Miracle-dz/UniFAR-FTUs huggingface dataset:
Prosocial Development in Adolescence: A Longitudinal Study

Change in prosocial moral reasoning over an 11-year period, gender differences in prosocial reasoning in adolescence, and the interrelations of moral reasoning, prosocial behavior, and empathy-related emotional responses were examined with longitudinal data and data from adolescents interviewed for the first time. Hedonistic reasoning declined in use until adolescence and then increased somewhat (primarily for boys). Needs-oriented reasoning, direct reciprocity reasoning, and approval and stereotypic reasoning increased until midchildhood or early adolescence and then declined. Several modes of higher level reasoning emerged in late childhood or adolescence. Girls' overall reasoning was higher than boys'. Consistent with expectations, there was some evidence of high level prosocial reasoning being associated with procosial behavior and empathy and of a relation between sympathy or empathy and prosocial behavior
Computer Measurement of Central Visual Acuity under Mesopic and Glare Conditions in Eyes with Nuclear Cataract

Purpose: The CVA is an interactive, automated computer device that rapidly thresholds central acuity under conditions mimicking customary photopic and mesopic activities. In sequence, the CVA may test up to 6 environments, in this series under 3 mesopic environments (98%, 50% MC against 1.6 cd/m2 background, 25% MC against 5 cd/m2), then 3 glare environments (98%, 10% and 8% MC, against 200 cd/m2). This report compares the CVA thresholded acuity with that measured with letter charts, as well as with C-Quant derived glare testing and patient responses to the Activities of Daily Vision Scale (ADVS) in eyes with nuclear cataract. Methods: In 33 eyes with nuclear cataract compared with 69 emmetropic eyes without lens opacity, best refracted acuity was measured under CVA modules and with ETDRS charts presenting similar contrast and luminance. Both groups were also tested with 15% MC charts placed outdoors with sun overhead and with sun at 15° off-axis and compared with the CVA acuty at 10% MC and 8% MC thresholded in a darkened room. In 22 of the eyes with nuclear cataract, C-Quant analysis of straylight glare was also performed along with the ADVS. Results: Acuities thresholded with CVA modules demonstrated high Pearson correlation coefficients, and Bland and Altman statistical similarity with the acuities measured from similar contrast charts. The acuities measured with CVA glare modules correlated significantly with charts placed in sun glare and with C-Quant measurements of straylight. Significant correlations were noted between CVA acuities and near vision as well as distance driving tasks. Conclusions: The CVA demonstrates the ability to accurately threshold the acuity of eyes with nuclear cataract compared with chart acuity under conditions of contrast, luminance and fixation times simulating normal photopic and mesopic activities and to provide the physician with glare evaluation and ability to function under multiple types of activities.
QCF, A useful tool for Quantum Neural Network implementation in Matlab

Most proposals for quantum neural networks have skipped over the implementation of the Qubit, superposition, entanglement and measurement in order to be used in MATLAB environment. Quantum computing uses unitary operators acting on discrete state vectors. Matlab is a well-known (classical) matrix computing environment, which makes it well suited for simulating quantum algorithms. The Quantum Computing Function (QCF) library extends Matlab by adding functions to represent and visualize common quantum operations. On the other hand a new mathematical model of computation called Quantum Neural Networks (QNNs) is defined, building on Deutsch's model of quantum computational network. The Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. In this paper the use and importance of those functions is illustrated with the help of few examples. This paper presents a brief overview of QCF that how it can be useful in Quantum Neural Network simulation.
An immersive virtual reality mobile platform for self-attachment

Psychotherapy is among the most effective techniques for combating mental health issues, and virtual reality is beginning to be explored as a way to enhance the efficacy of various psychotherapeutic treatments. In this paper we propose an immersive virtual reality mobile platform for Self-Attachment psychotherapy. Under the SelfAttachment therapeutic framework, the causes of disorders such as chronic anxiety and depression are traced back to the quality of the individual’s attachment with their primary caregiver during childhood. Our proposed platform aims to assist the user in enhancing their capacities for self-regulation of emotion, by means of earning secure attachment through the experience of positive attachment interactions, missed in their childhood. In the virtual environment provided by the platform, the adult-self of the user learns to create and strengthen an affectional and supportive bond with the inner-child. It is hypothesised that by long term potentiation and neuroplasticity, the user gradually develops new neural pathways and matures into an effective secure attachment object for the inner-child, thereby enabling the self-regulation of emotions.
Testing a Neuro-Evolutionary Theory of Social Bonds and Addiction: Methadone Associated With Lower Attachment Anxiety, Comfort With Closeness, and Proximity Maintenance

Evidence from non-human mammals for the involvement of the endogenous opioid system in prosocial behavior is reasonably extensive and robust; however, studies in humans are lacking. This study tests the neuro-evolutionary hypothesis that exogenous opiates, including morphine, heroine, and methadone, decrease separation anxiety and proximity by hijacking the neuro-peptide endogenous opioid system modulating social bonds. Participants were 486 subjects, 43% male, with ages between 18 and 62 years (M = 26.4; SD = 9.4), divided in three naturalistic groups: 1: addicts in drug-free treatment; 2: addicts in methadone programs; 3: normative non-clinical controls. Instruments: 1) Adult Attachment Scale (AAS) composed of three subscales: Anxiety about being rejected (α = 0.83), Comfort with Intimacy (α = 0.68), and Comfort Depending on Others (α = 0.70). 2) Caregiving Questionnaire composed of four subscales: Proximity Maintenance: (α = 0.83), Sensitivity: (α = 0.76), Controlling Caregiving (α = 0.77) and Compulsive Caregiving (α = 0.68). Results: Multivariate Analysis of Co-variance (MANCOVA) models were computed; gender, age, and education were included in the models. Methadone patients and drug-free treatment addicts were equivalent and reported significantly lower Comfort Depending on Others, Comfort with Intimacy, and Caregiving Proximity. However, methadone users reported significantly lower Anxiety about being rejected than drug-free addicts and were equivalent to non-clinical controls. In addition, correlations between the methadone intake dose and the questionnaires’ scales showed that dose was significantly and negatively correlated with Comfort with Closeness (rs = −0.36; p < 0.01) and with Caregiving Proximity (rs = −0.28; p < 0.05).
Evaluating Cognitive-Linguistic Deficits Postconcussion in Adults

Purpose: We sought to document the cognitive-linguistic challenges experienced by 3 adults with concussion at varying lengths postinjury. Method: A multiple case study design utilized motivational interviewing techniques, 4 self-report measurement tools, and 5 standardized neurocognitive tests. The 1 female and 2 male participants were 1, 21, and 37 months postconcussion. Results: All participants self-reported cognitive and linguistic challenges significantly impacting daily functioning and quality of life. Cognitively, participants demonstrated deficits in independence, metacognition, and cognitive flexibility. Linguistically, participants demonstrated deficits in verbal memory, verbal fluency, and reading. The participant 1-month postconcussion demonstrated deficits on multiple standardized measures; however, participants in the chronic phase of recovery demonstrated substantially more self-reported deficits than were noted on standardized testing. Discussion: Evaluation of cognitive-linguistic deficits postconcussion requires both self-report and standardized measurement; however, limitations of both tools exist. We discuss clinical implications for professionals selecting testing measures for use in this population.
Functional encryption for inner-product evaluations

Functional encryption is an emerging framework in which a master authority can distribute keys that allow some computation over encrypted data in a controlled manner. The trend on this topic is to try to build schemes that are as expressive possible, i.e., functional encryption that supports any circuit evaluation. These results are at the cost of efficiency and security. They rely on recent, not very well studied assumptions, and no construction is close to being practical. The goal of this thesis is to attack this challenge from a different angle: we try to build the most expressive functional encryption scheme we can get from standard assumption, while keeping the constructions simple and efficient. To this end, we introduce the notion of functional encryption for inner-product evaluations, where plaintexts are vectors ~x, and the trusted authority delivers keys for vectors ~y that allow the evaluation of the inner-product h~x, ~yi. This functionality already offers some direct applications, and it can also be used for theoretical constructions, as inner-product is a widely used operation. Finally, we present two generic frameworks to construct inner-product functional encryption schemes, as well as some concrete instantiations whose security relies on standard assumptions. We also compare their pros and cons.
A Heterogeneous Ensemble of Extreme Learning Machines with Correntropy and Negative Correlation

The Extreme Learning Machine (ELM) is an effective learning algorithm for a Single-Layer Feedforward Network (SLFN). It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning (NCL) to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs (HE 2 LM) for classification has different ELM algorithms including the Regularized ELM (RELM), the Kernel ELM (KELM), and the L 2 -norm-optimized ELM (ELML2). The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE 2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers.
Graphical User Interface For Multi-Objective Decision Support

i Multi-objective problems arise in various fields of application. Generally there is not a single optimal solution to these kind of problems but a set of Pareto optimal solutions with relative trade-offs. Which solution is the ”best” in a given situation depends on the judgment of a decision maker. To support the decision maker in the decision process software tool support is desirable. This research proposal outlines a study focused on visual representation of alternative solutions and interactive solution space navigation. The study will explore a method to narrow the solution space considered by a search algorithm to the decision maker’s areas of interest. The methods to specify the decision maker’s preference through a graphical user interface and how this information is considered by the search algorithm are part of the research. According to the design science research approach a prototype of a software tool has been implemented. The application features a graphical user interface whereby a user is able to specify the preferred objective range for solutions. Two variants of a method to enable the multi-objective search algorithm to consider these preferences have been implemented and tested on example problems. The validation through experiments shows that the proposed methods are suitable to reduce computing time to find solutions in the preferred range compared to solving the problem without considering the decision maker’s input. Acknowledgements ii I would like to thank Prof. Dr. Thomas Hanne for his valuable support and coaching in this thesis. It has been a very pleasant collaboration and I appreciate your effort and time. I also thank my wonderful wife who motivated and encouraged me always. Thanks for pushing me beyond my limitations. I love you. And I thank my God who makes everything possible.
The Types of Visual Metaphors in Advertisements of High-Tech Products and the Persuasion Effect that Social-Face Sensitivity has on the Participation Behavior and Citizenship Behavior of Consumers

This study has the purpose of analyzing through ex-perimentation, the assumption that the social-face level of consumers has a direct eﬀect on following behavior based on the type of visual metaphor, a method often used as a persuasion cue in high-tech product advertisements. This study can suggest a message strategy framework to morpho-logically expand the advertising creativity, and therefore be able oﬀer practical utility. A total of 163 responses (re-sponse rate 96.4%), excluding non-responders and incomplete responses, were used in the ﬁnal analysis. The results of the study were that the interaction eﬀect based on visual metaphor type and consumer social-face sensitivity levels appeared in sympathy eﬀort, compliance eﬀort, verbal behavior, and feedback. The level of social-face sensitivity was classiﬁed by calculating the cumulative of the total average, with 50% as the standard, 40% and below was set as low, 60% and above was set as high, with the result of 59 people (48.4%) who were classiﬁed as low, and 63 people (51.6%) who were classiﬁed as high. The results of the study were that the interaction eﬀect based on visual metaphor type and consumer social-face sensitivity levels appeared in sympathy eﬀort, compliance eﬀort, verbal behavior, and feedback. We evaluate communication eﬀort to be higher in cases of concrete metaphor over abstract metaphor, and in cases where social-face sensitivity levels are lower. Research utilizing the concept of social-face in the ﬁeld of marketing communication is ongoing. Especially as social-face in Korean society is very closely related to the consumption patterns of consumers, it can be considered a core factor in marketing strategy. Abstract metaphors express the characteristics of the product through characteristics that the consumer cannot objectively iden-tify or characteristics that reﬂect preferences. For example, if a consumer considers that color of a running shoe to be an important characteristic, as there is a subjective diﬀerence in the colors that consumers have, it can be considered to be an abstract character-istic. In addition, the reﬁnement of a suit, the safety of a car, the information processing speed of a smartphone can change based on the psychological characteristics of the individual, and therefore is included in the realm of abstract metaphor expression (Weelden et al., 2011). The novelty factor of visual metaphor cues in advertisements come from abstract metaphoric expressions, and the process of eliciting meaning also does not have one answer, however on the contrary this type of vague and complex visual design element can create positive emotions in the consumer.
Predicting loan default in peer‐to‐peer lending using narrative data

Peer‐to‐peer (P2P) lending is facing severe information asymmetry problems and depends highly on the internal credit scoring system. This paper provides a novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision‐making in P2P lending. The proposal is expected to improve the existing credit scoring models in P2P lending from two aspects, namely the classifier and the usage of narrative data. We utilize an advanced gradient boosting decision tree technique (i.e., CatBoost) to predict default loans. Moreover, a soft information extraction technique based on keyword clustering is developed to compensate for the insufficient hard credit data. Validated on three real‐world datasets, the experimental results demonstrate that variables extracted from narrative data are powerful features, and the utilization of narrative data significantly improves the predictability relative to solely using hard information. The results of sensitivity analysis reveal that CatBoost outperforms the industry benchmark under different cluster numbers of extracted soft information; meanwhile a small number of clusters (e.g., three) is preferred for consideration of model performance, computational cost, and comprehensibility. We finally facilitate a discussion on practical implication and explanatory considerations.
Fast ultra wideband microwave imaging for early stage breast cancer detection

Microwave Imaging (MWI) is one of the most promising imaging modalities to emerge in recent years. It has been used in a range of applications, and one of the most notable application is medical imaging. Microwave medical imaging is based on the contrast between the dielectric properties of healthy and cancerous tissue. Compared to the existing imaging modalities including the widely used X-ray mammography, MWI is non-ionising, non-invasive and cost effective. Among other medical imaging applications, MWI has been investigated to detect small tumours in breast at relatively early stage, and it has shown promising results in the detection of malignancies in moderately dense human breast. The heterogeneity of healthy breast tissue, high computational cost of the MWI algorithms, and sensitivity of the microwaves to tissue boundaries are the major challenges in development of a microwave based breast imaging system. This thesis focusses on the major challenges in MWI and proposes: (1) a novel pre-filtering technique to compensate for phase variations and frequency dependent loss that arise due to the dispersive nature of the biological tissue, (2) a massively parallel execution framework for microwave tomography to reduce the image reconstruction time, and (3) a multistage ultra wideband microwave tomography algorithm for quantitative breast imaging. Results indicate that the proposed pre-filtering improves the performance of conventional beamformers, enabling detection and accurate localization of multiple tumours in dense human breast. The proposed multi-stage microwave tomography algorithm has been applied to quantitative imaging of the breast, and the performance of the proposed algorithm has been validated on anatomically and dielectrically realistic breast phantoms. In order to minimise the image reconstruction time, a single-instruction multiple-data based parallel execution framework has been developed, and a gain factor of more than 50 in the computational throughput has been achieved.
Pseudowords and guessing in the Yes/No format vocabulary test

The main purpose of this study is to investigate guessing in the Yes/No (YN) format vocabulary test. One-hundred-and-five university students took a YN test, a translation task and a multiple-choice vocabulary size test (MC VST). With matched lexical properties between the real words and the pseudowords, pseudowords could index guessing in the YN test as correlations between false alarms and real word guessing were high (> .80). This finding provides important validity evidence for correction formulas that employ false alarm information to adjust the YN test scores. Another important finding is that the neighborhood size of pseudowords was related to false alarm rates, which has useful implications for the designing of future YN tests. Finally, the corrected scores of the YN test correlated highly (> .70) with those of the translation task. In comparison, correlations between the YN corrected scores and the MC VST scores were lower, indicating a difference in guessing between the two tests.
Peer Perceptions of Students With Autism Spectrum Disorders

This quasi-experimental study investigated behavioral intentions of 70 middle school participants toward engaging in activities with a student displaying characteristics of autism spectrum disorder (ASD) in a video vignette. Three video conditions were investigated: moderate to severe ASD, high-functioning ASD, and a typically developing control. Results of an analysis of variance indicated that participants reported significantly higher behavioral intentions toward the typically developing student and the student displaying more severe ASD symptomatology as compared to the student depicting high-functioning ASD. Participants were also able to differentially rate the behaviors they observed across the three conditions. These findings suggest that peers are capable of distinguishing among different behaviors typically displayed by students with ASD but may be less willing to socially engage with a student with mild or subtler characteristics. Implications regarding essential elements of interventions for peers based on the target student’s level of functioning are discussed.
The effect of social capital on exploratory and exploitative innovation

Purpose The purpose of this paper is to advance new insights into how internal and external social capital (SC) facets influence exploratory and exploitative innovation directly, and indirectly through absorptive capability (AC), by drawing on the relational and knowledge-based views. Design/methodology/approach The paper empirically tests the developed model using 135 survey responses from managers in construction contractor firms. Data were factor analysed, and path estimates determined using partial least squares structural equation modelling to test the hypotheses. Findings The results reveal that each social capital (SC) facet has direct benefits for both exploratory and exploitative innovation. The findings also show a mix of full and partial mediation paths between the facets of SC and innovation types through AC. Originality/value Extant research linking SC facets with innovation categories is fragmented. Added to this fragmentation is the dearth of studies linking both intra-firm and inter-firm SC with exploratory and exploitative innovation in firms. This paper makes a novel contribution by testing a model of the direct and indirect links (through AC) between internal and external SC and both exploratory and exploitative innovation in the context of construction contractor firms. The findings show how both facets of SC are necessary for exploratory and exploitative innovation. It reveals the types of relationships and capabilities necessary for specific innovation objectives.