Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploration of Diversity Status of Beneficial Microorganisms in Iron Ore Mines of Chitradurga District , Karnataka , South India

An attempt was made to investigate the diversity status of different beneficial microbes such as Arbuscular Mycorrhizal (AM) fungi and Plant Growth Promoting Rhizobacteria (PGPR) in roots and soil samples collected from 40 different sample plots of iron ore mine lease (core zone) and outside mine lease (buffer zone) areas including agricultural fields in Chitradurga, Hiriyur, Holalkere and Hosadurga Taluks in Chitradurga District, Karnataka state. Data on percent root colonization and soil spore population of these microbes were recorded from different sample locations. It was observed that all the samples had AM fungal association and PGPRs, but exhibited variation in population density. The samples collected from mined out areas (core zone) had very less population of AM fungi and PGPR organisms, as compared to outside mine lease (buffer zone) areas and agriculture fields. It was also recorded that a total of 23 different AM fungal species belonging to three genera such as Acaulosopora, Gigaspora and Glomus were recorded during the period of investigation. Among them, the genus Glomus was found to be the dominant one with 17 species, followed by Gigaspora and Acaulospora with 3 species each. Apart from this, four types of PGPR organisms viz., Azotobacter, Azosprtillum, Bacillus and Pseudomonas were also recorded from different study locations but variation in population density.
Analysis of IfE-Robot based Group Delay Variations for the Positioning and Navigation of Mobile Platforms -

This contribution demonstrates the analysis and application of antenna specific Group Delay Variations determined by the Hannover Concept of absolute antenna calibration and the robot of the Institut für Erdmessung (IfE). Group Delay Variations (GDV) may affect the correctness of the position solution in wide area differential GPS applications. The paper demonstrates that antenna specific GDV can occur above the code noise level and influences the correctness of the code observation by systematic effects. A detailed study provides strong evidence that code based positioning is improved by up to 0.3 m (or 30%) when GDV are considered. It will be shown if also navigation applications can be improved. Furthermore, we will discuss in detail that GDV do not appear with significant magnitudes on all GPS/GNSS antennas. BIOGRAPHIES Tobias Kersten received his diploma in Geodesy from the Technische Universität Berlin in April 2009. Currently he is a research associate at the Institut für Erdmessung (IfE) in the workgroup of Prof. Schön. His research interests are focused on the analyses and studies of absolute antenna calibration with respect to the application of determined corrections for mobile approaches. Steffen Schön holds a professor-ship in Positioning and Navigation at the Institut für Erdmessung (IfE) since 2006. He has been involved in several GPS projects at DGFI Munich (Germany) and IGMS TU Graz (Austria), especially in the stochastic modelling of errors within GPS processing. The current research interests cover the modelling and correction of systematic errors in GNSS processing like absolute antenna calibration, receiver clock modelling and improved stochastic models for GNSS observations as well as innovative concepts of GNSS navigation.
A Formal Model for the Analysis of Wireless Network Security Protocols ∗

We propose a timed broadcasting calculus for wireless networks, enhanced with cryptographic constructs for modelling security protocols. The operational semantics of our calculus is given both in terms of a Reduction Semantics and in terms of a Labelled Transition Semantics. We prove that the two semantics coincide. The labelled transition system is used to derive a standard notion of (weak) bisimilarity which is proved to be a congruence. We then adapt Gorrieri and Martinelli’s tGNDC , a general scheme for the definition of security properties, to investigate the safety of non-trivial wireless network security protocols.
Foreign Aid and Economic Growth : Panel Cointegration Analysis for Cambodia , Lao PDR , Myanmar , and Vietnam By Pahlaj Moolio

Given the paradoxical results of impact studies of foreign aid on economic growth in aid-growth literature, the effectiveness of aid for growth still remains a subject of intense debate. Thus, applying panel cointegration tests, and panel fully modified ordinary least squares (FMOLS) and panel dynamic ordinary least squares (DOLS) estimators, this paper estimates the magnitude of long run relationship between aid and economic growth in Cambodia, Lao PDR, Myanmar, and Vietnam using panel data from 1997 to 2014. The results of panel cointegration tests revealed that aid and economic growth cointegrate in a panel framework for the period of the study, indicating a robust long run relationship between the variables. Panel FMOLS and Panel DOLS estimation results revealed a positive impact of aid on economic growth. Thus, study concludes that foreign aid has a favorable effect on economic growth in the sample of four countries from ASEAN region.
Classification system for the predicting of psychosocial risk level in public-school teachers based on Artificial Intelligence

A new method to identify psychosocial risk level classification in Colombian public-school teachers, based on new hybrid approach on Artificial Intelligence. In this paper, we propose a first approach to hybrid prediction method using the algorithm hill climbing and Support Vector Machines (HC-SVM) to predict the psychosocial risk level on Colombian teachers public-schools in the metropolitan area of a Colombian city, where uses HC to optimize the input parameters of the prediction model based on SVM. A database with 5,340 epidemiological records that correspond to the psychosocial evaluations of publicschool teachers in the metropolitan area of a Colombian city (five municipalities). In the best classification performance, general precision and stability will obtained with 95% effectiveness compared to the psychosocial clinical diagnosis. Three new physiological variables will be implemented to be associated with psychosocial risk levels (heart rate, skin electrodermal activity, and electromyography). To validate the proposed prediction method, a 480 individuals who were representative of the sample will randomly chosen and evaluated with three new physiological variables is selected as a case study. Use of the model as an instrument for prediction of psychosocial risk level in publicschool teachers is suggested as a tool for occupational psychologists to improve psychosocial risk prevention programs within administrative and occupational safety systems. Keywords— Support vector machine; hill climbing; psychosocial risk level; management; work organization; publicschool teachers.
Using Semantic and Pragmatic Information to Determine Syntactic Decisions in Translation of The Passive Voice From English to French

USING SEMANTIC AND PRAGMATIC INFORMATION TO DETERMINE SYNTACTIC DECISIONS IN TRANSLATION OF THE PASSIVE VOICE FROM ENGLISH TO FRENCH Ashli Fain, M.S. Department of Computer Science Northern Illinois University, 2018 Dr. Reva Freedman, Director I investigated statistically significant factors that effect syntactic changes in French and English during translation of by phrases using the passive voice. Relative clauses containing this structure were also looked at. This feature was chosen from information that was gathered from experts in the form of academic texts on English-French translation as well as handbooks intended for an audience of professional translators. Data came from the proceedings of the Canadian Parliament (the Hansard), which are published in French and English. Passive sentences were collected from the output of the Stanford Parser. Animacy was found to be a significant factor in whether or not the arguments of a passive by phrase would change order in translation. Information status will need further investigation. While relative clauses are still subject to this effect, this syntactic shift occurs far less often than in sentences not containing relative clauses. NORTHERN ILLINOIS UNIVERSITY DE KALB, ILLINOIS
Studying Disability Related Terms with Swe-Clarin Resources

In Swedish, as in other languages, the words used to refer to disabilities and people with disabilities are manifold. Recommendations as to which terms to use have been changed several times over the last hundred years. In this exploratory paper we have used textual resources provided by Swe-Clarin to study such changes quantitatively. We demonstrate that old and new recommendations co-exist for long periods of time, and that usage sometimes converges.
Degradation feature extraction method for piezoelectric ceramic of ultrasonic motor based on DCT-SV cross entropy

Crack on piezoelectric ceramic is the main reason leading to failure of ultrasonic motors. A novel degradation feature extraction method based on discrete cosine transform (DCT) -singular value (SV) cross entropy was proposed in this paper. In order to improve the correlation with the crack, the DCT coefficients with the property of energy aggregation, were used to extract fault information. To avoid the influence of human factors in traditional DCT de-noising method, a matrix composed of DCT coefficients was constructed, and the SV cross entropy of the matrix was taken as the degradation feature for ultrasonic motor. A numerical simulated noise was added to the measured signal to verify the anti-noise performance of the feature. Analysis of the experimental results demonstrates that the proposed DCT-SV cross entropy is feasible and effective in indicating the degradation of piezoelectric ceramic in ultrasonic motor.
Effect of Slag as a Fine Aggregate on Mechanical, Corrosion, and Nuclear Attenuation Properties of Concrete

: A series of concrete maxis were designed and prepared by replacement percentages (0, 5, 10, and 20 wt%) of their fine aggregates by blast furnace slag. Mechanical properties, corrosion in the steel reinforcement, and different types of Nuclear radiation parameters of four concrete maxis were studied. The corrosion behavior was investigated using different electrochemical techniques and optical microscope. Also, three types of neutron energies as well as nine gamma ray energy lines (121.78 – 1407.92 keV) were used to evaluate the macroscopic neutron cross-sections (Σ, cm -1 ) and mass attenuation coefficients (σ, cm 2 /g) of gamma ray, respectively, in investigated concrete samples. The results showed that, the mechanical properties of concrete and the corrosion resistance of steel imbedded in concrete were increased with increasing blast furnace slag up to 10 wt. %. While, 20 wt % was the optimum percentage for the values of mass attenuation coefficients of most investigated γ-ray energy lines in concrete maxis. Moreover, there is no a significant variation of the values of Σ for all used neutron energy by adding a different percentage of slag as a fine aggregate in concrete.
Agglomeration Economies : Localisation or Urbanisation ? Structural Models for More and Less Developed European Regions

Agglomeration economies have a heterogeneous impact on growth and development, which has been confirmed in the literature. Thus, the aim of this paper is to find precise relatedness between these externalities and the performance of regions. Structural models reveal that impact depends on development level. Both urbanisation and localisation economies are beneficial in different circumstances, but they play a greater role in developed regions. In less developed regions, growth rate is only correlated with regional urbanisation economies, including low-technology and less knowledge-intensive services, but mostly with the share of manufacturing sectors. Nevertheless, regions with higher GDP per capita have a lower share of industry sectors and a higher share of knowledge-intensive services (KIS), but also higher localisation economies within both technology-intensive and KIS market services. Therefore, there is a dichotomy in the less developed regions. Although they need urbanisation economies and a high share of industries for growth, specialised KIS and knowledge-intensive activities become crucial afterwards, when they are mature. Less developed regions are moving in the direction of the development models of more developed regions. Developed regions, in turn, can follow two types of development paths (or these paths could mutually interfere), with positive correlations of localisation and urbanisation economies with both development level and growth. One path requires a high share of high-tech sectors and knowledge-intensive activities, with urbanisation economies within manufacturing and service sectors. The second path is inverse because it requires a lower share of manufacturing sectors and a higher share of KIS and knowledge-intensive activities with localisation economies throughout all sectors, including medium-low and low-technology and service sectors.
UAV-Enabled Spatial Data Sampling in Large-Scale IoT Systems Using Denoising Autoencoder Neural Network

Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size $\boldsymbol K$ -means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.
AN MCTS AGENT FOR EINSTEIN WÜRFELT NICHT!

Since the invention of the computer, attempts have been made to create programs, which are able to compete against humans in games. For a long time, several variants of the Minimax algorithm dominated the field of board games. However, the recent invention of the Monte-Carlo Tree Search (MCTS) algorithm allowed computer players, to drastically improve their performance in some games, in which Minimax did not perform well. This thesis tries to answers the question, how MCTS can be used to form a strong player in the game of “EinStein würfelt nicht! ”. “EinStein würfelt nicht! ” (EWN) is a board game invented by Ingo Althöfer in 2005. On a 5 × 5 board, two players try to be the first who reach the opposite corner, while movement is restricted by a die roll performed each turn. Despite its relatively simple rules, EWN is complex enough to be researched in the field of Artificial Intelligence MCTS is a fairly recent best-first search algorithm that is best known for its performance in Go. Many different enhancements have been proposed so far, from which several are examined in this thesis for the case of EWN. It is described how MCTS has to be modified to work with the die rolls of EWN and which approaches have been used to reduce memory consumption. Several enhancements and combinations of them are assessed to form the strongest-possible EWN agent. The enhancements are playout strategy, Prior Knowledge, Progressive History, MAST, Variance Reduction and Quality-based Rewards. The most beneficial enhancements seem to be Lorentz’s (2012) playout strategy and Prior Knowledge. The resulting agent uses the both these enhancements and is then compared to the state-of-the-art EWN agent MeinStein. In the performed experiments, the performance of both agents has shown to be on equal footing.
Concept-By-Example in EL Knowledge Bases

In dieser Arbeit wird das Concept-by-Example-Problem in Wissensbasen, die in der Beschreibungslogik EL formuliert sind, untersucht. Bei diesem Problem gibt der Benutzer positive und negative Individuen als Beispiele an. Daraufhin soll ein EL Konzept gelernt werden, welches die positiven Beispiele beschreibt, aber nicht die negativen. Automatisches Lernen von Konzepten unterstützt Benutzer beim Entwickeln von Wissensbasen sowie beim Schreiben von Konzeptanfragen und wird bereits mit verschiedenen Ansätzen umgesetzt. Wir zeigen erstmals, dass zu entscheiden ob ein solches Konzept existiert, ExpTime-vollständig ist und dass ein Algorithmus, der ein solches Konzept lernt doppelt exponentielle Laufzeit haben muss. Wenn man das zu lernende Konzept auf eine bestimmte Rollentiefe beschränkt, zeigen wir NP-Vollständigkeit des Entscheidungsproblems und geben einen Exponentialzeit-Algorithmus, der das Konzept lernt, an. In this thesis, we look at the concept-by-example problem in EL knowledge bases. The problem consists of automatically learning an EL concept that describes user provided positive examples, but does not describe user provided negative examples with regard to a given EL knowledge base. Concept learning supports the user during knowledge base engineering and concept query writing, and is actively pursued with multiple approaches. We show that deciding, if such a concept exists, is ExpTime-complete and that an algorithm that learns such a concept must take double exponential time in the worst case. If one restricts the concept that is learned to a fixed role depth, we show that the decision problem is only NP-complete and give an exponential time algorithm to learn it.
Formal Software Development of Android Games

We present a formal software development methodology for implementing Android video games. We use formal methods embedded within a Model-View-Controller (MVC) design pattern for software development. The Model of the game is written in Event-B, which is then translated to JML (Java Modeling Language) by using the EventB2Jml tool. A Java code implementation is manually written that closely follows the JML specification. Writing a formal model in EventB avoids the costly testing phase in traditional incremental software development methodologies for games. We validate our ideas on the proposed methodology by presenting the software development of a car racing game for the Android platform. Our methodology incorporates rigour in a number of ways. All the proof-obligations of the Event-B model are discharged prior to the generation of JML specifications, and we use OpenJML to verify the Java implementation against the JML specification obtained from Event-B.
1 Deciding Memory Safety for Forest Datastructures

Memory safety is the problem of determining if a heap manipulating program that allocates/frees memory locations and manipulates heap pointers, does not dereference a memory location that is not allocated. Memory safety errors are serious security vulnerabilities that can be exploited systematically to attack systems. In this paper we consider the problem of checking if a program, whose initial allocated heap forms a forest structure (i.e., a disjoint set of trees and lists), is memory safe. While the problem of checking memory safety of programs whose initial heap is a forest structure is undecidable, we identify a class of caching programs for which the problem of checking memory safety is decidable. Our experimental evaluation demonstrates that common library routines that manipulate forest data-structures using a single pass are almost always caching. We show that our decision procedure for such programs is effective in both proving memory safety and in identifying memory safety vulnerabilities.
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks

Recent studies identify that Deep learning Neural Networks (DNNs) are vulnerable to subtle perturbations, which are not perceptible to human visual system but can fool the DNN models and lead to wrong outputs. A class of adversarial attack network algorithms has been proposed to generate robust physical perturbations under different circumstance. These algorithms are the first efforts to move forward secure deep learning by providing an avenue to train future defense networks, however, the intrinsic complexity of them prevents their broader usage.In this paper, we propose the first hardware accelerator for adversarial attacks based on memristor crossbar arrays. Our design significantly improves the throughput of a visual adversarial perturbation system, which can further improve the robustness and security of future deep learning systems. Based on the algorithm uniqueness, we propose four implementations for the adversarial attack accelerator (A3) to improve the throughput, energy efficiency, and computational efficiency.
Differential expression of biochemicals with reference to the degree of juvenility in C asuarina equisetifolia

Introduction Casuarinas are widely planted in the tropics, subtropics and Mediterranean countries because of t heir ready adaptability to a variety of environmental conditions and also for their rapid growth performa nce. Among the 96 species of trees and shrubs in the fam ily Casuarinaceae (Turnbull, 1990), Casuarina equisetifolia L. has gained much attention due to its multiple en duses. India is the largest Casuarina growing country in the world. Its amenability to short rotation and a sustained market demand as scaffolding in the build ing industry, cheap housing material, banana stakes and excellent fuel wood are keys for its success. It i s an ideal tree for agro forestry practices. Casuarinas have also gained importance as a major pulpwood species. Propagation by seeds is the major practice this species. However, the seedling raised plantations s how considerable variations resulting in unpredictable yield. Though cloning can easily be carried out in Casuarina, there exists variability in rooting with respect to the degree of juvenility of the material. Venkataraman an et al. (1998) reported a decreasing trend in rooting ability as cuttings were collected progressively from lower to upper parts of the tree. The juvenile material responded fully to rooting whereas in matu re tissues, the rooting response was reduced but not completely suppressed. An experiment was carried o ut at the Institute of Forest Genetics and Tree Breedi ng, Coimbatore to understand the difference in tissue characteristics between the juvenile and adult mate rials Abstract Biochemical studies including total phenol content , peroxidase activity, total chlorophylls, total crude protein c ontent and DNA content were carried out to understand the tissue characteristic s between juvenile and adult tissues of Casuarina equisetifolia. Phylloclad cuttings collected from four different positions from lower (juvenile) to upper (mature) parts of 9 year old male, female and monoecious trees were subjected to the analyses at the Institute of Forest Genetics and Tree Breeding, Coi mbatore. Total phenol content and peroxidase activity exhibited an increa sing trend when tissues from lower to upper positions were examined (7.43 t o 14.30 mg g -1 and 27.35 to 39.31 enzyme units (mg protein) -1 respectively) whereas, chlorophylls, total crude proteins and DNA content recorded a decreasin g tre d (3.95 to 2.85 mg g, 37.05 to 33.99 mg g -1 and 354.09 to 292.59 μg g -1 respectively). They could be used as biochemical indicators of juvenili ty. Degree of juvenility plays an important role in successful clonal propag ation of this species.