Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of routing protocol in wireless sensor network for energy consumption to maximize network lifetime

Wireless Sensor Networks (WSNs) consist of thousands of tiny nodes having the capability of sensing, computing, and wireless communications. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy consumption is an essential design issues. Due to energy constraints, the deployment and maintenance of WSNs should be easy and scalable to maintain the network lifetime. A comprehensive energy efficient hierarchical cluster-based routing protocol was proposed for continuous stream queries in wireless sensor network. The routing scheme and algorithm has the common objective of trying to extend the lifetime of the sensor network. We introduce cluster head-set idea for cluster-based routing where several clusters are formed with the deployed sensors to collect information from target field. On rotation basis, a head-set member receives data from the neighbour nodes and transmits the aggregated results to the distance base station. For a given number of data collecting sensor nodes, the number of control and management nodes can be systematically adjusted to reduce energy consumption quite significantly and prolongs the life time of sensor network. This document is a study about hierarchical cluster-based routing protocol algorithm where the implementation was done using Matlab simulator to study the performance of this algorithm in term of lifetime. We show that existing energy models over-estimate life expectancy of a sensor node by 30–58% and also yield an “optimised” number of clusters which is too large. Simulation results show that our hierarchical clustering protocol balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime.
Efficient Coreset Constructions via Sensitivity Sampling

A coreset for a set of points is a small subset of weighted points that approximately preserves important properties of the original set. Specifically, if P is a set of points, Q is a set of queries, and f : P × Q → R is a cost function, then a set S ⊆ P with weights w : P → [0 , ∞ ) is an ϵ -coreset for some parameter ϵ> 0 if (cid:80) s ∈ S w ( s ) f ( s,q ) is a (1+ ϵ ) multiplicative approximation to (cid:80) p ∈ P f ( p,q ) for all q ∈ Q . Coresets are used to solve fundamental problems in machine learning under various big data models of computation. Many of the suggested coresets in the recent decade used, or could have used a general framework for constructing coresets whose size depends quadratically on the total sensitivity t . In this paper we improve this bound from O ( t 2 ) to O ( t log t ). Thus our results imply more space efficient solutions to a number of problems, including projective clustering, k -line clustering, and subspace approximation. The main technical result is a generic reduction to the sample complexity of learning a class of functions with bounded VC dimension. We show that obtaining an ( ν,α )-sample for this class of functions with appropriate parameters ν and α suffices to achieve space efficient ϵ -coresets. Our result implies more efficient coreset constructions for a number of interesting problems in machine learning; we show applications to k -median/ k -means, k -line clustering, j -subspace approximation, and the integer ( j,k )-projective clustering problem.
Fleurya Aestuans And Tetrahydroxyflavone Effects On Hepatological Enzymes Of Lead Acetate Induced Toxicity

1 Department of Human Physiology, Faculty of Basic Medical Sciences, College of Medicine, Madonna University, PMB 48, Elele, Nigeria. Department of Human Physiology, Faculty of Basic Medical Sciences, College of Health Sciences, University of Port Harcourt, PMB 5323, Port Harcourt, Nigeria. Department of Human Physiology, Faculty of Basic Medical Sciences, College of Health Sciences, Alex Ekwueme University, Ndufu-Alike, Nigeria.
The Role of Legal Theory in Legal Education: A Reflection on Professional and Scholarly Ideals in Nordic Legal Education

The article addresses the role of legal theory in legal education. Today, a multitude of perspectives is present within legal theory and, as a subject, it is not as distinct as it is sometimes claimed to be. It is evident when considering syllabuses in the Nordic countries that nearly all LLM programmes have ambitions to teach legal theory as embracing a multitude of theoretical and methodological perspectives. This multitude of perspectives promises adaptable content for the subject of legal theory in various legal contexts, and could facilitate a reflection on how knowledge is acquired and why.
NEW METRIC THAT USES A MEASURE OF RESEMBLANCE BETWEEN TERMS TO TAKE INTO ACCOUNT THE NOTION OF SEMANTIC PROXIMITY

In this article, we extended the vector model by adapting the parameter by combining it with the formula for index word extraction and evaluation in order to describe the relevant principles that describe a text. Indeed, by combining the calculation with an approach, we have proposed a new metric that uses a measure of resemblance between terms to take into account the notion of semantic proximity. This indexation approach is supported by a contextual and semantic appraisal. In order to have a comprehensive descriptor index, we used not only a semantic graph to illustrate the semantic relationships between words, but also an auxiliary dictionary to strengthen the cohesion of the established graph and thus the semantic weight of indexation phrases. In the presented article, two semantic similarity approaches were explored in Kazakh-Russian, namely, the direct path-based and distributional model, and their cross-lingual counterparts were synthesized in the light of English. The suggested approaches were evaluated on a specific dataset of 1000 Russian and Kazakh word pairs, formatted by analysis. The correlation scores obtained between the four tests and the human evaluation scores suggest a major shift that brings the cross-lingual approach to the semantic similarity estimation process in the Kazakh and Russian languages.
Syntactic predictors for text quality in Dutch upper-secondary school students’ L1 argumentative writing

Among other things, learning to write entails learning how to use complex sentences effectively in discourse. Some research has therefore focused on relating measures of syntactic complexity to text quality. Apart from the fact that the existing research on this topic appears inconclusive, most of it has been conducted in English L1 contexts. This is potentially problematic, since relevant syntactic indices may not be the same across languages. The current study is the first to explore which syntactic features predict text quality in Dutch secondary school students’ argumentative writing. In order to do so, the quality of 125 argumentative essays written by students was rated and the syntactic features of the texts were analyzed. A multilevel regression analysis was then used to investigate which features contribute to text quality. The resulting model (explaining 14.5% of the variance in text quality) shows that the relative number of finite clauses and the ratio between the number of relative clauses and the number of finite clauses positively predict text quality. Discrepancies between our findings and those of previous studies indicate that the relations between syntactic features and text quality may vary based on factors such as language and genre. Additional (cross-linguistic) research is needed to gain a more complete understanding of the relationships between syntactic constructions and text quality and the potential moderating role of language and genre.
Secured Supply Chain Management for Medical Companies using Blockchain

Supply chain management in pharmaceutical industry is a complex process which comprises of managing tasks like manufacturing, storage, and sales of medical drugs. Though supply chain management is a challenge on every industry, in health care, compromised supply chain adds risk to consumers’ safety. Increased adoption of technology, globalization and industry populated with multiple stakeholders in various jurisdictions has given rise to a complicated health supply chain. In this operation process, due to imbalance and asymmetry of information among the stakeholders arises a fraud problem such as compromising the consumer drug information. In such cases, in order to improve the security and reliability of drug information we suggest blockchain based data storage technology. In this paper we discuss about hoe fraudulent drug information problem can be resolved using blockchain and by the way presenting reliable information to the concerning consumers. We discuss about smart contract based on Consensus algorithm. Blockchain in pharmaceutical supply chain not only reduces the risk of counterfeiting and theft, but also allows efficient inventory management.
T RAINING DATA SIZE INDUCED DOUBLE DESCENT FOR DENOISING NEURAL NETWORKS AND THE ROLE OF TRAINING NOISE LEVEL .

When training a denoising neural network, we show that more data isn’t more beneficial. In fact the generalization error versus number of of training data points is a double descent curve. Training a network to denoise noisy inputs is the most widely used technique for pre-training deep neural networks. Hence one important question is the effect of scaling the number of training data points. We formalize the question of how many data points should be used by looking at the generalization error for denoising noisy test data. Prior work on computing the generalization error focus on adding noise to target outputs. However, adding noise to the input is more in line with current pre-training practices. In the linear (in the inputs) regime, we provide an asymptotically exact formula for the generalization error for rank 1 data and an approximation for the generalization error for rank r data. We show using our formulas, that the generalization error versus number of data points follows a double descent curve. From this, we derive a formula for the amount of noise that needs to be added to the training data to minimize the denoising error and see that this follows a double descent curve as well.
An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic

Wind power forecasting is crucial for wind power systems, grid load balance, maintenance, and grid operation optimization. The utilization of wind energy in the Arctic regions helps reduce greenhouse gas emissions in this environmentally vulnerable area. In the present study, eight various models, seven of which are representative machine learning algorithms, are used to make 1, 2, and 3 step hourly wind power predictions for five wind parks inside the Norwegian Arctic regions, and their performance is compared. Consequently, we recommend the persistence model, multilayer perceptron, and support vector regression for univariate time-series wind power forecasting within the time horizon of 3 hours.
Experimental Control of Curing & Structural Health Monitoring for Composite Patch Repairs

Unquestionably structural integrity of composite materials is of great importance and spans from production to service. The presented experimental investigation, supported initially by numerical analysis, addresses the capability of sensing during the curing cycle of Composite Patch Repair (CPR) guaranteeing achievement of optimal cure levels. Results have exhibited identification and completion of cure leading to probable reduction of repair and downtime costs. The sensors were further used post curing for structural integrity where results proved that the optical fibres can render a general state of the strain field inside the patches and possible crack propagation (real time).
Indicators for Sustainable Communities: A Strategy Building on Complexity Theory and Distributed Intelligence

Indicators and performance measures have become an important element in policy initiatives relating to sustainability and to the re-invention of government. This article reviews the research and practice of indicator development and use, summarizing several key lessons from this review. One of the key lessons is that to be useful, indicators must be developed with the participation of those who will use and learn from them. The article then proposes a strategy for community indicators based upon the conception that cities are like living organisms functioning as complex adaptive systems. Three types of indicators are needed. System performance indicators are required to provide information to the public about the overall health of a community or region. Policy and program measures are required to provide policy-makers with feedback about how specific programs and policies are working. Rapid feedback indicators are required to assist individuals and businesses to make more sustainable decisions on a day-to-day basis. There is no simple formula for how to develop a system of indicators. Each community and region should develop a system based upon their own circumstances and needs.
Does FDI Affect Domestic Employment in OECD Countries?

To verify the employment impact of two-directional FDI, the study analyzes panel data composed of 26 OECD countries from 2006 to 2018 by using the system GMM. Furthermore, we decompose domestic employment into types of industries and skill compositions to identify the heterogeneous employment impact. The results show that inward and outward FDI at lag one period promote domestic employment at the overall level. In terms of workers’ skill levels, lagged inward FDI significantly persistently promotes high-skilled workers’ employment, likewise, the positive employment impact also appears with a time lag in low-skilled labor subgroups. Outward FDI, on the other hand, initially inhibits both highand low-skilled labor demand, but then changes to a positive effect in the highskilled labor subgroups. Although there is a time difference between inward and outward FDI, it has a significant and positive impact on employment in the manufacturing and service industries. The results indicate that the relationship between manufacturing and service employment is a mutual substitute. To attract international investors, governments should promote a favorable investment climate and maintain stable economic growth. Because low-skilled labor is more susceptible to changes in FDI, policy measures are required to ensure employment stability.
Improved computational neutronics methods and validation protocols for the advanced test reactor

The Idaho National Laboratory (INL) is in the process of updating the various reactor physics modeling and simulation tools used to support operation and safety assurance of the Advanced Test Reactor (ATR). Key accomplishments so far have encompassed both computational as well as experimental work. A new suite of stochastic and deterministic transport theory based reactor physics codes and their supporting nuclear data libraries (HELIOS, KENO6/SCALE, NEWT/SCALE, ATTILA, and an extended implementation of MCNP5) has been installed at the INL. Corresponding models of the ATR and ATRC are now operational with all five codes, demonstrating the basic feasibility of the new code packages for their intended purposes. On the experimental side of the project, new hardware was fabricated, measurement protocols were finalized, and the first four of six planned physics code validation experiments based on neutron activation spectrometry have been conducted at the ATRC facility. Data analysis for the first three experiments, focused on characterization of the neutron spectrum in one of the ATR flux traps, has been completed. The six experiments will ultimately form the basis for flexible and repeatable ATR physics code validation protocols that are consistent with applicable national standards. (authors)
A COMPREHENSIVE APPROACH FOR CONVERTING RELATIONAL TO GRAPH DATABASE USING SPARK

Nowadays, data processing requirements is growing exponentially, and relational database is not always the best solution for all situations in big data such as increasing growth of data. Thus, NoSQL databases emerged to overcome the limitations of relational database and work with big data. NoSQL databases have four types of models, namely, key-value model, document database, column database, and graph database. Many approaches have been proposed to convert relational database to NoSQL models. However, most of them map relational database to key-value or column or document. Converting relational to graph database is slightly disregarded by the researchers. This paper proposes a comprehensive approach, based on Spark framework, for transformation and migration of relational database to graph database without semantic loss. The approach also supports conversions from Sql commands to cypher commands . It is categorized into two parts. The first part is concerned with “transformation and migration using Spark”, which encompasses three phases: Meta data analyzer, transformation algorithm, and migration algorithm. The second part focuses on “SQL to cypher”, which divides into two phases: SQL parser and Translator. The suggested approach has been applied, results and validation for the proposed approach
IMPLEMENTATION OF A CELEBRITY FACE RECOGNITION AI FOR VIDEO METADATA GENERATION

In this paper, we introduce a celebrity face recognition AI for video metadata generation. Face recognition performance has shown significant improvement thanks to deep learning. We implemented a face recognition AI using our customized dataset composed of mostly Korean celebrity faces designed for the content analysis of KBS. Bothersome dataset labelling process was enhanced by using MTCNN face detection and face clustering. Inception-ResNet v1 model was used and test set accuracy was measured with respect to iterations. We compared our model with a commercial cloud-based celebrity recognition AI with which our celebrity database is thought to have about 26% in common. In the experiment, our model showed better performance in the precision.
MOOC of mathematics, a strategic for strengthening basic competencies and concepts in higher education

Due to the high rates of failure in mathematics, the Department of Basic Sciences of Santo Tomás University Bucaramanga (USTABUCA), with the support of its Virtual Campus, proposes the design of a Massive Open Online Course (MOOC) to help in the development of mathematical competencies and processes that allow students to perform successfully in the Differential Calculus course. The course was designed in four sections: Arithmetic, Algebra, Trigonometry and Geometry, which were structured in lessons with practical exercises and entry and exit tests to check the competencies achieved in each section of the course. To determine the effectiveness of the MOOC a research with a mixed methodological approach was carried out in two stages: a quantitative one with the application of questionnaires and satisfaction surveys and a qualitative one with the application of interviews to students who participated in the MOOC and teachers in charge of the Differential Calculus course. The results indicated the relevance of the course to improve students’ catching-up processes and the improvement.
Spatial localization of sound sources: azimuth and elevation estimation

A model-based bio-mimetic sound localization method has been proposed for concurrent and continuous speech sources located in reverberant environment. However, the method only provides the estimation of azimuths of sound sources. The current paper is to extend the previous method to be available for sound sources existing in the 3D space by using a four microphone set. As the procedure of signal processing, arrival time differences of different microphone pairs are calculated at each available onset of sound components. The locus circles of different microphone pairs in the surface of Gaussian Sphere are then calculated and projected topographically to a horizontal plane, which consists the north pole, by the south pole projection. A two dimensional histogram in the projected plane corresponding to the source direction is created by summing all the projected maps of every onsets. Finally, the directions of sound sources are determined by the position of the major peaks in the two-dimensional histogram.
Is It Who You Are or What You Get? Comparing the Impacts of Loans and Grants for Microenterprise Development∗

Is the type of financial support provided to businesses more important than which businesses receive it? Loans and grants can lead to differences in optimal investments and in scope for moral hazard. We randomize 3,294 business-loan applicants into receiving a loan, cash grant, in-kind grant or nothing. All treatments equally increase income, yet there are large differences within a treatment group with impacts concentrated at the top of the distribution. Those who succeed with loans are observationally equivalent to those who succeed with grants, showcasing that owner heterogeneity is more important than the type of support received in microenterprise development. JEL classification: D22, J23, J24, O12, C93.
Using Physics Knowledge for Learning Rigid-body Forward Dynamics with Gaussian Process Force Priors

: If a robot’s dynamics are difﬁcult to model solely through analytical mechanics, it is an attractive option to directly learn it from data. Yet, solely data-driven approaches require considerable amounts of data for training and do not extrapolate well to unseen regions of the system’s state space. In this work, we emphasize that when a robot’s links are sufﬁciently rigid, many analytical functions such as kinematics, inertia functions, and surface constraints encode informative prior knowledge on its dynamics. To this effect, we propose a framework for learning probabilistic forward dynamics that combines physics knowledge with Gaussian processes utilizing automatic differentiation with GPU acceleration. Compared to solely data-driven modeling, the model’s data efﬁciency improves while the model also respects physical constraints. We illustrate the proposed structured model on a seven joint robot arm in PyBullet. Our implementation of the proposed framework can be found here: https://git.io/JP4Fs