically, the TARM is deployed in a residual learning module that employs a novel attention learning network to recalibrate the temporal attention of frames in a skeleton sequence. The STCM treats the attention calibrated skeleton joint sequences as images and leverages the Convolution Neural Networks (CNNs) to further model the spatial and temporal information of skeleton data. These two modules (TARM and STCM) seamlessly form a single network architecture that can be trained in an end-to-end fashion. MANs significantly boost the performance of skeleton-based action recognition and achieve the best results on four challenging benchmark datasets: NTU RGB+D, HDM05, SYSU-3D and UT-Kinect.1