nils-herrmann/clean_scirepeval huggingface dataset:
Management of functional nonretentive fecal incontinence in children: Recommendations from the International Children's Continence Society.

Fecal incontinence (FI) in children is frequently encountered in pediatric practice, and often occurs in combination with urinary incontinence. In most cases, FI is constipation-associated, but in 20% of children presenting with FI, no constipation or other underlying cause can be found - these children suffer from functional nonretentive fecal incontinence (FNRFI).



To summarize the evidence-based recommendations of the International Children's Continence Society for the evaluation and management of children with FNRFI.


RECOMMENDATIONS
Functional nonretentive fecal incontinence is a clinical diagnosis based on medical history and physical examination. Except for determining colonic transit time, additional investigations are seldom indicated in the workup of FNRFI. Treatment should consist of education, a nonaccusatory approach, and a toileting program encompassing a daily bowel diary and a reward system. Special attention should be paid to psychosocial or behavioral problems, since these frequently occur in affected children. Functional nonretentive fecal incontinence is often difficult to treat, requiring prolonged therapies with incremental improvement on treatment and frequent relapses.
PICS: Private Image Classification with SVM

The advent of Machine Learning as a Service (MLaaS) makes it possible to outsource a visual object recognition task to an external (e.g. cloud) provider. However, outsourcing such an image classification task raises privacy concerns, both from the image provider’s perspective, who wishes to keep their images confidential, and from the classification algorithm provider’s perspective, who wishes to protect the intellectual property of their classifier. We propose PICS, a private image classification system, based on polynomial kernel support vector machine (SVM) learning. We selected SVM because it allows us to apply only low-degree functions for the classification on private data, which is the reason why our solution remains computationally efficient. Our solution is based on Secure Multiparty Computation (MPC), it does not leak any information about the images to be classified, nor about the classifier parameters, and it is provably secure. We demonstrate the practicality of our approach by conducting experiments on realistic datasets. We show that our approach achieves high accuracy, comparable to that achieved on non-privacy-protected data while the input-dependent phase is at least 100 times faster than the similar approach with Fully Homomorphic Encryption.
Design and Implementation of IoT-Based Automation System for Smart Home

Home Automation System (HAS) gains popularity due to communication technology advancement. Smart home is one of the Internet of Things (IoT) applications that facilitates the control of home appliances over the Internet using automation system. This paper proposes a low-cost Wi-Fi based automation system for Smart Home (SH) in order to monitor and control home appliances remotely using Android-based application. An Arduino Mega microcontroller provided with Wi-Fi module is utilized to build the automation system. In addition, several sensors are used to monitor the temperature, humidity and motion in home. A relay board is exploited to connect the HAS with home under controlled appliances. The proposed automation system, can easily and efficiently control the electrical appliances via Wi-Fi and Virtuino mobile application.
Mitigating Observability Loss of Toggle-Based X-Masking via Scan Chain Partitioning

The Toggle-based <italic>X</italic>-masking method requires a single toggle at a given cycle, there is a chance that non-<italic>X</italic> values are also masked. Hence, the non-<italic>X</italic> value over-masking problem may cause a fault coverage degradation. In this paper, a scan chain partitioning scheme is described to alleviate non-<italic>X </italic> bit over-masking problem arising from Toggle-based <italic>X</italic>-Masking method. The scan chain partitioning method finds a scan chain combination that gives the least toggling conflicts. The experimental results show that the amount of over-masked bits is significantly reduced, and it is further reduced when the proposed method is incorporated with <italic>X</italic>-canceling method. However, as the number of scan chain partitions increases, the control data for decoder increases. To reduce a control data overhead, this paper exploits a Huffman coding based data compression. Assuming two partitions, the size of control bits is even smaller than the conventional <italic>X </italic>-toggling method that uses only one decoder. In addition, selection rules of <italic>X</italic>-bits delivered to <italic>X</italic>-Canceling MISR are also proposed. With the selection rules, a significant test time increase can be prevented.
Cancer Immunotherapy with Cytokine-Induced Killer Cells

Cytokine-induced killer (CIK) cells form under certain stimulation conditions in cultures of peripheral blood mononuclear cells (PBMCs). They are a heterogeneous immune cell population and contain a high percentage of cells with a mixed T-NK phenotype (CD3+CD56+). The ready availability of a lymphocyte source, together with the high proliferative rate and potent anti-tumor activity of CIK cells, has allowed their use as immunotherapy in a wide variety of neoplasms. Cytotoxicity mediated by CD3+CD56+ T cells depends on the major histocompatibility antigen (MHC)-independent recognition of tumor cells and the activation of signaling pathways through the natural killer group 2 member D (NKG2D) cell-surface receptor. Clinical trials have demonstrated the feasibility and efficacy of CIK cell immunotherapy even in advanced stage cancer patients or those that have not responded to first-line treatment. This review summarizes biological and technical aspects of CIK cells, as well as past and current clinical trials and future trends in this form of immunotherapy.
Dual-phase spinel MnCo2O4 and spinel MnCo2O4/nanocarbon hybrids for electrocatalytic oxygen reduction and evolution.

Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are essential reactions for energy-storage and -conversion devices relying on oxygen electrochemistry. High-performance, nonprecious metal-based hybrid catalysts are developed from postsynthesis integration of dual-phase spinel MnCo2O4 (dp-MnCo2O4) nanocrystals with nanocarbon materials, e.g., carbon nanotube (CNT) and nitrogen-doped reduced graphene oxide (N-rGO). The synergic covalent coupling between dp-MnCo2O4 and nanocarbons effectively enhances both the bifunctional ORR and OER activities of the spinel/nanocarbon hybrid catalysts. The dp-MnCo2O4/N-rGO hybrid catalysts exhibited comparable ORR activity and superior OER activity compared to commercial 30 wt % platinum supported on carbon black (Pt/C). An electrically rechargeable zinc-air battery using dp-MnCo2O4/CNT hybrid catalysts on the cathode was successfully operated for 64 discharge-charge cycles (or 768 h equivalent), significantly outperforming the Pt/C counterpart, which could only survive up to 108 h under similar conditions.
Technology Acceptance and User-Centred Design of Assistive Exoskeletons for Older Adults: A Commentary

Assistive robots are emerging as technologies that enable older adults to perform activities of daily living with autonomy. Exoskeletons are a subset of assistive robots that can support mobility. Perceptions and acceptance of these technologies require understanding in a user-centred design context to ensure optimum experience and adoption by as broad a spectrum of older adults as possible. The adoption and use of assistive robots for activities of daily living (ADL) by older adults is poorly understood. Older adult acceptance of technology is affected by numerous factors, such as perceptions and stigma associated with dependency and ageing. Assistive technology (AT) models provide theoretical frameworks that inform decision-making in relation to assistive devices for people with disabilities. However, technology acceptance models (TAMs) are theoretical explanations of factors that influence why users adopt some technologies and not others. Recent models have emerged specifically describing technology acceptance by older adults. In the context of exoskeleton design, these models could influence design approaches. This article will discuss a selection of TAMs, displaying a chronology that highlights their evolution, and two prioritised TAMs—Almere and the senior technology acceptance model (STAM)—that merit consideration when attempting to understand acceptance and use of assistive robots by older adults.
Cross-Device Search

Ownership and use of multiple devices such as desktop computers, smartphones, and tablets is increasing rapidly. Search is popular and people often perform search tasks that span device boundaries. Understanding how these devices are used and how people transition between them during information seeking is essential in developing search support for a multi-device world. In this paper, we study search across devices and propose models to predict aspects of cross-device search transitions. We characterize multi-device search across four device types, including aspects of search behavior on each device (e.g., topics of interest) and characteristics of device transitions. Building on the characterization, we learn models to predict various aspects of cross-device search, including the next device used for search. This enables many applications. For example, accurately forecasting the device used for the next query lets search engines proactively retrieve device-appropriate content (e.g., short documents for smartphones), while knowledge of the current device combined with device-specific topical interest models may assist in better query-sense disambiguation. %to help the searcher once they transition to the target device.
Multiclass Classification of Unconstrained Handwritten Arabic Words Using Machine Learning Approaches

In this paper, we propose and describe efficient multiclass classification and recognition of unconstrained handwritten Arabic words using machine learning approaches which include the K-nearest neighbor (K-NN) clustering, and the neural network (NN). The technical details are presented in terms of three stages, namely preprocessing, feature extraction and classification. Firstly, words are segmented from input scripts and also normalized in size. Secondly, from each of the segmented words various feature extraction methods are introduced. Finally, these features are utilized to train the K-NN and the NN classifiers for classification. In order to validate the proposed techniques, extensive experiments are conducted using the K-NN and the NN. The proposed algorithms are tested on the IFN/ENIT database which contains 32492 Arabic words; the proposed algorithms give good accuracy when compared with other methods.
Symbol Digit Modalities Test : Normative data for the Latin American Spanish speaking adult population

To generate normative data on the Symbol Digit Modalities Test (SDMT) across 11 countries in Latin America, with country-specific adjustments for gender, age, and education, where appropriate.  The sample consisted of 3,977 healthy adults who were recruited from Argentina, Bolivia, Chile, Cuba, El Salvador, Guatemala, Honduras, Mexico, Paraguay, Peru, and, Puerto Rico. Each subject was administered the SDMT as part of a larger neuropsychological battery. A standardized five-step statistical procedure was used to generate the norms.  The final multiple linear regression models explained 29–56% of the variance in SDMT scores. Although there were gender differences on the SDMT in Mexico, Honduras, Paraguay, and Guatemala, none of the four countries had an effect size greater than 0.3. As a result, gender-adjusted norms were not generated.  This is the first normative multicenter study conducted in Latin America to create norms for the SDMT; this study will have an impact on the future practice of neuropsychology throughout the global region.
Network Anomaly Detection: Methods, Systems and Tools

Network anomaly detection is an important and dynamic research area. Many network intrusion detection methods and systems (NIDS) have been proposed in the literature. In this paper, we provide a structured and comprehensive overview of various facets of network anomaly detection so that a researcher can become quickly familiar with every aspect of network anomaly detection. We present attacks normally encountered by network intrusion detection systems. We categorize existing network anomaly detection methods and systems based on the underlying computational techniques used. Within this framework, we briefly describe and compare a large number of network anomaly detection methods and systems. In addition, we also discuss tools that can be used by network defenders and datasets that researchers in network anomaly detection can use. We also highlight research directions in network anomaly detection.
Does the G-spot exist? A review of the current literature

In 1950, Gräfenberg described a distinct erotogenic zone on the anterior wall of the vagina, which was referred to as the Gräfenberg spot (G-spot) by Addiego, Whipple (a nurse) et al. in 1981. As a result, the G-spot has become a central topic of popular speculation and a basis of a huge business surrounding it. In our opinion, these sexologists have made a hotchpotch of Gräfenberg’s thoughts and ideas that were set forth and expounded in his 1950 article: the intraurethral glands are not the corpus spongiosum of the female urethra, and Gräfenberg did not report an orgasm of the intraurethral glands. G-spot amplification is a cosmetic surgery procedure for temporarily increasing the size and sensitivity of the G-spot in which a dermal filler or a collagen-like material is injected into the bladder–vaginal septum. All published scientific data point to the fact that the G-spot does not exist, and the supposed G-spot should not be identified with Gräfenberg’s name. Moreover, G-spot amplification is not medically indicated and is an unnecessary and inefficacious medical procedure.
WhatsAnalyzer: A Tool for Collecting and Analyzing WhatsApp Mobile Messaging Communication Data

WhatsAnalyzer is a web-based service, which collects and analyzes chat histories of the mobile messaging application WhatsApp. Thereby, it leverages the e-mail export feature of WhatsApp to obtain the chat histories, which cannot be accessed otherwise due to encrypted storage on the mobile device and end-to-end encrypted transmission over the Internet. Thus, the major asset of the service is that real communication data can be collected without the bias introduced by observing or surveying participants. The collected communication data can be analyzed and provides valuable insights into the communication in WhatsApp and the resulting network traffic. To incentivize users to send chat histories, the privacy of users is respected by anonymizing all communication data. Moreover, some analyses of each chat history can be accessed on a web page by the sender of the chats.
Real-time expression transfer for facial reenactment

We present a method for the real-time transfer of facial expressions from an actor in a source video to an actor in a target video, thus enabling the ad-hoc control of the facial expressions of the target actor. The novelty of our approach lies in the transfer and photorealistic re-rendering of facial deformations and detail into the target video in a way that the newly-synthesized expressions are virtually indistinguishable from a real video. To achieve this, we accurately capture the facial performances of the source and target subjects in real-time using a commodity RGB-D sensor. For each frame, we jointly fit a parametric model for identity, expression, and skin reflectance to the input color and depth data, and also reconstruct the scene lighting. For expression transfer, we compute the difference between the source and target expressions in parameter space, and modify the target parameters to match the source expressions. A major challenge is the convincing re-rendering of the synthesized target face into the corresponding video stream. This requires a careful consideration of the lighting and shading design, which both must correspond to the real-world environment. We demonstrate our method in a live setup, where we modify a video conference feed such that the facial expressions of a different person (e.g., translator) are matched in real-time.
Demystifying Surrogate Modeling for Circuits and Systems

In this article, grey-box and black-box surrogate modeling are described, with some key findings. The important point is that surrogate modeling has a solid mathematical basis leading to what has become a dramatic increase in our ability to develop engineering models and to engineer systems. In Section 2, a systematic approach to constructing surrogate models is provided. Each step is explained using published methods. Section 3 presents surrogate modeling examples from the domain of circuits and systems.
Oil of Oregano Properties and Uses

Origanum vulgare (the scientific name of oregano) has been studied in depth due to a number of interesting and exciting potential clinical uses. There is also an ongoing interest in a number of industries to replace synthetic chemicals with natural products that have similar properties. Many bioactive compounds can be found in aromatic plants, and there are a number of different ways they can be extracted. In one study, the major components of oregano essential oils were found to be carvacrol, beta-fenchyl alcohol, thymol, and gamma-terpinene.[1] A hot-water extraction was found to be the best method of extracting antioxidant properties and provided the highest phenolic content. This study also tested the oregano extracts against seven bacterial cultures, but they were ineffective. However, the essential oil itself was able to inhibit the growth of all bacteria, causing greater reductions on both Listeria strains that were tested.
Spectral-spatial classification of hyperspectral image using autoencoders

Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine PCA on spectral dimension and autoencoder on the other two spatial dimensions to extract spectral-spatial information for classification. The experimental results show that this framework achieves the highest classification accuracy among all methods, and outperforms classical classifiers such as SVM and PCA-based SVM.
A Course Recommender System based on Graduating Attributes

Assessing learning outcomes for students in higher education institutes is an interesting task with many potential applications for all involved stakeholders (students, administrators, potential employers, etc.). In this paper, we propose a course recommendation system for students based on the assessment of their “graduate attributes” (i.e. attributes that describe the developing values of students). Students rate the improvement in their graduating attributes after a course is finished and a collaborative filtering algorithm is utilized in order to suggest courses that were taken by fellow students and rated in a similar way. An extension to weigh the most recent ratings as more important is included in the algorithm which is shown to have better accuracy than the baseline approach. Experimental results using correlation thresholding and the nearest neighbors approach show that such a recommendation system can be effective when an active neighborhood of 10-15 students is used and show that the numbers of users used can be decreased effectively to one fourth of the whole population for improving the performance of the algorithm.
Current methods of diagnosis and treatment of scaphoid fractures

Fractures of the scaphoid bone mainly occur in young adults and constitute 2-7% of all fractures. The specific blood supply in combination with the demanding functional requirements can easily lead to disturbed fracture healing. Displaced scaphoid fractures are seen on radiographs. The diagnostic strategy of suspected scaphoid fractures, however, is surrounded by controversy. Bone scintigraphy, magnetic resonance imaging and computed tomography have their shortcomings. Early treatment leads to a better outcome. Scaphoid fractures can be treated conservatively and operatively. Proximal scaphoid fractures and displaced scaphoid fractures have a worse outcome and might be better off with an open or closed reduction and internal fixation. The incidence of scaphoid non-unions has been reported to be between 5 and 15%. Non-unions are mostly treated operatively by restoring the anatomy to avoid degenerative wrist arthritis.
Simulation-based functional verification of dynamically reconfigurable FPGA-based systems

Dynamically Reconfigurable Systems (DRS), implemented using Field-Programmable Gate Arrays (FPGAs), allow hardware logic to be partially reconfigured while the rest of a design continues to operate. By mapping multiple reconfigurable hardware modules to the same physical region of an FPGA, such systems are able to time-multiplex their circuits at run time and can adapt to changing execution requirements. This architectural flexibility introduces challenges for verifying system functionality. New simulation approaches need to extend traditional simulation techniques to assist designers in testing and debugging the time-varying behavior of DRS. Another significant challenge is the effective use of tools so as to reduce the number of design iterations. This thesis focuses on simulation-based functional verification of modular reconfigurable DRS designs. We propose a methodology and provide tools to assist designers in verifying DRS designs while part of the design is undergoing reconfiguration. This thesis analyzes the challenges in verifying DRS designs with respect to the user design and the physical implementation of such systems. We propose using a simulationonly layer to emulate the behavior of target FPGAs and accurately model the characteristic features of reconfiguration. The simulation-only layer maintains verification productivity by abstracting away the physical details of the FPGA fabric. Furthermore, since the design does not need to be modified for simulation purposes, the design as implemented instead of some variation of it is verified. We provide two possible implementations of the simulation-only layer. Extended ReChannel is a SystemC library that can be used to model DRS at a high level. ReSim is a library to support RTL simulation of a DRS reconfiguring both its logic and state. Through a number of case studies, we demonstrate that with insignificant overheads, our approach seamlessly integrates with the existing, mainstream DRS design flow and with wellestablished verification methodologies such as top-down modeling and coverage-driven verification. The case studies also serve as a guide in the use of our libraries to identify bugs that are related to Dynamic Partial Reconfiguration. Our results demonstrate that using the simulation-only layer is an effective approach to the simulation-based functional verification of DRS designs.