nils-herrmann/clean_scirepeval huggingface dataset:
QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today’s heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients’ Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80% compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time.
Guided Zoom: Questioning Network Evidence for Fine-grained Classification

We propose Guided Zoom, an approach that utilizes spatial grounding to make more informed predictions. It does so by making sure the model has “the right reasons” for a prediction, being defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom questions how reasonable the evidence used to make a prediction is. In state-of-the-art deep single-label classification models, the top-k (k = 2, 3, 4, . . . ) accuracy is usually significantly higher than the top-1 accuracy. This is more evident in fine-grained datasets, where differences between classes are quite subtle. We show that Guided Zoom results in the refinement of a model’s classification accuracy on three finegrained classification datasets. We also explore the complementarity of different grounding techniques, by comparing their ensemble to an adversarial erasing approach that iteratively reveals the next most discriminative evidence.
Conditional Infilling GANs for Data Augmentation in Mammogram Classification

Deep learning approaches to breast cancer detection in mammograms have recently shown promising results. However, such models are constrained by the limited size of publicly available mammography datasets, in large part due to privacy concerns and the high cost of generating expert annotations. Limited dataset size is further exacerbated by substantial class imbalance since “normal” images dramatically outnumber those with findings. Given the rapid progress of generative models in synthesizing realistic images, and the known effectiveness of simple data augmentation techniques (e.g. horizontal flipping), we ask if it is possible to synthetically augment mammogram datasets using generative adversarial networks (GANs). We train a class-conditional GAN to perform contextual in-filling, which we then use to synthesize lesions onto healthy screening mammograms. First, we show that GANs are capable of generating high-resolution synthetic mammogram patches. Next, we experimentally evaluate using the augmented dataset to improve breast cancer classification performance. We observe that a ResNet-50 classifier trained with GAN-augmented training data produces a higher AUROC compared to the same model trained only on traditionally augmented data, demonstrating the potential of our approach.
A SWOT analysis of successful construction waste management

Recent years have witnessed a significant amount of construction waste as a result of rapid urbanization and large scale of construction activities in China. However, compared to many advanced western countries, very few studies have been carried out for investigating the construction waste management problems in China. This study, by conducting a strength, weakness, opportunity, and threat (SWOT) analysis, aims to help understand the status quo of construction waste management based on the particular context of Shenzhen city in south China. Data supporting the analysis are derived from multiple channels including governmental reports, waste management related regulations, literature review, and focus group meetings. The study opens a window through which major stakeholders involved can perceive the internal and external conditions of construction waste management in Shenzhen. The seven critical strategies, which are presented based on the SWOTs identified, could be useful for Shenzhen to develop and promote its future construction waste management at the strategic
The Impacts of E-service Quality on Customers ' Repurchase Intention in Platform Online Retailing : An Empirical Investigation

Online retailing has been an important channel or business model for many firms in China. How to win, satisfy and retain customers has become the urgent practical problem to solve in front of the online retailers. Based on the theory of reasoned action, an empirical study of factors influencing consumers’ repurchase is conducted by using the questionnaire survey method. The results indicate that in the setting of platform online retail, consumer repurchase intention is significantly affected by consumer satisfaction, consumer satisfaction is positively and significantly affected by product quality and price, but not by website design, information quality, order fulfillment, and consumer service quality. So platform online retailers should reasonably allocate marketing resources accordingly, and pay attention to improve customer satisfaction by controlling product quality and enhance price advantage of product.
Fast median and bilateral filtering

Median filtering is a cornerstone of modern image processing and is used extensively in smoothing and de-noising applications. The fastest commercial implementations (e.g. in Adobe® Photoshop® CS2) exhibit O(r) runtime in the radius of the filter, which limits their usefulness in realtime or resolution-independent contexts. We introduce a CPU-based, vectorizable O(log r) algorithm for median filtering, to our knowledge the most efficient yet developed. Our algorithm extends to images of any bit-depth, and can also be adapted to perform bilateral filtering. On 8-bit data our median filter outperforms Photoshop's implementation by up to a factor of fifty.
The use of 3D surface scanning for the measurement and assessment of the human foot

A number of surface scanning systems with the ability to quickly and easily obtain 3D digital representations of the foot are now commercially available. This review aims to present a summary of the reported use of these technologies in footwear development, the design of customised orthotics, and investigations for other ergonomic purposes related to the foot.



The PubMed and ScienceDirect databases were searched. Reference lists and experts in the field were also consulted to identify additional articles. Studies in English which had 3D surface scanning of the foot as an integral element of their protocol were included in the review.



Thirty-eight articles meeting the search criteria were included. Advantages and disadvantages of using 3D surface scanning systems are highlighted. A meta-analysis of studies using scanners to investigate the changes in foot dimensions during varying levels of weight bearing was carried out.



Modern 3D surface scanning systems can obtain accurate and repeatable digital representations of the foot shape and have been successfully used in medical, ergonomic and footwear development applications. The increasing affordability of these systems presents opportunities for researchers investigating the foot and for manufacturers of foot related apparel and devices, particularly those interested in producing items that are customised to the individual. Suggestions are made for future areas of research and for the standardization of the protocols used to produce foot scans.
Sorting of finely-grinded granular mixtures using a belt-type corona-electrostatic separator

The commercial roll-type corona-electrostatic separators, which are currently employed for the recovery of metals and plastics from mm-size granular mixtures, are inappropriate for the processing of finely-grinded wastes. The aim of the present work is to demonstrate that a belt-type corona-electrostatic separator could be an appropriate solution for the selective sorting of conductive and non-conductive products contained in micronized wastes. The experiments are carried out on a laboratory-scale multi-functional electrostatic separator designed by the authors. The corona discharge is generated between a wire-type dual electrode and the surface of the metal belt conveyor. The distance between the wire and the belt and the applied voltage are adjusted to values that permit particles charging without having an electric wind that puts them into motion on the surface of the belt. The separation is performed in the electric field generated between a high-voltage roll-type electrode (diameter 30 mm) and the grounded belt electrode. The study is conducted according to experimental design methodology, to enable the evaluation of the effects of the various factors that affect the efficiency of the separation: position of the roll-type electrode and applied high-voltage. The conclusions of this study will serve at the optimum design of an industrial belt-type corona-electrostatic separator for the recycling of metals and plastics from waste electric and electronic equipment.
Unified Deep Supervised Domain Adaptation and Generalization

This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high “speed” of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.
Wiki-based process framework for blended learning

With few exceptions, currently published research on theeducational use of wikis does not include how the learningactivities should be shaped, planned or enforced in a wiki [11]. Inthis paper we aim to fill that gap by providing a framework forlearning and teaching processes supported by the use of wikis. Aninstance of that process framework ("feedback-driven" process) wasformulated and implemented through a series of trials performed atUniversity of Hertfordshire Business School during the course ofthe last two academic years to 2006/7. The results of the trialhave been collected and analyzed using the quantitative andqualitative methods and have led to the conclusion that studentsengagement with wiki-based learning activities is directlyproportional to the quality and frequency of tutors feedback andthe clarity of the underlying learning and teaching process.
Concept analysis of reflection in nursing professional development.

The aim of this concept analysis was to describe attributes, antecedents, and consequences of reflection in nursing professional development, as well as surrogate terms and a model case to inform nursing educators, students, and nurses about developing reflective skills.



Rodgers' evolutionary cycle for concept analysis was used. The published work search was conducted using five databases: Education Resources Information Center (ERIC), Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ichushi, and British Nursing Index. The inclusion criteria were: (i) academic articles; (ii) written in English or Japanese; and (iii) including descriptions about reflection. From the pool of extant published work, 50 articles were selected for the thematic analysis.



For the concept of reflection in nursing professional development, four antecedents were identified: (i) theory and practice gap; (ii) expansion of the role and competency in nursing; (iii) educational and learning needs; and (iv) educational responsibility. The major attribute was the process of learning from experience, and the circular process included emotional reaction, description, internal examination, critical analysis, evaluation, and planning new action. The consequences were transforming new perspective, enhanced communication, professional development, and quality of care.



Reflection is used as a method or tool that connects knowledge and experiences. Through the process of reflection, students/nurses become aware of themselves, which helps them review and improve clinical skills. They also become more able to communicate with patients and colleagues. Reflection enhances self-directed learning and professional maturity. Nurses who use reflection can be better positioned to provide excellent patient care.
Processing performance on Apache Pig, Apache Hive and MySQL cluster

MySQL Cluster is a famous clustered database that is used to store and manipulate data. The problem with MySQL Cluster is that as the data grows larger, the time required to process the data increases and additional resources may be needed. With Hadoop and Hive and Pig, processing time can be faster than MySQL Cluster. In this paper, three data testers with the same data model will run simple queries and to find out at how many rows Hive or Pig is faster than MySQL Cluster. The data model taken from GroupLens Research Project  showed a result that Hive is the most appropriate for this data model in a low-cost hardware environment.
A Linux-based firewall for the DNP3 protocol

Firewall solutions, specifically designed for smart power grids and other industrial control systems, are quite limited, with only a few commercial offerings. This paper presents a novel methodology that extends existing Linux-based firewalls for use in systems that use DNP3 protocol for industrial control. The proposed solution uses the u32 byte-matching feature of the iptables firewall, a firewall solution available in most Linux distributions. To demonstrate the approach, filtering rules for common attacks on the DNP3 protocol were developed. DNP3 is an industrial control protocol typically used in the electric power sector. The main goal of our work is to leverage an openly available and robust firewall solution for use in protecting the U.S. smart grid. The prototype was tested on a scaled-down electric power substation which runs the DNP3 protocol for communication between the field devices and the SCADA master.
Evolution of Modular Neural Network in Medical Diagnosis

Intelligent systems have been extensively used in the area of biomedical engineering for assisting the doctors in medical diagnosis. The inability of simple neural networks to solve the diagnosis problem, due to extensively large data size as well as complex mapping of inputs to outputs, has resulted in the growth of modular neural networks that try to exploit the modularity in the problem for better performance. Inability of humans to effectively set the training parameters of the neural networks has further resulted in the use of evolutionary algorithms that automatically compute the best configuration. In this paper we present a novel mechanism of combining the neural and the evolutionary approaches to build an evolutionary neural network classifier for solving the problem of diagnosis of PIMA Indian Diabetes. The modular neural network is in form of a mixture of exerts model with multi-layer perceptron, radial basis function network and self organizing maps as the experts. Results show that the proposed approach is better than the conventionally used models over the database used.
A graph-based model for malware detection and classification using system-call groups

In this paper we present a graph-based model that, utilizing relations between groups of System-calls, detects whether an unknown software sample is malicious or benign, and classifies a malicious software to one of a set of known malware families. More precisely, we utilize the System-call Dependency Graphs (or, for short, ScD-graphs), obtained by traces captured through dynamic taint analysis. We design our model to be resistant against strong mutations applying our detection and classification techniques on a weighted directed graph, namely Group Relation Graph, or Gr-graph for short, resulting from ScD-graph after grouping disjoint subsets of its vertices. For the detection process, we propose the $$\Delta $$ Δ -similarity metric, and for the process of classification, we propose the SaMe-similarity and NP-similarity metrics consisting the SaMe-NP similarity. Finally, we evaluate our model for malware detection and classification showing its potentials against malicious software measuring its detection rates and classification accuracy.
Grasper having tactile sensing function using acoustic reflection for laparoscopic surgery

In current minimally invasive surgery techniques, the tactile information available to the surgeon is limited. Improving tactile sensation could enhance the operability of surgical instruments. Considering surgical applications, requirements such as having electrical safety, a simple structure, and sterilization capability should be considered. The current study sought to develop a grasper that can measure grasping force at the tip, based on a previously proposed tactile sensing method using acoustic reflection. This method can satisfy the requirements for surgical applications because it has no electrical element within the part that is inserted into the patient’s body. We integrated our acoustic tactile sensing method into a conventional grasping forceps instrument. We designed the instrument so that acoustic cavities within a grasping arm and a fork sleeve were connected by a small cavity in a pivoting joint. In this design, when the angle between the two grasping arms changes during grasping, the total length and local curvature of the acoustic cavity remain unchanged. Thus, the grasping force can be measured regardless of the orientation of the grasping arm. We developed a prototype sensorized grasper based on our proposed design. Fundamental tests revealed that sensor output increased with increasing contact force applied to the grasping arm, and the angle of the grasping arm did not significantly affect the sensor output. Moreover, the results of a grasping test, in which objects with different softness characteristics were held by the grasper, revealed that the grasping force could be appropriately adjusted to handle different objects on the basis of sensor output. Experimental results demonstrated that the prototype grasper can measure grasping force, enabling safe and stable grasping.
How Do Humans Teach: On Curriculum Learning and Teaching Dimension

We study the empirical strategies that humans follow as they teach a target concept with a simple 1D threshold to a robot. 1 Previous studies of computational teaching, particularly the teaching dimension model and the curriculum learning principle, offer contradictory predictions on what optimal strategy the teacher should follow in this teaching task. We show through behavioral studies that humans employ three distinct teaching strategies, one of which is consistent with the curriculum learning principle, and propose a novel theoretical framework as a potential explanation for this strategy. This framework, which assumes a teaching goal of minimizing the learner’s expected generalization error at each iteration, extends the standard teaching dimension model and offers a theoretical justification for curriculum learning.
Development of a 10 kW high power density three-phase ac-dc-ac converter using SiC devices

This paper presents the development and experimental performance of a 10 kW high power density three-phase ac-dc-ac converter. The converter consists of a Vienna-type rectifier front-end and a two-level voltage source inverter (VSI). In order to reduce the switching loss and achieve a high operating junction temperature, a SiC JFET and SiC Schottky diode are utilized. Design considerations for the phase-leg units, gate drivers, integrated input filter — combining EMI and boost inductor stages — and the system protection are described in full detail. Experiments are carried out under different operating conditions, and the results obtained verify the performance and feasibility of the proposed converter system.
Learning to See in the Dark

Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pipeline for processing low-light images, based on end-to-end training of a fully-convolutional network. The network operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data. We report promising results on the new dataset, analyze factors that affect performance, and highlight opportunities for future work.