Miracle-dz/UniFAR-FTUs huggingface dataset:
Secrets of a successful outsourcing contract: a risk analysis

Abstract Outsourcing information systems on a large scale is a difficult challenge. The public sector has seen some spectacular information systems (IS) outsourcing failures. Public officials have come under fire for sloppy management of IS outsourcing engagements and legislators are demanding higher standards of outsourcing practice and more accountability from public sector managers. This paper analyzes a large outsourcing contract for software maintenance services in Alberta, Canada from a risk perspective. This contract offers insights into how one public organization approaches the risks inherent in the practice of IS outsourcing. We use a risk framework and content analysis to identify which risk management strategies are included in the contract and how they are implemented. Although IS outsourcing risk factors are widely acknowledged in the literature, they may not be fully specified in the outsourcing contracts implemented in some public organizations. Our findings highlight some of the differences in the suggestions made in the academic literature on IS outsourcing and the techniques implemented in an actual outsourcing contract to manage the risks inherent in this growing sector of the international economy. We also find that not all risks need to be addressed in the contract to have a successful outsourcing relationship. Keywords: Outsourcing, Public Sector IT Management, Information Systems Management, Risk Management, Contracts.
Hardware/software co-design of embedded real-time systems from an undergraduate perspective

The increasing complexity of embedded systems parallels the difficulty of adequately preparing students to design them. Two topics key to the success of a graduate in the area of embedded systems are hardware/software co-design and real-time computing. This paper serves as a case study describing how an undergraduate applied hardware/software co-design in the design of a spectrum analyzer with real-time constraints for a Capstone senior design project. The goal of this work is to produce a co-design approach more suited for undergraduates having little design experience.
Relationship between the apolipoprotein E gene and headache following sports-related concussion

ABSTRACT Introduction: Headache is one of the most commonly reported and longest lasting symptoms that concussed athletes report, yet the etiology of headache symptoms following concussion is not entirely clear. The purpose of this study was to determine whether the e4 allele of the apolipoprotein E (APOE) gene influences the presence and severity of postconcussion headache. Method: Participants were composed of 45 concussed athletes and 43 healthy/nonconcussed athletes who were involved in a clinically based sports concussion management program. All athletes completed the Post-Concussion Symptom Scale (PCSS). The “headache” symptom from the PCSS was the primary outcome variable. Buccal samples were collected and analyzed to determine APOE genotype. Results: A significantly greater proportion of concussed e4+ athletes than e4– athletes endorsed headache. Furthermore, concussed e4+ athletes endorsed more severe headaches than e4– athletes. When examining the healthy/nonconcussed sample (i.e., athletes at baseline), results showed no differences between e4 allele groups with respect to the presence and severity of headache. Conclusions: These findings show that when compared to concussed e4– athletes, e4+ athletes are more likely to (a) endorse postconcussion headache and (b) report more severe headache symptoms following concussion. Conversely, it appears that the e4 allele does not influence baseline reports of headache. Thus, results suggest that those with the e4 genotype may be at a higher risk for experiencing headache-related difficulties only after a concussion is sustained.
Occupational asthma caused by isocyanates: patterns of asthmatic reactions to increasing day-to-day doses.

Inhalation challenges to isocyanates are conducted in specialized centers to confirm occupational asthma. The pattern of asthmatic reactions due to consecutively increasing daily doses of isocyanates is unknown. We conducted a study involving 24 subjects who had undergone specific inhalation challenges to isocyanates (toluene diisocyanate [TDI], n = 8; hexamethylene diisocyanate [HDI], n = 10; and methylene diisocyanate [MDI], n = 6) on three or more consecutive days. Challenge tests were given through a closed-circuit apparatus (n = 12) or in small cubicles (n = 12), allowing assessment of the total inhaled dose (concentration x duration). The pattern of asthmatic reactions was described. In addition, dose-response curves were analyzed and tested for their linear and quadratic trends. Four patterns of response were observed: (1) linear (n = 10); (2) minimal effect followed by a brisk change (n = 7); (3) significant change followed by tachyphylaxis or a plateau (n = 4); (4) biphasic (i.e., significant change followed by a reduction in the effect and significant change on the last day of exposure [n = 3]). Subjects with a linear dose-response pattern had been exposed to isocyanates at work for a significantly shorter interval (7.2 +/- 6.7 yr) than subjects with a nonlinear pattern (20.0 +/- 13.1 yr). An analysis of variance covering a 3-d period for all subjects showed a significant linear model for the response (p < 0.0001); there was no quadratic trend. However, when the analysis was done on subjects with four or more days of challenge (n = 10), we found both linear and quadratic significant components. This analysis shows that the most common pattern of asthmatic reactions to inhaled isocyanates generated on consecutive days is linear; however, other patterns are also observed. In some individuals, particularly those in whom more days of challenge are required, we observed in addition to a strong linear component a quadratic component manifested by a brisk change on the last day of exposure.
Diagnosis strategies and reconfiguration of a 5-level double-boost PFC with fault-tolerant capability

A new 5-level PFC topology with fault-diagnosis and fault-tolerant capability is proposed and compared to known structures. It is derived from a 3-level non differential double-boost PFC including 2-stacked fly-cap cells. The series-connection of the two low-voltage switching-cells is decoupled by a single flying capacitor that provides a failed-safe tolerant capability increasing the availability of converter. Two fault-diagnosis strategies are presented. The first one directly monitors the voltages across the flying capacitors to detect and to localize the faulty switch by means of two sensors. The second one is based on a harmonic detection (magnitude and phase-shift) at the switching frequency through only one voltage sensor at the input of the PFC. All these strategies are analyzed and simulated with the efficient PD-PWM technique that exhibits a lower THD of the leg-leg input voltage for three-phase application. The design and the most important features are highlighted thanks to a digital control frame and a single-phase mock-up rated to AC115V - DC400V - 4kW - 2 × 31kHz.
Close correlation between telomerase expression and adenomatous polyp progression in multistep colorectal carcinogenesis.

To investigate the role of telomerase in the multistep colorectal carcinogenesis, we examined telomerase activity in 31 adenomatous polyps and 22 paired cancer-normal mucosa specimens from non-hereditary nonpolyposis colorectal cancer patients. Telomerase activity was detected in 18% of normal mucosa, 16% of small (<1.0 cm) polyps, 20% of intermediate polyps, 71% of large (>2.0 cm) polyps, and 96% of adenocarcinoma samples (P for trend, <0.0001). High-level enzyme activities were seen in none of the normal mucosa, 5% of small polyps, 20% of intermediate polyps, 43% of large polyps, and 73% of adenocarcinoma samples (P for trend, <0.0001). These data indicate telomerase reactivation occurs with adenomatous polyp progression in multistep colorectal carcinogenesis.
The body as a communication medium

Body sensor networks are currently not very intuitive and not very reliable. In the last couple of years body coupled communications has been rediscovered after a period of disinterest. In this paper an overview of recent research into body coupled communications is given. This research into body coupled communications shows that body sensor networks are not the only ubiquitous computing application where it could be useful. From recent research a number of guidelines for using body coupled communications are developed to help future research in ubiquitous computing.
Weakly-supervised hashing in kernel space

The explosive growth of the vision data motivates the recent studies on efficient data indexing methods such as locality-sensitive hashing (LSH). Most existing approaches perform hashing in an unsupervised way. In this paper we move one step forward and propose a supervised hashing method, i.e., the LAbel-regularized Max-margin Partition (LAMP) algorithm. The proposed method generates hash functions in weakly-supervised setting, where a small portion of sample pairs are manually labeled to be “similar” or “dissimilar”. We formulate the task as a Constrained Convex-Concave Procedure (CCCP), which can be relaxed into a series of convex sub-problems solvable with efficient Quadratic-Program (QP). The proposed hashing method possesses other characteristics including: 1) most existing LSH approaches rely on linear feature representation. Unfortunately, kernel tricks are often more natural to gauge the similarity between visual objects in vision research, which corresponds to probably infinite-dimensional Hilbert spaces. The proposed LAMP has a natural support for kernel-based feature representation. 2) traditional hashing methods assume uniform data distributions. Typically, the collision probability of two samples in hash buckets is only determined by pairwise similarity, unrelated to contextual data distribution. In contrast, we provide such a collision bound which is beyond pairwise data interaction based on Markov random fields theory. Extensive empirical evaluations are conducted on five widely-used benchmarks. It takes only several seconds to generate a new hashing function, and the adopted random supporting-vector scheme enables the LAMP algorithm scalable to large-scale problems. Experimental results well validate the superiorities of the LAMP algorithm over the state-of-the-art kernel-based hashing methods.
A state transition analysis of online information-seeking behavior

Statistical analyses of online searching patterns compared the usage of a query language by various groups of searchers. Data were gathered by an experimental project, Individualized Instruction for Data Access (IIDA), concerned with developing and testing a system which serves as a teacher and assistant to users of Lockheed's DIALOG system. Sequential listings of user commands were classified by corresponding state codes to represent phases of searching. Zero- through fourth-order Markovian analyses of individual commands and strings of like commands were performed to compare searching procedures used by three classes of users. Class 1 compared novices differing in their use of IIDA as an adjunct trainer; Class 2 compared inexperienced, IIDA-trained searchers with professional searchers; Class 3 compared inexperienced searchers taught by either IIDA or professional searchers. Analysis revealed that searchers tend to follow the advice of their trainer, whether it be human or computer. The use of IIDA as the sole training method appears to produce searchers who use similar patterns of searching. The use of another training method in addition to or instead of IIDA appears to produce searchers with varied and individual “styles” of online searching.
SVM kernel based predictive analytics on faculty performance evaluation

In recent years, higher education has been gaining importance in graduate students to make successful careers. So, academic organizations are given utmost importance for quality in academics to build the careers of the students. Faculty performance plays a vital role in academic institutions. In this paper, the performance of faculty members is evaluated on the basis of different parameters are taken for assessment and predicted by building models using data mining techniques. In this evaluation, the sample data is collected, preprocessed, and model learning is done using Support vector machines (SVM) with several kernel methods such as linear, sigmoid, radial basis, polynomial and Pearson VII function-based universal kernel (PUKF). The idea of this proposed paper is to investigate and analyze by considering various parameters for predicting the performance of faculty. The parameters considered are Faculty profile, Quality of Teaching, Maintaining Relationships, Learning Assessment, Counseling and Mentoring, Administrative Functions, Research and Development, Organizational Qualities and Outcome. Performance of various kernels is evaluated with the data and models with SVM-PUKF yields better accuracy by 97.84% when compared with other three standard kernels.
(μ, λ) evolutionary and particle swarm hybrid algorithm over cloud computing, with an application to dinosaur gait optimization

A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for numerical optimization problems. In order to evaluate the performance of the hybrid, a computer experiment was conducted on a dinosaur's gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster at the beginning of the search.
Preparing mental health professionals for new directions in mental health practice: Evaluating the sensory approaches e‐learning training package

The application of sensory modulation approaches in mental health settings is growing in recognition internationally. However, a number of barriers have been identified as limiting the implementation of the approach, including workplace culture and a lack of accessible and effective sensory approaches training. The aim of this project was to investigate the efficacy of providing this training through a custom-designed e-learning package. Participants in the present study were predominately nurses and occupational therapists working in mental health settings in Queensland, Australia. Data were collected from 121 participants using an online survey. Significant improvements were found between pre- and post-training in participants' real and perceived levels of knowledge, their perceived levels of confidence, and their attitudes towards using sensory modulation approaches in mental health settings. The findings of the study suggest that the custom-designed sensory approaches e-learning package is an effective, accessible, acceptable, and usable method to train health professionals in sensory modulation approaches. As this study is the first to analyse the efficacy of an e-learning sensory approaches package, the results are considered preliminary, and further investigation is required.
Integrated Cloud Framework for Farm Management

Smart or intelligent farming is a relatively new concept, but it's becoming an important factor for the agricultural sectors, as a way to raise productivity through technology. This article presents a global management and decision system for smart farming. Our system is addressed to farmers and other actors involved in farming operations, such as rural service companies and banks. It can manage up to several groups of smart or technology-based farms. The system is functioning on two levels, local and Cloud. First of all, it must be deployed on the farm's local wireless network. And secondly, it uses the Cloud's resources and flexibility to offer even more complex services.
Lynx: Automatic Elderly Behavior Prediction in Home Telecare

This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder's daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user's health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.
A wearable human activity recognition system on a chip

The availability of cheap wearable motion and biometric sensors has favoured the research on wearable human activity recognition (HAR) systems. However, a HAR system comprehends many complex signal processing stages that usually require some computationally demanding operations which can hardly be directly performed in an embedded system. Modern FPGA technologies and the system-on-chip (SoC) approach open the door to the implementation of complex single-chip signal processing systems to produce tiny, wearable and autonomous embedded HAR systems. However, compared to a pure embedded software approach, the potentially higher performance-to-power ratio of FPGAs can only be exploited in very demanding applications and by a careful design of the implemented system. In this work we describe a first step in the consecution of an FPGA-based completely autonomous singlechip HAR system which can be adapted and optimized to the user with no need of external computing means neither of human intervention. The system includes all stages in a HAR process, i.e., signal segmentation, signal processing for feature extraction, input space dimensionality reduction (feature selection), and activity estimation by means of a neural classifier. A physical activity recognition example is used as a reference design to evaluate the performance of the system and to draw conclusions on the potential benefits of using FPGAs in future wearable HAR applications.
Some Verb-Intensifier Collocations in American and British English

N A RECENT ISSUE of American Speech, Dwight Bolinger made an eloquent plea for the importance of understanding the lexicon, pointing out that linguists have generally been content to leave lexical study to the dictionary-makers.1 The language models adopted by most American linguists allow little space for the treatment of vocabulary: the relationships between words-in the flow of speech and in paradigmatic sets-is allocated to the two major components, syntax and semantics, and the lexicon is seen as a ragbag of the irregularities and idiosyncracies in language. In a period when most linguists are concerned exclusively with rules it is not surprising that the lexicon is neglected as of little interest. Many British linguists, on the other hand, have recognized lexis as a separate component or level of analysis of language. In particular, they have drawn attention to one aspect of lexical relationships, the tendency for particular lexical items to cooccur. These relationships are seen as forming lexical patterns syntagmatically and as constituting the basis for lexical sets paradigmatically. So far, most of what has been said has been suggestive and programmatic.2
A Comprehensive Comparison of the Continual Reassessment Method to the Standard 3 + 3 Dose Escalation Scheme in Phase I Dose-Finding Studies

Background An extensive literature has covered the statistical properties of the Continual Reassessment Method (CRM) and the modifications of this method. While there are some applications of CRM designs in recent Phase I trials, the standard method (SM) of escalating doses after three patients with an option for an additional three patients SM remains very popular, mainly due to its simplicity. From a practical perspective, clinicians are interested in designs that can estimate the MTD using fewer patients for a fixed number of doses, or can test more dose levels for a given sample size. Purpose This article compares CRM-based methods with the SM in terms of the number of patients needed to reach the MTD, total sample size required, and trial duration. Methods The comparisons are performed under two alternative schemes: a fixed or a varying sample approach with the implementation of a stopping rule. The stopping rule halts the trial if the confidence interval around the MTD is within a pre-specified bound. Our simulations evaluated several CRM-based methods under different scenarios by varying the number of dose levels from five to eight and the location of the true MTD. Results CRM and SM are comparable in terms of how fast they reach the MTD and the total sample size required when testing a limited number of dose levels (≤5), but as the number of dose levels increases, CRM reaches the MTD in fewer patients when used with a fixed sample of 20 patients. However, a sample size of 20—25 patients is not sufficient to achieve a narrow precision around the estimated toxicity rate at the MTD. Limitations We focused on methods with practical design features that are of interest to clinicians. However, there are several alternative CRM-based designs that are not investigated in this manuscript, and hence our results are not generalizable to other designs. Conclusions We show that CRM-based methods are an improvement over the SM in terms of accuracy and optimal dose allocation in almost all cases, except when the true dose is among the lower levels. Clinical Trials 2008; 5: 465—477. http://ctj.sagepub. com
Auto-Categorization of Businesses on Yelp

We built a system to infer semantic categories of businesses (Auto-Categorization) from Yelp.com based on business titles and reviews. Auto-Categorization is accomplished by training a Näıve Bayes classifier on labeled word frequencies (supervised learning). We extend this classifier by using Expectation-Maximization to bootstrap off of unlabeled word frequencies (semi-supervised learning). Given more time, we would have liked to look at adding named entities and chunking of words with high mutual information as additional features to our model.