nils-herrmann/clean_scirepeval huggingface dataset:
Long-term DHEA replacement in primary adrenal insufficiency: a randomized, controlled trial.

Dehydroepiandrosterone (DHEA) and DHEA sulfate (DHEAS) are the major circulating adrenal steroids and substrates for peripheral sex hormone biosynthesis. In Addison's disease, glucocorticoid and mineralocorticoid deficiencies require lifelong replacement, but the associated near-total failure of DHEA synthesis is not typically corrected.



In a double-blind trial, we randomized 106 subjects (44 males, 62 females) with Addison's disease to receive either 50 mg daily of micronized DHEA or placebo orally for 12 months to evaluate its longer-term effects on bone mineral density, body composition, and cognitive function together with well-being and fatigue.



Circulating DHEAS and androstenedione rose significantly in both sexes, with testosterone increasing to low normal levels only in females. DHEA reversed ongoing loss of bone mineral density at the femoral neck (P < 0.05) but not at other sites; DHEA enhanced total body (P = 0.02) and truncal (P = 0.017) lean mass significantly with no change in fat mass. At baseline, subscales of psychological well-being in questionnaires (Short Form-36, General Health Questionnaire-30), were significantly worse in Addison's patients vs. control populations (P < 0.001), and one subscale of SF-36 improved significantly (P = 0.004) after DHEA treatment. There was no significant benefit of DHEA treatment on fatigue or cognitive or sexual function. Supraphysiological DHEAS levels were achieved in some older females who experienced mild androgenic side effects.



Although further long-term studies of DHEA therapy, with dosage adjustment, are desirable, our results support some beneficial effects of prolonged DHEA treatment in Addison's disease.
Credit Card Default Predictive Modeling

Predicting credit card payment default is critical for the successful business model of a credit card company. An accurate predictive model can help the company identify customers who might default their payment in the future so that the company can get involved earlier to manage risk and reduce loss. It is even better if a model can assist the company on credit card application approval to minimize the risk at upfront. However, credit card default prediction is never an easy task. It is dynamic. A customer who paid his/her payment on time in the last few months may suddenly default his/her next payment. It is also unbalanced given the fact that default payment is rare compared to non-default payments. Unbalanced dataset will easily fail using most machine learning techniques if the dataset is not treated properly.
Paleolimnology of a small oligotrophic lake on Wolin Island, Baltic Sea, Poland

Paleolimnological studies which included analyses of diatoms, fossil pigments and physico-chemical characteristics of bottom sediments have been used to describe the limnological history of Racze Lake. The influx of terrigenous material into the lake have been determined on the basis of stratigraphy of elements associated with mineral content. The successively eroded soils as well as process of chemical erosion caused increase leaching of metals Mg, Fe, Al into the lake basin. However the concentration of these metals finally deposited in bottom sediments was also effected by the oxygen regime at the sediment-water interface. Both ratios, chlorophyll derivatives to total carotenoids (CD:TC) and Fe:Mn indicated hypolimnetic oxygen depletion in the middle part of the profile. The development of blue-green algal population, estimated by the ratio epiphasic to hypophasic carotenoids (EC:HC) was correlated with periods of redox conditions in the lake. The pH changes ranged from 6.5 to 7.7. The most important factors effecting pH changes were inflow of mineral matter from the watershed and structural changes in the littoral biocenosis.
Application of semi-supervised deep learning to lung sound analysis

The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of automated lung sound identification and classification has remained elusive. Over the past 40 years, published work in this field has demonstrated only limited success in identifying lung sounds, with most published studies using only a small numbers of patients (typically N<;20) and usually limited to a single type of lung sound. Larger research studies have also been impeded by the challenge of labeling large volumes of data, which is extremely labor-intensive. In this paper, we present the development of a semi-supervised deep learning algorithm for automatically classify lung sounds from a relatively large number of patients (N=284). Focusing on the two most common lung sounds, wheeze and crackle, we present results from 11,627 sound files recorded from 11 different auscultation locations on these 284 patients with pulmonary disease. 890 of these sound files were labeled to evaluate the model, which is significantly larger than previously published studies. Data was collected with a custom mobile phone application and a low-cost (US$30) electronic stethoscope. On this data set, our algorithm achieves ROC curves with AUCs of 0.86 for wheeze and 0.74 for crackle. Most importantly, this study demonstrates how semi-supervised deep learning can be used with larger data sets without requiring extensive labeling of data.
Data-Driven Diagnosis of Cervical Cancer With Support Vector Machine-Based Approaches

Cervical cancer, as the fourth most common cause of death from cancer among women, has no symptoms in the early stage. There are few methods to diagnose cervical cancer precisely at present. Support vector machine (SVM) approach is introduced in this paper for cervical cancer diagnosis. Two improved SVM methods, support vector machine-recursive feature elimination and support vector machine-principal component analysis (SVM-PCA), are further proposed to diagnose the malignant cancer samples. The cervical cancer data are represented by 32 risk factors and 4 target variables: Hinselmann, Schiller, Cytology, and Biopsy. All four targets have been diagnosed and classified by the three SVM-based approaches, respectively. Subsequently, we make the comparison among these three methods and compare our ranking result of risk factors with the ground truth. It is shown that SVM-PCA method is superior to the others.
Affinity Derivation and Graph Merge for Instance Segmentation

We present an instance segmentation scheme based on pixel affinity information, which is the relationship of two pixels belonging to a same instance. In our scheme, we use two neural networks with similar structure. One is to predict pixel level semantic score and the other is designed to derive pixel affinities. Regarding pixels as the vertexes and affinities as edges, we then propose a simple yet effective graph merge algorithm to cluster pixels into instances. Experimental results show that our scheme can generate fine grained instance mask. With Cityscapes training data, the proposed scheme achieves 27.3 AP on test set.
Zigbee Based Centralised Patient Monitoring System

Centralised patient monitoring systems are in huge demand as they not only reduce the labour work and cost but also the time of the clinical hospitals. Earlier wired communication was used but now Zigbee which is a wireless mesh network is preferred as it reduces the cost. Zigbee is also preferred over Bluetooth and infrared wireless communication because it is energy efficient, has low cost and long distance range (several miles). In this paper we proposed wireless transmission of data between a patient and centralised unit using Zigbee module. The paper is divided into two sections. First is patient monitoring system for multiple patients and second is the centralised patient monitoring system. These two systems are communicating using wireless transmission technology i.e. Zigbee. In the first section we have patient monitoring of multiple patients. Each patient's multiple physiological parameters like ECG, temperature, heartbeat are measured at their respective unit. If any physiological parameter value exceeds the threshold value, emergency alarm and LED blinks at each patient unit. This allows a doctor to read various physiological parameters of a patient in real time. The values are displayed on the LCD at each patient unit. Similarly multiple patients multiple physiological parameters are being measured using particular sensors and multiple patient's patient monitoring system is made. In the second section centralised patient monitoring system is made in which all multiple patients multiple parameters are displayed on a central monitor using MATLAB. ECG graph is also displayed on the central monitor using MATLAB software. The central LCD also displays parameters like heartbeat and temperature. The module is less expensive, consumes low power and has good range.
Augmented Reality-based Mimicry Attacks on Behaviour-Based Smartphone Authentication

We develop an augmented reality-based app that resides on the attacker's smartphone and leverages computer vision and raw input data to provide real-time mimicry attack guidance on the victim's phone. Our approach does not require tampering or installing software on the victim's device, or specialized hardware. The app is demonstrated by attacking keystroke dynamics, a method leveraging the unique typing behaviour of users to authenticate them on a smartphone, which was previously thought to be hard to mimic. In addition, we propose a low-tech AR-like audiovisual method based on spatial pointers on a transparent film and audio cues. We conduct experiments with 31 participants and mount over 400 attacks to show that our methods enable attackers to successfully bypass keystroke dynamics for 87% of the attacks after an average mimicry training of four minutes. Our AR-based method can be extended to attack other input behaviour-based biometrics. While the particular attack we describe is relatively narrow, it is a good example of using AR guidance to enable successful mimicry of user behaviour---an approach of increasing concern as AR functionality becomes more commonplace.
A privacy preserving three-factor authentication protocol for e-Health clouds

E-Health clouds are gaining increasing popularity by facilitating the storage and sharing of big data in healthcare. However, such an adoption also brings about a series of challenges, especially, how to ensure the security and privacy of highly sensitive health data. Among them, one of the major issues is authentication, which ensures that sensitive medical data in the cloud are not available to illegal users. Three-factor authentication combining password, smart card and biometrics perfectly matches this requirement by providing high security strength. Recently, Wu et al. proposed a three-factor authentication protocol based on elliptic curve cryptosystem which attempts to fulfill three-factor security and resist various existing attacks, providing many advantages over existing schemes. However, we first show that their scheme is susceptible to user impersonation attack in the registration phase. In addition, their scheme is also vulnerable to offline password guessing attack in the login and password change phase, under the condition that the mobile device is lost or stolen. Furthermore, it fails to provide user revocation when the mobile device is lost or stolen. To remedy these flaws, we put forward a robust three-factor authentication protocol, which not only guards various known attacks, but also provides more desired security properties. We demonstrate that our scheme provides mutual authentication using the Burrows–Abadi–Needham logic.
Complications from toxins and fillers in the dermatology clinic: recognition, prevention, and treatment.

Adverse reactions and unintended effects can occasionally occur with toxins for cosmetic use, even although they generally have an outstanding safety profile. As the use of fillers becomes increasingly more common, adverse events can be expected to increase as well. This article discusses complication avoidance, addressing appropriate training and proper injection techniques, along with patient selection and patient considerations. In addition to complications, avoidance or amelioration of common adverse events is discussed.
Equalization techniques for nonlinear analog circuits

Technology advancement entails an analog design scenario in which sophisticated signal processing algorithms are deployed in mixed-mode and radio frequency circuits to compensate for deterministic and random deficiencies of process technologies. This article reviews one such approach of applying a common communication technique, equalization, to correct for nonlinear distortions in analog circuits, which is analogized as non-ideal communication channels. The efficacy of this approach is showcased by a few latest advances in data conversion and RF transmission integrated circuits, where unprecedented energy efficiency, circuit linearity, and post-fabrication adaptability have been attained with low-cost digital processing.
Justification Narratives for Individual Classifications

Machine learning models are now used extensively for decision making in diverse applications, but for non-experts they are essentially black boxes. While there has been some work on the explanation of classifications, these are targeted at the expert user. For the non-expert, a better model is one of justification not detailing how the model made its decision, but justifying it to the human user on his or her terms. In this paper we introduce the idea of a justification narrative: a simple model-agnostic mapping of the essential values underlying a classification to a semantic space. We present a package that automatically produces these narratives and realizes them visually or textually.
Incremental effects of reward on creativity.

The authors examined 2 ways reward might increase creativity. First, reward contingent on creativity might increase extrinsic motivation. Studies 1 and 2 found that repeatedly giving preadolescent students reward for creative performance in 1 task increased their creativity in subsequent tasks. Study 3 reported that reward promised for creativity increased college students' creative task performance. Second, expected reward for high performance might increase creativity by enhancing perceived self-determination and, therefore, intrinsic task interest. Study 4 found that employees' intrinsic job interest mediated a positive relationship between expected reward for high performance and creative suggestions offered at work. Study 5 found that employees' perceived self-determination mediated a positive relationship between expected reward for high performance and the creativity of anonymous suggestions for helping the organization.
Arc-flash in large battery energy storage systems — Hazard calculation and mitigation

This paper deals with the arc-flash haz ard calculation in battery energy storage systems (BESSs). The lack of international harmonized standards, coupled with a foreseeable increasing use of BESSs, makes this subject very interesting, especially due to the practical involvements related to arc-flash hazard associated with BESS maintenance operation. A Li-ion battery circuit model, based on experimental short-circuit tests conducted by TERNA (the Italian transmission system operator) and a non-linear arc resistance model are used to assess the incident energy related to arc-flash. Efficient and cost-effective solutions, able to reduce incident energy below the safe value 8 cal/cm2 and based on the installation of fast-acting fuses, are then proposed and discussed.
Estimation of Composite Load Model Parameters Using an Improved Particle Swarm Optimization Method

Power system loads are one of the crucial elements of modern power systems and, as such, must be properly modelled in stability studies. However, the static and dynamic characteristics of a load are commonly unknown, extremely nonlinear, and are usually time varying. Consequently, a measurement-based approach for determining the load characteristics would offer a significant advantage since it could update the parameters of load models directly from the available system measurements. For this purpose and in order to accurately determine load model parameters, a suitable parameter estimation method must be applied. The conventional approach to this problem favors the use of standard nonlinear estimators or artificial intelligence (AI)-based methods. In this paper, a new solution for determining the unknown load model parameters is proposed-an improved particle swarm optimization (IPSO) method. The proposed method is an AI-type technique similar to the commonly used genetic algorithms (GAs) and is shown to provide a promising alternative. This paper presents a performance comparison of IPSO and GA using computer simulations and measured data obtained from realistic laboratory experiments.
Defense genes missing from the flight division.

Birds have a smaller repertoire of immune genes than mammals. In our efforts to study antiviral responses to influenza in avian hosts, we have noted key genes that appear to be missing. As a result, we speculate that birds have impaired detection of viruses and intracellular pathogens. Birds are missing TLR8, a detector for single-stranded RNA. Chickens also lack RIG-I, the intracellular detector for single-stranded viral RNA. Riplet, an activator for RIG-I, is also missing in chickens. IRF3, the nuclear activator of interferon-beta in the RIG-I pathway is missing in birds. Downstream of interferon (IFN) signaling, some of the antiviral effectors are missing, including ISG15, and ISG54 and ISG56 (IFITs). Birds have only three antibody isotypes and IgD is missing. Ducks, but not chickens, make an unusual truncated IgY antibody that is missing the Fc fragment. Chickens have an expanded family of LILR leukocyte receptor genes, called CHIR genes, with hundreds of members, including several that encode IgY Fc receptors. Intriguingly, LILR homologues appear to be missing in ducks, including these IgY Fc receptors. The truncated IgY in ducks, and the duplicated IgY receptor genes in chickens may both have resulted from selective pressure by a pathogen on IgY FcR interactions. Birds have a minimal MHC, and the TAP transport and presentation of peptides on MHC class I is constrained, limiting function. Perhaps removing some constraint, ducks appear to lack tapasin, a chaperone involved in loading peptides on MHC class I. Finally, the absence of lymphotoxin-alpha and beta may account for the observed lack of lymph nodes in birds. As illustrated by these examples, the picture that emerges is some impairment of immune response to viruses in birds, either a cause or consequence of the host-pathogen arms race and long evolutionary relationship of birds and RNA viruses.
Distributed SDN controller system: A survey on design choice

Although within SDN community, the notion of logically centralized network control is well understood and agreed upon, many different approaches exist on how one should deliver such a logically centralized view to multiple distributed controller instances. In this paper, we survey and investigate those approaches. We discover that we can classify the methods into several design choices that are trending among SDN adopters. Each design choice may influence several SDN issues such as scalability, robustness, consistency, and privacy. Thus, we further analyze the pros and cons of each model regarding these matters. We conclude that each design begets some characteristics. One may excel in resolving one issue but perform poor in another. We also present which design combinations one should pick to build distributed controller that is scalable, robust, consistent
Deep Multi-Task Learning with Shared Memory for Text Classification

Neural network based models have achieved impressive results on various specific tasks. However, in previous works, most models are learned separately based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we propose two deep architectures which can be trained jointly on multiple related tasks. More specifically, we augment neural model with an external memory, which is shared by several tasks. Experiments on two groups of text classification tasks show that our proposed architectures can improve the performance of a task with the help of other related tasks.
Perinatal management of harlequin ichthyosis: a case report and literature review

Harlequin ichthyosis (HI) is a rare and severe form of congenital ichthyosis. Linked to deletion and truncation mutations of a keratinocyte lipid transporter, HI is characterized by diffuse epidermal hyperkeratinization and defective desquamation. At birth, the HI phenotype is striking with thick hyperkeratotic plate-like scales with deep dermal fissures, severe ectropion and eclabium, among other findings. Over the first months of life, the hyperkeratotic covering is shed, revealing a diffusely erythematous, scaly epidermis, which persists for the remainder of the patient's life. Although HI infants have historically succumbed in the perinatal period related to their profound epidermal compromise, the prognosis of HI infants has vastly improved over the past 20 years. Here, we report a case of HI treated with acitretin, focusing on the multi-faceted management of the disease in the inpatient setting. A review of the literature of the management of HI during the perinatal period is also presented.
A New Attempt to Silhouette-Based Gait Recognition for Human Identification

Human identification at distance by analysis of gait patterns extracted from video has recently become very popular research in biometrics. This paper presents multi-projections based approach to extract gait patterns for human recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Eigenspace transformation is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Based on normalized correlation on the distance vectors, gait cycle estimation is also performed to extract the gait cycle. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles.