Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of M-PIRO system text output

ii Declaration I hereby declare that this MSc dissertation is of my own composition and that it contains no material previous submitted for the award of any other degree. The word reported in this MSc Dissertation has been executed by myself, except where due acknowledgement is made in the text. iii Acknowledgements I desire to express my gratitude to all those people without whom this task would have been much harder and difficult to accomplish. Above all, tha nk you Amy and Colin, for being so patient, helpful, supportive and cooperative , and for being there every time I need you. You make many things clear and easy with your useful and clever advice. It was said that the beginning and the end of a dissertatio n is the supervisors. Many thanks to all these people who participated in both experiments. Without their participation the evaluation would be impossible to be done. I appreciate the time you spent for my experiment. And special thanks to my classmate, Behzad whom I own my dissertation's topic and I am grateful for all these useful conversations. Many thanks to Ellen Burk for guiding me out of the statistics labyrinth. care and support throughout the whole year. Additionally, special thanks to Alexander Melengoglou, who offered his valuable knowledge and comments. I could not be here now without the strong support and love of my family. Thank you Anna and Sotiria for yo ur corrections. iv To my mother, my uncle and specially to my sister, Abstract Half of the problem in Natural Language Generation (NLG) is the evaluation of a NLG system. In the last few decades the research about evaluation has increased and made some serious steps on this direction. This study describes an evaluation of a multilingual personalized information objects system (M-PIRO), which dynamically generates descriptions for exhibits in a virtual museum exhibition. In the evaluation, learning outcomes in-between subjects who read two sets of texts about coins and vessels were compared to those of subjects who read these text sets with different text structure. The aim was to attempt to prove that the text type factors, comparison and aggregation are essential for a better performance. Several types of data were collected by post-session tests of factual recall knowledge and a questionnaire about the evaluated system. Results showed that performance measures did differ between subjects in the two conditions (presence and absence …
The Role of Peers and Friends on Mexican-Origin Female Adolescents ’ Psychological Adjustment

Despite the importance of peers and friends in adolescence, little work has been done that explores the contributions that peers and friends have in Latino adolescents’ lives, specifically on their psychological adjustment. To fill in this gap, the current study examined the contribution that peer stressors had on Mexican-origin female adolescents’ (n = 153) self-esteem and depressive symptomatology and explored the moderating role that the quality of the relationship with friends and best friends played in shaping this association. Results from hierarchical regressions indicated that more peer stress was significantly associated with lower levels of selfesteem and greater levels of depressive symptoms. Further, friendship support moderated the association between peer stressors and self-esteem such that this association was only significant among adolescents reporting low friendship support. Finally, contrary to expectations, relationship quality with best friend was neither a direct predictor of adjustment or moderated any associations. This study stresses the importance of supportive friendships in adolescent psychological adjustment against peer stressors. Introduction Latinos are the largest minority group in the United States (U.S. Census Bureau, 2000), with a large percentage of this population being children and youth (U.S. Census Bureau). Latino adolescents, along with other ethnic minority youth, report psychological adjustment issues such as depressed mood more so than White adolescents (Gore & Aseltine, 2003), and Latina adolescents show significantly higher levels of depression than their White, Black, and Asian counterparts (Siegel, Aneshensel, Taub, Cantwell, & Driscoll, 1998). As a result, understanding the psychological adjustment of Latino adolescent girls is extremely relevant considering the scarcity of knowledge and needs on this population. Depressive symptoms and self-esteem are two important components of psychological adjustment (Feldman, Rubenstein, & Rubin, 1988; Turner, Finkelhor, & Ormrod, 2009) that appear to be affected by adolescents’ stressful experiences with peers. For instance, adolescents
Transcribing broadcast news with the 1997 Abbot System

Previous DARPA CSR evaluations have focused on the transcription of broadcast news from both television and radio programmes. This is a challenging task because the data includes a variety of speaking styles and channel conditions. This paper describes the development of a connectionist-hidden Markov model (HMM) system, and the enhancements designed to improve the performance on broadcast news data. Both multilayer perceptron (MLP) and recurrent neural network acoustic models have been investigated. We asses the effect of using gender-dependent acoustic models, and the impact on the performance of varying both the number of parameters and the amount of training data used for acoustic modelling. The use of a context-dependent phone models is described, and the effect of the number of context classes is investigated. We also describe a method for incorporating syllable boundary information during search. Results are reported on the 1997 DARPA Hub-4 development test set.
Agricultural Productivity , Carbon Dioxide Emission And Nuclear Energy Consumption In Pakistan : An Econometric Analysis

The main principle of this study is to observe the association among carbon dioxide emission (CDE), nuclear energy consumption (NEC) and agricultural productivity (AGP) in the case of Pakistan. The carbon dioxide (CO2) emission, nuclear energy consumption (NEC), and the agricultural productivity (AGP) are used as the environmental indicator, energy consumption indicator, and economic indicator respectively. The Augmented dickey Fuller Unit Root test, Johansen Co-integration, VAR and Granger causality tests are used to make analysis of data from 1971 to 2016. The findings of Augmented Dicky Fuller and Phillip and Perron test indicated that series are integrated at order I (1). The long-run relationship was determined by applying Johansen cointegration test. The results show that there is no long run association among these three variables. VAR estimation and the impulse response function show that there is significant short run relationship among CO2 emission, and agricultural productivity. The CO2 emission has negative effect on agricultural productivity in short run in second lag and it is significant, while nuclear energy has no effect on agricultural productivity. The Granger causality test shows bidirectional causality between CO2 emissions, nuclear energy consumption and agricultural productivity. Further, the variance decomposition shows that CO2 emission contributed about 83.28 percent and nuclear energy consumption 83.78 percent to the variation in AGP. The empirical findings will help the policy makers in understanding the impact of the CO2 emissions therefore; they suggest adopting carbon tax policy and cleaning power plan for reduction of carbon dioxide emission.
IoT 1 AlignmentAsynchronized measurement IoT n IoT Devices Intermittent RF Traffic Reconstruction Noise Floor Measurements Alignment Asynchronized measurement Signal Source Distinguishing Server Figure 2

ABSTRACT The exponentially increasing number of Internet-of-Thing (IoT) devices introduces a spectrum crisis in the shared ISM band. However, it also introduces opportunities for conducting radio frequency (RF) sensing using pervasively available signals generated by heterogeneous IoT devices. In this paper, we explore how to leverage the ambient wireless traffic that i) generated by uncontrollable IoT devices and ii) sensed by ambient noise floor measurements (a widely available metric in IoT devices) for human gesture recognition. Specifically, we introduce our system EAR, which can conduct fine-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources. We conducted extensive evaluations in both residential and academic buildings. Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%. Moreover, EAR can recognize fine-grained human gestures with high accuracy even under extremely low traffic rate (i.e., 4%) from uncontrollable ambient signal sources.
Assessing the role of matching bias in reasoning with disjunctions

On mental models theories, reasoners create mental representations of information which they manipulate in order to derive new conclusions. These theories have been uniquely successful at explaining a class of attractive fallacies involving disjunctions. In this article we examine a crucial ingredient of mental models accounts of these illusions, a matching procedure. In three experiments, we show that what is explained in terms of low-level matching or overlap in content in these theories must in fact take place at a higher level of cognition. We introduce variants of illusory inferences from disjunction whose acceptance by participants is accurately predicted by their confidence in causal connections that rely on worldknowledge.
Regional Compliance of Brachial Artery and Saline Infusion in Patientswith Arteriosclerosis Obliterans

Simultaneous brachial artery pressure and blood flow measurements were made in 15 patients with arteriosclerosis of the lower limbs (AOLL) and in controls of the same age and sex. Blood flow was evaluated by a pulsed Doppler device with a doubletransducer probe. From analysis of the pressure-flow curves during diastole, regional arterial compliance (RAC) was determined by using as a model of the forearm arterial tree a system of tubes, each with a storage capacity, in series with the arteriolar resistance vessels. In AOLL patients, RAC was significantly reduced (102 ± 13 vs 173 ± 14-10−4 ml/mm Hg, p < 0.01), and systolic pressure was significantly increased. After saline infusion, systolic pressure continued to increase and arterial compliance, to decrease; brachial blood flow did not change. Study of the baroreflex sensitivity in AOLL patients under basal conditions indicated that a higher pulse pressure was required to obtain the same heart rate as in the controls. The study provided evidence that in AOLL patients: 1) compliance was reduced in the brachial artery (a regional circulation with no clinical evidence of arterial occlusion); 2) an increase in systolic pressure resulted from the decreased arterial compliance; and 3) saline infusion exaggerated the observed reduction in arterial compliance and increase in systolic pressure.
One-Time , Directed and Catalytic Activation of 1-D DNA Tiles

Experimental demonstration of the Turing universal tile based algorithmic DNA self-assembly has been limited by significant assembly errors. An important class of errors, called co-ordinated growth errors, occur when an incorrect tile binds to a growing assembly even when some of its pads are mismatched with its neighbors. Activatable DNA tiles, introduced originally by [12], employ a protection/deprotection strategy to strictly enforce the direction of tiling assembly growth that prevents these errors, ensuring the robustness of the assembly process. Tiles are initially inactive, meaning that each tile’s pads are protected and cannot bind with other tiles. After an activation event, the tile transitions to an active state and its pads are exposed, allowing further growth. In this paper we demonstrate the formation of three different DNA nano-assemblies using activatable tiles. In the first assembly, we use two tiles A and B that have complementary sticky ends such that they can form a linear co-polymeric chain . ABABABAB. Additionally, the sticky ends of A can be rendered inactive (non-sticky) by attachment of a protection strand to tile A. This protection strand can be subsequently removed from tile A (thus activating it) by the polymerization extension of a primer strand bound to the protection strand. Upon the introduction of the polymerase, tile A gets deprotected enabling the formation of the linear co-polymeric chain. The second type of assembly, consists of three tiles A,B,C, each of which is a DNA hairpin. On activation of each tile, the assembly is of the form of a ribbon . ABCABCABC. The stem loop of each tile acts as a protection strand, and constitutes an inactive state of the tile. On activation, the stem loop is open, and can activate another tile. Our third system, is a system which shows catalytic assembly of the hairpin shaped tiles of system 2. These initial results point to the possibility of more complex activatable systems that can alleviate some of the errors encountered in tile based DNA self-assembly.
Volumetric Hierarchical Heavy Hitters

Hierarchical heavy hitters (HHH) identification is useful for various network utilities such as anomaly detection, DDoS mitigation, and traffic analysis. However, the increasing support for jumbo frames enables attackers to overload the system with fewer packets, avoiding detection by packet counting techniques. This paper suggests an efficient algorithm for detecting HHH based on their traffic volume that asymptotically improves the runtime of previous works. We implement our algorithm in Open vSwitch (OVS) and incur a 4−6% overhead compared to a 42% throughput reduction experienced by the state-of-the-art.
Constraints to Growth in Indian Agriculture: Needed Technology, Resource Management and Trade Strategies

At the outset, I would immensely thank the Society for selecting me as President for the 63rd Annual Conference and I think this annual meet provides a greater opportunity to bring together the members of agricultural economics tribe in this country to meet, know each other, take stock of the developments in the profession, motivate the young agricultural economists to actively participate in professional activities and explore the scope to nurture the profession. Indian agriculture is fast changing as a result of new developments in natural resource supplies and demand, emerging economic opportunities due to transformation taking place both within the national border and outside, and to meet the challenges of persistence of socioeconomic problems such as poverty, unemployment, gender bias, worsening income distribution, etc. I take this opportunity, so generously given to me by the members of the Society to place before you, my understanding of the current problems plaguing Indian agriculture. In this address, I am not attempting to propose solutions for the issues, but venture to place before you a collection of wisdom of leading researchers and eminent economists working on the issues of agricultural development in this country. I also, with all humility, make an attempt to throw some light on complex relationships, which exist among the socio-economic and technological variables, which have significant implications for agricultural development and for the welfare of the people who are dependent on agriculture so that this learned and high profile audience can continue to debate, discern out issues and propose framework for designing policies and programmes and for their implementation. I
A recursive approach to network management

Nowadays there is an increasing need for a general management paradigm which can simplify network management and further enable network innovations. In this paper, in response to limitations of current Software Defined Networking (SDN) management solutions, we propose a recursive approach to enterprise network management, where network management is done through managing various Virtual Transport Networks (VTNs). Different from the traditional virtual network model which mainly focuses on routing/tunneling, our VTN provides communication service with explicit Quality-of-Service (QoS) support for applications via transport flows, and it involves all mechanisms (e.g., routing, addressing, error and flow control, resource allocation) needed to support such transport flows. Based on this approach, we design and implement a management layer, which recurses the same VTN-based management mechanism for enterprise network management. Comparing with an SDN-based management approach, our experimental results show that our management layer achieves better network performance.
Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices

Leaf diseases, such as powdery mildew and leaf rust, frequently infect barley plants and severely affect the economic value of malting barley. Early detection of barley diseases would facilitate the timely application of fungicides. In a field experiment, we investigated the performance of fluorescence and reflectance indices on (1) detecting barley disease risks when no fungicide is applied and (2) estimating leaf chlorophyll concentration (LCC). Leaf fluorescence and canopy reflectance were weekly measured by a portable fluorescence sensor and spectroradiometer, respectively. Results showed that vegetation indices recorded at canopy level performed well for the early detection of slightly-diseased plants. The combined reflectance index, MCARI/TCARI, yielded the best discrimination between healthy and diseased plants across seven barley varieties. The blue to far-red fluorescence ratio (BFRR_UV) and OSAVI were the best fluorescence and reflectance indices for estimating LCC, respectively, yielding R 2 of 0.72 and 0.79. Partial
Performance Comparison between Sliding Mode Control and Active Force Control for A Nonlinear Anti-Lock Brake System

The Anti-lock braking system (ABS) is the most common safety system in the vehicles, which works to increase the generating braking force between the tire and the road surface in way to keep the stability in vehicles during braking. The high nonlinearity due to the interaction between the tire and road, the uncertainties in vehicle dynamics always exist and using the standard PID controller will not be accurate enough to the extent required to deal with the uncertainties. Two robust control techniques, the sliding mode control (SMC) and an intelligent active force control (AFC) that both of them deal with the dynamic systems to provide durability and quell the unrest that may found in the system. In this paper highlights the ability of this proposed technique to improve the dynamic system robustly by combining them with standard PID in one control scheme (PID-AFC) comparing with (PID-SMC). Results showed the robustness of AFC and solo performance to improving the slip track without chattering phenomenon and thus reduce the stopping distance of the vehicle compared to other control schemes.
A General Variance-based Outlier Mining Model

To solve the outlier mining problems where the outliers are highly intermixed with normal data, a general Variance-based Outlier Mining Model (VOMM) is presented, in which the information of data is decomposed into normal and abnormal components according to their variances. With minimal loss of normal information in our model, outliers are viewed as the top k samples holding maximal abnormal information in a dataset. And then, the principal curve that is a smooth nonparametric one-dimensional curve passing through the “middle” of the dataset and that provides a good nonlinear summary of the data is introduced as an algorithm of our model. Being a general approach to outlier mining, our proposed VOMM is proved to be feasible by experiments carried out on abnormal returns detection in stock market. Additionally, the model is potentially applicable to many other domains such as credit cards, telecommunication, medicine and traffic etc.
Volatility Linkages between Agricultural Commodity Prices, Oil Prices and Real USD Exchange Rate

This study examines the dynamic nexus betwixt oil prices, twenty-two world agricultural commodity prices and given the evolution of the relative strength of the US dollar in a panel setting. We use panel cointegration and Panel Granger causality methods for a panel of twenty-two agricultural products based on annual observations ranging from 1980 to 2015. The empirical results provide a strong evidence of long-term relationship between Agricultural Commodity Prices, Oil Prices and Real USD Exchange Rate. Contrary to the findings of many studies in the literature that report neutrality of agricultural prices to oil price changes, we find strong support of bi-directional causal linkages among Agricultural Commodity Prices, Oil Prices and Real USD Exchange Rate. The long-run causality analysis thereby implies that the oil prices and the dollar have a predictive power to forecast the agricultural prices, which could be a good tool to prioritize the allocation of resources across industries to ensure agricultural scenario in general and economic outcomes.
The α-Dendrotoxin Footprint on a Mammalian Potassium Channel (*)

α-Dendrotoxin, a 59-amino acid basic peptide from the venom of Dendroaspis angusticeps (green mamba snake), potently blocks some but not all voltage-dependent potassium channels. Here we have investigated the relative contribution of the individual α-subunits constituting functional Kv1.1 potassium channels to α-dendrotoxin binding. Three residues critical for α-dendrotoxin binding and located in the loop between domains S5 and S6 were mutated (A352P, E353S, and Y379H), and multimeric cDNAs were constructed encoding homo- and heterotetrameric channels composed of all possible ratios of wild-type and mutant α-subunits. Complete mutant channels were about 200-fold less sensitive for the α-dendrotoxin block than complete wild-type channels, which is attributable to a smaller association rate. Analysis of the bimolecular reaction between α-dendrotoxin and the different homo- and heteromeric channel constructs revealed that the association rate depends on the number of wild-type α-subunits in the functional channel. Furthermore, we observed a linear relationship between the number of wild-type α-subunits in functional channels and the free energy for α-dendrotoxin binding, providing evidence that all four α-subunits must interact with α-dendrotoxin to produce a high affinity binding site.
Tracking Algorithms for Multiagent Systems

This paper is devoted to the consensus tracking issue on multiagent systems. Instead of enabling the networked agents to reach an agreement asymptotically as the time tends to infinity, the consensus tracking between agents is considered to be derived on a finite time interval as accurately as possible. We thus propose a learning algorithm with a gain operator to be determined. If the gain operator is designed in the form of a polynomial expression, a necessary and sufficient condition is obtained for the networked agents to accomplish the consensus tracking objective, regardless of the relative degree of the system model of agents. Moreover, the H∞ analysis approach is introduced to help establish conditions in terms of linear matrix inequalities (LMIs) such that the resulting processes of the presented learning algorithm can be guaranteed to monotonically converge in an iterative manner. The established LMI conditions can also enable the iterative learning processes to converge with an exponentially fast speed. In addition, we extend the learning algorithm to address the relative formation problem for multiagent systems. Numerical simulations are performed to demonstrate the effectiveness of learning algorithms in achieving both consensus tracking and relative formation objectives for the networked agents.