Miracle-dz/UniFAR-FTUs huggingface dataset:
What happens to psychological depth in brief-term therapy with trainee therapists? : clients' experiences of therapy and the therapeutic relationship using Interpretative Phenomenological Analysis (IPA)

The purpose of this doctoral research project was to explore the conscious and subconscious thoughts and feelings clients had about their experience of brief-term therapy and the therapeutic relationship. The methodology chosen was IPA because it examines in systematic detail the complex understandings from individuals’ making sense of a phenomenon. Face-to-face semi-structured interviews were conducted individually with 10 clients who had recently ended their therapy with trainee integrative practitioners at one low-cost counselling agency. To reach psychological depth, an ‘object-tray’ of miniature objects was used in each interview to facilitate access to clients’ intrapsychic and intersubjective processes, and to trigger metaphorical thinking. Findings detail a wide range of clients’ multi-faceted fears and thoughts, and reveal how these relate to client negativity, defensiveness and resistance. They demonstrate how clients were active in overcoming their fears (or not! ) and illustrate how clients reflected upon their therapeutic relationships. This study also offers clients’ perceptions of various helpful interventions found to be therapeutically successful for reducing fears, and for facilitating psychological recovery and personal growth. The impact of this study could influence assessment procedures, and therapeutic practice and trainings by helping assessors and therapists develop their skills in demystifying therapy for clients and addressing client fears; it could also potentially help to understand the reasons for drop-outs. Furthermore, this study emphasises the need for therapists to obtain client feedback both early on and continually throughout the course of therapy and for therapists to work collaboratively according to clients’ needs and wants. However it must be recognised that, as this was small scale research carried out in one particular location, it is possible that another group of ex-client participants would have different sense-making narratives and another researcher might have responded differently in the interview process, perhaps drawing out different data with correspondingly different considerations.
BART: A modular toolkit for coreference resolution

Developing a full coreference system able to run all the way from raw text to semantic interpretation is a considerable engineering effort. Accordingly, there is very limited availability of off-the shelf tools for researchers whose interests are not primarily in coreference or others who want to concentrate on a specific aspect of the problem. We present BART, a highly modular toolkit for developing coreference applications. In the Johns Hopkins workshop on using lexical and encyclopedic knowledge for entity disambiguation, the toolkit was used to extend a reimplementation of Soon et al.s proposal with a variety of additional syntactic and knowledge-based features, and experiment with alternative resolution processes, preprocessing tools, and classifiers. BART has been released as open source software and is available from http://www.sfs. uni-tuebingen.de/~versley/BART
Do we miss depressive disorders and suicidal behaviours in clinical practice?

This study involved a detailed standardized initial research assessment which was carried out with 100 young people aged 12–15 years newly referred to a child and adolescent mental health service. The assessment involved the K-SADS interview with the young person and their parent, the Strengths and Difficulties Questionnaire, the Clinical Global Impression Scale, and the Children’s Global Assessment Scale. Diagnoses resulting from these ‘research assessments’ were compared with clinical diagnoses, which were determined by case note analysis and discussion with the key clinician. Results showed that a clinical diagnosis of depressive disorder was made in only one-third of those who received a ‘research assessment’ diagnosis of depressive disorder, and suicidality was missed in a significant proportion of cases. Those with a diagnosis of depressive disorder had significantly more problems, more comorbidity, more suicidality and greater functional impairment than those without. It is important to keep depression and suicidality in mind when assessing young people with complex mental health difficulties. Unless specific pointers are sought, it is easy to miss these, which may mean that vulnerable young people do not benefit from potentially effective treatments.
Grand Challenges for Evolutionary Robotics

Evolutionary Robotics is a field that “aims to apply evolutionary computation techniques to evolve the overall design or controllers, or both, for real and simulated autonomous robots” (Vargas et al., 2014). This approach is “useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes” (Floreano et al., 2008). However, as noted in Bongard (2013) “the use of metaheuristics (i.e., evolution) sets this subfield of robotics apart from the mainstream of robotics research,” which “aims to continuously generate better behavior for a given robot, while the long-term goal of Evolutionary Robotics is to create general, robot-generating algorithms. ” One could say that Evolutionary Robotics is a test ground or experimental toolbox to study various issues arising on the road to intelligent and autonomous machines. The related issues include embodied cognition and intelligence, self-organization and collective behavior, the emergence of communication and cooperation, co-evolution, neuro-evolution, and many more with robots forming the substrate or medium for the experiments (Nolfi and Floreano, 2000; Wang et al., 2006; Floreano et al., 2008; Trianni, 2008; Doncieux et al., 2011; Bongard, 2013; Vargas et al., 2014). Given the fact that many Evolutionary Robotics investigations are performed in simulation, there is a big overlap with a subfield in Artificial Life research that is concerned with evolving virtual creatures and societies. However, I think it is safe to say that robotics can be distinguished because it ultimately aims at real physical robots (a.k.a. intelligent machines, animate artifacts, or artificial organisms) that exist and operate in the real world. Their bodies can be made of traditional mechatronic components, (self-) assembled from simple modular units, formed by some soft material, 3D printed plastics, some fancy new stuff invented by material scientists, or any combination of these,but in the end the robots must be physical entities. Therefore, Grand Challenges for Evolutionary Robotics must be tangible. Furthermore, Grand Challenges should be demonstrations of evolution, either a particular property of it, or the process of evolution as a whole. In the following, I propose three Grand Challenges, subject to discussion. This list is not meant to capture the ultimate goals for the field. Rather, it is meant to inspire the community to deliberate and collectively identify the bold dreams that can lead further developments. This paper will have achieved its main goal if the list of Grand Challenges is discussed and revised, leading to an adjusted version that has a broad support.
MEMOS: an interactive assistive system for prospective memory deficit compensation-architecture and functionality

The Mobile Extensible Memory Aid System (MEMOS) is an electronic memory aid system which was developed to support patients with deficits in the prospective memory after a brain injury. A special palmtop computer, the Personal Memory Assistant (PMA), reminds the patient of important tasks and supervises the patient's actions. The PMA communicates with a stationary care system via a bidirectional cellular radio connection (GPRS). MEMOS features structured interactive reminding impulses, a flexible task scheduling, integration of a heterogeneous group of caregivers and integration in the patient's everyday life. The bidirectional communication allows for reporting of critical situations back to a responsible care-giver, so MEMOS can be used even in a critical context. This paper describes the requirements for a memory aid system, the design and functionality of MEMOS as well as its application in the practical care of patients.
Determinants of research citation impact in nanoscience and nanotechnology

This study investigates a range of metrics available when a nanoscience and nanotechnology article is published to see which metrics correlate more with the number of citations to the article. It also introduces the degree of internationality of journals and references as new metrics for this purpose. The journal impact factor; the impact of references; the internationality of authors, journals, and references; and the number of authors, institutions, and references were all calculated for papers published in nanoscience and nanotechnology journals in the Web of Science from 2007 to 2009. Using a zero‐inflated negative binomial regression model on the data set, the impact factor of the publishing journal and the citation impact of the cited references were found to be the most effective determinants of citation counts in all four time periods. In the entire 2007 to 2009 period, apart from journal internationality and author numbers and internationality, all other predictor variables had significant effects on citation counts.
Power-efficient calibration and reconfiguration for optical network-on-chip

Recent advances in nanophotonic fabrication have made the optical network-on-chip an attractive interconnect option for next-generation multi-/many-core systems, providing high bandwidth and power efficiency. Both postfabrication and runtime calibration of the optical components (ring resonators) are essential to building a robust optical communication system, as they are highly sensitive to process and thermal variation. Existing tuning methods based on bias voltage and temperature adjustment require excessive power to fully compensate for these variations. In this work, we propose a set of complementary techniques to address this challenge and significantly reduce the tuning power consumption: 1) a subchannel remapping scheme to decrease the required tuning from the free spectral range to less than one channel (typically less than 1 nm); 2) a transceiver-based network topology capable of building and tuning far fewer rings while maintaining the same system throughput. Our results show that the proposed methods can together reduce the tuning power by as much as 99.85%.
Knowledge infusion into content-based recommender systems

Content-based recommender systems try to recommend items similar to those a given user has liked in the past. The basic process consists of matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object (item). Common-sense and domain-specific knowledge may be useful to give some meaning to the content of items, thus helping to generate more informative features than "plain" attributes. The process of learning user profiles could also benefit from the infusion of exogenous knowledge or open source knowledge, with respect to the classical use of endogenous knowledge (extracted from the items themselves). The main contribution of this paper is a proposal for knowledge infusion into content-based recommender systems, which suggests a novel view of this type of systems, mostly oriented to content interpretation by way of the infused knowledge. The idea is to provide the system with the "linguistic" and "cultural" background knowledge that hopefully allows a more accurate content analysis than classic approaches based on words. A set of knowledge sources is modeled to create a memory of linguistic competencies and of more specific world "facts", that can be exploited to reason about content as well as to support the user profiling and recommendation processes. The modeled knowledge sources include a dictionary, Wikipedia, and content generated by users (i.e. tags provided on items), while the core of the reasoning component is a spreading activation algorithm.
Generalized regression neural networks in time-varying environment

The current state of knowledge regarding nonstationary processes is significantly poorer then in the case of stationary signals. In many applications, signals are treated as stationary only because in this way it is easier to analyze them; in fact, they are nonstationary. Nonstationary processes are undoubtedly more difficult to analyze and their diversity makes application of universal tools impossible. In this paper we propose a new class of generalized regression neural networks working in nonstationary environment. The generalized regession neural networks (GRNN) studied in this paper are able to follow changes of the best model, i.e., time-varying regression functions. The novelty is summarized as follows: 1) We present adaptive GRNN tracking time-varying regression functions. 2) We prove convergence of the GRNN based on general learning theorems presented in Section IV. 3) We design in detail special GRNN based on the Parzen and orthogonal series kernels. In each case we precise conditions ensuring convergence of the GRNN to the best models described by regression function. 4) We investigate speed of convergence of the GRNN and compare performance of specific structures based on the Parzen kernel and orthogonal series kernel. 5) We study various nonstationarities (multiplicative, additive, "scale change," "movable argument") and design in each case the GRNN based on the Parzen kernel and orthogonal series kernel.
One-year predictive factors for various aspects of neck disorders.

STUDY DESIGN A longitudinal epidemiologic study conducted over 12 months among active workers in different occupations. The study was primarily designed to evaluate intervention for prevention of low back and other spinal disorders. OBJECTIVE To determine factors that predict incidence, recurrence, and persistence of neck disorders (ND), taking into account various dimensions of ND. SUMMARY OF BACKGROUND DATA Neck pain is often supposed to have essentially the same risk factors as back pain; however, there is comparatively little data relevant to this issue. Moreover, there is a lack of prospective studies that take into account a diversity of predictive factors. METHODS The Nordic questionnaire for the analysis of musculoskeletal symptoms was completed twice at a 12-month interval by 568 workers. Predictive factors were studied with logistic models for four dimensions of ND from the second questionnaire: 1) any ND in the past 6 months; 2) ND for more than 30 days; 3) treatment for ND; and 4) visit to a health care professional for ND. The predictive factors were obtained from the first questionnaire and included gender, age, occupational group, level of psychological distress and psychosomatic problems, and ND at baseline. RESULTS Female gender and older age were predictors of ND. Headaches or pain in the head, psychological distress, and psychosomatic problems were predictors for all dimensions of ND. These effects were observed for both incidence and persistence of ND. CONCLUSIONS The results emphasize the role of psychosomatic and psychological factors in the occurrence and course of ND for various dimensions of the disorder.
Event recognition in egocentric videos using a novel trajectory based feature

This paper proposes an approach for event recognition in Egocentric videos using dense trajectories over Gradient Flow - Space Time Interest Point (GF-STIP) feature. We focus on recognizing events of diverse categories (including indoor and outdoor activities, sports and social activities and adventures) in egocentric videos. We introduce a dataset with diverse egocentric events, as all the existing egocentric activity recognition datasets consist of indoor videos only. The dataset introduced in this paper contains 102 videos with 9 different events (containing indoor and outdoor videos with varying lighting conditions). We extract Space Time Interest Points (STIP) from each frame of the video. The interest points are taken as the lead pixels and Gradient-Weighted Optical Flow (GWOF) features are calculated on the lead pixels by multiplying the optical flow measure and the magnitude of gradient at the pixel, to obtain the GF-STIP feature. We construct pose descriptors with the GF-STIP feature. We use the GF-STIP descriptors for recognizing events in egocentric videos with three different approaches: following a Bag of Words (BoW) model, implementing Fisher Vectors and obtaining dense trajectories for the videos. We show that the dense trajectory features based on the proposed GF-STIP descriptors enhance the efficacy of the event recognition system in egocentric videos.
Speech-Based Non-Prototypical Affect Recognition for Child-Robot Interaction in Reverberated Environments

We present a study on the effect of reverberation on acousticlinguistic recognition of non-prototypical emotions during child-robot interaction. Investigating the well-defined Interspeech 2009 Emotion Challenge task of recognizing negative emotions in children’s speech, we focus on the impact of artificial and real reverberation conditions on the quality of linguistic features and on emotion recognition accuracy. To maintain acceptable recognition performance of both, spoken content and affective state, we consider matched and multi-condition training and apply our novel multi-stream automatic speech recognition system which outperforms conventional Hidden Markov Modeling. Depending on the acoustic condition, we obtain unweighted emotion recognition accuracies of between 65.4 % and 70.3 % applying our multi-stream system in combination with the SimpleLogistic algorithm for joint acoustic-linguistic analysis.
SHAKE, rattle, and roll: Efficient constraint algorithms for linked rigid bodies

We present an iterative constraint algorithm, QSHAKE, for use with semirigid molecules in molecular dynamics simulations. The algorithm is based on “SHAKE‐ing” bond constraints between rigid bodies, whose equations of motion are solved in the quaternion framework. The algorithm is derived and its performance compared with SHAKE for liquid octane. QSHAKE is significantly more efficient whenever SHAKE requires triangles (or tetrahedra) of constraints to maintain molecular shape. Efficiencies are gained because QSHAKE reduces the number of holonomic constraints that must be solved iteratively and requires fewer iterations to obtain convergence. The gains in efficiency are most noticeable when a high degree of precision is imposed on the constraint criteria. QSHAKE is also stable at larger time steps than SHAKE, thus allowing for even more efficient exploration of phase space for semirigid molecules. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 102–111, 1998
Cluster Identiication on a Distributed Memory Multiprocessor Cluster Identiication on a Distributed Memory Multiprocessor

The cluster identiication step is often the bottleneck in multiprocessor simulations of spin models for statistical mechanics. We have applied a connected component labeling algorithm originally developed for VLSI circuit extraction to the cluster identiication problem. The algorithm is extended to more than two dimensions , abstracting away unnecessary spatial information to simplify implementation in higher dimensions. We identify two types of spatial locality in cluster con-gurations, and present optimizations to exploit each type of locality. Performance results are presented from two and three-dimensional Ising model simulations .
Resistant Use of Project Management Methodologies - Using Psychology to Rethink the Influence of Methodology Attributes

Even though practitioners and researchers generally agree that pro- ject management methodologies are very useful in managing IT-related pro- jects, organizations are rarely able to motivate their staff to use them. Even when employees use these methodologies, the question of how they are being used still remains. To better understand the way in which employees use project management methodologies, we develop the construct "resistant use. " Applying a diffusion of innovations perspective, we develop a conceptual model to exam- ine how methodology attributes interact with users psychological needs to in- fluence a user's resistant use behavior. Based on a sample size of 2645 partici- pants, and using the structural equation modeling technique, we find that a us- er's need for achievement and need for cognition moderate the impact of pro- ject management methodologies' attributes (relative advantage, complexity, and compatibility) on their resistant usage behavior.
Using the Max-Sum Algorithm for Supply Chain Emergence in Dynamic Multiunit Environments

Supply chain formation (SCF) is the process of determining the set of participants and exchange relationships within a network with the goal of setting up a supply chain that meets some predefined social objective. Many proposed solutions for the SCF problem rely on centralized computation, which presents a single point of failure and can also lead to problems with scalability. Decentralized techniques that aid supply chain emergence offer a more robust and scalable approach by allowing participants to deliberate between themselves about the structure of the optimal supply chain. Current decentralized supply chain emergence mechanisms are only able to deal with simplistic scenarios in which goods are produced and traded in single units only and without taking into account production capacities or input-output ratios other than 1:1. In this paper, we demonstrate the performance of a graphical inference technique, max-sum loopy belief propagation (LBP), in a complex multiunit unit supply chain emergence scenario which models additional constraints such as production capacities and input-to-output ratios. We also provide results demonstrating the performance of LBP in dynamic environments, where the properties and composition of participants are altered as the algorithm is running. Our results suggest that max-sum LBP produces consistently strong solutions on a variety of network structures in a multiunit problem scenario, and that performance tends not to be affected by on-the-fly changes to the properties or composition of participants.