nils-herrmann/clean_scirepeval huggingface dataset:
A randomized, double-blind, placebo-controlled trial of a β-hydroxyl β-methyl butyrate, glutamine, and arginine mixture for the treatment of cancer cachexia (RTOG 0122)

Cancer cachexia is a common problem among advanced cancer patients. A mixture of β-hydroxyl β-methyl butyrate, glutamine, and arginine (HMB/Arg/Gln) previously showed activity for increasing lean body mass (LBM) among patients with cancer cachexia. Therefore a phase III trial was implemented to confirm this activity. Four hundred seventy-two advanced cancer patients with between 2% and 10% weight loss were randomized to a mixture of β-hydroxyl β-methyl butyrate, glutamine, and arginine or an isonitrogenous, isocaloric control mixture taken twice a day for 8 weeks. Lean body mass was estimated by bioimpedance and skin-fold measurements. Body plethysmography was used when available. Weight, the Schwartz Fatigue Scale, and the Spitzer Quality of Life Scale were also measured. Only 37% of the patients completed protocol treatment. The majority of the patient loss was because of patient preference (45% of enrolled patients). However, loss of power was not an issue because of the planned large target sample size. Based on an intention to treat analysis, there was no statistically significant difference in the 8-week lean body mass between the two arms. The secondary endpoints were also not significantly different between the arms. Based on the results of the area under the curve (AUC) analysis, patients receiving HMB/Arg/Gln had a strong trend higher LBM throughout the study as measured by both bioimpedance (p = 0.08) and skin-fold measurements (p = 0.08). Among the subset of patients receiving concurrent chemotherapy, there were again no significant differences in the endpoints. The secondary endpoints were also not significantly different between the arms. This trial was unable to adequately test the ability of β-hydroxy β-methylbutyrate, glutamine, and arginine to reverse or prevent lean body mass wasting among cancer patients. Possible contributing factors beyond the efficacy of the intervention were the inability of patients to complete an 8-week course of treatment and return in a timely fashion for follow-up assessment, and because the patients may have only had weight loss possible not related to cachexia, but other causes of weight loss, such as decreased appetite. However, there was a strong trend towards an increased body mass among patients taking the Juven® compound using the secondary endpoint of AUC.
On the Iterative Refinement of Densely Connected Representation Levels for Semantic Segmentation

State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions. However, it is not clear which operation leads to best results. In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a densely connected backbone composed of residual networks. Following standard image segmentation architectures, receptive field enlargement operations that change the representation level are interleaved among residual networks. This allows the model to exploit the benefits of both residual and dense connectivity patterns, namely: gradient flow, iterative refinement of representations, multi-scale feature combination and deep supervision. In order to highlight the potential of our model, we test it on the challenging CamVid urban scene understanding benchmark and make the following observations: 1) downsampling operations outperform dilations when the model is trained from scratch, 2) dilations are useful during the finetuning step of the model, 3) coarser representations require less refinement steps, and 4) ResNets (by model construction) are good regularizers, since they can reduce the model capacity when needed. Finally, we compare our architecture to alternative methods and report state-of-the-art result on the Camvid dataset, with at least twice fewer parameters.
Fake Face Detection Based on Skin Elasticity

Biometric system provides a way of automatic verification or identification a person. But nowadays due to lack of secrecy, there is lot of security threat due to spoofing. Spoofing with photograph or video is one of the most common manners to attack a face recognition system. Liveness detection is a technique that can be used for validating whether the data originate is from a valid user or not. Liveness detection can be hardware based or software based or a combination of both. In this paper, we present a non intrusive and real time method to address this problem, based on skin elasticity of human face. In this technique user is asked to do some movement like chewing and forehead movement simultaneously, so that a full movement to face skin can be given and then sequence of face images is captured with a gap of few milliseconds. Then by applying correlation coefficient between images and then discriminate analysis using some method, face skin is discriminate from the other materials like gelatin, rubber, cadaver, clay etc. In comparison to other face liveness detection, this method will be much user friendly. On the other hand, one of the images captured for liveness detection can be used for face recognition.  Biometrics, Face Recognition, Fake Face Detection, Liveness Detection, Skin Elasticity,
Biomechanical design of escalading lower limb exoskeleton with novel linkage joints.

In this paper, an obstacle-surmounting-enabled lower limb exoskeleton with novel linkage joints that perfectly mimicked human motions was proposed. Currently, most lower exoskeletons that use linear actuators have a direct connection between the wearer and the controlled part. Compared to the existing joints, the novel linkage joint not only fitted better into compact chasis, but also provided greater torque when the joint was at a large bend angle. As a result, it extended the angle range of joint peak torque output. With any given power, torque was prioritized over rotational speed, because instead of rotational speed, sufficiency of torque is the premise for most joint actions. With insufficient torque, the exoskeleton will be a burden instead of enhancement to its wearer. With optimized distribution of torque among the joints, the novel linkage method may contribute to easier exoskeleton movements.
Human urinary carcinogen metabolites: biomarkers for investigating tobacco and cancer.

Measurement of human urinary carcinogen metabolites is a practical approach for obtaining important information about tobacco and cancer. This review presents currently available methods and evaluates their utility. Carcinogens and their metabolites and related compounds that have been quantified in the urine of smokers or non-smokers exposed to environmental tobacco smoke (ETS) include trans,trans-muconic acid (tt-MA) and S-phenylmercapturic acid (metabolites of benzene), 1- and 2-naphthol, hydroxyphenanthrenes and phenanthrene dihydrodiols, 1-hydroxypyrene (1-HOP), metabolites of benzo[a]pyrene, aromatic amines and heterocyclic aromatic amines, N-nitrosoproline, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides (NNAL and NNAL-Gluc), 8-oxodeoxyguanosine, thioethers, mercapturic acids, and alkyladenines. Nitrosamines and their metabolites have also been quantified in the urine of smokeless tobacco users. The utility of these assays to provide information about carcinogen dose, delineation of exposed vs. non-exposed individuals, and carcinogen metabolism in humans is discussed. NNAL and NNAL-Gluc are exceptionally useful biomarkers because they are derived from a carcinogen- 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)- that is specific to tobacco products. The NNAL assay has high sensitivity and specificity, which are particularly important for studies on ETS exposure. Other useful assays that have been widely applied involve quantitation of 1-HOP and tt-MA. Urinary carcinogen metabolite biomarkers will be critical components of future studies on tobacco and human cancer, particularly with respect to new tobacco products and strategies for harm reduction, the role of metabolic polymorphisms in cancer, and further evaluation of human carcinogen exposure from ETS.
Critical factors for knowledge management in project business

The purpose of this study is to identify and examine various factors that influence the success or failure of knowledge management (KM) initiatives in project-based companies.  Following a literature review, the study proposes a conceptual model of six factors of potential importance to the success of KM initiatives. The model is then examined through an online survey of project managers and assistant managers from project-based businesses in Finland.  The study finds that a lack of incentives and the absence of an appropriate information system are the most significant barriers to successful KM initiatives in projects.  The findings of the study may be restricted in terms of generalisability because of the limited empirical study.  Project managers should formulate an attractive incentive package to encourage project members to participate in KM initiatives and to suggest ideas for new KM opportunities. Managers should also ensure that an effective user-friendly information system is in place before introducing KM initiatives.  The study proposes a new model of critical success factors for KM initiatives in the context of project-based business.
Current practice in measuring usability: Challenges to usability studies and research

How to measure usability is an important question in HCI research and user interface evaluation. We review current practice in measuring usability by categorizing and discussing usability measures from 180 studies published in core HCI journals and proceedings. The discussion distinguish several problems with the measures, including whether they actually measure usability, if they cover usability broadly, how they are reasoned about, and if they meet recommendations on how to measure usability. In many studies, the choice of and reasoning about usability measures fall short of a valid and reliable account of usability as quality-in-use of the user interface being studied. Based on the review, we discuss challenges for studies of usability and for research into how to measure usability. The challenges are to distinguish and empirically compare subjective and objective measures of usability; to focus on developing and employing measures of learning and retention; to study long-term use and usability; to extend measures of satisfaction beyond post-use questionnaires; to validate and standardize the host of subjective satisfaction questionnaires used; to study correlations between usability measures as a means for validation; and to use both micro and macro tasks and corresponding measures of usability. In conclusion, we argue that increased attention to the problems identified and challenges discussed may strengthen studies of usability and usability research.
Classroom teachers' perceptions of the impact of barriers to teaching physical education on the quality of physical education programs.

A major aim of the current study was to determine what classroom teachers perceived to be the greatest barriers affecting their capacity to deliver successful physical education (PE) programs. An additional aim was to examine the impact of these barriers on the type and quality of PE programs delivered. This study applied a mixed-mode design involving data source triangulation using semistructured interviews with classroom teachers (n = 31) and teacher-completed questionnaires (n = 189) from a random sample of 38 schools. Results identified the key factors inhibiting PE teachers, which were categorized as teacher-related or institutional. Interestingly, the five greatest barriers were defined as institutional or out of the teacher's control. The major adverse effects of these barriers were evident in reduced time spent teaching PE and delivering PE lessons of questionable quality.
Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

Suggested Citation: Berens, Johannes; Oster, Simon; Schneider, Kerstin; Burghoff, Julian (2018) : Early Detection of Students at Risk Predicting Student Dropouts Using Administrative Student Data and Machine Learning  Schumpeter Discussion Papers, No. 2018-006, University of Wuppertal, Schumpeter School of Business and Economics, Wuppertal, http://nbn-resolving.de/urn:nbn:de:hbz:468-20180719-085420-5
Privacy policies as decision-making tools: an evaluation of online privacy notices

Studies have repeatedly shown that users are increasingly concerned about their privacy when they go online. In response to both public interest and regulatory pressures, privacy policies have become almost ubiquitous. An estimated 77% of websites now post a privacy policy. These policies differ greatly from site to site, and often address issues that are different from those that users care about. They are in most cases the users' only source of information.This paper evaluates the usability of online privacy policies, as well as the practice of posting them. We analyze 64 current privacy policies, their accessibility, writing, content and evolution over time. We examine how well these policies meet user needs and how they can be improved. We determine that significant changes need to be made to current practice to meet regulatory and usability requirements.
Bilingual Advantages in Inhibition or Selective Attention: More Challenges

A large sample (N = 141) of college students participated in both a conjunctive visual search task and an ambiguous figures task that have been used as tests of selective attention. Tests for effects of bilingualism on attentional control were conducted by both partitioning the participants into bilinguals and monolinguals and by treating bilingualism as a continuous variable, but there were no effects of bilingualism in any of the tests. Bayes factor analyses confirmed that the evidence substantially favored the null hypothesis. These new findings mesh with failures to replicate language-group differences in congruency-sequence effects, inhibition-of-return, and working memory capacity. The evidence that bilinguals are better than monolinguals at attentional control is equivocal at best.
Near-Optimal Algorithms for the Assortment Planning Problem Under Dynamic Substitution and Stochastic Demand

Assortment planning of substitutable products is a major operational issue that arises in many industries, such as retailing, airlines and consumer electronics. We consider a single-period joint assortment and inventory planning problem under dynamic substitution with stochastic demands, and provide complexity and algorithmic results as well as insightful structural characterizations of near-optimal solutions for important variants of the problem. First, we show that the assortment planning problem is NP-hard even for a very simple consumer choice model, where each customer is willing to buy only two products. In fact, we show that the problem is hard to approximate within a factor better than 1− 1/e. Secondly, we show that for several interesting and practical choice models, one can devise a polynomial-time approximation scheme (PTAS), i.e., the problem can be solved efficiently to within any level of accuracy. To the best of our knowledge, this is the first efficient algorithm with provably near-optimal performance guarantees for assortment planning problems under dynamic substitution. Quite surprisingly, the algorithm we propose stocks only a constant number of different product types; this constant depends only on the desired accuracy level. This provides an important managerial insight that assortments with a relatively small number of product types can obtain almost all of the potential revenue. Furthermore, we show that our algorithm can be easily adapted for more general choice models, and present numerical experiments to show that it performs significantly better than other known approaches.
Automated monitoring and control system for shrimp farms based on embedded system and wireless sensor network

This paper presents a versatile solution in an effort of improving the accuracy in monitoring the environmental conditions and reducing manpower for industrial households shrimp farming. A ZigBee-based wireless sensor network (WSN) was used to monitor the critical environmental conditions and all the control processes are done with the help of a series of low-power embedded MSP430 microcontrollers from Texas Instruments. This system is capable of collecting, analyzing and presenting data on a Graphical User Interface (GUI), programmed with LabVIEW. It also allows the user to get the updated sensor information online based on Google Spreadsheets application, via Internet connectivity, or at any time through the SMS gateway service and sends alert message promptly enabling user interventions when needed. Thereby the system minimizes the effects of environmental fluctuations caused by sudden changes and reduces the expended labor power of farms. Because of that, the proposed system saves the cost of hiring labor as well as the electricity usage. The design promotes a versatile, low-cost, and commercial version which will function best for small to medium sized farming operations as it does not require any refitting or reconstruction of the pond.
Pattern recognition in multivariate time series - A case study applied to fault detection in a gas turbine

Advances in information technology, together with the evolution of systems in control, automation and instrumentation have enabled the recovery, storage and manipulation of a large amount of data from industrial plants. This development has motivated the advancement of research in fault detection, especially based on process history data. Although a large amount of work has been conducted in recent years on the diagnostics of gas turbines, few of them present the use of clustering approaches applied to multivariate time series, adopting PCA similarity factor (SPCA) in order to detect and/or prevent failures. This paper presents a comprehensive method for pattern recognition associated to fault prediction in gas turbines using time series mining techniques. Algorithms comprising appropriate similarity metrics, subsequence matching and fuzzy clustering were applied on data extracted from a Plant Information Management System (PIMS) represented by multivariate time series. A real case study comprising the fault detection in a gas turbine was investigated. The results suggest the existence of a safe way to start the turbine that can be useful to support the development of a dynamic system for monitoring and predicting the probability of failure and for decision-making at operational level. & 2015
Blind PARAFAC receivers for DS-CDMA systems

This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic blind PARAFAC DS-CDMA receiver with performance close to nonblind minimum mean-squared error (MMSE). The proposed PARAFAC receiver capitalizes on code, spatial, and temporal diversity-combining, thereby supporting small sample sizes, more users than sensors, and/or less spreading than users. Interestingly, PARAFAC does not require knowledge of spreading codes, the specifics of multipath (interchip interference), DOA-calibration information, finite alphabet/constant modulus, or statistical independence/whiteness to recover the information-bearing signals. Instead, PARAFAC relies on a fundamental result regarding the uniqueness of low-rank three-way array decomposition due to Kruskal (and generalized herein to the complex-valued case) that guaranteesidentifiability of all relevant signals and propagation parameters. These and other issues are also demonstrated in pertinent simulation experiments.
Exceptionally high Young's modulus observed for individual carbon nanotubes

CARBON nanotubes are predicted to have interesting mechanical properties—in particular, high stiffness and axial strength—as a result of their seamless cylindrical graphitic structure1–5. Their mechanical properties have so far eluded direct measurement, however, because of the very small dimensions of nanotubes. Here we estimate the Young's modulus of isolated nanotubes by measuring, in the transmission electron microscope, the amplitude of their intrinsic thermal vibrations. We find that carbon nanotubes have exceptionally high Young's moduli, in the terapascal (TPa) range. Their high stiffness, coupled with their low density, implies that nanotubes might be useful as nanoscale fibres in strong, lightweight composite materials.
Implementing UTM based on PfSense platform

Today, as Network environments become more complex and cyber and Network threats increase, Organizations use wide variety of security solutions against today's threats. For proper and centralized control and management, range of security features need to be integrated into unified security package. Unified threat management (UTM) as a comprehensive network security solution, integrates all of security services such as firewall, URL filtering, virtual private networking, etc. in a single appliance. PfSense is a variant of UTM, and a customized FreeBSD (Unix-like operating system). Specially is used as a router and statefull firewall. It has many packages extend it's capabilities such as Squid3 package as a as a proxy server that cache data and SquidGuard, redirector and access controller plugin for squid3 proxy server. In this paper, with implementing UTM based on PfSense platform we use Squid3 proxy server and SquidGuard proxy filter to avoid extreme amount of unwanted uploading/ downloading over the internet by users in order to optimize our organization's bandwidth consumption. We begin by defining UTM and types of it, PfSense platform with it's key services and introduce a simple and operational solution for security stability and reducing the cost. Finally, results and statistics derived from this approach compared with the prior condition without PfSense platform.
Cardiac tests in asymptomatic type 2 diabetics.

To evaluate the use of noninvasive procedures for the detection of myocardial ischemia and its relation with other coexistent clinical factors in patients with asymptomatic type 2 diabetes mellitus.



A total of 42 patients with type 2 diabetes mellitus, aged 41-72 years with no clinical history suggestive of coronary heart disease, were evaluated for silent myocardial ischemia by stress cardiac exercise tolerance test (ETT), 12-lead electrocardiography (ECG), transthoracic echocardiography and stress myocardial perfusion scan using technetium-99m tetrofosmin.



Eleven patients (26.2%) showed an ischemic pattern on ETT, the resting ECG was suggestive of ischemia in only 2 (4.8%), echocardiography showed diastolic dysfunction in 9 (21.4%), and the stress myocardial perfusion scan was ischemic in 3 (7.3%). For subjects over the age of 57, a significant difference was found between age and ischemic ETT (p = 0.026) and diastolic dysfunction by echocardiography (p = 0.044). Patients with microalbuminuria and/or diastolic dysfunction were more likely than others to have ischemic ETT (p = 0.036 and 0.024, respectively) and patients with diastolic dysfunction had a higher prevalence of ischemic ETT. There was no relation between ischemic ETT and other major cardiac risk factors (hypertension, dyslipidemia, smoking, sex, duration of diabetes, BMI, and glycated hemoglobin levels).



The cardiac ETT was most helpful for detecting myocardial ischemia in asymptomic type 2 diabetics. For equivocal ETT findings, echocardiography is recommended. The prevalence of myocardial ischemia was high in patients with type 2 diabetes mellitus.