Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensembles of Neural Morphological Inflection Models

We investigate different ensemble learning techniques for neural morphological inflection using bidirectional LSTM encoder-decoder models with attention. We experiment with weighted and unweighted majority voting and bagging. We find that all investigated ensemble methods lead to improved accuracy over a baseline of a single model. However, contrary to expectation based on earlier work by Najafi et al. (2018) and Silfverberg et al. (2017), weighting does not deliver clear benefits. Bagging was found to underperform plain voting ensembles in general.
The Role of Youth in Political Participation in Somalia

Opportunities for engaging political participation are a leading way to reach an inclusive politics in which citizens can participate in the public decision-making process. PoliticalParticipationis a crucial part of democracy. In principle, it is a constructive way those members who participated in the politics should be able to further educated a democracy and the political leadership. However, the understanding and perceptions of young people in the participation of politics have remained disillusion in many countries around the world. This makes it indispensable to examine the exclusion of young people in political participation. The study used a consuming theory to study and explain the research questions. The empirical research design of the study is qualitative oriented and data was collected through semi-structural interview.Based on the observation of the study there is strong evidence to suggest that Somali youths had a particular interest in political participation. However, this interest was confined space by the influence of clan identity politics in Somalia. As claimed youths, Political leaders have come to power through purporting to represent the wishes of their clan‟s instead of promoting to the national interest. Despite the fact, young people were articulated that the adoption of the multiparty system across Somalia are crucial and abolish the contemporary institutionalized clan-based politics in the country.
Combining Decision Tree Predictions under Consideration of Uncertainty

The random forest algorithm is a popular classification method in machine learning. Thereby, the prediction of this ensemble model is made by combining decision tree predictions within the forest. Those predictions are made based on the instance numbers and distributions of the respective leafs. Therefore, if those leafs are small (i. e. there are only a few number of instances in that leaf), the prediction might be uncertain and false. This seems to be increasingly problematic for smaller leafs when using the probability as the prediction methods. The purpose of this bachelor thesis is examining existing and introducing new prediction methods which can be used instead of probability and which consider the uncertainty of the leafs based on the number of instances used for the prediction. The evaluation of those methods showed that probability gives bad estimations on single decision trees. However, none of the tested methods managed to achieve significantly better results than the probability in random forest classification. The experiments in this thesis indicate that random forest are not as affected by the uncertainty of small leafs as single decision trees, although it was shown that large trees (i. e. small leafs) on average perform better than smaller trees, when used in random forests.
Modelling of diaphragm motion for simulation of liver access Surgery and Anaesthesia

Purpose To generate a realistic three-dimensional model of human diaphragm motion, using CT and MR data, for incorporation into virtual reality simulators. Background Liver surgery and radiotherapy, for which targeting positions is crucial, are often complicated by displacement secondary to breathing. This may hinder accurate instrument placement during minimally invasive surgery or compromise the therapeutic dose achieved in tumour tissue. Since the diaphragm is the major determinant of respiratory motion, it is important to study its activity during the respiratory cycle. Incorporation of diaphragm motion models into virtual reality simulators will enhance the accuracy of tumour targeting and liver access simulation, and aid surgical education. Methods and Materials A literature review was performed to establish segmentation criteria for the diaphragm. 4D CT data, displaying ten breathing phases of the respiratory cycle, was sourced retrospectively from two different patients undergoing lung radiotherapy. Additional MR data, consisting of three breathing phases, was obtained from a healthy volunteer. The diaphragm was manually segmented and a 3D mesh generated, for two phases of the 4D CT data and for three phases of the MR data, using Insight SnAPTM software. Non-rigid registration of the initial to final breathing phases was performed using Insight Segmentation and Registration ToolkitTM (ITK) software. The geometric differences obtained from non-rigid registration were compared using MESHTM software. Finally, standard algorithms are applied to the segmented data sets to produce smooth 3D reconstructions of the diaphragms which were used in a virtual reality simulator. Results We present a 3D model of diaphragm motion, which considers physiological boundary conditions, rib and sternum kinematics, and the muscular and tendinous aspects of the diaphragm. Conclusion The validation results suggest that the diaphragm model proposed closely resembles the behaviour of the human diaphragm. It may be integrated into virtual reality simulators of liver access procedures or into radiotherapy treatment planning software.
A STUDY ON CUSTOMER SATISFACTION OF VALUE ADDED SERVICES OFFERED BY SELECTED PUBLIC SECTOR BANKS IN PUNE CITY

Banking industry is the blood vascular system of our economy. It has a positive role to play in the economic development of the country as repositories of people’s savings and purveyors of credit, especially as the success of economic development depends on the mobilization of resources and their investment in an appropriate manner. Banking sector is the back bone of any financial system of the economy. Commercial banks play an important role in the development of developing economies by mobilization of resources and their better allocation. The rapid growth of banks, especially since nationalization of major commercial banks in 1969, brought in both quantitative and qualitative changes in their functioning and also approach towards socio-economic development of the country.
THE EFFECT OF WORK ENGAGEMENT AND ORGANIZATIONAL CLIMATE ON JOB SATISFACTION IN A NONPROFIT ORGANIZATION

The concept that work engagement facilitates the organizational climate is recognized widely, yet it has not been widely tested in nonprofit organization. To fill the gap between concept and practice, this study adopted the three-dimensional model of work engagement (vigor, dedication, and absorption) proposed by Salanova and colleagues (2005) with the aim of investigating the relationship between work engagement and job satisfaction mediated by organizational climate in a nonprofit organization, i.e., an educational institution. This study surveyed 99 workers. The effect of work engagement and organizational climate was explored. Furthermore, the mediating effect of organizational climate on job satisfaction was also examined. The study employing hierarchical linear modeling (HLM). The statistical analysis indicated that up to 66 percent variance in organizational climate could potentially be explained by job satisfaction. The three dimensions studied namely; vigor, dedication, and absorption were positively related and significant to organizational climate. However, the results showed the mediating role of organizational climate on the prediction of job satisfaction and work engagement of workers in a nonprofit organization. The results obtained were dissimilar to those found in prior studies. This result shed new insights on work engagement and organizational climate in nonprofit organizations. The implications of the study are discussed, together with limitations and suggestions are made for future research.
Encrypted Distributed Hash Tables

Distributed hash tables (DHT) are a fundamental building block in the design of distributed systems with applications ranging from content distribution networks to off-chain storage networks for blockchains and smart contracts. When DHTs are used to store sensitive information, system designers use end-to-end encryption in order to guarantee the confidentiality of their data. A prominent example is Ethereum’s off-chain network Swarm. In this work, we initiate the study of end-to-end encryption in DHTs and the many systems they support. We introduce the notion of an encrypted DHT and provide simulation-based security definitions that capture the security properties one would desire from such a system. Using our definitions, we then analyze the security of a standard approach to storing encrypted data in DHTs. Interestingly, we show that this “standard scheme” leaks information probabilistically , where the probability is a function of how well the underlying DHT load balances its data. We also show that, in order to be securely used with the standard scheme, a DHT needs to satisfy a form of equivocation with respect to its overlay. To show that these properties are indeed achievable in practice, we study the balancing properties of the Chord DHT—arguably one of the most influential DHT—and show that it is equivocable with respect to its overlay in the random oracle model. Finally, we consider the problem of encrypted DHTs in the context of transient networks, where nodes are allowed to leave and join. execute DHT . Overlay , DHT . Alloc , DHT . FrontEnd and DHT . to and output the paramters ω , ψ and ϕ . The Put algorithm takes as input the secret key K and a label/value pair ( ℓ, v ). It first computes t := F K 1 ( ℓ ) and e ← Enc ( K 2 , v ) and then executes DHT . Put ( t, e ). The Get algorithm takes as input the secret key K and a label ℓ . It computes t := F K 1 ( ℓ ) and executes e ← DHT . Get ( t ). It then outputs SKE . Dec ( K, e ).
Survey of various road detection and extraction techniques for autonomous vehicles 1

Identification of the safe usable road is a crucial task for autonomous vehicles and robots. It is one of the main parameters which help in elevating the level of automation. The autonomous vehicle needs to understand the difference between various types of roads present in various conditions. External factors like lighting effects, shadows, weather conditions, occlusions, and mobile passengers also need to be considered. It helps in avoiding an obstacle, path planning, and decision making. It is useful in conditions where lane markings are not visible, exampleroad covered by snow or due to poor lighting conditions or lane markings not present (in certain rural and urban roads). There are many different ways to overcome these difficult situations using multiple sensors like monocular camera images, stereo images, RADAR sensor, LiDAR sensor, Inertial Measurement Unit (IMU), Global Positioning System (GPS) information and pre-loaded maps. Here we have presented a survey of various road extraction techniques.
Effects of ammonium and nitrate on the growth of vesicular . arbuscular mycorrhizal Erythrina poeppigiana 0 . 1 .

Erythrinapoeppigiana, a woody tropical plant, was inoculated with vesicular-arbuscular mycorrhizal (VAM) fungi Glomus etunicatum Becker and Gerdeman, G. mosseae Nicol. and Gerd. Gerdeman and Trappe, or G. intraradices Schenk and Smith. Growth, N uptake, and nutrition were evaluated in VAM-inoculated plants and controls fertilized with two levels (3 or 6 raM) of either NH~-N or NOf-N. The response by the mycorrhizal plants to N fertilization, according to N source and/or level differed significantly from that of the control plants. In general, the growth of the mycorrhizal plants was similar to that of the non-mycorrhizal plants when N was provided as NH~. When the N source was NOr the control plants grew significantly less than the VAM plants. Inoculation with VAM fungi gave yield increases of 255 and 268% for G. etunicatum-colonized plants, 201 and 164% for G. mosseae-colonized plants and 286 and 218% for G. intraradices-colonized plants fertilized with 3 and 6 mM NO3-N, respectively. The increased growth and acquisition of nutrients by plants fertilized with NO3-N and inoculated with VAM shows that VAM mycelium has a capacity for NO 3 absorption. The results also showed that E. poeppigiana seedlings preferred NH~ as an N source. G. etunicatum was the most effective endophyte, not only increasing N, P, Ca, Mg, and Zn uptake in the presence of NO~ fertilizer but also P and Mg in the presence of NH~ applications. From these results we conclude that VAM symbiosis affects N metabolism in E. poeppigiana plants and that this species can overcome limitations on the use of NO~--N by the mediation of VAM fungi. G. Cuenca (~) Centro de Ecologia y Ciencias Ambientales (IVIC), Apartado 21827, Caracas 1020 A, Venezuela R. Azc6n Departamento de Microbiologia. Estaci6n Experimental del Zaidfn (CSIC), Apartado 419, E-18008 Granada, Spain
Clinical Study Developing Interventions for Frailty

Frailty is a well-recognised, complex syndrome, predominantly affecting older people. Currently, there are two main conceptualizations of frailty: the phenotypic and the accumulated deficit models, with the phenotypic model being more widely applied in clinical research. Based on these models, we contend that a number of the phenotypic frailty components (weakness, slowness, and low energy expenditure) are potentially reversible. This paper outlines the results of a frailty research program. It summarizes the initial frailty treatment model and reports its effectiveness. Briefly, the effect of a twelve-month individually tailored multifactorial, interdisciplinary intervention targeting frailty was compared with usual care. The intervention reduced phenotypically defined frailty and improved mobility. Further, it is contended that there is evidence of substantial unmet need due to treatment fragmentation and the absence of a comprehensive approach for this population. Our paper then outlines the current stage of our research in which the model is now being extended to prefrailty. Interventions aimed at reversing the frailty syndrome or its consequences are only in their early stages.There is significant scope for future research to identify optimal management programs for frail older people.
ON IMAGE RETARGETING TECHNIQUES

In this digital era, there is rapid growth of mobile technologies and web page layout so, there is need to resize the media content to adapt the preferred display sizefor various layouts of web pages and small size of portable devices such as tab or mobile. Image retargeting method is used to display the original image without changing the important information. It also maintains important and visual major features. There are three main objectives; first preserving important information of original image, secondlimiting visual object in resulting image, third preserving internal structure of original image. Two methods used for semantic preserving deep image retargeting are; first, extracting semantic components which is further used to preserve semantic of the source image. Each semantic component is represented by importance map of source image that preserved in target image and second, the semantic collage method is fed into the carrier to generate the target image and this target image will fit into aspect resolution.
Unattended baggage detection using deep neural networks

As the world becomes ever more attuned to potential security threats, the need for sophisticated surveillance system is increasing to monitor and detect any potential threats. Sophisticated surveillance system should functions as an intuitive “robotic eye” for accurate and real-time detection of threats. Unattended baggage has become a critical need for security personnel at airports, stations, malls, and in other public or crowded areas. However, an effective system for detection of objects like baggage and people with a real-time video input requires high processing power and storage to just process the video frames using the typical digital image processing technique. This will require a very high development cost and time in order to make the system work which is impractical for commercial use. Moreover, manual configuration is needed which is not flexible to be for multiple application. Therefore, the objective of this thesis is to improve the object detection accuracy and flexibility compared to existing digital image processing techniques. This proposed system uses deep neural networks approach through collection of datasets thus providing a more accurate detection and flexible application. Tensorflow framework is used as the deep neural network framework for the development of this system. This system utilizes the Single Shot multibox Detection detection algorithm to the ’MobileNet’ neural network architecture which is optimized to provide a promising performance even in embedded system. This project is developed by implementing the Tensorflow Object Detection Application Programming Interface (API). This method enables 4 main classes of detection which are suitcase, backpack, handbag and person. The datasets used for benchmarking are surveillance video sample that contain unattended baggage scenario used by most existing works like AVSS2007, PETS2006 and ABODA. The overall accuracy and flexibility of the proposed system improved up to 43% thus unattended baggage is able to be detected. The system is able to be applied in various environment due to the excellent flexibility of the system.
Improving Collection Understanding in Web Archives

Ever since the Internet Archive started large-scale web archiving in 1996, historians, sociologists, and journalists have found web archives to be an important source of information for their work. Archive-It, a service focused on creating collections, allows curators to generate their own web archive collections. Many of these collections are vast, consisting of thousands of documents. This makes collection understanding a difficult, if not impossible, task. We seek to improve collection understanding by summarizing these collections and visualizing the summaries. Focusing on Archive-It, we seek to identify the different types of web archive collections, the algorithms that can be used summarize those collections, and the best visualizations of those summaries to support better collection understanding. ACM Reference Format: ShawnM. Jones. 2018. Improving Collection Understanding inWebArchives: Extended Abstract for the JCDL 2018 PhD Consortium. In Proceedings of ACM-IEEE Joint Conference on Digital Libraries Doctoral Consortium (JCDL’18). ACM, New York, NY, USA, 9 pages.
Informal Laboratory Practices in Psychology

Method sections in psychology articles differ in the amount of information they provide, or the level of specificity at which they do so. This can make incremental research (e.g., replication efforts) difficult, because potentially relevant methodological decisions or practices may not be reported. As yet, these unwritten practices have not been systematically studied; the current work represents a first attempt in this direction. For this paper, we interviewed 22 experimental psychologists in the Netherlands to find out about these unwritten aspects of experimenting, as well as their opinions and beliefs regarding these practices. Thematic analysis of the transcripts suggests that (a) experimental psychologists indeed employ a variety of methods that they consider relevant yet do not routinely report in method sections, and (b) these unwritten practices seem to fall into two broad categories: ‘professionalism’ and ‘the production of good data’. We discuss implications for psychological research generally, and the replication debate more specifically.
A Semantically Enriched Recommendation and Visualization Approach for Academic Literature

Researchers must be aware of the current developments and novel findings in their field. To gain an overview of relevant prior work and ongoing research, academics must review the published literature. However, the search for related academic literature is tedious. Furthermore, researchers can easily oversee potentially valuable information in today’s increasing volume of academic literature. While academic search and recommendation engines have greatly simplified the information acquisition process, current recommendation approaches are not adequately considering specialized semantic similarity measures, such as citation-based similarity, mathematical formulae-based similarity, or image-based similarity to recommend and visualize academic literature. This paper proposes to take into account combinations of semantic features that have previously not been considered for the use case of literature recommendation. Additionally, supporting researchers in the sense-making of semantic similarities present in recommended literature remains a largely unsupported task. Researchers must review the content of each academic paper, manually identify or compare the sections that are of interest to them, and then arrive at a judgment regarding the relevance of the recommendation. We propose a supportive process that allows researchers to more quickly compare academic literature with regard to the user-selected semantic features that are of interest to them. Such user-selected features can be the citations to other literature, the contained mathematical formulae, graphs, or figures, i.e. a range of semantic features that go beyond pure text-based similarity. The proposed semantically-enriched literature recommendation and visualization concept could help researchers more quickly identify the specific content that is of interest to them within a large set of recommended literature.
Social Security Aided D 2 D Communications : Performance Bound and Implementation Mechanism

In a device-to-device (D2D) communications underlaying cellular network, any user is a potential eavesdropper for the transmissions of others that occupy the same spectrum. Physical-layer security mechanism is typically employed to guarantee secure communications, at the cost of reducing the system’s throughput. As hand-held devices are carried by human beings, we may leverage their social trust to decrease the number of potential eavesdroppers. Aiming to establish a new paradigm for solving the challenging problem of security and efficiency tradeoff, we propose a social security aware D2D communication architecture that exploits social-domain trust for securing physical-domain communication. In order to understand the impact of social trust on the security of transmissions, we analyze the system ergodic rate of social security aided communications via stochastic geometry, and our result based on a real dataset shows that the proposed social security aided D2D communication increases the system secrecy rate by about 63% compared to the scheme without considering social trust relation. Furthermore, in order to provide implementation mechanism, we utilize matching theory to implement efficient resource allocation among multiple users. Numerical results show that our proposed mechanism increases the system secrecy rate by 28% with fast convergence over the social oblivious approach.
A Syntactically Expressive Morphological Analyzer for Turkish

We present a broad coverage model of Turkish morphology and an open-source morphological analyzer that implements it. The model captures intricacies of Turkish morphology-syntax interface, thus could be used as a baseline that guides language model development. It introduces a novel fine part-of-speech tagset, a fine-grained affix inventory and represents morphotactics without zero-derivations. The morphological analyzer is freely available. It consists of modular reusable components of human-annotated gold standard lexicons, implements Turkish morphotactics as finite-state transducers using OpenFst and morphophonemic processes as Thrax grammars.