Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of Service and Power Consumption Optimization on the IEEE 802.15.4 Pulse Sensor Node based on Internet of Things

The Purpose of this research is to determine the Quality of Service (QoS) Zigbee or IEEE 802.15. 4 sensor Node use the indicators, i.e. the Receiver Signal Strength and PathLoss (attenuation (-dB)) at the time of communication of the sensor node end device to the sensor Router node or Coordinator sensor node (sink). The factor power consumption sensor node is important to maintain the lifetime sensor node, The Sensor data in this research is the pulse sensor. The development of the Wireless Sensor Network communication system is in multi-hop communication, with efforts to obtain low power consumption on each sensor node. This study utilizes the Routing Protocol for Low Power and Lossy Network (RPL) method with position management on the sensor node on DODAGs consequently, that the average power consumption value for each sensor node is low. The benefit of the Sensor node is to send pulse sensor data from various nodes that are interconnected at different distances in multi-hop so that power consumption and Quality of Services (QoS) can be identified from the sensor node. From the research results, the average PathLoss value of IEEE 802.15. 4 or Zigbee in free space is obtained by comparing the various simulation values and field experiments at a distance of 50 m at -75.4 dB and the Average Receiver Signal Strength (RSS) with a comparison of Equation and Experiments in the field with parameters The minimum Power Transmitter is 0 dBm and the Power Transmitter is maximum +20 dBm at a distance of 50m at - 66.6 dBm. Therefore, Pulse Sensor data will be displayed on the Web Page and stored in the MySQL database using Raspberry PI 3 as the Internet Gateway.
Passivity-based induction motor speed control: unknown load torque case

The problem of specifying a desired torque trajectory to achieve speed tracking in passivity-based control of induction motors is addressed. The paper presents a solution to the problem that does not require acceleration measurement nor knowledge of the load torque. Simulation results show the feasibility of the scheme when used as an improvement to the non-observer based scheme proposed by Espinosa et al. (1994).
Inside SSL: accelerating secure transactions

By defining a protocol that supplies Web clients and servers with cryptographic parameters, the Secure Sockets Layer protocol enables the safe exchange of sensitive data, a crucial aspect of any e-business. The protocol's sticking point is that encrypting and decrypting data requires a tremendous amount of CPU processing power. The burden is especially apparent on the server side, because multiple Web clients often connect to a single Web server. For e-commerce transactions, it's important to implement SSL in a way that doesn't overburden your Web server's CPU and slow down the entire operation. Although the original Web servers that supported SSL did so exclusively in software, SSL adapter cards soon became available to help off-load the server's CPU load and increase performance. Today, content switches with SSL accelerators can encrypt and decrypt data at the network edge, eliminating the need for a Web server's CPU to perform any SSL-related calculations. The article focuses on the relative merits of these newer implementations. A look at the original software-only approach and its drawbacks clarify the reasons that hardware acceleration for SSL became necessary.
Automated information extraction from free-text EEG reports

In this study we have developed a supervised learning to automatically detect with high accuracy EEG reports that describe seizures and epileptiform discharges. We manually labeled 3,277 documents as describing one or more seizures vs no seizures, and as describing epileptiform discharges vs no epileptiform discharges. We then used Naïve Bayes to develop a system able to automatically classify EEG reports into these categories. Our system consisted of normalization techniques, extraction of key sentences, and automated feature selection using cross validation. As candidate features we used key words and special word patterns called elastic word sequences (EWS). Final feature selection was accomplished via sequential backward selection. We used cross validation to predict out of sample performance. Our automated feature selection procedure resulted in a classifier with 38 features for seizure detection, and 23 features for epileptiform discharge detection. The average [95% CI] area under the receiver operating curve was 99.05 [98.79, 99.32]% for detecting reports with seizures, and 96.15 [92.31, 100.00]% for detecting reports with epileptiform discharges. The methodology described herein greatly reduces the manual labor involved in identifying large cohorts of patients for retrospective neurophysiological studies of patients with epilepsy.
The Machine-SUIF Cookbook

This cookbook contains several complete (runnable) examples of a variety of simple MachineSUIF compiler passes. Along with the OPI User’s Guide, these examples should help you to start writing Machine-SUIF optimization passes quickly. We also provide a target library for a very simple machine. Please feel free to use these examples as the starting point for your work.
Distributed Energy Efficient Channel Allocation

Design of energy efficient protocols for modern wireless systems has become an important area of research. In this paper, we propose a distributed optimization algorithm for the channel assignment problem for multiple interfering transceiver pairs that cannot communicate with each other. We first modify the auction algorithm for maximal energy efficiency and show that the problem can be solved without explicit message passing using the carrier sense multiple access (CSMA) protocol. We then develop a novel scheme by converting the channel assignment problem into perfect matchings on bipartite graphs. The proposed scheme improves the energy efficiency and does not require any explicit message passing or a shared memory between the users. We derive bounds on the convergence rate and show that the proposed algorithm converges faster than the distributed auction algorithm and achieves near-optimal performance under Rayleigh fading channels. We also present an asymptotic performance analysis of the fast matching algorithm for energy efficient resource allocation and prove the optimality for a large enough number of users and number of channels. Finally, we provide numerical assessments that confirm the faster convergence of the proposed algorithm compared to the distributed auction algorithm.
A Survey of Vehicle Position Prediction System based on GSM and ANN Structure

– The need for determining the positioning information of any vehicle is becoming popular in recent year. This survey paper compares and summarizes the different techniques used to find the position of any vehicle using Received Signal Strength (RSS) from Global System for Mobile Communication (GSM) network available at the instant, GSM and Global Positioning System (GPS) modem, GSM and Google Map and other positioning Algorithms. The existing system uses the GSM and Artificial Neural Network (ANN) structure in location estimation so that the positioning accuracy of the overall system is increased. The role of ANN structure in location estimation is that it not only a substitute for positioning algorithms but it will also substitute of overloaded map-matching
Autosomal recessive polycystic kidney disease: the importance of autopsy of suspected cases and genetic counselling

Autosomal recessive polycystic kidney disease (ARPKD) is a common inheritable cystic renal disease that has a profound effect on the growing fetus and on subsequent pregnancies, being fatal in 30% to 50% cases in the neonatal period. Prenatal imaging studies are only suggestive of the disorder and it is evident only from the second trimester onwards. Prenatal molecular linkage analysis can identify the disorder, but have a prerequisite of previous DNA analysis of parents and one sibling. ARPKD can be accurately diagnosed on characteristic histopathological features if an autopsy is performed in cases of infant death where the prenatal imaging studies and clinical findings are suggestive of the disease. A case of the characteristic histopathological findings of ARPKD on autopsy of a live-born neonate who died shortly after birth, with prenatal ultrasonographic findings suggestive of renal cystic disease, is presented.
Alternative method for the connectionist learning of k-DNF expressions

We show that the extremely long learning times imposed upon connectionist systems by the use of general learning methods such as back propagation and simulated annealing can be drastically shortened by the use of methods geared toward the problem at hand. In particular, the learning of k-DNF expressions is analyzed and a new, more efficient, algorithm is proposed.
Evaluating Machine Translations from Arabic into English and Vice Versa

Machine translation (MT) allows direct communication between two persons without the need for the third party or via dictionary in your pocket, which could bring significant and per formative improvement. Since most traditional translational way is a word-sensitive, it is very important to consider the word order in addition to word selection in the evaluation of any machine translation. To evaluate the MT performance, it is necessary to dynamically observe the translation in the machine translator tool according to word order, and word selection and furthermore the sentence length. However, applying a good evaluation with respect to all previous points is a very challenging issue. In this paper, we first summarize various approaches to evaluate machine translation. We propose a practical solution by selecting an appropriate powerful tool called iBLEU to evaluate the accuracy degree of famous MT tools (i.e. Google, Bing, Systranet and Babylon). Based on the solution structure, we further dihttp://researchplusjournals.com/index.php/IRJECE/workflow/index/291/5scuss the performance order for these tools in both directions Arabic to English and English to Arabic. After extensive testing, we can decide that any direction gives more accurate results in translation based on the selected machine translations MTs. Finally, we proved the choosing of Google as best system performance and Systranet as the worst one.
Les Coléoptères du Monde

resume Les Coleopteres du Monde est une serie d’ouvrages consacres a l’etude des especes les plus spectaculaires de coleopteres presentes dans le monde. Chaque ouvrage est ecrit par un ou plusieurs specialistes et richement illustre. Le premier livre de la collection a ete publie en 1981 par les editions Sciences Nat. Le dernier volume (n°30 ci-dessus) est paru en 2005 aux editions Hillside Books. exposition Cette notice est publiee en echo a l'exposition temporaire Coleopteres, insectes extraordinaires (21/12/2018-28/06/2020). source Bibliotheque du musee des Confluences.
Measurements for managing software reliability

Accurate software reliability evaluation requires the collection of comprehensive and consistent data sets on the ongoing software project. We define some measurements that can be performed on the software during its development or in operation to help evaluating and managing its reliability, and discuss organizational and feedback aspects. Since failure data are difficult to collect, the raw data set may include extraneous data: validation is thus needed before processing. Emphasis is put on near-term objectives, implying timely and efficient feedback for the ongoing project. However, as reliability measurement is to be considered in the first step in a software reliability improvement program, we report some success stories in which improvement programs have increased productivity and reliability at no extra cost or even with cost reduction.
A Review of Graph-based Dependency Parsing

Dependency parsing has been the mainstream of Syntactic parsing in NLP community. With the rapid development of deep neural network, more researches tend to utilize the neural network for dependency parsing, all these efforts has improved the accuracy of dependency parsing greatly. In this paper, we first introduce the theory of dependency parsing briefly. Then the main process of dependency parsing is analyzed in detail, including feature learning, training and decoding process. Lastly, we summarize the current problems and challenges during dependency parsing research, as well as discussing the further development.
Album-oriented face recognition for online social networks

In this paper, we propose an album-oriented face-recognition model that exploits the album structure for face recognition in online social networks. Albums, usually associated with pictures of a small group of people at a certain event or occasion, provide vital information that can be used to effectively reduce the possible list of candidate labels. We show how this intuition can be formalized into a model that expresses a prior on how albums tend to have many pictures of a small number of people. We also show how it can be extended to include other information available in a social network. Using two real-world datasets independently drawn from Facebook, we show that this model is broadly applicable and can significantly improve recognition rates.
Individual Bioequivalence—A Review of the FDA Draft Guidance

As more generic drugs become available in the marketplace, it is a public concern whether the quality, safety, and efficacy of generic drugs are the same as the brand name drug. Under current regulation, the United States Food and Drug Administration (FDA) only requires that evidence of bioequivalence in average bioavailability be provided for assessment of bioequivalence. FDA, however, does not require that bioequivalence among generic copies of the same brand name drug be demonstrated. As a result, it is a concern whether these generic drugs can be used interchangeably. To address this question, the concepts of population bioequivalence for drug prescribability and individual bioequivalence for drug switchability have been proposed. In this paper, we provide a comprehensive review of the draft FDA guidance on “In Vivo Bioequivalence Studies Based on Population and Individual Bioequivalence Approaches” which was distributed for comment in October 1997. The discussion will be primarily on the FDA proposed criteria, design, statistical methods, and interpretations on the assessment of individual bioequivalence from the pharmaceutical industry perspective.
Energy-based analysis and control of power networks and markets: Port-Hamiltonian modeling, optimality and game theory

This research studies the modeling, control and optimization of power networks. A unifying mathematical approach is proposed for the modeling of both the physical power network as well as market dynamics. For the physical system, several models of varying complexity describing the changes in frequency and voltages are adopted. For the electricity market, various dynamic pricing algorithms are proposed that ensure a optimal dispatch of power generation and demand (via flexible loads). Such pricing algorithms can be implemented in real-time and using only local information that is available in the network (such as the frequency). By appropriately coupling the physical dynamics with the pricing algorithms, stability of the combined physical-economical system is proven. This in particular shows how real-time dynamic pricing can be used as a control method to achieve frequency regulation and cost efficiency in the network.
Variant distribution of the calf cutaneous nerves - case report and clinical impact

We report a cadaveric case of unusual innervation pattern of the right calf and ankle. During dissection of an 82 years-old, formalin-fixed, male cadaver, the posterior femoral cutaneous nerve was found providing sensory supply to the lateral knee region, while it terminated at the level of the ankle. Moreover, the medial sural cutaneous nerve did not anastomose with a peroneal communicating branch and it continued as the lateral dorsal cutaneous nerve of the foot. In cases that the peroneal communicating branch is absent, the medial sural cutaneous nerve is called sural nerve. Additionally, the lateral sural cutaneous nerve travelled over the calf crossing the sural nerve at its middle and thus, providing sensory supply to the upper lateral calf and the lower medial aspect of the calf and ankle. This peculiar sensory supply may cause diagnostic hazard in cases of nerve injury with neurological symptomatology, whereas due to the presented variability, nerves are prone to iatrogenic injury during procedures in the area.
Named entity recognition goes to old regime France: geographic text analysis for early modern French corpora

ABSTRACT Geographic text analysis (GTA) research in the digital humanities has focused on projects analyzing modern English-language corpora. These projects depend on temporally specific lexicons and gazetteers that enable place name identification and georesolution. Scholars working on the early modern period (1400–1800) lack temporally appropriate geoparsers and gazetteers and have been reliant on general purpose linked open data services like Geonames. These anachronistic resources introduce significant information retrieval and ethical challenges for early modernists. Using the geography entries of the canonical eighteenth-century Encyclopédie, we evaluate rule-based named entity recognition (NER) systems to pinpoint areas where they would benefit from adjustments for processing historical corpora. As we demonstrate, annotating nested and extended place information is one way to improve early modern GTA. Working with Enlightenment sources also motivates a critique of the landscape of digital geospatial data.
Quantitative Models for Older Adults Hierarchical Structure Browsing

Hierarchical structures are one of the most commonly used methods of presenting electronic information. Older users, however, may experience problems accessing it because of ageing-related decline. This paper demonstrates two quantitative models of the time older adults took to browse expandable and sequential structures to reach the target information. The first model utilises GOMS to break down browsing activities into various components, such as mouse movements, reading and eye movements. The second model presents a parametric model of browsing time. Our research demonstrates that these two models managed to model the experimental data quite well, suggesting that it is possible to model older adults’ browsing activity quantitatively.
Design and implementation of a standardised framework for the management of a wireless body network in an Mobile Health environment

The design and implementation of a framework that facilitates the development of Mobile Health applications to manage the communications with biomedical sensors in compliance with the CEN ISO/IEEE 11073 standard family are presented. The framework includes a set of functional modules that are responsible, among other tasks, of the communication of sensors and the processing and storage of data. The mobile terminal acts as an intermediary or hub, collecting and presenting the data received in a standardised way, regardless of the sensor type used. In this context, as proof of concept it is presented a mobile app built on the top of the framework to manage the communications with a smart fall detector.
以Generalized Hebbian Algorithm 為基礎的主成分分析之硬體實現

This paper presents a novel hardware architecture for fast principle component analysis (PCA). The architecture is based on generalized Hebbian algorithm (GHA). In the architecture, the updating of different synaptic weight vectors are divided into a number of stages. The results of precedent stages will be used for the computation of subsequent stages for expediting training speed and lowering the area cost. The proposed architecture has been embedded in a system-on-programmable-chip (SOPC) platform for physical performance measurement. Experimental results show that the proposed architecture is an effective alternative for fast PCA attaining both high performance and low computational time.
Orthopaedic basic science : foundations of clinical practice

The goal of the "Orthopaedic Basic Science: Foundations of Clinical Practice edition 3" is to be a useful, comprehensive, clinically relevant and accessible synthesis of the basic knowledge in orthopaedics. Its main audience is doctors-in-training and orthopaedic surgeons who desire information on what's new in basic science and how it effects their practice. Section topics include Basic Principles in Orthopaedic Surgery, Physiology of Musculoskeletal Tissues and basic principles of musculoskeletal Disease and its Treatment. New to "OBs 3" are chapters on Evidence Based Medicine and Metabolic Bone Diseases and a collection of topic correlated case studies that will be accessible on the web only.
Observing Human Behaviour to Identify Risk in Task Performance

Small mistakes in task performance can have large consequences. Administering a wrong dosage of a medicine, ignoring a system warning or failing to check whether certain values are set can have severe negative effects for patient and process safety. The repetitive nature of such tasks and the often high cognitive load of the worker can easily lead to decreased attention, with inaccurate or incorrect task performance as a result. These effects are even stronger when the normal workflow is interrupted, for example when the worker receives a phone call or when other unexpected events occur. These deviations from a typical task performance can be indicative of risky task performance. By observing the behaviour of a worker, we aim at identifying these moments in time and to alert the worker. We specifically focus on training scenarios in which we can provide understandable feedback to the worker, so the risky behaviour can be avoided in the future. In this paper, we present the Patria system, which observes gestures, head movement, facial expressions, eye gaze and key strokes from subjects operating a simple medical device.
Progress on Artificial Neural Networks for Big Data Analytics: A Survey

Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of “big data. ” Artificial neural networks (ANNs) are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of the ANN in big data analytics and is still ongoing, although it is in it is early stages. The purpose of this paper is to summarize recent progress, challenges, and opportunities for future research. This paper presents a concise view of the state of the art, challenges, and future research opportunities regarding the applications of the ANN in big data analytics and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, and several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of the ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of the ANN in big data analytics.