Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the Human Values behind Arguments

This paper studies the (often implicit) human values behind natural language arguments, such as to have freedom of thought or to be broadminded. Values are commonly accepted answers to why some option is desirable in the ethical sense and are thus essential both in real-world argumentation and theoretical argumentation frameworks. However, their large variety has been a major obstacle to modeling them in argument mining. To overcome this obstacle, we contribute an operationalization of human values, namely a multi-level taxonomy with 54 values that is in line with psychological research. Moreover, we provide a dataset of 5270 arguments from four geographical cultures, manually annotated for human values. First experiments with the automatic classification of human values are promising, with F_1-scores up to 0.81 and 0.25 on average.
Determination of Bioactive Compounds of Boerhavia diffusa Linn. Leaf Using IR Irradiation

Plants possess a lot of beneficial effects in traditional systems of medicine. Boerhavia diffusa Linn. , a potential medicinal plant used in the system of Ayurvedha, belongs to the family Nyctaginaceae. The bioactive compounds, Alkane, CO 2 , Alkene, Sulfate, phenol, Alkyl aryl ether and Alkyl halide are found to be present in the leaves of B. diffusa through IR irradiation studies. The comparison of FTIR spectrum of raw plant leaf with aqueous extract and ethanol extract in the combined form shows that the effect of B. diffusa is same in any extract form since the bioactive components determined are identical in all the three studies. The possible medicinal values of the active groups detected are discussed in the context of pharmaceutical interest.
An unsupervised clustering method for assessing the degradation state of cutting tools used in the packaging industry

The objective of the present work is to develop a method for the identification of the degradation state of cutting tools (knives) used in the packaging industry. The main difficulties to be addressed are that i) only measurements of a physical quantity indirectly related to the knives degradation are available and ii) only the beginning and the end of operation of the knives are known, whereas no information is available on the component degradation state during its operation life. A method to identify the component degradation state is here proposed. First the general setting for extracting health indicators to measure the amount of knife degradation from a set of signals measured during operation is discussed. Then, an optimal subset of health indicators is selected based on monotonicity and trendability indexes. Finally, the optimal subset of health indicators is fed to a Fuzzy C-Means (FCM) clustering algorithm, which allows assessing the knife degradation state. The application of the proposed method to real condition monitoring knife data is shown to lead to satisfactory results. S2) Selection among the set of extracted features of an optimal subset of Health Indicators (HIs) for the identification of the component degradation; S3) Unsupervised clustering of HI data in order to identify the component degradation state. The main novelty of our work lies in the strategy to select the optimal subset of HIs based on monotonicity and trendability indexes and in its application to knives used in the packaging industry. The paper is organized as follows: in Section 2, the general setting for HI development is proposed; in Section 3, Fuzzy C-Means (FCM) clustering for identifying the knife degradation state is discussed. The application of the methodology to Tetra Pak® A3/Flex filling data is described in Section 4, whereas in Section 5 conclusions are drawn.
ANALYSIS OF LONG TERM SUGARCANE CULTIVATION IN TAMILNADU

: The work presented in this paper determines the forecast of the cultivation of Sugarcane which is the major cash crop of Tamil Nadu. This paper work demonstrates how the data for last decades could be used to predict future demand to cultivate the sugarcane. The best fitted model corresponds to the ARIMA(Auto Regressive Integrated Moving Average) with seasonal effect. The major problem of sugarcane in Tamil Nadu is based on monsoon and water supply. This study analyses the production and reasons for the changes in cultivation of sugarcane.The performance is evaluated by the statistical Package for the Social Science (SPSS) These results will predict the area to be cultivated for the sugarcane the next ten decades with the best fit for ARIMA Methods.
Virtual Reality Observations: Using Virtual Reality to Augment Lab-Based Shoulder Surfing Research

Given the difficulties of studying the shoulder surfing resistance of authentication systems in a live setting, researchers often ask study participants to shoulder surf authentications by watching two-dimensional (2D) video recordings of a user authenticating. How-ever, these video recordings do not provide participants with a realistic shoulder surfing experience, creating uncertainty in the value and validity of lab-based shoulder surfing experiments. In this work, we exploit the unique characteristics of virtual reality (VR) and study the use of non-immersive/immersive VR recordings for shoulder surfing research. We conducted a user study (N=18) to explore the strengths and weaknesses of such a VR-based shoulder surfing research approach. Our results suggest that immersive VR observations result in a more realistic shoulder surfing experience, in a significantly higher sense of being part of the authentication environment, in a greater feeling of spatial presence, and in a higher level of involvement than 2D video observations without impacting participants’ observation performance. This suggests that studying shoulder surfing in VR is advantageous in many ways compared to currently used approaches, e.g., participants can freely choose their observation angle rather than being limited to a fixed observation angle as done in current methods. We discuss the strengths and weaknesses of using VR for shoulder surfing research and conclude with four recommendations to help researchers decide when (and when not) to employ VR for shoulder surfing research in the authentication research domain.
Failure of the Omai Tailings Dam

The Omai gold mine is in the humid tropics of Guyana, a small fonner Britishcolony tucked between Venezuelaand Surinam onthe northeast coast of South America. Omai Gold Mining Ltd. (OGML) started the open-pit mining operation in 1993,processing some 13,000tJdayof gold-bearing oreusing cyanide extractionin a conventionalcarbon-in-pulp process. Finelyground tailings slimes,predominantly minus 200 mesh, and residual cyanide solutions remain. Both the tailings dam to contain these wastes and the mine itself lie on the banks of the Omai River. Only several meters wide, the Omai carries a flow of 4.5 m3/s for a short distance where it joins the Essequibo, one of the principal rivers of South America with a mean annual flow of 2100 m3/s. By mid-1995, the dam was only one meter short of its final planned height, and its operation was proceeding seemingly uneventfully. As late as 4:00 PM on the afternoon of the failure, inspection of the dam crest showed nothing unusual.
Relative Clauses and LSTMs

—One robust phenomenon of English and many other subject-verb-object ordered languages is that people are able to read sentences with subject-extracted relative clauses (SRC) more quickly than sentences with object-extracted relative clauses (ORC). This paper explores how Long Short-Term Memory neural networks may be used to model this difference. Experiments 1 and 2 ﬁnd that the models tend to place a heavier weight on purely statistical patterns, like subjects being followed by verbs, than humans do. Experiment 3 models a previous experiment done in humans
Body Warping Versus Change Blindness Remapping: A Comparison of Two Approaches to Repurposing Haptic Proxies for Virtual Reality

When using tangible props as proxies for virtual objects, it is important that these haptic proxies are similar to and co-located with their virtual counterparts. This makes it challenging to scale virtual scenarios because more proxies are needed as scenarios grow more complex. Haptic retargeting, or virtual remapping, makes it possible to repurpose the same physical prop as a proxy for multiple virtual objects. This paper details a user study comparing two techniques for repurposing haptic proxies; namely haptic retargeting based on body warping and change blindness remapping. Participants performed a simple button-pressing task, and 24 virtual buttons were mapped onto four haptic proxies with varying degrees of misalignment. Body warping and change blindness remapping were used to realign the real and virtual buttons, and the results indicate that users failed to reliably detect realignment of up to 7.9 cm for body warping and up to 9.7 cm for change blindness remapping. Moreover, change blindness remapping yielded significantly higher self-reported agency, and marginally higher ownership. Taken together these results suggest that this less explored technique has potential when it comes to repurposing haptic proxies for virtual reality.
Modelling of soil water and salt dynamics and prediction of salinity risks in Lezíria (Portugal) in response to different irrigation water qualities

The mitigation of soil salinization and/or sodicization phenomena are considered one of the main challenges of irrigated agriculture. In Portugal, Lezíria Grande of Vila Franca de Xira is a region with increased salinization and sodicization risks due to the influence of tidal movement on groundwater. This study aimed to evaluate soil water and salt dynamics in an irrigated Fluvisol with a crop rotation of maize and annual ryegrass and to predict irrigation-induced risks of soil salinization. We first calibrated the Hydrus-1D model for predicting soil water flow and solute transport with observed field data (from June 2017 to October 2018). Hydrus-1D successfully simulated soil water content (RMSE= 0.019 m 3 m -3 and R 2 = 0.94), the total dissolved solids (TDS) in the soil (RMSE= 0.212 g L -1 and R 2 = 0.77) and the reduction in water uptake by roots due to osmotic stress. Afterward we evaluate future risks of soil salinization performing a scenario analysis considering irrigation with different water qualities (with an electrical conductivity of the irrigation water, ECw, of 1.5, 3 and 5 dS m -1 ). We observed that when the ECw increases to 5 dS m -1 , the average solute concentration in the root zone rises to levels above the threshold tolerance of maize. Hydrus-1D successfully simulated soil water content and the total dissolved solids and is a very useful tool in planning and managing irrigation and in predicting soil salinity risks.
Independent of Granzymes A and B Cytotoxicity by Cytotoxic T Cells Is Cutting Edge: Rapid and Efficient In Vivo

Cytotoxic T (Tc) cells lyse target cells via exocytosis of granules containing perforin (perf) and granzymes (gzm). In vitro, gzm delivery into the target cell cytosol results in apoptosis, and in the absence of gzm A and B the induction of apoptosis is severely impaired. How-ever, using in vivo Tc cell killing assays, we find that virus-immune, gzm A (cid:1) B-deficient (gzmA (cid:1) B (cid:2) / (cid:2) ) mice are competent to eliminate adoptively transferred target cells pulsed with an immunodominant Tc cell determinant as rapidly and completely as their wild-type counterparts. Specific target cell elimination occurred with similar kinetics in both spleen and lymph nodes. Thus, neither gzmA nor gzmB are required for rapid and efficient in vivo cytotoxicity by Tc cells. The Journal of Immunology, 2009, 183: 37–40.
Graph Representation Learning for Fraud Prediction: A Nearest Neighbour Approach

The rise of graph representation learning has introduced a vast collection of techniques for the automated featurization of network data. With new algorithms empirically evaluated on a set of publicly available data sets, this study takes a different approach: the social graph resulting from a large, real-life credit card transaction data set is scrutinized. The social mechanisms at work in credit card fraud are exploited by means of node embeddings from the transaction network. Node embedding algorithms automatically map each node in a graph into a low-dimensional, latent vector representation. The credit card fraud application introduces a number of challenges for representation learning, such as: volume of transactions, on-line requirements and imbalanced data. As a result, we put forward a fast and efﬁcient inductive extension based on a nearest neighbour search which overcomes these challenges. The extension is empirically evaluated against state-of-the art inductive representation learning algorithms.
Phytochemical and vitamin constituents of Senna occidentalis Linn (Uzaki Mma)

This study dealt with the phytochemical and vitamin constituents of Sennaoccidentalis (Uzakimma). The qualitative analysis of the plant indicated the presence of high presence of flavonoids, glycosides, alkaloids, terpenoids and, moderate presence of tannins, saponin, steroids, and phenol. The quantitative results were tannins 1.4775  0.0495 mg/g, alkaloids 2.2280  0.0283 mg/g, saponin 1.5830  0.0141 mg/g, glycosides 2.1295  0.1909 mg/g, terpenoids 2.7560  0.1414 mg/g, flavonoids 2.1755  0.0212 mg/g, steroids 1.1030  0.1273 mg/g, phenol 1.3290  0.0283 mg/g. The results of vitamin contents were vitamin A (0.6545  0.0778 mg/g), vitamin B (1.5600  0.0283 mg/g), vitamin B1 (0.8145  0.9405 mg/g), vitamin B2 (0.2325  0.1202 mg/g), vitamin B3 (0.1200  0.0566 mg/g), vitamin B6 (0.2390  0.0283 mg/g), vitamin B9 (1.2280  0.3536 mg/g), vitamin C (1.0610  0.8061 mg/g), vitamin D (0.3430  0.1273 mg/g), vitamin E (0.5285  0.0212 mg/g) and vitamin K (0.6170  for the presence of flavonoids, terpenoids, steroids, saponins, alkaloids, tannins, glycosides and phenols. The steroid concentration was determined using the following relationship.
Estimating lower extremity joint angles during gait using reduced number of sensors count via deep learning

Estimating lower extremity joint angle during gait is essential for biomechanical analysis and clinical purposes. Traditionally infrared light-based motion capture systems are used to get the joint angle information. However, such an approach is restricted to the lab environment, limiting the applicability of the method in daily living. Inertial Measurement Units (IMU) sensors can solve this limitation but are needed in each body segment, causing discomfort and impracticality in everyday living. As a result, it is desirable to build a system that can measure joint angles in daily living while ensuring user comfort. For this reason, this paper uses deep learning to estimate joint angle during gait using only two IMU sensors mounted on participants' shoes under four different walking conditions, i.e., treadmill, overground, stair, and slope. Specifically, we leverage Gated Recurrent Unit (GRU), 1D, and 2D convolutional layers to create sub-networks and take their average to get a final model in an end-to-end manner. Extensive evaluations are done on the proposed method, which outperforms the baseline and improves the Root Mean Square Error (RMSE) of joint angle prediction by up to 32.96%.
MARCUS: An Event-Centric NLP Pipeline that generates Character Arcs from Narratives

Character arcs are important theoretical devices employed in literary studies to understand character journeys, identify tropes across literary genres, and establish similarities between narratives. This work addresses the novel task of computationally generating event-centric, relation-based character arcs from narratives. Providing a quantitative representation for arcs brings tangibility to a theoretical concept and paves the way for subsequent applications. We present MARCUS (Modelling Arcs for Understanding Stories), an NLP pipeline that extracts events, participant characters, implied emotion, and sentiment to model inter-character relations. MARCUS tracks and aggregates these relations across the narrative to generate character arcs as graphical plots. We generate character arcs from two extended fantasy series, Harry Potter and Lord of the Rings. We evaluate our approach before outlining existing challenges, suggesting applications of our pipeline, and discussing future work.
THE EFFECTS OF URBANIZATION GRADIENT ON MOSQUITO DISTRIBUTION IN BALTIMORE, MARYLAND

Mosquitos are insect vectors that transmit pathogens that lead to millions of human deaths every year. Species presence and population abundance is influenced by environmental conditions, such as temperature and precipitation. Human land modifications can also alter habitat conditions, thereby making them more optimal for some mosquito species. Environmental conditions experienced by juvenile mosquitoes can determine the diversity and abundance of adult mosquitoes. We used standardized ovitraps paired with iButton temperature loggers to collect data across five years (2011-2015) to examine how mosquito composition and abundance are influenced by temperature variation along an urbanization gradient in Baltimore, Maryland. Our results focus on the four most commonly observed species, which include two Culex ( restuans and pipiens ) and two Aedes ( albopictus and japonicus ) species. All species were highly abundant at sites with both high impervious surface and vegetation cover (urban green sites), while Ae. japonicus and Cx. restuans were also abundant in rural sites, and Ae. albopictus and Cx. pipiens were most abundant in urban sites. Ae. albopictus and Cx. pipiens were positively correlated with impervious surface cover within a 50-meter buffer. All four species were positively affected by temperature, but with dampening association at higher temperatures. Species that bite humans, Ae. albopictus and Cx. pipiens , were more likely to be found in warm, urban areas, which will likely lead to an increase in population sizes of these species due to future growth of urbanization and climate change.
Using Keyqueries to Reduce Misinformation in Health-Related Search Results

In the scenario of health-related searches, we investigate whether explicit relevance feedback by experts can guide query expansion methods to formulate queries that return fewer misleading or wrong results. In contrast to standard query expansion methods that pay no attention to the ranks of the feedback documents in the results of the expanded query, we experiment with a keyquery-based approach to identify expanded queries for which the feedback documents are ranked as high as possible. Experiments on the TREC 2019–2021 Decision and Health Misinformation tracks show that our keyquery-based method substantially reduces the portion of harmful results and improves the overall retrieval effectiveness.
Alignment: The Duality of Decision Problems: Working Paper Series--02-16

This paper makes a strong case that there exists an underlying pattern to decision problems. To exploit this pattern, the decision order framework is developed to guide researchers and decision makers toward appropriate solution methodologies. This process is called alignment and is based on the duality of decision problems. The process is validated using a number of examples from quantitative research, decision-making, economic theory, entrepreneurship, theory of the firm, education, and eCommerce. Extensions to the decision orders framework are presented to offer insight into the evolution and decomposition of complex decision problems.
Hybrid Integration of Screen-Printed Smart RFID Tags with Rigid Microchip

— Hybrid or heterogenous integration of silicon technologies and printed electronics is a promising approach for high-performance flexible electronics. It is essentially the integration of rigid microchips with the printed circuit on a flexible substrate. However, microchips with no-lead and mm size pitch introduce practical challenges in terms of precise electrical bonding onto the printed pattern. The differences in mechanical and thermal requirements also make it challenging to use conventional bonding and interconnect technology to robustly connect the microchips on flexible substrates. The focus of this work is to present these challenges with respect to different substrate-adhesive combinations and to provide a facile technique for integrating NFC microchips on different flexible substrates. Following the successful integration, the smart tags were characterised to demonstrate reliable performance in the NFC reading range. Whilst presenting a hybrid integration for the smart tag, this work also shows a potential solution for several other applications of flexible electronics where high-performance cannot be achieved by printed electronics alone.
Virtual reality exposure therapy for treatment of selective mutism in children

Selective mutism is a rare anxiety disorder where a person does not speak in certain situations. Exposure therapy has proven to be an eﬀective psychological treatment, but generally involves intensive in vivo exposure supervised by a therapist. This research in-vestigates whether virtual reality exposure therapy can make treatment of selective mutism in children more accessible. Studies on the adoption of virtual reality treatment in related disorders exists, but our intended use is novel. Findings are based on the development of an experimental application, qualitatively evaluated by domain experts. The application is based on an existing protocol, which revolves around the school environment. We present a robust approach for systematically implementing this protocol using reusable components, driven by intuitive interaction methods, and guided by a virtual teacher. We aim to empower therapists by supporting the ability to practice freely in a tailored virtual environment, while relaying its state through a dashboard for monitoring. In conclusion, the application was positively received by the interviewed domain experts, who regarded it as a suﬃcient foundation for further research and development.
Talking on handsfree and handheld cellphones while driving in association with handheld phone bans.

Introduction: Concurrent use of a cellphone while driving impairs driving abilities, and studies of policy effectiveness in reducing distracted driving have yielded mixed results. Furthermore, few studies have considered how hands-free phone use associates with handheld phone bans. It is not clear whether hand-held phone bans dissuade some drivers from using the phone while driving completely, or whether it simply promotes a shift to hands-free use. The present study estimates the association between handheld phone policies and self-reported talking on handsfree and handheld cellphones while driving. Methods: Our data consisted of 16,067 respondents to annual administrations of the Traffic Safety Culture Index from 2012-2017. Our primary exposure variable was handheld phone policy, and our primary outcome variables were self-reported talking on any phone, self-reported talking on a handheld phone, and self-reported talking on a hands-free phone while driving. We estimated adjusted prevalence ratios of the outcomes associated with handheld phone bans via modified Poisson regression. Results: Drivers in states with handheld bans were 13% less likely to self-report talking on any type of cellphone (handheld or handsfree) while driving. When broken down by cellphone type, drivers in states with handheld bans were 38% less likely to self-report talking on a handheld phone and 10% more likely to self-report talking on a hands-free phone while driving. Conclusions: Handheld phone bans were associated with more self-reported talking on hands-free phones and less talking on handheld phones, consistent with a substitution hypothesis. Handheld bans were also associated with less talking on any phone while driving, supporting a net safety benefit. Practical Applications: In the absence of a national ban on handheld phone use while driving, our study supports state handheld phone bans to deter distracted driving and improve traffic safety.