nils-herrmann/clean_scirepeval huggingface dataset:
Effectiveness of training of Neuro linguistic programming (NLP) strategies on reducing social anxiety

The paper was aimed at exploring the efficacy of Neuro linguistic programming (NLP) strategies on social anxiety people referring to counseling centers. To do this, the number of 30 people with social anxiety disorder who had referred for counseling was selected as the sample, and completed Liebowitz Social Anxiety Scale (1987) The pretest having been administered, 30 people were randomly placed in experimental and control groups respectively. The experimental group received ten sessions of training of neuro linguistic programming strategies. The interventions were a combination of NLP tools, including the art of self assertiveness, WACO system, Neuro linguistic logical level, goal setting, Switch system, chart sections, rapport, Metal model, encore and modeling. After the interventions were ended, the subjects of both groups were tested with Liebowitz Test twice. The data obtained were examined by using SPSS software ver.17 and statistical independent T Test. Research findings illustrated a significant difference between the experimental and control groups as regards posttest social anxiety scores (P 0/001) Also, with regards to performance anxiety, a significant difference was seen between the two groups in the posttest (P 0/001)
Real time text detection and recognition on hand held objects to assist blind people
Venue: 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT)

This paper presents camera based system which will help blind person for reading text patterns printed on hand held objects. This is the framework to assist visually impaired persons to read text patterns and convert it into the audio output. To obtain the object from the background and extract the text pattern from that object, the system first proposes the method that will capture the image from the camera and object region is detected. The text which are maximally stable are detected using Maximally Stable External Regions (MSER) feature. A novel algorithm is evaluated on variety of scenes. The detected text is compared with the template and converted into the speech output. The text patterns are localized and binarized using Optical Character Recognition (OCR) The recognized text is converted to an audio output. The speech output is given to the blind user. Experimental results shows the analysis of MSER and OCR for different text patterns. MSER shows that it is robust algorithm for the text detection. Therefore, this paper deals with analysis of detection and recognition of different text patterns on different objects.
RELATIONSHIP BETWEEN OUTSOURCING ACCOUNTING TASKS AND FINANCIAL PERFORMANCE OF SMALL AND MEDIUM ENTERPRISES IN NIGERIA STRATEGIC RELATEDNESS FACTOR USING THE PARTIAL LEAST SQUARE APPROACH

The overall objective of the study was to investigate the relationship between outsourcing accounting tasks and the financial performance of small and medium enterprises in Nigeria with emphasize on strategic relatedness factor. Several hypotheses were formulated based on the constructs of the Independent Variable. The study specifically seeks to explore the role of outsourcing of accounting functions based on some drivers, as given by the Transaction Cost Economies theory (TCE) The target population of the study are the SMEs in three Geo political zones of Southern part of Nigeria consisting of 5,796 SMEs. The sampling technique adopted was a two stage sampling technique applied chronologically as follows: stratified and simple random sampling techniques which eventually produced a sample size of 411 used for the study. Both primary and secondary data options were explored with the main aim of making sufficient data available for the study. Structured Questionnaires were used to collect primary data from the respondent organisations and the secondary data involved the collection of the Annual Financial Reports of the respondent organisations for the 5 year period covering 2008 to 2012 for the extraction of the financial performance indices. The data were subjected to various statistical screening for reliability of the instrument and validity of the variables (in terms of Construct and Convergent validity) Structural Equation Model (SEM) was employed to analyse the data vide SPSS 23 and SmartPLS packages in order to obtain the statistical significance and the direction of the relationships between Inner and Outer models of the study. The study revealed that there was significant relationship between outsourcing of accounting tasks and the financial performance of SMEs in Nigeria when strategic relatedness factor is put into consideration. The study finally recommended that SMEs should take their external accountants as mentors and advisors on how to further devise their firm strategies. This is easily achieved when the outsourcing relationship established is for long term as against the usual short term practices. Here the parties involved would align their goals (goals congruency) and put an arrangement in place for easy knowledge sharing among all the parties involved in the relationship.
Relationship Determinants of Performance in Service Triads: A Configurational Approach

The increasing popularity of service based strategies among manufacturers, such as solution provision, makes service triads commonplace within business. While there is some consensus that "relational" (i.e. close or collaborative) relationships are beneficial for the performance of individual actors and the triad as a whole, there is little known about what exactly affects the service performance of an actor in these triads. In this study, we investigate the influence of the manufacturer service supplier relationship on the performance of the service supplier toward the manufacturer's customers. As this phenomenon is causally complex and context dependent, we assume that there will be alternative configurations of relationship characteristics and contingent factors that lead to high service performance. To uncover potential configurations, we deployed fuzzy set qualitative comparative analysis, on data collected from 38 triads within the network of a large Anglo German commercial vehicle manufacturer. Our research shows that in this context superior service performance cannot be generalized to one relationship configuration and is also contingent upon exogenous factors that is, contract support and service site size. We uncovered four "core" configurations of relationship dimensions and two exogenous factors. Three of the configurations exhibited relational properties, while the fourth configuration had transactional properties. This is counter to extant research findings. We extend the perspective that within triads, service performance is not an outcome of a single "close," or "collaborative" relationship and is a combination of multiple configurations consisting of varying relationship dimensions and exogenous factors.
Single Image Haze Removal Using Dark Channel Prior

Haze brings troubles to many computer vision/graphics applications. It reduces the visibility of the scenes and lowers the reliability of outdoor surveillance systems; it reduces the clarity of the satellite images; it also changes the colors and decreases the contrast of daily photos, which is an annoying problem to photographers. Therefore, removing haze from images is an important and widely demanded topic in computer vision and computer graphics areas. The main challenge lies in the ambiguity of the problem. Haze attenuates the light reflected from the scenes, and further blends it with some additive light in the atmosphere. The target of haze removal is to recover the reflected light (i.e. the scene colors) from the blended light. This problem is mathematically ambiguous: there are an infinite number of solutions given the blended light. How can we know which solution is true? We need to answer this question in haze removal. Ambiguity is a common challenge for many computer vision problems. In terms of mathematics, ambiguity is because the number of equations is smaller than the number of unknowns. The methods in computer vision to solve the ambiguity can roughly categorized into two strategies. The first one is to acquire more known variables, e.g. some haze removal algorithms capture multiple images of the same scene under different settings (like polarizers).But it is not easy to obtain extra images in practice. The second strategy is to impose extra constraints using some knowledge or assumptions .All the images in this thesis are best viewed in the electronic version. This way is more practical since it requires as few as only one image. To this end, we focus on single image haze removal in this thesis. The key is to find a suitable prior. Priors are important in many computer vision topics. A prior tells the algorithm "what can we know about the fact beforehand" when the fact is not directly available. In general, a prior can be some statistical/physical properties, rules, or heuristic assumptions. The performance of the algorithms is often determined by the extent to which the prior is valid. Some widely used priors in computer vision are the smoothness prior, sparsity prior, and symmetry prior. In this thesis, we develop an effective but very simple prior, called the dark channel prior, to remove haze from a single image. The dark channel prior is a statistical property of outdoor haze free images: most patches in these images should contain pixels which are dark in at least one color channel. These dark pixels can be due to shadows, colorfulness, geometry, or other factors. This prior provides a constraint for each pixel, and thus solves the ambiguity of the problem. Combining this prior with a physical haze imaging model, we can easily recover high quality haze free images.
Single image haze removal using light and dark channel prior
Venue: 2016 IEEE/CIC International Conference on Communications in China (ICCC)

In this paper, we propose a simple but effective method to remove haze from a single input image. This method is not only prior on dark channel, but also light. Based on dark channel prior (DCP) a new haze removal scheme is proposed by Dr. He in  and it is becoming popular because of its dark channel statistics of outdoor haze free images. Whereas, there are two problems in the scheme proposed in  the cost of computing the transmission map using soft mapping is high and the atmospheric light is over exposure when a bright area is shown in images. This paper proposes a novel dehazing algorithm with dark channel and light channel where the light channel is a kind of statistics of outdoor hazy images. Moreover, the guided filter is introduced to refine the dark channel and the light channel in this paper. The objective of proposed dehazing algorithm (PDA) is to alleviate or remove these problems found in  To verify the PDA and compare it with the DCP scheme, several examples are given in this paper. The results show that the PDA is about 25 times faster than the DCP since the soft matting is avoided on average, and visual quality in the PDA without over exposure problem is better than that in the DCP. With these improvements, the proposed method may be applied in video surveillance, intelligent transportation system and remote sensing.
Single Image Haze Removal Using Single Pixel Approach Based on Dark Channel Prior with Fast Filtering
Venue: ICCVG

We propose a fast image dehazing method that uses a pixelwise dark channel prior (PDCP) We argue that the neighbor minimum filter in the dark channel prior (DCP) is not crucial in image dehazing. We prove this assertion in experiments that compare dehazed images and histograms of dark channel images obtained using PDCP and DCP. To refine the transmission map in the Koschmieder model of the degradation of a hazy image, we use a fast guided filter to replace the soft matting (SM) used in DCP, because SM is the main reason for the slowness of DCP. The proposed algorithm is faster than existing methods, but achieves similar dehazing. This new method is useful for applications that require fast dehazing.
The Effect of Reward Management on Employees Commitment in the Universities in Nakuru County Kenya

The aim of this study is to examine the effect of financial and non financial rewards on organisational commitment of employees in Universities in Nakuru County. The specific objectives of the study were as follows: to determine the effect of financial reward on normative commitment of employees in universities in Nakuru County; to determine the effect of financial rewards on affective commitment of employees in universities in Nakuru County; to establish the effect of financial rewards on continuance commitment of employees in universities in Nakuru County; and finally, to determine the combined effect of rewards management on employees organisational commitment in universities in Nakuru County. The sample size of the study comprised 224 full time lecturers working in 10 universities within Nakuru County. Data was collected using the questionnaire method. The data obtained was processed using the Statistical Package for Social Sciences (SPSS) and was analysed using both descriptive and inferential statistics. The analysed data was presented using graphs, charts and tables. Results indicated that there was a moderate significant positive relationship between financial rewards and affective commitment (r 0.344, p 0.000) and a weak significant positive relationship between financial rewards and normative commitment (r 0.249, p 0.008) The study found that financial reward management practices collectively have significant effect on organizational commitment. The study recommended that universities in Nakuru County should carry out salaries reviews in order to develop a reward management structure that is externally competitive, internally fair and consistent with the current acceptable international rates. It was also recommended that the top management in the Universities should encourage employees' to participate in decision making and they should implement employee decision.
EFFECT OF REWARD MANAGEMENT PRACTICES ON ORGANIZATIONAL COMMITMENT IN STATE CORPORATIONS IN KENYA: A CASE STUDY OF KEFRI

Studies indicate that when employees are committed to organizations they work for they produce better outcomes for such organizations. Rewards are considered as one of the tools for enhancing organizational commitment. The pertinent issues of reward management practices that KEFRI could be facing are similar to what other research institutions are facing. This study therefore sought to investigate the effect of reward management practices on organizational commitment in KEFRI. In this regard, the study was guided by two specific objectives: to establish the effect of benefits administration and to find out what employees perceived as the base for rewards. This study adopted a case study research design using stratified sampling since data was gathered from a single source. The population was divided into sets or strata and which were mutually exclusive. The unit of analysis was the entire population of KEFRI employees, 1012 in number from which a sample of 100 was derived at from top management, middle and lower level staff. The study used questionnaires and interviews as the data collection instruments. The research carried out a pilot study to pretest and validate the instruments. Quantitative data collected were analyzed using SPSS (Version 22) Content analysis was used to test data that was qualitative in nature or aspect of the data collected from the open ended questions. The information was presented using tables, charts and in prose form.  Benefits of Administration, Base for Rewards, Rewards Management Practices, Organizational Commitment,
"Letting stories breathe" Using Patient Stories for Organizational Learning and Improvement

There has been a recent upsurge in the use of patient stories to better understand patients' experiences of illness and of care, and to inspire leaders and staff for quality and safety within healthcare. However, to fully realize the potential of patient stories, a more nuanced understanding is needed of how they are used, who tells them, for what purpose, and in what context. Using a constructivist case study methodology with qualitative methods, this study examined four healthcare organizations that are known leaders in the systematic and deliberate use of patient stories, exploring the storytellers, the types of stories told and their purposes. It also examined the contexts that enable the use of stories and the impact they have had on organizational learning and quality improvement. An interpretivist approach to analysis highlighted the specific types of stories told by patients and of patients, and how they were co constructed from stories of chaos into quest stories for learning, "authorized stories" to be shared for particular purposes. The storytellers who emerged were those who had extended their involvement as patient advisors/members, determined by leaders to be the "right fit" and at the "right time" to share their stories. Strong leaders modeled and supported the philosophical orientation toward patient and familycentred care that patient stories helped to develop and sustain. Leaders also created the
What can be learned from patient stories about living with the chronicity of heart illness? A narrative inquiry
Venue: Contemporary nurse

Patients' illness stories are valuable information that supports person centred care across the illness trajectory.  To learn how older South Asian immigrant women experience living with heart illness long after discharge from hospital.  We used narrative inquiry, a personal experience method that explores and interprets lived and told stories through the three dimensions of experience.  Four participants, over the age of sixty, living with heart illness for over ten years, were invited to engage in narrative interview and Narrative Reflective Process.  Giving patients voice, allows caregivers insight into the human experience of illness beyond hospitalization. Considering the increased migration of people around the globe, this knowledge is significant in provision of person centred care.  Person centred care does not end with the hospitalization and outpatient clinics. Inter disciplinary teams need to reconsider the trajectory of chronic illnesses and the care required throughout, especially for marginalized populations.
Using patient stories to reflect on care.
Venue: Nursing times

There is an increasing emphasis on, and commitment to, using patient narratives in nursing practice and nurse education. Listening to the voices of those receiving our care is just the beginning. The challenge is to use these narratives to improve practice and the patient experience. This seven part series will present narratives from three fields of nursing: adult, mental health and learning disability. Each article will include opportunities to reflect on the stories presented and consider their implications for practice. Part 1 explores how patient narratives can be used as an evidence base for nursing practice. It uses a patient story to explore the unintended consequences of communication between a nurse and a patient, as well as how the environment in which patients find themselves can relay important messages.
Bridging the boundaries between scientists and clinicians mechanistic hypotheses and patient stories in risk assessment of drugs

The cultural divide between scientists and clinicians has been described as undermining the advance of medical science, by hindering the production of practice relevant research and of research informed clinical decisions. Here, I consider the field of post marketing risk assessment of drugs as an example of strict interdependence between basic biomedical research, clinical research, and clinical evaluation and show how it would benefit from a closer collaboration between scientists and clinicians. The risk assessment of drugs after their marketing relies on spontaneous adverse effect reports to drug agencies and on peer reviewed case reports. I emphasize the importance of qualitative analysis of such reports for the improvement of mechanistic understanding of harmful effects of drugs. I argue that mechanistic explanations of drug effects are at least as important as determination of their frequency, in order to establish causation. An ideal risk assessment, then, verifies not only the frequency of undesired effects but also why and how the harm happens. For this purpose, the frequency or novelty of the unintended outcome, although contextually indicative, should not determine the epistemic value of a report. Details about the context that generated an unexpected outcome, instead, can offer the chance of improving causal understanding about how the intervention works. This is illustrated through examples from medical research. Mechanistic understanding is a domain of joint collaboration among (1) clinicians, in charge of detailed, qualitative reporting of patient stories about side effects, (2) qualitative clinical researchers, in charge of analyzing clinical contexts or harmful effects and formulating explanatory hypotheses, and (3) basic biomedical researchers, in charge of verifying such hypotheses. In addition, direct information flow can on one side focus clinicians' attention on knowledge gaps about drugs/effects where more research is needed, while on the other side create a more contextualized concept of mechanism among scientists.
Improving the Reliability of Decision Support Systems for Nuclear Emergency Management by Leveraging Software Design Diversity
Venue: J. Comput. Inf. Technol.

This paper introduces a novel method of continuous verification of simulation software used in decision support systems for nuclear emergency management (DSNE) The proposed approach builds on methods from the field of software reliability engineering, such as N Version Programming, Recovery Blocks, and Consensus Recovery Blocks. We introduce a new acceptance test for dispersion simulation results and a new voting scheme based on taxonomies of simulation results rather than individual simulation results. The acceptance test and the voter are used in a new scheme, which extends the Consensus Recovery Block method by a database of result taxonomies to support machine learning. This enables the system to learn how to distinguish correct from incorrect results, with respect to the implemented numerical schemes. Considering that decision support systems for nuclear emergency management are used in a safety critical application context, the methods introduced in this paper help improve the reliability of the system and the trustworthiness of the simulation results used by emergency managers in the decision making process. The effectiveness of the approach has been assessed using the atmospheric dispersion forecasts of two test versions of the widely used RODOS DSNE system. ACM CCS (2012) Classification Information systems Information systems applications Decision support systems Expert systems *To cite this article: T. B. Ionescu and W. Scheuermann, "Improving the Reliability of Decision Support Systems for Nuclear Emergency Management by Leveraging Software Design Diversity" CIT. Journal of Computing and Information Technology vol. 24, no. 1, pp. 45 63, 2016.