nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the quality of medical care.

This paper is an attempt to describe and evaluate current methods for assessing the quality of medical care and to suggest some directions for further study. It is concerned with methods rather than findings, and with an evaluation of methodology in general, rather than a detailed critique of methods in specific studies. This is not an exhaustive review of the pertinent literature. Certain key studies, of course, have been included. Other papers have been selected only as illustrative examples. Those omitted are not, for that reason, less worthy of note. This paper deals almost exclusively with the evaluation of the medical care process at the level of physician-patient interaction. It excludes, therefore, processes primarily related to the effective delivery of medical care at the community level. Moreover, this paper is not concerned with the administrative aspects of quality control. Many of the studies reviewed here have arisen out of the urgent need to evaluate and control the quality of care in organized programs of medical care. Nevertheless, these studies will be discussed only in terms of their contribution to methods of assessment and not in terms of their broader social goals. The author has remained, by and large, in the familiar territory of care provided by physicians and has avoided incursions into other types of
Social rejection shares somatosensory representations with physical pain.

How similar are the experiences of social rejection and physical pain? Extant research suggests that a network of brain regions that support the affective but not the sensory components of physical pain underlie both experiences. Here we demonstrate that when rejection is powerfully elicited--by having people who recently experienced an unwanted break-up view a photograph of their ex-partner as they think about being rejected--areas that support the sensory components of physical pain (secondary somatosensory cortex; dorsal posterior insula) become active. We demonstrate the overlap between social rejection and physical pain in these areas by comparing both conditions in the same individuals using functional MRI. We further demonstrate the specificity of the secondary somatosensory cortex and dorsal posterior insula activity to physical pain by comparing activated locations in our study with a database of over 500 published studies. Activation in these regions was highly diagnostic of physical pain, with positive predictive values up to 88%. These results give new meaning to the idea that rejection "hurts." They demonstrate that rejection and physical pain are similar not only in that they are both distressing--they share a common somatosensory representation as well.
PowerHammer: Exfiltrating Data from Air-Gapped Computers through Power Lines

In this paper we provide an implementation, evaluation, and analysis of PowerHammer, a malware (bridgeware ) that uses power lines to exfiltrate data from air-gapped computers. In this case, a malicious code running on a compromised computer can control the power consumption of the system by intentionally regulating the CPU utilization. Data is modulated, encoded, and transmitted on top of the current flow fluctuations, and then it is conducted and propagated through the power lines. This phenomena is known as a ’conducted emission’. We present two versions of the attack. Line level powerhammering: In this attack, the attacker taps the in-home power lines that are directly attached to the electrical outlet. Phase level power-hammering: In this attack, the attacker taps the power lines at the phase level, in the main electrical service panel. In both versions of the attack, the attacker measures the emission conducted and then decodes the exfiltrated data. We describe the adversarial attack model and present modulations and encoding schemes along with a transmission protocol. We evaluate the covert channel in different scenarios and discuss signal-to-noise (SNR), signal processing, and forms of interference. We also present a set of defensive countermeasures. Our results show that binary data can be covertly exfiltrated from air-gapped computers through the power lines at bit rates of 1000 bit/sec for the line level power-hammering attack and 10 bit/sec for the phase level power-hammering attack.
A Study on Immersion of Hand Interaction for Mobile Platform Virtual Reality Contents

This study proposes gaze-based hand interaction, which is helpful for improving the user’s immersion in the production process of virtual reality content for the mobile platform, and analyzes efficiency through an experiment using a questionnaire. First, three-dimensional interactive content is produced for use in the proposed interaction experiment while presenting an experiential environment that gives users a high sense of immersion in the mobile virtual reality environment. This is designed to induce the tension and concentration of users in line with the immersive virtual reality environment. Additionally, a hand interaction method based on gaze—which is mainly used for the entry of mobile virtual reality content—is proposed as a design method for immersive mobile virtual reality environment. The user satisfaction level of the immersive environment provided by the proposed gaze-based hand interaction is analyzed through experiments in comparison with the general method that uses gaze only. Furthermore, detailed analysis is conducted by dividing the effects of the proposed interaction method on user’s psychology into positive factors such as immersion and interest and negative factors such as virtual reality (VR) sickness and dizziness. In this process, a new direction is proposed for improving the immersion of users in the production of mobile platform virtual reality content.
Securing Proof-of-Stake Blockchain Protocols

Proof-of-Stake (PoS) protocols have been actively researched for the past five years. PoS finds direct applicability in open blockchain platforms and has been seen as a strong candidate to replace the largely inefficient Proof of Work mechanism that is currently plugged in most existing open blockchains. Although a number of PoS variants have been proposed, these protocols suffer from a number of security shortcomings; for instance, most existing PoS variants suffer from the nothing at stake and the long range attacks which considerably degrade security in the blockchain. In this paper, we address these problems and we propose two PoS protocols that allow validators to generate at most one block at any given “height”—-thus alleviating the problem of nothing at stake and preventing attackers from compromising accounts to mount long range attacks. Our first protocol leverages a dedicated digital signature scheme that reveals the identity of the validator if the validator attempts to work on multiple blocks at the same height. On the other hand, our second protocol leverages existing pervasive Trusted Execution Environments (TEEs) to limit the block generation requests by any given validator to a maximum of one at a given height. We analyze the security of our proposals and evaluate their performance by means of implementation; our evaluation results show that our proposals introduce tolerable overhead in the block generation and validation process when compared to existing PoS protocols.
The study and application of the IOT technology in agriculture

in recent years, greenhouse technology in agriculture is to automation, information technology direction with the IOT (internet of things) technology rapid development and wide application. In the paper, control networks and information networks integration of IOT technology has been studied based on the actual situation of agricultural production. Remote monitoring system with internet and wireless communications combined is proposed. At the same time, taking into account the system, information management system is designed. The collected data by the system provided for agricultural research facilities.
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations. We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in both temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.
Nanotechnology in agriculture, livestock, and aquaculture in China. A review

Nanoscience emerged in the late 1980s and is developed and applied in China since the middle of the 1990s. Although nanotechnologies have been less developed in agronomy than other disciplines, due to less investment, nanotechnologies have the potential to improve agricultural production. Here, we review more than 200 reports involving nanoscience in agriculture, livestock, and aquaculture. The major points are as follows: (1) nanotechnologies used for seeds and water improved plant germination, growth, yield, and quality. (2) Nanotechnologies could increase the storage period for vegetables and fruits. (3) For livestock and poultry breeding, nanotechnologies improved animals immunity, oxidation resistance, and production and decreased antibiotic use and manure odor. For instance, the average daily gain of pig increased by 9.9–15.3 %, the ratio of feedstuff to weight decreased by 7.5–10.3 %, and the diarrhea rate decreased by 55.6–66.7 %. (4) Nanotechnologies for water disinfection in fishpond increased water quality and increased yields and survivals of fish and prawn. (5) Nanotechnologies for pesticides increased pesticide performance threefold and reduced cost by 50 %. (6) Nano urea increased the agronomic efficiency of nitrogen fertilization by 44.5 % and the grain yield by 10.2 %, versus normal urea. (7) Nanotechnologies are widely used for rapid detection and diagnosis, notably for clinical examination, food safety testing, and animal epidemic surveillance. (8) Nanotechnologies may also have adverse effects that are so far not well known.
Rapid Style Adaptation Using Residual Error Embedding for Expressive Speech Synthesis

Synthesizing expressive speech with appropriate prosodic variations, e.g., various styles, still has much room for improvement. Previous methods have explored to use manual annotations as conditioning attributes to provide variation information. However, the related training data are expensive to obtain and the annotated style codes can be ambiguous and unreliable. In this paper, we explore utilizing the residual error as conditioning attributes. The residual error is the difference between the prediction of a trained average model and the ground truth. We encode the residual error into a style embedding via a neural networkbased error encoder. The style embedding is then fed to the target synthesis model to provide information for modeling various style distributions more accurately. The average model and the error encoder are jointly optimized with the target synthesis model. Our proposed method has two advantages: 1) the embedding is automatically learned with no need of manual style annotations, which helps overcome data sparsity and ambiguity limitations; 2) For any unseen audio utterance, the style embedding can be efficiently generated. This enables rapid adaptation to the desired style to be achieved with only a single adaptation utterance. Experimental results show that our proposed method outperforms the baseline model in both speech quality and style similarity.
Probabilistic Linear Discriminant Analysis

Linear dimensionality reduction methods, such as LDA, are often used in object recognition for feature extraction, but do not address the problem of how to use these features for recognition. In this paper, we propose Probabilistic LDA, a generative probability model with which we can both extract the features and combine them for recognition. The latent variables of PLDA represent both the class of the object and the view of the object within a class. By making examples of the same class share the class variable, we show how to train PLDA and use it for recognition on previously unseen classes. The usual LDA features are derived as a result of training PLDA, but in addition have a probability model attached to them, which automatically gives more weight to the more discriminative features. With PLDA, we can build a model of a previously unseen class from a single example, and can combine multiple examples for a better representation of the class. We show applications to classification, hypothesis testing, class inference, and clustering, on classes not observed during training.
Value Chain Creation in Business Analytics

Firms are awash in big data and analytical technology as part of deriving values in the turbulent environment. The literature has somewhat reached a consensus that investments in technology only may not reap benefits from business analytics (BA). The main purpose of BA is not about how to install technical capabilities, but about how to make a process whereby a firm builds a value chain converting data into insights, leading to quality decisions. Drawing upon the theory of the information value chain, this study develops a BA value chain model and tests it with 268 data scientists. Results show that organizational resilience, absorptive capacity, and analytical IT capabilities are critical antecedents to analytical decision-making quality which in turn influences BA net benefits. Particularly, results illustrate that organizational resilience is a more significant variable impacting analytical decision-making quality than the influence of people and technology. Theoretical and practical implications are also discussed.
Contour-aware network for semantic segmentation via adaptive depth

Semantic segmentation has been widely investigated for its important role in computer vision. However, some challenges still exist. The first challenge is how to perceive semantic regions with various attributes, which can result in unbalanced distribution of training samples. Another challenge is accurate semantic boundary determination. In this paper, a contour-aware network for semantic segmentation via adaptive depth is proposed which particularly exploits the power of adaptive-depth neural network and contouraware neural network on pixel-level semantic segmentation. Specifically, an adaptive-depth model, which can adaptively determine the feedback and forward procedure of neural network, is constructed. Moreover, a contour-aware neural network is respectively built to enhance the coherence and the localization accuracy of semantic regions. By formulating the contour information and coarse semantic segmentation results in a unified manner, global inference is proposed to obtain the final segmentation results. Three contributions are claimed: (1) semantic segmentation via adaptive depth neural network; (2) contouraware neural network for semantic segmentation; and (3) global inference for final decision. Experiments on three popular datasets are conducted and experimental results have verified the superiority of the proposed method compared with the state-of-the-art methods.
The Flash Crash : A New Deconstruction *

The “Flash Crash” of May 6th, 2010 comprised an unprecedented 1,000 point, five-minute decline in the Dow Jones Industrial Average that was followed by a rapid, disorderly recovery of prices. We illuminate the causes of this singular event with the first analysis that tracks the full order book activity at millisecond granularity. We document previously overlooked market data anomalies and establish that these anomalies Granger-caused liquidity withdrawal. We offer a simulation model that formalizes the process by which large sell orders, combined with widespread liquidity withdrawal, can generate Flash Crash-like events in the absence of fundamental information arrival. ∗This work was supported by the Hellman Fellows Fund and the Rock Center for Corporate Governance at Stanford University. †Email: ealdrich@ucsc.edu. ‡Email: grundfest@stanford.edu §Email: gregory.laughlin@yale.edu
Geometric deep learning

Machine Learning on graphs and manifolds are important ubiquitous tasks with applications ranging from network analysis to 3D shape analysis. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph or mesh. Recently, there has been an increasing interest in geometric deep learning [6] that automatically learns signals defined on graphs and manifolds. We are then motivated to apply such methods to address the multifaceted challenges arising in computational biology and computer graphics for decades, i.e. protein function prediction and 3D facial expression recognition. Here we propose a deep graph neural network to successfully address the semi-supervised multi-label classification problem (i.e. protein function prediction). With regard to 3D facial expression recognition, we propose a deep residual B-Spline graph convolution network, which allows for end-to-end training and inference without using hand-crafted feature descriptors. Our method outperforms the current baseline results on 4DFAB  dataset.
Big Data Analytics in IOT: Challenges, Open Research Issues and Tools

Terabytes of data are generated day-to-day from modern information systems, cloud computing and digital technologies, as the increasing number of Internet connected devices grows. However, the analysis of these massive data requires many efforts at multiple levels for knowledge extraction and decision making. Therefore, Big Data Analytics is a current area of research and development that has become increasingly important. This article investigates cutting-edge research efforts aimed at analyzing Internet of Things (IoT) data. The basic objective of this article is to explore the potential impact of large data challenges, research efforts directed towards the analysis of IoT data and various tools associated with its analysis. As a result, this article suggests the use of platforms to explore big data in numerous stages and better understand the knowledge we can draw from the data, which opens a new horizon for researchers to develop solutions based on open research challenges and topics.
Validity Testing of Technology Acceptance Model Based on Factor Analysis Approach

Successful implementation of Information Technology can be judged or predicted from the user acceptance. Technology acceptance model (TAM) is a model that is built to analyze and understand the factors that influence the acceptance of the use of technologies based on the user's perspective. In other words, TAM offers a powerful explanation related to acceptance of the technology and its behavior. TAM model has been applied widely to evaluate various information systems or information technology (IS/IT), but it is the lack of research related to the evaluation of the TAM model itself. This study aims to determine whether the model used TAM is still relevant today considering rapid development of information & communication technology (ICT). In other words, this study would like to test whether the TAM measurement indicators are valid and can represent each dimension of the model. The method used is quantitative method with factor analysis approach. The results showed that all indicators valid and can represent each dimension of TAM, those are perceived usefulness, perceived ease of use and behavioral intention to use. Thus the TAM model is still relevant used to measure the user acceptance of technology.
A Personalized Service for Scheduling Express Delivery Using Courier Trajectories

With the increasing demand for express delivery, a courier needs to deliver many tasks in one day and it's necessary to deliver punctually as the customers expect. At the same time, they want to schedule the delivery tasks to minimize the total time of a courier's one-day delivery, considering the total travel time. However, most of scheduling researches on express delivery focus on inter-city transportation, and they are not suitable for the express delivery to customers in the “last mile”. To solve the issue above, this paper proposes a personalized service for scheduling express delivery, which not only satisfies all the customers' appointment time but also makes the total time minimized. In this service, personalized and accurate travel time estimation is important to guarantee delivery punctuality when delivering shipments. Therefore, the personalized scheduling service is designed to consist of two basic services: (1) personalized travel time estimation service for any path in express delivery using courier trajectories, (2) an express delivery scheduling service considering multiple factors, including customers' appointments, one-day delivery costs, etc., which is based on the accurate travel time estimation provided by the first service. We evaluate our proposed service based on extensive experiments, using GPS trajectories generated by more than 1000 couriers over a period of two months in Beijing. The results demonstrate the effectiveness and efficiency of our method.
Occupational Therapy Wellness Program: Youth and Parent Perspectives.

In this study, we explored the impact of an occupational therapy wellness program on daily habits and routines through the perspectives of youth and their parents.



Data were collected through semistructured interviews with children and their parents, the Pizzi Healthy Weight Management Assessment(©), and program activities.



Three themes emerged from the interviews: , and Time as a Barrier to Health. The most common areas that both youth and parents wanted to change were time spent watching television and play, fun, and leisure time. Analysis of activity pie charts indicated that the youth considerably increased active time in their daily routines from Week 1 to Week 6 of the program.



An occupational therapy program focused on health and wellness may help youth and their parents be more mindful of their daily activities and make health behavior changes.
Learning Graph While Training: An Evolving Graph Convolutional Neural Network

Convolution Neural Networks on Graphs are important generalization and extension of classical CNNs. While previous works generally assumed that the graph structures of samples are regular with unified dimensions, in many applications, they are highly diverse or even not well defined. Under some circumstances, e.g. chemical molecular data, clustering or coarsening for simplifying the graphs is hard to be justified chemically. In this paper, we propose a more general and flexible graph convolution network (EGCN) fed by batch of arbitrarily shaped data together with their evolving graph Laplacians trained in supervised fashion. Extensive experiments have been conducted to demonstrate the superior performance in terms of both the acceleration of parameter fitting and the significantly improved prediction accuracy on multiple graph-structured datasets.