Miracle-dz/UniFAR-FTUs huggingface dataset:
MOBILE AD HOC NETWORK ROUTING STABLE PATH SELECTION USING ENRDP PROTOCOL

MANET is a network where no fixed infrastructure exists. Such networks are expected to play vital role in future civilian and military settings, being useful to provide communication support where no fixed infrastructure exists or the deployment of a fixed infrastructure is not economically profitable and movement of communicating parties is possible. The topology of MANETs is dynamic, because the link among the nodes may vary with time due to device mobility, new device arrivals, and the possibility of having mobile devices. Neighbor discovery is the process by which a node in a network determines the total number and identity of other nodes in its neighborhood position. Neighbor discovery is especially important to the proper functioning of wireless networks. In wireless networks, neighbors are usually defined as nodes that lie within radio range of each other. Thus, neighbor discovery can be considered as the exploration of the volume of space or “neighborhood” immediately surrounding a wireless node. To the problems identified, there must be a protocol which is fully distributed and light weight to solve the verification of node position in mobile Adhoc networks. It should not depend on trusted nodes and should be secure for various kinds of attacks. A neighbor position verification (NPV) is used to discover and verify the position of the neighbors. Particularly, in a mobile Adhoc network, where a pervasive infrastructure is not present, and the location data must be attained through node-to-node communication. This situation is a chance for the adversarial nodes to misuse the location-based services. By advertise the forged positions, adversaries could bias data gathering processes, attracting network traffic and then discard the data. The primary objective of this paper is to selects the stable path to reduce the fake position and packet lost using ENRDP protocol. It selects the stable path, so neighbor discovery failure decreases and there by increases the packet delivery ratio and reduces energy consumption, End-to-End delay and packet lost. The proposed work is analyzed and simulates in the software Network Siimulator-2(NS2) and compares its effectiveness with the existing protocol. The simulation will cover different network scenarios with varying network performance parameters such as Packet delivery ratio, End-to-End delay, Packet lost and Energy Consumption.
Common Misconceptions in Subsurface and Surface Risk Analysis

This paper is intended to highlight some of the misconceptions that enter decision making whether as a consequence of human bias or as statistical inaccuracies and misuse of mathematically robust equations. There are three basic questions in the oil and gas industry: 1) How big is it? i.e.: Resources, 2) How quickly can we get it out? i.e.: Rate, and 3) How much can we make from it? i.e.: Value. All three contain risk and uncertainty. Several methods are used by oil and gas professionals to manage these variables, ranging from the simple toss of a coin, to complex mathematical algorithms and statistics. If only it were so simple to use a formula for the decision and have the confidence to trust it. However, business decisions are often more than just complex logic and the worlds of subsurface geoscience and reservoir engineering add yet more variables and uncertainty. The paper uses real world examples in the oil and gas industry and highlights the difference between Risk Management and simple Gambling. The paper draws upon the following three themes: 1) Human Factors: No matter how precise the mathematics of risk analysis, ultimately decision making relies upon individuals who are subjective by definition. A group of similar but independent professionals will have a different perception of risk and their own biases depending upon the number of parameters used to calculate for example, chance of success and different biases on marking at extremes of a range. 2) Statistics: One solution to deal with the human variable is to attempt to take individuals out of the decision making process completely and trust mathematics instead. Unfortunately, many decision makers have forgotten what assumptions exist in Portfolio Theory and often use the methods blindly, placing too much confidence in the predictions and unaware of other equally likely outcomes. 3) Popular Decision tools: a) Expected Monetary Value – ‘Expected’ is a subjective term and numerically tends towards the Mode of the distribution. The authors question whether EMV really has any true meaning and instils a false sense of confidence in the outcome.
A Novel Active Cell Balancing Circuit and Charging Strategy in Lithium Battery Pack

A novel, active cell balancing circuit and charging strategy in lithium battery pack is proposed in this paper. The active cell balancing circuit mainly consists of a battery voltage measurement circuit and switch control circuit. First, all individual cell voltages are measured by an MSP430 microcontroller equipped with an isolation circuit and a filter circuit. Then, the maximum cell voltage difference is calculated by subtracting the minimum cell voltage from the maximum cell voltage. When the maximum cell voltage difference exceeds 0.05 V, the balancing action starts to carry on. The MSP430 microcontroller output controls signals to close the switches corresponding to the battery cell with the maximum voltage. At this time, the balancing charge power performs a balancing charge for other batteries, except for the one that is switched on. In addition, a three-stage balancing charge strategy is also proposed in this paper to achieve the goal of speedy charging with balancing action. In the first stage, a 0.5 C balancing current is used to perform pre-balanced charging on all battery cells until the maximum cell voltage difference is less than 0.05 V, which is required for entry to the second stage of charging. In the second stage, constant current charging of 1 C, coupled with 0.2 C balancing current charging is carried out, until the maximum battery cell voltage reaches 4.2 V, which is required for entry into the third stage of charging. In the third stage, a constant voltage charging is coupled with 0.2 C balancing current charging, until the maximum battery cell voltage reaches 4.25 V, which is required to complete the balancing charge. The imbalance of power between the battery cells during battery pack charging, which reduces battery charging efficiency and battery life, is thus effectively improved. In this paper, a six-cells-in-series and two-in parallel lithium battery pack is used to perform a balancing charge test. Test results show that the battery cells in the battery pack are capable of quickly completing a balancing charge under different initial voltages, the maximum voltage difference is reduced to within the range of 0.05 V, and the total time required for each balancing charge is approximately 3600 s.
ENHANCING PERSONAL RECOMMENDATION SYSTEM USING FAMILIARITY FACTOR ON SOCIAL NETWORK

Social media has become one of the most popular web platforms, with an enormous user base. Many application systems use social media information to generate search results. A recommendation system is one of technologies that may have applications on social media. Numerous studies investigate recommendation systems by combining semantic web reasoning mechanisms. They exploit technologies such as ontologies to achieve intelligent recommendations. Users may then more easily use mobile devices to obtain digital content. We propose a novel method to personalize a recommendation system through the user’s affecting index to exploit user similarity and familiarity between users.
Recognition of Arbitrary 2D Shapes for Pick and Place Solutions using Robot Manipulators

Robotic platforms are consistently playing greater roles in manufacturing processes. In many production lines robots are required to recognize different objects of varying shapes and sizes. They also need to reliably manipulate these objects to achieve some goal. By improving the robustness of these components in uncertain conditions, a number of new applications could be enabled. We present a complete pick and place system using a robot manipulator which assembles a child’s puzzle. The problem was chosen based on the arbitrary nature of the shapes involved and because of the intricate manipulation necessary for the problem. Our proposed solutions to shape recognition, localization and placement are described in the paper. We chose a Fourier based descriptor for shape recognition and servo the manipulator in vision space for fine manipulation. System performance is presented with promising results.
Effects of Melatonin on Sleep after Laparoscopic Cholecystectomy: A Randomized, Placebo-Controlled Clinical Trial

Effects of Melatonin on Sleep after Laparoscopic Cholecystectomy: A Randomized, Placebo-Controlled Clinical Trial Objective: We set out to investigate whether administration of an oral dose of 5 mg melatonin in the evenings after laparoscopic cholecystectomy could reduce sleep disturbances measured by actigraphy. Methods: We performed a double-blinded, placebo-control, randomised clinical trial where 54 patients received 5 mg melatonin or placebo approximately 30 minutes before bedtime three nights postoperatively. Participants were monitored with actigraphy two nights preoperatively and three nights postoperatively. Likewise sleep, pain, general wellbeing, and fatigue were measured via visual analogue scales. Results: Melatonin administration in the postoperative period did not affect any of the objectively determined sleep parameters compared with placebo. No group difference was observed during the night time or daytime even though the overall sleep was disturbed postoperatively. Melatonin intervention did not result in improvements in the subjective parameters. Conclusions: We did not show an effect of 5 mg melatonin administration on sleep after laparoscopic cholecystectomy.
Acceleration Techniques for Monte Carlo Ray Tracing Acceleration Techniques for Monte Carlo Ray Tracing

Monte Carlo (MC) ray tracing has been considered as the most effective technique to produce a variety of realistic visual effects. However, its performance tends to be very slow since a lot of ray samples should be generated until we achieve converged images. In this thesis, we propose three novel techniques to accelerate performance of MC ray tracing. We fist develop a ray reordering framework based on a novel ray ordering measure hit point heuristic to compute a cache-coherent access pattern of ray traversals on acceleration hierarchies. In addition to the cache optimization technique, we propose an efficient and robust image-space denoising method for reducing noise generated by MC ray tracing while preserving image features. Our denoising is built upon a novel edge-stopping function virtual flash image which captures a wide variety of image features without taking additional ray samples. Furthermore, we present a new image-space adaptive rendering method based on locally weighted regression. In our adaptive framework, we locally guide ray budgets on high error regions, and estimate optimal filtering bandwidths for each rendering feature in terms of minimizing filtering errors. We have demonstrated that the proposed acceleration techniques improve the performance of MC ray tracing using different realistic benchmarks compared to state-of-the-art methods.
THE FUTURE OF COMPUTING: VLSI, STORAGE, AND NETWORKS REFLECTIONS ON A LEGACY OF INNOVATION

Building on the legacy of innovation in computing by Stamatis Vassiliadis and others, the future of computing in the principal areas of CMOS VLSI technology, storage, and networks will be explored, with extrapolations to large-scale systems. Applications and the societal impact of these technological advancements will be considered.
Consistency of Proof-of-Stake Blockchains with Concurrent Honest Slot Leaders

We improve the fundamental security threshold of eventual consensus Proof-of-Stake (PoS) blockchain protocols under the longest-chain rule by showing, for the first time, the positive effect of rounds with concurrent honest leaders. Current security analyses reduce consistency to the dynamics of an abstract, round-based block creation process that is determined by three events associated with a round: (i) event A: at least one adversarial leader, (ii) event S: a single honest leader, and (iii) event M: multiple, but honest, leaders. We present an asymptotically optimal consistency analysis assuming that an honest round is more likely than an adversarial round (i.e., Pr[S]+Pr[M] > Pr[A]); this threshold is optimal. This is a first in the literature and can be applied to both the simple synchronous communication as well as communication with bounded delays.In all existing consistency analyses, event M is either penalized or treated neutrally. Specifically, the consistency analyses in Ouroboros Praos (Eurocrypt 2018) and Genesis (CCS 2018) assume that Pr[S] − Pr[M] > Pr[A]; the analyses in Sleepy Consensus (Asiacrypt 2017) and Snow White (Fin. Crypto 2019) assume that Pr[S] > Pr[A]. Moreover, all existing analyses completely break down when Pr[S] < Pr[A]. These thresholds determine the critical trade-off between the honest majority, network delays, and consistency error.Our new results can be directly applied to improve the security guarantees of the existing protocols. We also complement these results by analyzing the setting where S is rare, even allowing Pr[S] = 0, under the added assumption that honest players adopt a consistent chain selection rule.
Hierarchical Dirichlet Process Hidden Markov Model for unsupervised learning from bioacoustic data

Hidden Markov Models (HMM) are one of the most used models in statistics and machine learning for modeling sequential data. One of the main issues in HMM is the one of selecting the number of hidden states required for the expectation-maximization (EM) learning scheme. The infinite Hidden Markov Model (IHMM) is a Bayesian non-parametric alternative for standard parametric HMMs that offers a principled way to tackle this challenging problem by relying on a Hierarchical Dirichlet Process (HDP) prior. In this paper, we present an application of the the Hierarchical Dirichlet Process for Hidden Markov Model (HDP-HMM) to a challenging problem of unsupervised learning from bioacoustic data. We investigate two different approaches, the first one uses Gibbs sampling and the second one uses Beam sampling. The problem for humpback whale song decomposition consists in simultaneously finding the structure of possible hidden whale song units and automatically inferring the unknown number of the hidden units from the data. The considered data are Mel Frequency Cepstral Coefficients (MFCC) of recording of bioacoustic signals. The experimental results show the good performance of the proposed Bayesian non-parametric approach.
NUMERICAL OPTIMIZATION OF THE WING OF A SUPERSONIC AIRPLANE

A direct optimization method is developed to design the wing of a supersonic civil airplane. Aerodynamic characteristics of the airplane are calculated within the framework of a model based on Euler’s equations with taking into account surface friction drag. The optimization algorithm is based on a modification of Newton’s method. Drag is used as the objective function minimized under lift, longitudinal trim and volume constraints. A quadratic form describing dependence of drag on the design variables is obtained on the basis of a local analysis of the load distribution on the wing. It gives approximations to the true Hessian matrix and gradient vector, and wing form variations that enable the aerodynamic performance to be improved. A fast convergence to the optimum in case of the large number (more than 500) of the variables is provided. The efficiency of the method is demonstrated on examples of the isolated wing and the wing situated under interference with other elements of the airplane. For the supersonic civil airplane a possibility of lift-todrag ratio increase on 9.3% is shown. The airplane is designed for cruise flight at Mach number M=2 and performed in tail-first configuration with two air-breathing engines established underneath the wing.
The status quo of Intervention Methodology on Childhood and Adolescents Obesity: A qualitative systematic review

s assessed for eligibility (n = 45) Reviews excluded (by Abstract) (n = 128) Unsuitable age group (n=7) Not addressed to childhood obesity (n=11) No results (n= 21) No BMI/BMI z-score outcome (n=32) Follow-up <6 months (n=4) Majority of the included trials published before 2008 (n=19) Repeated results (n=8) Other aspects (n= 26) Full-text Reviews assessed for eligibility (n=25) 1–25 Healthcare (5) 2,4,7,10,12 Pharmacotherapy (2) 1,10 Bariatric Surgery (2) 1,13 Family (10) 1–3,5,7,14,16,17,19,20 Preschool (2) 11,22 School (11) 2,6,8–12,15,16,18,23,24 Technology (5) 2,12,15,21,25 Public Health (1) 11 1–25 Reviews included in qualitative synthesis (n=41) 1–41 Full-text Reviews excluded (n =20) No results (n= 12) Short follow-up (n=3) Low quality (n=5) Additional Reviews identified through other sources (n=16) 26–41 Healthcare (3) 26,27,34 Pharmacotherapy (4) 35–38 Bariatric Surgery (2) 35,39
The effect of pressure- and volume-controlled one -lung ventilation on lung dynamics and plasma malondialdehyde level

Ozkan Yavuz1, Hanife Karakaya Kabukcu1, Nursel Sahin1, Sebahat Ozdem2, Abdullah Erdogan3, Tulin Aydogdu Titiz1 1Departments of Anesthesiology and Reanimation, 2Clinical Biochemistry, 3Thoracic Surgery, Akdeniz University, Antalya, Turkey The effect of pressureand volume-controlled The effect of pressureand volume-controlled one -lung ventilation on lung dynamics and plasma malondialdehyde level
SYNOPTIC-SCALE PRECURSORS AND CHARACTERISTICS OF HIGH-END TORNADO OUTBREAKS IN THE SOUTHEASTERN REGION OF THE UNITED STATES

In the southeastern region of the United States, the spring is the primary tornado outbreak season with most events occurring between January and June (Fuhrmann et al. 2014). These temporal and spatial domains were used in attempt to identify the differences between differing severities of tornado outbreaks. Objective categorization of tornado outbreaks resulted in two types: HETO and Moderate-end Tornado Outbreaks (METO). The focus of this study was to identify synoptic-scale features corresponding to HETO by comparing HETO and METO. After qualitatively comparing HETO and METO, weighted anomalies were calculated with respect to a 30-year climatology and statistical significance was assessed.
UDC: 612.349.8: 577.175.4/8:636.234+618.6: 159.922.5 LEVELS OF INSULIN, INSULIN-LIKE GROWTH FACTOR-I AND THYROID HORMONES IN RELATION TO THE BODY CONDITION SCORE CHANGES IN PERIPARTURIENT DAIRY COWS

The objective of this study was to determine the levels of insulin, insulin like growth factor I (IGF-I) and thyroid hormones in relation to the body condition score (BCS) of periparturient dairy cows. The study was carried out on twenty Holstein-Friesian dairy cows with average milk production of 7000 L/305 days in the previous lactation, parity ranging from 2-4. All cows were BCS scored during the early dry period, 7±3 days before and after parturition. Based on the BCS at the early dry period, cows were divided in two groups: cows with high BCS (3.754.25, HBCS, n=10), and cows with moderate BCS (2.75-3.75, MBCS, n=10). Blood samples were taken at the time of BCS evaluation. Concentrations of insulin, IGF-I, triiodothyroinine (T3) and thyroxine (T4) were determined by radioimmunoassay (RIA, INEP-Zemun, Serbia). Statistical differences between mean values were determined using Student t-test (p<0.01). Mean BCS values at early dry period were significantly different compared to two other periods (p<0.01) within both groups, but BCS was similar in both groups at 7±3 days after calving (2.69±0.67:2.62± 0.27, p>0.05). IGF-I level in HBCS cows at days 7±3 before calving was significantly higher (16.28±3.07:11.76±2.28, p<0.01), with a reverse relationship after calving (3.77±1.64:8.46±2.37, p<0.01). Insulin level was significantly lower at 7±3 days before calving in HBCS cows (16.26±4.60:20.18±4.96mIU/L, p<0.05). Thyroid hormones levels were significantly lower in HBCS group et all examined periods.
Asymptotic Estimate for the Multinomial Coefficients

The multinomial coefficient ( n,q k ) is defined to be the coefficient of xk in (1 + x + x2 + · · · + xq−1)n. It is conjectured that for given n > 2, T (n, q) := ( n,q cn ) − ( n,q−1 cn ) is unimodal and the maximum occurs at q = ⌊log1+ 1 c n⌋ or q = ⌊log1+ 1 c n⌋ + 1. As an attempt to prove this conjecture, we give an asymptotic estimate for ( n,q cn ) as n tends to infinity, where c is a positive integer.
State of art of real-time singing voice synthesis

This paper describes the state of art of realtime singing voice synthesis and presents its concept, applications and technical aspects. A technological mapping and a literature review are made in order to indicate the latest developments in this area. We made a brief comparative analysis among the selected works. Finally, we have discussed challenges and future research problems. Keywords: Real-time singing voice synthesis, Sound Synthesis, TTS, MIDI, Computer Music.
Heavy flavor identification at CMS with deep neural networks

At the Large Hadron Collider, the identification of jets originating from heavy flavour quarks (b or c-tagging) is important for searches for new physics and for measurements of standard model processes. A variety of b-tagging algorithms has been developed by CMS to select b-quark jets based on variables such as the impact parameters of the charged-particle tracks, the properties of reconstructed decay vertices, and the presence or absence of a lepton, or combinations thereof. These algorithms heavily rely on machine learning tools and are thus natural candidates for advanced tools like deep neural networks. A new algorithm, DeepCSV, uses a deep neural network. The input is the same set of observables used by the existing CSVv2 b-tagger, with the extension that it uses information of more tracks. Also, the training strategy was adapted and about 50 million jets are used for the training of the deep neural network. The new DeepCSV algorithm outperforms the CSVv2 tagger, with an absolute b-tagging efficiency improvement of about 4% for a misidentification probability for light-flavour jets of 1%. In addition, DeepCSV is a multiclassifier simultaneously trained for c-tagging. For c-tagging DeepCSV outperforms the other taggers in CMS.
Mining Influentials and their Bot Activities on Twitter Campaigns

Twitter is increasingly used for political, advertising and marketing campaigns, where the main aim is to influence users to support specific causes, individuals or groups. We propose a novel methodology for mining and analyzing Twitter campaigns, which includes: (i) collecting tweets and detecting topics relating to a campaign; (ii) mining important campaign topics using scientometrics measures; (iii) modelling user interests using hashtags and topical entropy; (iv) identifying influential users using an adapted PageRank score; and (v) various metrics and visualization techniques for identifying bot-like activities. While this methodology is generalizable to multiple campaign types, we demonstrate its effectiveness on the 2017 German federal election.