Miracle-dz/UniFAR-FTUs huggingface dataset:
An argumentation-based design rationale approach to reflective design of persuasive systems

Technology has significantly influenced human lifestyle, industrial sector, businesses and the way people interact with each other. Evidently, information technology is no longer a neutral tool. Intentionally or otherwise, system designers and developers influence our attitudes and/or behaviors for varying motives including promotion of socially valued activities. However, promoting moral values using technologies raises several ethical concerns. For example, what might be ethical for one person could be completely unethical from the other. Moral justification of persuasive systems requires involving all stakeholders in a rationale and open argumentation process. The moral issues are difficult to perceive and comprehend and more importantly, the entire process involves different stakeholders with conflicting viewpoints. Therefore, it is challenging to find an optimal solution to satisfy all stakeholders and argumentation process encompasses both cognitive and social complexities. In an attempt to address the problem; this study outlines an argumentation-based design rationale application. The proposed solution is to mitigate social and cognitive complexities pertaining to moral justification process. The main aim of this thesis is to study construction and evaluation of the said solution in order to facilitate reflective design and evaluation of persuasive information systems. This study follows formal problem-centric approach and was conducted by applying Design Science Research (DSR) methodology. We carried out an experiment to examine socio-technical aspects of the designed artifact focusing on system usefulness and its effect on the problem context. System usefulness of the artifact was studied by analyzing perceived usefulness and usability of the solution from participants' opinions. The intended implication of this study was to examine that argumentation-based design rationale could potentially decrease complexity of argumentation process. Likewise, it could lead to improved communication and reasoning between stakeholders. These aspects do not guarantee design and development of information systems that are morally justifiable by all standards. However, it is suggested that there are certain prerequisites that are needed for moral justification of persuasive information systems. From a practical perspective, we developed and evaluated a discourse-supported application that effectively establishes rationale and autonomous communication process. Theoretically, we acknowledge that three major aspects of argumentationbased design rationale applications including communication, reasoning and problem comprehension capabilities have a significant influence on mitigating social and cognitive complexities of ethical reflection relating to persuasive information systems.
Accurate Eye Centre Localisation by Means of Gradients

The estimation of the eye centres is used in several computer vision applications such as face recognition or eye tracking. Especially for the latter, systems that are remote and rely on available light have become very popular and several methods for accurate eye centre localisation have been proposed. Nevertheless, these methods often fail to accurately estimate the eye centres in difficult scenarios, e.g. low resolution, low contrast, or occlusions. We therefore propose an approach for accurate and robust eye centre localisation by using image gradients. We derive a simple objective function, which only consists of dot products. The maximum of this function corresponds to the location where most gradient vectors intersect and thus to the eye’s centre. Although simple, our method is invariant to changes in scale, pose, contrast and variations in illumination. We extensively evaluate our method on the very challenging BioID database for eye centre and iris localisation. Moreover, we compare our method with a wide range of state of the art methods and demonstrate that our method yields a significant improvement regarding both accuracy and robustness.
Anytime AND/OR depth-first search for combinatorial optimization

One popular and efficient scheme for solving exactly combinatorial optimization problems over graphical models is depth-first Branch and Bound. However, when the algorithm exploits problem decomposition using AND/OR search spaces, its anytime behavior breaks down. This paper 1) analyzes and demonstrates this inherent conflict between effective exploitation of problem decomposition (through AND/OR search spaces) and the anytime behavior of depth-first search (DFS), 2) presents a first scheme to address this issue while maintaining desirable DFS memory properties, 3) analyzes and demonstrates its effectiveness. Our work is applicable to any problem that can be cast as search over an AND/OR search space.
Robot Selection Using Analytic Hierarchy Process and System Of Equations of Matrices

The problem of robot selection plays an important key role concern to various fields of different applications since three decades. This problem has become more difficult in recent years due to increasing complexity of applications of the environment, features and specifications, and facilities offered by various manufactures. The primary objective of this paper is to select the suitable type of robot based on various factors such as type of application, payload, working environment, accuracy, lifespan, weight, purchasing cost etc. The selection procedure is developed for selection of particular type of robot based evaluating the various alternative selection factors using AHP technique and systems of equations of the matrices i.e. Eigen values and Eigen vectors. The ranking evolution will provide a good guidance for the robot selection to the end user. The concept of this work is an attempt has been made to create exhaustive database for identifying maximum possible number of attributes.
Testing the Cognitive-Affective Consistency Model of Intercultural Attitudes: Do Stereotypical Perceptions Influence Prejudicial Feelings?

This paper explores how cognitive beliefs, emotional feelings, and attitudinal evaluations toward racial/ethnic out-groups are inter-related. The first two studies examined the content and strength of contemporary cultural stereotypes associated by White-American participants with African-Americans and Asian-Indians. Path analyses using empirical data from the final survey (N = 227) reveal a complex set of relationships among stereotypical beliefs, prejudicial feelings, and overall favorability toward African-Americans and Asian-Indians. Interestingly, even seemingly positive stereotypes can activate negative emotions toward out-groups. Additionally, hostile and benevolent prejudicial feelings lead to decreased favorability toward out-groups. Results find support for mixed emotion models such as the stereotype content model rather than the traditional tripartite model of attitudinal consistency.
EXTRACELLULAR GLYCOLIPIDS FROM SACCHAROMYCES CEREVISIAE AS BIOEMULSIFYING AGENTS IN FOOD PROCESSING

The development of food processing sector and increasing diversity in consumers’ taste has continuously put forth different demands in the interdisciplinary research. In this research study, a demand for bio-based emulsifying agent to improve the oil-aqueous based food has been taken as a predominant destination. Extracellular glycolipids were produced using Saccharomyces cerevisiae MTCC 181 strain and basic characterization studies were performed.Further, the scope of the partially purified bioemulsifier in food processing was examined using different experiments and found to be more promising.
Measures of Participation Outcomes and Environmental Considerations for Children With Acquired Brain Injury: A Systematic Review

Abstract This systematic review examined the psychometric properties and application of assessments used to evaluate participation outcomes and environmental factors for children with acquired brain injury (ABI). An electronic search of eight databases for articles published up to June 2009 revealed reference to 98 outcome measures. According to inclusion criteria based on the International Classification of Functioning, Disability and Health (ICF: Child and Youth version), five were identified as measures of participation (Children's Assessment of Participation and Enjoyment [CAPE], Child and Adolescent Scale of Participation [CASP], Assessment of Life Habits for Children [LIFE-H], Participation Index of the Mayo-Portland Adaptability Index [M2PI], and the Participation Subscale of the School Function Assessment [SFA-PS]). Six were identified as measures of environment (Child and Adolescent Scale of Environment [CASE], Craig Hospital Inventory of Environmental Factors [CHIEF], European Child Environment Questionnaire [ECEQ], Family Inventory of Life Events and Changes [FILE], HOME Inventory, and the Multidimensional Scale of Perceived Social Support Scale [MSPSS]). The measures were critiqued in relation to content, validity, reliability, clinical utility, responsiveness, and overall strengths/weaknesses. These measures need to be evaluated more extensively with children who have ABI to further determine their psychometric properties and clinical usefulness with this population.
Asking scientists: A decade of questions analyzed by age, gender, and country

Nearly 79,000 questions sent to an Internet-based Ask-A-Scientist site during the last decade were analyzed according to the surfer's age, gender, country of origin, and the year the question was sent. The sample demonstrated a surprising dominance of female contributions among K-12 students (although this dominance did not carry over to the full sample), where offline situations are commonly characterized by males' greater interest in science. This female enthusiasm was observed in different countries, and had no correlation to the level of gender equity in those countries. This suggests that the
Methodologies for Time Series Prediction and Missing Value Imputation

The amount of collected data is increasing all the time in the world. More sophisticated measuring instruments and increase in the computer processing power produce more and more data, which requires more capacity from the collection, transmission and storage. Even though computers are faster, large databases need also good and accurate methodologies for them to be useful in practice. Some techniques are not feasible to be applied to very large databases or are not able to provide the necessary accuracy. As the title proclaims, this thesis focuses on two aspects encountered with databases, time series prediction and missing value imputation. The first one is a function approximation and regression problem, but can, in some cases, be formulated also as a classification task. Accurate prediction of future values is heavily dependent not only on a good model, which is well trained and validated, but also preprocessing, input variable selection or projection and output approximation strategy selection. The importance of all these choices made in the approximation process increases when the prediction horizon is extended further into the future. The second focus area deals with missing values in a database. The missing values can be a nuisance, but can be also be a prohibiting factor in the use of certain methodologies and degrade the performance of others. Hence, missing value imputation is a very necessary part of the preprocessing of a database. This imputation has to be done carefully in order to retain the integrity of the database and not to insert any unwanted artifacts to aggravate the job of the final data analysis methodology. Furthermore, even though the accuracy is always the main requisite for a good methodology, computational time has to be considered alongside the precision. In this thesis, a large variety of different strategies for output approximation and variable processing for time series prediction are presented. There is also a detailed presentation of new methodologies and tools for solving the problem of missing values. The strategies and methodologies are compared against the state-of-the-art ones and shown to be accurate and useful in practice.
Power Strategies in Interpersonal Relationships

The present study was designed to examine the effects of both power and gender in the use of influence strategies. Women and men responded to three scenarios in which they interacted with an imagined partner in situations with different levels of interpersonal power: more power than their partner (expert), less power (novice), and the same amount of power (equivalent). Partners were either same sex or other sex. Participants used more direct strategies when they were experts and more indirect strategies when they were novices, and women and men were very similar in the strategies they selected. Overall, power differences had a more profound effect than gender in predicting the choice of influence strategies. What are often construed as gender differences in social influence probably are perceived power differences. As such, gender differences in behavior must be understood within a context of status and power.
Learning Models of Human Behaviour from Textual Instructions

There are various activity recognition approaches that rely on manual definition of precondition-effect rules to describe human behaviour. These rules are later used to generate computational models of human behaviour that are able to reason about the user behaviour based on sensor observations. One problem with these approaches is that the manual rule definition is time consuming and error prone process. To address this problem, in this paper we propose an approach that learns the rules from textual instructions. In difference to existing approaches, it is able to learn the causal relations between the actions without initial training phase. Furthermore, it learns the domain ontology that is used for the model generalisation and specialisation. To evaluate the approach, a model describing cooking task was learned and later applied for explaining seven plans of actual human behaviour. It was then compared to a hand-crafted model describing the same problem. The results showed that the learned model was able to recognise the plans with higher overall probability compared to the hand-crafted model. It also learned a more complex domain ontology and was more general than the hand-crafted model. In general, the results showed that it is possible to learn models of human behaviour from textual instructions which are able to explain actual human behaviour.
Multilayered information encryption scheme with fine-grained authentication

Secret communication has long been an import issue in recent years. To protect a secret, encryption is a general way to help owners encrypt the secret into meaningless information for non-owners in order to prevent secret leakage. Since fine-grained encryption provides more flexibility in information protection, it has drawn more attention in recent years, but effective methods are rare in this new research domain. In this study, a multilayered and authentication-enhanced scheme for information encryption was proposed, and its application was focused on image encryption. The scheme encrypts the whole or parts of a secret image according to its owners' authorization. Owners of the image can also specify their own permissions encrypted in the image by embedding the corresponding authentication codes into the encrypted image. Later, the receivers can extract the hidden authentication codes to judge whether the decrypted image is fake or not. The experiments have demonstrated the effectiveness of the proposed encryption scheme, and the conceptual framework can also be applied to other applications requiring information encryption.
Energy Efficient Use of Multirobot Production Lines in the Automotive Industry: Detailed System Modeling and Optimization

This paper quantitatively reports about potential energy savings on robotic assembly lines for the automotive industry. At first, a detailed system model is described, which improves previously published results by explicitly considering both manipulator and electrical drive dynamics. The model closely captures experimental data in terms of actuation torques and servodrive voltages, which are directly used to derive the plant input power. Two practical methods are then evaluated for reducing the overall energy consumption. The methods rely on: 1) implementation of energy-optimal trajectories obtained by means of time scaling, concerning the robots' motion from the last process point to the home positions and 2) reduction of energy consumption by releasing the actuator brakes earlier when the robots are kept stationary. Simulation results, based on the production timing characteristics measured at a real plant, clearly shows that the system energy consumption can be effectively reduced without negative effects on the production rate. Note to Practitioners - The global industry trend towards sustainability demands energy optimization as a primary goal. Currently, industrial multirobot systems are not efficiently programmed as long as effective simulation tools are mostly lacking. In this context, a detailed model of the production line is proposed, which can be readily integrated into commercial tools for robot programming. Furthermore, the impact of practical optimization methods which are readily applicable to existing equipment is quantitatively highlighted. The key aspect of the proposed approach is that both the production rate of each cell and the robot hardware limitations are considered as strict constraints, so that no significant changes to the plant are required.
A Novel Model of Working Set Selection for SMO Decomposition Methods

In the process of training support vector machines (SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field. To improve working set selection, we propose a new model for working set selection in sequential minimal optimization (SMO) decomposition methods. In this model, it selects B as working set without reselection. Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods.
Use of Symmetric And Asymmetric Cryptography in False Report Filtering in Sensor Networks

Sensor networks are easily deployable networks of inexpensive sensor nodes. The sensors detect events of interest, for example forest fires, battlefield events, and so on. The sensor nodes form an ad-hoc network by communicating with each other using short-range radio. However, since the sensors are “in the field”, they may be compromised by adversaries. Compromising a sensor node gives the attacker keys and any other stored material, making it possible for him to inject false reports to the network. So far, mainly symmetric methods have been proposed to detect and filter out such false reports the low computational resources available to the nodes set constraints on the use of cryptography. However, recent research has shown that asymmetric methods may also be feasible. The purpose of this paper is to review both methods; so far, symmetric methods seem to tackle the problem of false reports more efficiently than asymmetric ones, but the issue remains not yet adequately solved.
An Ethnography of Two Teachers' Antiracist and Critical Multicultural Practices

Educational theorists concerned with antiracist and critical multicultural practices have identified the need for teachers to begin changing the processes of social exclusion which children of colour experience in the school system. An ethnographic study of critical educators teaching practices is provided as a useful directive. The study examines how two critical educators respond to the diversity represented in their school by incorporating antiracist and critical multicultural approaches in their teaching practice. Of interest in relation to Julia Neuman’s practice is the question: How do white teachers negotiate their ‘whiteness’ in relation to the diversity in student population? In the case of Sita Ramana a South Asian teacher, the study considers how cultural and linguistic congruence with her students engages their interests in culturally response reading programs that involve dual language books as well as parent - student active participation in heritage language programs.
Anti-Hyperglycemic and Antioxidant Effect of Saraca asoca (Roxb. De Wilde) Flowers in Streptozotocin-Nicotinamide Induced Diabetic Rats: A Therapeutic Study

50% Ethanolic extract of Saraca asoca Roxb. de Wilde flowers (SAE) was screened for its antihyperglycemic and antioxidant effect in streptozotocin-nicotinamide induced diabetic rats. Phytochemical analysis of SAE revealed the presence of phenolic compounds phytosterol, flavonoids and saponins. After the administration of extract 200 mg/kg body weight/day) in diabetic rats has improved the elevated levels of blood glucose (p<0.01). The decreased activities of key antioxidant enzymes such as superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase and glutathione in diabetic rats were brought back to near normal range upon SAE treatment. Our studies demonstrate the anti-hyperglycemic and anti-oxidative potential of Saraca asoca which could exert sympathetic effects against the diabetes and associated free radicals complications.
IgA antibodies to human tissue transglutaminase: audit of routine practice confirms high diagnostic accuracy

Background: The diagnostic accuracy of IgA tissue transglutaminase antibodies (TGA) for coeliac disease (CD) has been assessed following the introduction of the test into routine practice in 2002. Methods: Specificity was assessed in serum samples received from 1554 adults for routine coeliac serology. The population for assessing sensitivity was 75 consecutive new adult diagnoses of CD. TGA was measured by enzyme-linked immunoassay using human tissue transglutaminase as antigen. Concordance between TGA and endomysial antibody (EMA) was also assessed. Results: The prevalence of new diagnoses of CD in the population tested was 2.8% with similar proportions of new diagnoses in males and females. The positive predictive values at a cut-off of 3 units/mL were 0.77 for samples from Primary Care and 0.92 for samples from Hospital sources, with a sensitivity of 92%. At TGA <3 units/mL, EMA was usually negative; when TGA was >4.9 units/mL, EMA was rarely negative. Conclusions: We have assessed the role of TGA in routine clinical practice and confirmed high diagnostic accuracy. Sensitivity (92%) is identical to the sensitivity for EMA. IgA deficiency should be excluded in samples showing low absorbance readings in the TGA assay and interference from monoclonal and polyclonal IgA should be excluded in samples with slightly raised TGA levels and negative EMA. TGA is recommended as the first-line serological test for coeliac disease.
Insulin Antagonizes Ischemia-induced Thr172 Phosphorylation of AMP-activated Protein Kinase α-Subunits in Heart via Hierarchical Phosphorylation of Ser485/491*

Previous studies showed that insulin antagonizes AMP-activated protein kinase activation by ischemia and that protein kinase B might be implicated. Here we investigated whether the direct phosphorylation of AMP-activated protein kinase by protein kinase B might participate in this effect. Protein kinase B phosphorylated recombinant bacterially expressed AMP-activated protein kinase heterotrimers at Ser485 of the α1-subunits. In perfused rat hearts, phosphorylation of the α1/α2 AMP-activated protein kinase subunits on Ser485/Ser491 was increased by insulin and insulin pretreatment decreased the phosphorylation of the α-subunits at Thr172 in a subsequent ischemic episode. It is proposed that the effect of insulin to antagonize AMP-activated protein kinase activation involves a hierarchical mechanism whereby Ser485/Ser491 phosphorylation by protein kinase B reduces subsequent phosphorylation of Thr172 by LKB1 and the resulting activation of AMP-activated protein kinase.