Miracle-dz/UniFAR-FTUs huggingface dataset:
AN EXPLORATION OF RESILIENCE, SPIRITUALITY AND POST TRAUMATIC GROWTH IN THE FACE OF TRAUMA

Traumatic events acquire both negative and positive qualities that reflect a person’s life. The negative side includes psychopathologies and the positive side includes transformation in lifestyle. The focus of the present research is to explore the positive transformation of females experiencing trauma as a result of Breast Cancer, Sexual Abuse and Spousal Bereavement. It was hypothesised that there would be significant group differences among the three traumas on the measure of Post traumatic growth, Resilience, Spirituality and also to examine their relationship. The data was collected using purposive sampling method and the size of the total sample was 90 (i.e. 30 females in each group). A three group design was followed where Post traumatic growth inventory by Tedeschi and Calhoun (1996) and Spirituality and Resilience Assessment Packet by Kass and Kass (2000) was administered on the three groups of trauma to meet the objectives. The results revealed significant differences between the groups indicating breast cancer cases mean scores to be highest. Also, significant positive relations were found between the variables, supporting positive growth in the sample. Overall, it can be concluded that an increase in the above factors is associated with self-reported positive life changes over time. Further research on related topics, based on the findings of the present and other studies, will undoubtedly advance our understanding of the positive psychological changes following breast cancer, sexual abuse and spousal bereavement. Keywords : Post traumatic growth, Resilience, Spirituality, Trauma
The role of hand dominance in padel: performance profiles of professional players

Profiling methods allow coaches to determine players’ profiles for designing training programs as specific as possible to maximize performance. We aimed to define the distinctive performance profiles for professional padel players according to technical, spatial, effectiveness and hand dominance characteristics. A multivariate decision tree approach was used to identify and classify players’ profiles. Hand dominance comparison revealed a similar technical game pattern along the match (p = 0.330) but different effectiveness rates (p = 0.012). Left-handed scored more points using smashes (63.3 % vs. 40.7 %) but made more errors using the wall (37.7 % vs. 19.5 %); right-handed secured the ball with fewer errors (11.2 % vs. 8.2 %) and more continuity actions (84.9 % vs. 79.9 %). According to these findings, the hand dominance has an important role in players’ game style and effectiveness. These differences should be taken into account for designing optimal training programs in padel.
Effect of elevated carbon dioxide on shoal familiarity and metabolism in a coral reef fish

Atmospheric CO2 is expected to more than double by the end of the century. The resulting changes in ocean chemistry will affect the behaviour, sensory systems and physiology of a range of fish species. Although a number of past studies have examined effects of CO2 in gregarious fishes, most have assessed individuals in social isolation, which can alter individual behaviour and metabolism in social species. Within social groups, a learned familiarity can develop following a prolonged period of interaction between individuals, with fishes preferentially associating with familiar conspecifics because of benefits such as improved social learning and greater foraging opportunities. However, social recognition occurs through detection of shoal-mate cues; hence, it may be disrupted by near-future CO2 conditions. In the present study, we examined the influence of elevated CO2 on shoal familiarity and the metabolic benefits of group living in the gregarious damselfish species the blue-green puller (Chromis viridis). Shoals were acclimated to one of three nominal CO2 treatments: control (450 µatm), mid-CO2 (750 µatm) or high-CO2 (1000 µatm). After a 4–7 day acclimation period, familiarity was examined using a choice test, in which individuals were given the choice to associate with familiar shoal-mates or unfamiliar conspecifics. In control conditions, individuals preferentially associated with familiar shoal-mates. However, this association was lost in both elevated-CO2 treatments. Elevated CO2 did not impact the calming effect of shoaling on metabolism, as measured using an intermittent-flow respirometry methodology for social species following a 17–20 day acclimation period to CO2 treatment. In all CO2 treatments, individuals exhibited a significantly lower metabolic rate when measured in a shoal vs. alone, highlighting the complexity of shoal dynamics and the processes that influence the benefits of shoaling.
Towards generating human-oriented summaries of unit test cases

The emergence of usable unit testing frameworks (e.g., JUnit for Java code) and unit test generators (e.g., CodePro for Java code) make it easier to create more comprehensive unit testing suites for applications. Unfortunately, test code, especially generated test code, can be difficult to comprehend. In this paper, we propose generating human-oriented summaries of test cases. We suggest an initial approach based on a static analysis of the source code of the test cases. Our goal is to help improve a human's ability to quickly comprehend unit test cases so that appropriate decisions can be made about where to place effort when dealing with large unit test suites.
A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes, The IFR² Framework

Fraud is a million dollar business and it is increa sing every year. Both internal and external fraud present a substantial cost to ou r economy worldwide. A review of the academic literature learns that the academic commun ity only addresses external fraud and how to detect this type of fraud. Little or no effo rt to our knowledge has been put in investigating how to prevent and to detect internal fraud, which we call 'internal fraud risk reduction'. Taking together the urge for research i n internal fraud and the lack of it in academic literature, research to reduce internal fr aud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR≤ framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.
Balanced Low Noise High Dynamic Photodiode Interface for Automotive

A high performance balanced photodiode sensor interface for the automotive industry is introduced. A novel switched current based architecture provides 100dB input current dynamic range. Correlated double sampling technique is used for 1/f noise suppression in the MOS input circuitry. Sigma delta ADC (13 bit) is used. The measured SNR is >66dB. Embedded offset compensation supports fast 236mus conversion cycle per channel. Mixed signal chip architecture is described. EMC emissions measurement results meet the strict automotive limits
The Value of Cooperation: Minimizing User Costs in Multi-Broker Mobile Cloud Computing Networks

We study the problem of user cost minimization in mobile cloud computing (MCC) networks. We consider a MCC model where multiple brokers assign cloud resources to mobile users. The model is characterized by an heterogeneous cloud architecture (which includes a public cloud and a cloudlet) and by the heterogeneous pricing strategies of cloud service providers. In this setting, we investigate two classes of cloud reservation strategies, i.e., a competitive strategy, and a compete-then-cooperate strategy as a performance bound. We first study a purely competitive scenario where brokers compete to reserve computing resources from remote public clouds (which are affected by long delays) and from local cloudlets (which have limited computational resources but short delays). We provide theoretical results demonstrating the existence of disagreement points (i.e., the equilibrium reservation strategy that no broker has incentive to deviate unilaterally from) and convergence of the best-response strategies of the brokers to disagreement points. We then consider the scenario in which brokers agree to cooperate in exchange for a lower average cost of resources. We formulate a cooperative problem where the objective is to minimize the total average price of all brokers, under the constraint that no broker should pay a price higher than the disagreement price (i.e., the competitive price). We design new globally optimal solution algorithm to solve the resulting non-convex cooperative problem, based on a combination of the branch and bound framework and of advanced convex relaxation techniques. The resulting optimal solution provides a lower bound on the achievable user cost without complete collusion among brokers. Compared with pure competition, we found that (i) noticeable cooperative gains can be achieved over pure competition in markets with a few brokers only, and (ii) the cooperative gain is only marginal in crowded markets, i.e., with a high number of brokers, hence there is no clear incentive for brokers to cooperate.
Visualizing Block IO Workloads

Massive block IO systems are the workhorses powering many of today’s largest applications. Databases, health care systems, and virtual machine images are examples for block storage applications. The massive scale of these workloads, and the complexity of the underlying storage systems, makes it difficult to pinpoint problems when they occur. This work attempts to shed light on workload patterns through visualization, aiding our intuition. We describe our experience in the last 3 years of analyzing and visualizing customer traces from XIV, an IBM enterprise block storage system. We also present results from applying the same visualization technology to Linux filesystems. We show how visualization aids our understanding of workloads and how it assists in resolving customer performance problems.
Ignoring Hardware Differences

In this paper, we introduce a new graphics hardware architecture based on XMT (Explicit-Multithreading), a fine-grained PRAM (Parallel Random Access Model) design. We demonstrate the advantage of XMT architecture in graphics hardware applications. Compared with the stream architecture, the XMT-based graphics hardware provides higher flexibility, a more friendly programming interface and can achieve similar performance. With an XMT-based GPU architecture, it would be much easier to migrate most general-purpose computation tasks onto the GPU. This capability may increase the utilization of GPU and enhance the performance of the whole computer system. We base our conclusion on architectural analysis, simulation of code segments and example programs.
An Energy-Balanced Graph Routing Algorithm for WirelessHART Networks

Aiming at the high reliable wirelessHART protocol, an innovative energy-balanced graph routing algorithm (EBGRA) is proposed in this paper. Firstly, the network topology is established and initialized Based on graph routing. During the routing update process, by synthesizing the remaining energy, the communication cycles of nodes and the links in the network, the network manager calculates the robust coefficient of each node as the metric for nodes to select the proper next hop and generate the optimal path. Meanwhile, an upper threshold for the number of neighbors of each node is set so that the neighbors with smaller robust coefficient can be removed. EBGRA considers both the nodes' communication load and residual energy, not only making each path the shortest, but also greatly extending the lifetime of the network. Finally, simulations are carried out to demonstrate the long network lifetime and the high reliability of the proposed algorithm.
The Anomalous X-Ray Pulsar 4U 0142+61: A Neutron Star with a Gaseous Fallback Disk

The recent detection of the anomalous X-ray pulsar (AXP) 4U 0142+61 in the mid-infrared with the Spitzer observatory by Z. Wang and coworkers constitutes the first instance of a disk around an AXP. We show, by analyzing earlier optical and near-IR data together with the recent data, that the overall broadband data set can be reproduced by a single model of an irradiated and viscously heated disk.
The Structure of Features in Java Code: An Exploratory Investigation

Techniques to help software developers explicitly separate different concerns in their programs have been gaining increasing attention in the last few years. Three examples of such techniques are composition filters [ABV92], aspect-oriented programming [KLM97], and hyperspaces [TOHSJ99]. A central idea behind these techniques is that software systems would be easier to change if various design and programming decisions were modularized separately and simultaneously. But what is a concern? And how do different concerns interact within a code base? To gain some insight into these questions, we have conducted an exploratory investigation of concerns in two existing Java [GJS96] packages: gnu.regexp,1 and jFTPd.2 Each of the packages was marked for concerns by each author of this positionpaper. We then compared the concerns identified and analyzed how the concerns interacted with each other and across the existing structure of the Java package. The results of this investigation provide:
e-Training for Critical Microelectronic Disciplines over the Fraunhofer Knowledge Network (FKN)

The World Wide Web has opened up entirely novel ways of teaching and learning. To use these new chances, Germany’s Fraunhofer Gesellschaft (FhG) has launched the implementation of the so-called Fraunhofer Knowledge & Learning Network (FKN). This project is mainly aimed at providing graduates, scientists, engineers and also students with latest skills in Information and Communication Technologies (ICT) much needed in industry. For that purpose, dedicated training aids have been prepared and integrated in a Web-based training (WbT) system. Starting from FKN’s aims and business model, this paper describes the WbT system exemplified for using in microelectronics. A brief survey on content topics is also given.
Moral Identity Complexity: Situated Morality Within and Across Work and Social Roles

Moral identity, how individuals define themselves relative to various moral attributes, is an important factor in the regulation of moral thought and behavior in organizations. We advance both the conceptualization and operationalization of moral identity through considering the complexity of its content and structure. We do so through integrating self-complexity theory to establish a deeper understanding of how individuals both define themselves as moral beings and structure moral attributes within their self-concepts across various roles (e.g., co-worker, son/daughter, etc.). To date, moral identity has been conceptualized as a global unitary construct. Across five samples, we find that it is dimensional (having dimensions of Benevolence, Justice, Obligation, and Integrity) and that individuals construe themselves relative to each dimension differently across sub-identities/roles. We then assess the effects of this expanded construct across two samples and find that using the complex structure of moral identity, we can predict situated ethical intentions and deviance within and across roles. Further, a within-participants experiment using an additional sample demonstrates that when priming a focal role, the unique moral identity content constituting that role provides predominant influence on corresponding ethical choices.
Nonlinear Partial Least Squares An Overview

In many areas of research and industrial situations, including many data analytic problems in chemistry, a strong nonlinear relation between different sets of data may exist. While linear models may be a good simple approximation to these problems, when nonlinearity is severe they often perform unacceptably. The nonlinear partial least squares (PLS) method was developed in the area of chemical data analysis. A specific feature of PLS is that relations between sets of observed variables are modeled by means of latent variables usually not directly observed and measured. Since its introduction, two methodologically different concepts of fitting existing nonlinear relationships initiated development of a series of different nonlinear PLS models. General principles of the two concepts and representative models are reviewed in this chapter. The aim of the chapter is two-fold i) to clearly summarize achieved results and thus ii) to motivate development of new computationally efficient nonlinear PLS models with better performance and good interpretability.
Detecting Romanized Thai tokens in social media texts

Social media contents were created by a large number of users or writers. Additionally, each of them has their own writing styles, which depend on their creative thinking or attitudes. As commonly found in online social networks of Thai users, typed texts sometimes include Thai words that were transliterated with Roman letters. Therefore, text-to-speech systems cannot pronounce these transliterated tokens correctly. In this work, we propose and evaluate statistical methods for detecting Romanized Thai tokens. Both context-dependent and context-free classification features are proposed. Real social network texts are used for constructing the training set and the test set. Human subjects can detect Thai Romanized tokens at 91.16% accuracy on average when adjacent contexts are hidden while the accuracy is at 99.41% with contexts. With the proposed features, a decision tree-based classifier and an N-gram-based classifier yield 87.63% and 74.42% accuracy, respectively. In the later case, the accuracy increases to 82.60% when the tokens' existence in English dictionaries is considered. Combining the two methods results in a detection accuracy of 89.36%.
Exercise instructor-led functional training programme for stroke survivors in the community

Background: Action for Rehabilitation in Neurological Injury (ARNI) is a novel community-based training programme that promotes functional independence and physical fitness in stroke survivors after formal rehabilitation. Method: We investigated the experiences of 22 stroke survivors enrolled in the 12-week programme, through their additional participation in one of four focus groups. Guidance topics informed the discussions, which were audio-recorded and transcribed. Thematic analysis was conducted by two independent researchers and consensus reached on the overarching themes. Results: The programme was described as overwhelmingly positive, with ascribed benefits in physical, functional and social activities, leading to a reported increase in active participation in life. Key contextual factors were discussed as being critical to develop these benefits including: the balance of group and 1:1 training, the nature of challenge in the activities and the de-medicalisation of both the environment and the intervention itself. Conclusions: Group-based ARNI was reported as having a positive impact on function and participation in life post-stroke. This study indicates a need for further examination of the objective impact of the ARNI programme.
Sensor fusion and occlusion refinement for tablet-based AR

This paper presents a set of technologies which enable robust, accurate, high resolution augmentation of live video, delivered via a tablet PC to which a video camera has been attached. By combining several technologies, this is achieved without the use of contrived markers in the environment: An outside-in tracker observes the tablet to generate robust, low-accuracy pose estimates. An inside-out tracker running on the tablet observes the video feed from the tablet-mounted camera and provides high-accuracy pose estimates by tracking natural features in the environment. Information from both of these trackers is combined in an extended Kalman filter. Finally, to maximise the quality of the augmented imagery, boundaries where the real world occludes the virtual imagery are identified and another tracker is used to refine the boundaries between real and virtual imagery so that their synthesis is as convincing as possible.
Performance Comparison and Improvement of Broadcasting Protocols in Mobile Ad-hoc Network 1

Mobile Ad-hoc Network (MANET) is expected to be deployed in various scenarios having complex node mobility and connectivity dynamics in the future. Optimal broadcasting in mobile Ad-hoc networking is crucial for providing control and routing information for multicast and point to point communication algorithms. In this paper presents an overview on the state of the art of broadcasting techniques in mobile Ad-hoc networks, compare their performance and make recommendations to improve the efficiency performance of broadcasting techniques. In this research work we have introduced the four different types of broadcasting protocols, describe a clear concept of these protocols because we are going to work with these protocols. For this research work, we have used ns-2 simulator in Red Hat Linux environment successfully. We specially make comparison between simple flooding broadcast and probabilistic broadcast with 7 different probabilities by using two parameters which are average number of redundant broadcast and average number of single broadcast. For optimal power efficient broadcast we have analyzed the collected data and finally we have found that in probabilistic broadcast, the probability of approximately 0.275 is the best one.
Automatic Feature Generation for Setting Compilers Heuristics

Heuristics in compilers are often designed by manually analyzing sample programs. Recent advances have successfully applied machine learning to automatically generate heuristics. The typical format of these approaches reduces the input loops, functions or programs to a finite vector of features. A machine learning algorithm then learns a mapping from these features to the desired heuristic parameters. Choosing the right features is important and requires expert knowledge since no machine learning tool will work well with poorly chosen features. This paper introduces a novel mechanism to generate features. Grammars describing languages of features are defined and from these grammars sentences are randomly produced. The features are then evaluated over input data and computed values are given to machine learning tools. We propose the construction of domain specific feature languages for different purposes in different parts of the compiler. Using these feature languages, complex, machine generated features are extracted from program code. Using our observation that some functions can benefit from setting different compiler options, while others cannot, we demonstrate the use of a decision tree classifier to automatically identify the former using the automatically generated features. We show that our method outperform human generated features on problems of loop unrolling and phase ordering, achieving a statistically significant decrease in run-time compared to programs compiled using GCC’s heuristics.