Miracle-dz/UniFAR-FTUs huggingface dataset:
A classification framework for clinical information system implementation in hospitals

In the last decade, many information system (IS) implementations took place in the healthcare organisations. Mainstream reasons for this evolvement are the increase of quality and safety of care, and reducing costs. As in many other sectors IS implementations in healthcare are complex, and confronted with many types of difficulties that significantly hinder achieving the objected benefits. So far, a number of systematic overviews and classifications of critical success factors (CSFs) have been available for IS implementation in healthcare, but a CSF framework specifically for clinical information systems (CISs) in hospitals does not exist. This paper provides such a framework, validates it, and translates it into a practical contribution for IT project managers in hospitals. The framework is based on a systematic literature review that has been performed to extract relevant SCFs from recent academic publications. After a filtering process, 58 publications have been selected from which 248 success factors have been extracted. Building upon seven existing classifications, the classification framework for CSFs in CIS implementations was build. Expert interviews were conducted to validate the framework and the relative weights of eight CSF categories.
Both Market and Hierarchy : A MultiTask Synergy Theory of Hybrid Governance Forms

While research on alternative governance forms has focused mainly on intermediate forms lying on a single continuum between market and hierarchy, we capture a broader range of real-world governance forms by taking a multi-dimensional view of true hybrids that are very market-like on some dimensions while simultaneously being very hierarchy-like on others. Building on the multi-task principal-agent model of Holmstrom and Milgrom, we develop a taxonomy of hybrid forms, and derive conditions under which each would be optimal. Our predictions differ from those of the earlier theory because we allow for synergies among tasks that had arbitrarily been assumed away.
Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model

For the integrate-and-fire model with or without reversal potentials, we consider how correlated inputs affect the variability of cellular output. For both models, the variability of efferent spike trains measured by coefficient of variation (CV) of the interspike interval is a nondecreasing function of input correlation. When the correlation coefficient is greater than 0.09, the CV of the integrate-and-fire model without reversal potentials is always above 0.5, no matter how strong the inhibitory inputs. When the correlation coefficient is greater than 0.05, CV for the integrate- and-fire model with reversal potentials is always above 0.5, independent of the strength of the inhibitory inputs. Under a given condition on correlation coefficients, we find that correlated Poisson processes can be decomposed into independent Poisson processes. We also develop a novel method to estimate the distribution density of the first passage time of the integrate-and-fire model.
A Distributed Subgradient Method for Dynamic Convex Optimization Problems Under Noisy Information Exchange

We consider a convex optimization problem for non-hierarchical agent networks where each agent has access to a local or private time-varying function, and the network-wide objective is to find a time-invariant minimizer of the sum of these functions, provided that such a minimizer exists. Problems of this type are common in dynamic systems where the objective function is time-varying because of, for instance, the dependency on measurements that arrive continuously to each agent. A typical outer-loop optimization iteration for optimization problems of this type consists of a local optimization step based on the information provided by neighboring agents, followed by a consensus step to exchange and fuse local estimates of the agents. A great deal of research effort has been directed towards developing and better understanding such algorithms, which find many applications in distinct areas such as cognitive radio networks, distributed acoustic source localization, coordination of unmanned vehicles, and environmental modeling. Contrasting with existing work, which considers either dynamic systems or noisy links (but not both jointly), in this study we devise and analyze a novel distributed online algorithm for dynamic optimization problems in noisy communication environments. The main result of the study proves sufficient conditions for almost sure convergence of the algorithm as the number of iterations tends to infinity. The algorithm is applicable to a wide range of distributed optimization problems with time-varying cost functions and consensus updates corrupted by additive noise. Our results therefore extend previous work to include recently proposed schemes that merge the processes of computation and data transmission over noisy wireless networks for fast and efficient consensus protocols. To give a concrete example of an application, we show how to apply our general technique to the problem of distributed detection with adaptive filters.
A static low-power, high-performance 32-bit carry skip adder

In this paper, we present a full-static carry-skip adder designed to achieve low power dissipation and high-performance operation. To reduce the adder's delay and power consumption, the adder is divided into variable-sized blocks that balance the inputs to the carry chain. The optimum block sizes for minimizing the critical path delay with complementary carry generation are achieved. Within blocks, highly optimized carry look-ahead logic, which computes block generate and block propagate signals, is used to further decrease delay. The adder architecture decreases power consumption by reducing the number of logic levels, glitches, and transistors. To achieve balanced delay, input bits are grouped unevenly in the carry chain. This grouping reduces active power by minimizing extraneous glitches and transitions. The adder has been implemented in 130nm CMOS technology. At 1.2V and 25C, typical performance is 1.086GHz and power dissipation normalized to 600 MHz operation is 0.786 mW.
Cloud-based dynamic electrocardiogram monitoring and analysis system

Dynamic electrocardiogram (ECG) monitoring is significantly superior to conventional static ECG inspection in application scope and clinical study. With the increasing number of guardianship and healthcare category, health surveillance platform will produce a large number of monitoring data. The traditional monitoring technology has been unable to effectively resolve the management and analysis of those generated large data. To solve these problems, this paper proposed a cloud-based dynamic ECG monitoring and analysis system. First of all, the use of acquisition sensors for acquisition, direct access to dynamic ECG signal and acceleration data; Secondly, these sensors send the gathered data to the terminal by Bluetooth for display and further process; then, the terminal will send the generated data to Cloud Storage platform for storage. The Cloud platform utilize generic hierarchical Service Modeling Framework and Cloud Storage which is consist of relational database storage (MySQL) and distributed file system storage (HDFS). Finally, utilizing MapReduce computing framework for data mining, medical statistics and similarity correlation analysis of diseases can help expert to make a health improvement plan and guide users to manage their own health. The proposed system can also allow users to better understand their own physical condition and achieve the purpose of disease monitoring and early warning.
Conflict probability estimation for free flight

The safety and efficiency of free flight will benefit from automated conflict prediction and resolution advisories. Conflict prediction is based on trajectory prediction and is less certain the farther in advance the prediction, however. An estimate is therefore needed of the probability that a conflict will occur, given a pair of predicted trajectories and their levels of uncertainty. This paper presents a method to estimate that conflict probability. The trajectory prediction errors are modeled as normally distributed, and the two error covariances for an aircraft pair are combined into a single, equivalent covariance of the relative position. A coordinate transformation is then used to derive an analytical solution. Numerical examples and a Monte Carlo validation are presented. (Author)
The Relationship Between Information Technology Professionals' Skill Requirements and Career Stage in the E-Commerce Era: An Empirical Study

Building on prior research, a field study of IT professionals is performed to determine if and how IT professionalsâ€™ technical skills requirement change as they gain experience in the workplace. Based on the results of this study, it appears that IT professionals: (1) are required to have different technical skills as their careers progress, and (2) do possess different technical skills at different stages in their careers.
NEUROBEHAVIOURAL ACTIVITY IN MICE IN THE OPEN FIELD MAZE TASK FOLLOWING LONG TERM INGESTION OF COWPEA DIET

It was therefore the aim of this study to find out whether long term consumption of cooked and uncooked beans (cowpea) diet has effects on some neurobehavioral parameters such as anxiety and fear using Swiss white mice as experimental animals, since cowpea contain serotonin and its precursor 5-Hydroxytryptophan which have neurobehavioral effects. Forty (40) Swiss mice were randomly assigned into four groups. Control (group A) were fed normal rodent chow, cooked beans (group B) received cooked beans diet (50% w/w), uncooked beans (group C) also received uncooked beans diet (50% w/w), while another set of mice was placed on serotonin precursor (5-HTP)(group D) diet (0.2mg/50g w/w) for thirty days. All the mice had access to clean drinking water. Anxiety and fear were investigated alongside food and water intake and body weight change. Involvement of serotonin pathway was also investigated using the set of mice administered serotonin precursor for comparison with the beans diet fed mice. The open field test was used to assess anxiety &fear related behaviors. The results showed that the Centre square entry and Centre square duration for the cooked and uncooked cowpea diet was statistically higher (P<0.05 and P<0.01respectively) compared to control while the grooming frequency for the cooked, uncooked and 5-HTP was significantly higher (P<0.05., P<0.001) compared to control. Duration of grooming was also significantly higher (P<0.01) compare to control. The frequency of stretch attend posture and defecation was lower in the test group (p<0.05 respectively). Signifying a decrease in the level of anxiety & fear. The administration of serotonin precursor diet (5-HTP) produced similar results as cooked or uncooked beans, thus suggesting that serotonin may be involved in the action of beans on neurobehavioral parameters. In conclusion, long term consumption of cowpea diet causes calmness and sedation. It is therefore likely that it causes sleep, reduction of aggression and muscle tone. If the result of this finding is extrapolated to man, then, cowpea diet can be used to ameliorate post-traumatic stress disorders.
Antibody to the α4 Integrin Decreases Infarct Size in Transient Focal Cerebral Ischemia in Rats

Background and Purpose—Inflammation, a process that involves neutrophils, lymphocytes, and monocytes, contributes to cerebral ischemic injury. Blockade of neutrophil adhesion to endothelium improves outcome after experimental stroke. In this study we sought to assess the contribution of lymphocytes and monocytes to ischemic brain injury. Methods—Male Lewis rats underwent 3 hours of middle cerebral artery occlusion followed by 45 hours of reperfusion. Two hours after the onset of ischemia, one group of animals received an intraperitoneal injection of antibodies to the α4 integrin (n=16); another group was injected with an isotype control antibody (n=11). Neurological examination, body temperature, and body weight were assessed at different time points after stroke. Animals were killed 48 hours after the onset of ischemia for determination of infarct volume and leukocyte counts. Results—There were no significant differences in body temperature or weight at any time. Neurological scores (deficits) were signi.
Is Enjoyment Still Important in University Second Language Education

This study investigates if undergraduate students majoring in Science, Technology, Engineering and Medicine (STEM) who have studied Japanese in the Institution-Wide Language Provision (IWLP) context experienced enjoyment in Japanese language learning. This research was held at a British STEM university in London. Questionnaires were used to generate the quantitative and qualitative data. The participants were STEM undergraduate students who were studying Japanese in the 2015/16 academic year. The results showed that the majority of students experienced enjoyment and flow in Japanese language learning. These results show that the general assumption that all learning is grim and unpleasant is not necessarily true and that the assumption may be changed. Furthermore, language learning may be considered as the same as a leisure activity such as games, shopping or hobbies. As an implication for professional practices, language teachers are encouraged to be familiar with the concept of enjoyment and flow so that they are able to manipulate to invoke students’ enjoyment and flow.
Hidden translation and orbit coset in quantum computing

We give efficient quantum algorithms for the problems of <sc>Hidden Translation</sc> and <sc>Hidden Subgroup</sc> in a large class of non-abelian groups including solvable groups of constant exponent and of constant length derived series. Our algorithms are recursive. For the base case, we solve efficiently <sc>Hidden Translation</sc> in Z <inf>p</inf><sup>n</sup>, whenever <i>p</i> is a fixed prime. For the induction step, we introduce the problem <sc>Orbit Coset</sc> generalizing both <sc>Hidden Translation</sc> and <sc>Hidden Subgroup</sc>, and prove a powerful self-reducibility result: <sc>Orbit Coset</sc> in a finite group <i>G</i> is reducible to <sc>Orbit Coset</sc> in <i>G/N</i> and subgroups of <i>N</i>, for any solvable normal subgroup <i>N</i> of <i>G</i>.
A novel feature learning for image classification using wrapper approach in GA

In many pattern recognition applications, limiting the number of features is a very important requirement due to high dimensional cost as well as the risk of “overfitting” imposed by the high-dimensional feature vectors. Feature subset selection addresses the dimensionality reduction problem by determining a subset of available features which is most essential for classification. A novel feature learning for image classification is proposed here using wrapper approach in Genetic Algorithm. The proposed method applies GA for feature subset selection and neural network for classification. The method operates by trying to choose the subset of features which lead to the largest margin of class separation between two classes. Experiments are conducted on four benchmark datasets of iris, seed, glass and wine and then used on one domain dataset of rice. Comparison of the proposed approach is made with other approaches like Multi-SVM and GA-LDA to demonstrate its effectiveness and efficiency. Analysis of the experimental results shows that the proposed method outperforms the other two approaches in classification accuracy.
‘I never did anything wrong’ – Trojan Horse: a qualitative study uncovering the impact in Birmingham

Abstract In 2014, the city of Birmingham (UK) became embroiled in a high-profile scandal that would bring it and the wider education system under disrepute. The ‘Trojan Horse’ affair, as it become known, began with an anonymised letter that claimed hard-line ‘Islamists’ were plotting and planning to take over a number of schools in Birmingham. Amongst the allegations were that some of the schools had been promoting gender segregation and that children were not safeguarded against the risks of extremism. This article examines some of those issues in relation to the Trojan Horse scandal and uses a qualitative study focused on participants from the area of Birmingham. As the tribunal hearings against the teachers have now found no wrongdoing, the findings reported in this article suggest that Muslim communities feel unfairly categorised as extremists and that such issues have impacted upon their sense of identity and belonging.
Training Deep Bidirectional LSTM Acoustic Model for LVCSR by a Context-Sensitive-Chunk BPTT Approach

This paper presents a study of using deep bidirectional long short-term memory (DBLSTM) recurrent neural network as acoustic model for DBLSTM-HMM based large vocabulary continuous speech recognition (LVCSR), where a context-sensitive-chunk (CSC) back-propagation through time (BPTT) approach is used to train DBLSTM by splitting each training sequence into chunks with appended contextual observations, and a CSC-based decoding method with possibly overlapped CSCs is used for recognition. Our approach makes mini-batch based training on GPU more efficient and reduces the latency of DBLSTM-based LVCSR from a whole utterance to a short chunk. Evaluations have been made on Switchboard-I benchmark task. In comparison with epoch-wise BPTT training, our method can achieve more than three times speedup on a single GPU card without degrading recognition accuracy. In comparison with a highly optimized DNN-HMM system trained by a frame-level cross entropy (CE) criterion, our CE-trained DBLSTM-HMM system achieves relative word error rate reductions of 9% and 5% on Eval2000 and RT03S testing sets, respectively. Furthermore, by running model averaging based parallel training of DBLSTM on a cluster of GPUs, CSC-BPTT incurs less accuracy degradation than epoch-wise BPTT while achieves a linear speedup.
Editorial: Using neurophysiological signals that reflect cognitive or affective state

The central question of this Frontiers Research Topic is: What can we learn from brain and other physiological signals about an individual's cognitive and affective state and how can we use this information? This question reflects three important issues which are addressed by the 22 articles in this volume: (1) the combination of central and peripheral neurophysiological measures; (2) the diversity of cognitive and affective processes reflected by these measures; and (3) how to apply these measures in real world applications.
Dietary, anthropometric, blood-lipid, and performance patterns of American College Football Players during 8 weeks of training.

PURPOSE The purpose of this study was to determine the dietary, anthropometric, blood-lipid, and performance patterns of university-level American football players attempting to increase body mass during 8 wk of training. METHODS Three-day diet records, body composition (DEXA scan), blood lipids, and performance measures were collected in redshirt football players (N = 15, age 18.5 ± 0.6 yr) early season and after 8 wk of in-season training. RESULTS There was an increase (p < .05) from early-season to postseason testing for reported energy (+45%), carbohydrate (+82%), and protein (+29%) intakes and no change in the intake of fat. Fat intake was 41% of energy at the early-season test and 32% of energy at the postseason test. Increases (p < .05 for all) in performance measures, lean mass (70.5 ± 7.7-71.8 ± 7.7 kg), fat mass (15.9 ± 6.2-17.3 ± 6.8 kg), plasma total cholesterol (193.5 ± 32.4-222.6 ± 40.0 mg/dl), and low-density lipoproteins (LDL; 92.7 ± 32.7-124.5 ± 34.7 mg/dl) were measured. No changes were measured in triglycerides, very-low-density lipoproteins, or high-density lipoproteins. CONCLUSION Increases in strength, power, speed, total body mass, muscle mass, and fat mass were measured. Cholesterol and LDL levels increased during the study to levels associated with higher risk for cardiovascular disease. It is possible that this is a temporary phenomenon, but it is cause for concern and an indication that dietary education to promote weight gain in a manner less likely to adversely affect the lipid profile is warranted.
Repair locality from a combinatorial perspective

Repair locality is a desirable property for erasure codes in distributed storage systems. Recently, different structures of local repair groups have been proposed in the definitions of repair locality. In this paper, the concept of regenerating set is introduced to characterize the local repair groups. A definition of locality r(δ-1) (i.e., locality r with repair tolerance δ - 1) under the most general structure of regenerating sets is given. All previously studied locality notions turn out to be special cases of this definition. Furthermore, three representative notions of locality proposed before are reinvestigated under the framework of regenerating sets, and their respective upper bounds on the minimum distance are reproved in a uniform and brief form. Additionally, a tighter distance bound is derived for the square code which is a class of linear codes with locality r(2) and high information rate, and an explicit code construction attaining the optimal distance bound is obtained.
Game theory perspectives on client: vendor relationships in offshore software outsourcing

The objective of this paper is to provide the initial literature based insights into the game theory specifically with the viewpoint of client - vendor relationships in offshore software outsourcing. Game theory has been used for long in understanding various contexts in economics and other disciplines. Offshore software outsourcing relates to the situation in which client and vendor are operating from different countries. Subsequently, in this paper, the initial understanding of game theory focusing on software engineering community is developed. Particularly risk, rationality, payoffs, and other elements of game theory are explored in terms of how they affect offshore software outsourcing. The paper is structured as follows. Section one provides introduction to game theory concept. Section two explores the history, representation and types of games. Section three compares offshore software outsourcing with types and elements of game theory. Section four discusses one of the most famous game theory examples - 'prisoners-dilemma' and relates it to software outsourcing context. Finally, section five concludes this paper with the intended future work.