nils-herrmann/clean_scirepeval huggingface dataset:
Closed-loop control of blood glucose level in type-1 diabetics: A simulation study

Survey on diabetes is one of the popular fields of biomedical signal processing. In this paper, a closed-loop system which utilizes modified Stolwijk-Hardy glucose insulin interaction model is considered. The modified model was derived by adding an exogenous insulin infusion term. Two control algorithms are used for exogenous insulin infusion: a Mamdani type fuzzy logic controller (FLC), and a fuzzy-PID controller. Simulations are performed to assess control function in terms of keeping desired steady state plasma glucose level (0.81 mg/ml) against to exogenous glucose input. Simulation results are notable and significant in terms of controlling blood glucose level (BGL). The control algorithms that applied to the model are firstly proposed, therefore this study is made a contribution to the literature.
Examining the Peak-End Effects of Subjective Experience

Psychological research has shown that 'peak-end' effects influence people's retrospective evaluation of hedonic and affective experience. Rather than objectively reviewing the total amount of pleasure or pain during an experience, people's evaluation is shaped by the most intense moment (the peak) and the final moment (end). We describe an experiment demonstrating that peak-end effects can influence a user's preference for interaction sequences that are objectively identical in their overall requirements. Participants were asked to choose which of two interactive sequences of five pages they preferred. Both sequences required setting a total of 25 sliders to target values, and differed only in the distribution of the sliders across the five pages -- with one sequence intended to induce positive peak-end effects, the other negative. The study found that manipulating only the peak or the end of the series did not significantly change preference, but that a combined manipulation of both peak and end did lead to significant differences in preference, even though all series had the same overall effort.
A Neural Architecture for Automated ICD Coding

The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases. Medical coding – which assigns a subset of ICD codes to a patient visit – is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and errorprone. In this paper, we build a neural architecture for automated coding. It takes the diagnosis descriptions (DDs) of a patient as inputs and selects the most relevant ICD codes. This architecture contains four major ingredients: (1) tree-ofsequences LSTM encoding of code descriptions (CDs), (2) adversarial learning for reconciling the different writing styles of DDs and CDs, (3) isotonic constraints for incorporating the importance order among the assigned codes, and (4) attentional matching for performing many-toone and one-to-many mappings from DDs to CDs. We demonstrate the effectiveness of the proposed methods on a clinical datasets with 59K patient visits.
Modeling a Retweet Network via an Adaptive Bayesian Approach

Twitter (and similar microblogging services) has become a central nexus for discussion of the topics of the day. Twitter data contains rich content and structured information on users’ topics of interest and behavior patterns. Correctly analyzing and modeling Twitter data enables the prediction of the user behavior and preference in a variety of practical applications, such as tweet recommendation and followee recommendation. Although a number of models have been developed on Twitter data in prior work, most of these only model the tweets from users, while neglecting their valuable retweet information in the data. Models would enhance their predictive power by incorporating users’ retweet content as well as their retweet behavior. In this paper, we propose two novel Bayesian nonparametric models, URM and UCM, on retweet data. Both of them are able to integrate the analysis of tweet text and users’ retweet behavior in the same probabilistic framework. Moreover, they both jointly model users’ interest in tweet and retweet. As nonparametric models, URM and UCM can automatically determine the parameters of the models based on input data, avoiding arbitrary parameter settings. Extensive experiments on real-world Twitter data show that both URM and UCM are superior to all the baselines, while UCM further outperforms URM, confirming the appropriateness of our models in retweet modeling.
The Smartphone Psychology Manifesto.

By 2025, when most of today's psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today's iPhones, Androids, and Blackberries. Although smartphones were not designed for psychological research, they can collect vast amounts of ecologically valid data, easily and quickly, from large global samples. If participants download the right "psych apps," smartphones can record where they are, what they are doing, and what they can see and hear and can run interactive surveys, tests, and experiments through touch screens and wireless connections to nearby screens, headsets, biosensors, and other peripherals. This article reviews previous behavioral research using mobile electronic devices, outlines what smartphones can do now and will be able to do in the near future, explains how a smartphone study could work practically given current technology (e.g., in studying ovulatory cycle effects on women's sexuality), discusses some limitations and challenges of smartphone research, and compares smartphones to other research methods. Smartphone research will require new skills in app development and data analysis and will raise tough new ethical issues, but smartphones could transform psychology even more profoundly than PCs and brain imaging did.
A Meta-Top-Down Method for Large-Scale Hierarchical Classification

Recent large-scale hierarchical classification tasks typically have tens of thousands of classes on which the most widely used approach to multiclass classification--one-versus-rest--becomes intractable due to computational complexity. The top-down methods are usually adopted instead, but they are less accurate because of the so-called error-propagation problem in their classifying phase. To address this problem, this paper proposes a meta-top-down method that employs metaclassification to enhance the normal top-down classifying procedure. The proposed method is first analyzed theoretically on complexity and accuracy, and then applied to five real-world large-scale data sets. The experimental results indicate that the classification accuracy is largely improved, while the increased time costs are smaller than most of the existing approaches.
Survey of Automated Vulnerability Detection and Exploit Generation Techniques in Cyber Reasoning Systems

Software is everywhere, from mission critical systems such as industrial power stations, pacemakers and even household appliances. This growing dependence on technology and the increasing complexity software has serious security implications as it means we are potentially surrounded by software that contain exploitable vulnerabilities. These challenges have made binary analysis an important area of research in computer science and has emphasized the need for building automated analysis systems that can operate at scale, speed and efficacy; all while performing with the skill of a human expert. Though great progress has been made in this area of research, there remains limitations and open challenges to be addressed. Recognizing this need, DARPA sponsored the Cyber Grand Challenge (CGC), a competition to showcase the current state of the art in systems that perform; automated vulnerability detection, exploit generation and software patching. This paper is a survey of the vulnerability detection and exploit generation techniques, underlying technologies and related works of two of the winning systems Mayhem and Mechanical Phish. Cyber reasoning systems, automated binary analysis, automated exploit generation, dynamic symbolic execution, fuzzing
Multimodal Energy Harvesting System : Piezoelectric and Electromagnetic

In this study, we report a multimodal energy harvesting device that combines electromagnetic and piezoelectric energy harvesting mechanism. The device consists of piezoelectric crystals bonded to a cantilever beam. The tip of the cantilever beam has an attached permanent magnet which, oscillates within a stationary coil fixed to the top of the package. The permanent magnet serves two purpose (i) acts as a tip mass for the cantilever beam and lowers the resonance frequency, and (ii) acts as a core which oscillates between the inductive coils resulting in electric current generation through Faraday’s effect. Thus, this design combines the energy harvesting from two different mechanisms, piezoelectric and electromagnetic, on the same platform. The prototype system was optimized using the finite element software, ANSYS, to find the resonance frequency and stress distribution. The power generated from the fabricated prototype was found to be 0.25W using the electromagnetic mechanism and 0.25mW using the piezoelectric mechanism at 35 g acceleration and 20Hz frequency.
Flows of Dense Granular Media

We review flows of dense cohesionless granular materials, with a special focus on the question of constitutive equations. We first discuss the existence of a dense flow regime characterized by enduring contacts. We then emphasize that dimensional analysis strongly constrains the relation between stresses and shear rates, and show that results from experiments and simulations in different configurations support a description in terms of a frictional visco-plastic constitutive law. We then discuss the successes and limitations of this empirical rheology in light of recent alternative theoretical approaches. Finally, we briefly present depth-averaged methods developed for free surface granular flows. 1 A nn u. R ev . F lu id M ec h. 2 00 8. 40 :1 -2 4. D ow nl oa de d fro m a rjo ur na ls. an nu al re vi ew s.o rg by d r y oe l f or te rre o n 03 /1 9/ 08 . F or p er so na l u se o nl y. ANRV332-FL40-01 ARI 10 November 2007 15:46
Enhancing the Network Embedding Quality with Structural Similarity

Neural network techniques are widely used in network embedding, boosting the result of node classification, link prediction, visualization and other tasks in both aspects of efficiency and quality. All the state of art algorithms put effort on the neighborhood information and try to make full use of it. However, it is hard to recognize core periphery structures simply based on neighborhood. In this paper, we first discuss the influence brought by random-walk based sampling strategies to the embedding results. Theoretical and experimental evidences show that random-walk based sampling strategies fail to fully capture structural equivalence. We present a new method, SNS, that performs network embeddings using structural information (namely graphlets) to enhance its quality. SNS effectively utilizes both neighbor information and local-subgraphs similarity to learn node embeddings. This is the first framework that combines these two aspects as far as we know, positively merging two important areas in graph mining and machine learning. Moreover, we investigate what kinds of local-subgraph features matter the most on the node classification task, which enables us to further improve the embedding quality. Experiments show that our algorithm outperforms other unsupervised and semi-supervised neural network embedding algorithms on several real-world datasets.
A Normalized Convolutional Neural Network for Guided Sparse Depth Upsampling

Guided sparse depth upsampling aims to upsample an irregularly sampled sparse depth map when an aligned high-resolution color image is given as guidance. When deep convolutional neural networks (CNNs) become the optimal choice to many applications nowadays, how to deal with irregular and sparse data still remains a non-trivial problem. Inspired by the classical normalized convolution operation, this work proposes a normalized convolutional layer (NCL) implemented in CNNs. Sparse data are therefore explicitly considered in CNNs by the separation of both data and filters into a signal part and a certainty part. Based upon NCLs, we design a normalized convolutional neural network (NCNN) to perform guided sparse depth upsampling. Experiments on both indoor and outdoor datasets show that the proposed NCNN models achieve state-of-the-art upsampling performance. Moreover, the models using NCLs gain a great generalization ability to different sparsity levels.
Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment †

This research proposes an accurate vehicular positioning system which can achieve lane-level performance in urban canyons. Multiple passive sensors, which include Global Navigation Satellite System (GNSS) receivers, onboard cameras and inertial sensors, are integrated in the proposed system. As the main source for the localization, the GNSS technique suffers from Non-Line-Of-Sight (NLOS) propagation and multipath effects in urban canyons. This paper proposes to employ a novel GNSS positioning technique in the integration. The employed GNSS technique reduces the multipath and NLOS effects by using the 3D building map. In addition, the inertial sensor can describe the vehicle motion, but has a drift problem as time increases. This paper develops vision-based lane detection, which is firstly used for controlling the drift of the inertial sensor. Moreover, the lane keeping and changing behaviors are extracted from the lane detection function, and further reduce the lateral positioning error in the proposed localization system. We evaluate the integrated localization system in the challenging city urban scenario. The experiments demonstrate the proposed method has sub-meter accuracy with respect to mean positioning error.
Migrating running applications across mobile edge clouds: poster

Mobile edge clouds (MECs) are small cloud-like infrastructures deployed in close proximity to users, allowing users to have seamless and low-latency access to cloud services. When users move across different locations, their service applications often need to be migrated to follow the user so that the benefit of MEC is maintained. In this paper, we propose a layered framework for migrating running applications that are encapsulated either in virtual machines (VMs) or containers. We evaluate the migration performance of various real applications under the proposed framework.
A switched reluctance machine-based starter/alternator for more electric cars

Switched reluctance machines (SRMs) are considered as serious candidates for starter/alternator (S/A) systems in more electric cars. Robust performance in the presence of high temperature, safe operation, offering high efficiency, and a very long constant power region, along with a rugged structure contribute to their suitability for this high impact application. To enhance these qualities, we have developed key technologies including sensorless operation over the entire speed range and closed-loop torque and speed regulation. The present paper offers an in-depth analysis of the drive dynamics during motoring and generating modes of operation. These findings will be used to explain our control strategies in the context of the S/A application. Experimental and simulation results are also demonstrated to validate the practicality of our claims.
Energy-Efficient Indoor Localization of Smart Hand-Held Devices Using Bluetooth

Indoor localization of smart hand-held devices is essential for location-based services of pervasive applications. The previous research mainly focuses on exploring wireless signal fingerprints for this purpose, and several shortcomings need to be addressed first before real-world usage, e.g., demanding a large number of access points or labor-intensive site survey. In this paper, through a systematic empirical study, we first gain in-depth understandings of Bluetooth characteristics, i.e., the impact of various factors, such as distance, orientation, and obstacles on the Bluetooth received signal strength indicator (RSSI). Then, by mining from historical data, a novel localization model is built to describe the relationship between the RSSI and the device location. On this basis, we present an energy-efficient indoor localization scheme that leverages user motions to iteratively shrink the search space to locate the target device. An Motion-assisted Device Tracking Algorithm has been prototyped and evaluated in several real-world scenarios. Extensive experiments show that our algorithm is efficient in terms of localization accuracy, searching time and energy consumption.
Sentiment analysis on tweets for social events

Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of people's opinions. Tweets data can be efficiently used to infer people's opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general people's opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.
Evaluating capacitive touch input on clothes

Wearable computing and smart clothing have attracted a lot of attention in the last years. For a variety of applications, it can be seen as potential future direction of mobile user interfaces. In this paper, we concentrate on usability and applicability issues concerned with capacitive touch input on clothing. To be able to perform user studies, we built a generic platform for attaching, e.g., capacitive sensors of different types. On top of that, several prototypes of wearable accessories and clothing and implemented various application scenarios. We report on two studies we undertook with these implementations with a user group randomly sampled at a shopping mall. We provide a significant set of guidelines and lessons learned that emerged from our experiences and those studies. Thus, developers of similar projects have to put major efforts into minimizing the delay between button activation and feedback and to make location and identification of controls and their function as simple and quick as possible. Issues that have to be treated in all designs include the requirement of one-handed interaction and that, even for minimal functionality, to find a general solution with regard to layout and button-to-function mapping is hardly possible. Additionally, in order to generate a satisfactory user experience good usability must be combined with aesthetical factors.
Deep Learning Computed Tomography

In this paper, we demonstrate that image reconstruction can be expressed in terms of neural networks. We show that filtered backprojection can be mapped identically onto a deep neural network architecture. As for the case of iterative reconstruction, the straight forward realization as matrix multiplication is not feasible. Thus, we propose to compute the back-projection layer efficiently as fixed function and its gradient as projection operation. This allows a data-driven approach for joint optimization of correction steps in projection domain and image domain. As a proof of concept, we demonstrate that we are able to learn weightings and additional filter layers that consistently reduce the reconstruction error of a limited angle reconstruction by a factor of two while keeping the same computational complexity as filtered back-projection. We believe that this kind of learning approach can be extended to any common CT artifact compensation heuristic and will outperform hand-crafted artifact correction methods in the future.
Dynamic self-clamping at short-circuit turn-off of high-voltage IGBTs

Measurements show that the IGBT is able to clamp the collector-emitter voltage to a certain value at short-circuit turn-off despite a very low gate turn-off resistor in combination with a high parasitic inductance is applied. The IGBT itself reduces the turn-off diC/dt by avalanche injection. However, device destructions during fast turn-off were observed which cannot be linked with an overvoltage failure mode. Measurements and semiconductor simulations of high-voltage IGBTs explain the self-clamping mechanism in detail. Possible failures which can be connected with filamentation processes are described. Options for improving the IGBT robustness during short-circuit turn-off are discussed.
Recognition of QR Code with mobile phones

Quick Response Code has been widely used in the automatic identification fields. In order to adapting various sizes, a little dirty or damaged, and various lighting conditions of bar code image, this paper proposes a novel implementation of real-time Quick Response Code recognition using mobile, which is an efficient technology used for data transferring. An image processing system based on mobile is described to be able to binarize, locate, segment, and decode the QR Code. Our experimental results indicate that these algorithms are robust to real world scene image.
Controlling Linguistic Style Aspects in Neural Language Generation

Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controlling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts. We demonstrate the approach on the movie reviews domain and show that it is successful in generating coherent sentences corresponding to the required linguistic style and content.
Effects of poverty and maternal depression on early child development.

Researchers have renewed an interest in the harmful consequences of poverty on child development. This study builds on this work by focusing on one mechanism that links material hardship to child outcomes, namely the mediating effect of maternal depression. Using data from the National Maternal and Infant Health Survey, we found that maternal depression and poverty jeopardized the development of very young boys and girls, and to a certain extent, affluence buffered the deleterious consequences of depression. Results also showed that chronic maternal depression had severe implications for both boys and girls, whereas persistent poverty had a strong effect for the development of girls. The measures of poverty and maternal depression used in this study generally had a greater impact on measures of cognitive development than motor development.
Efficient Clustering of Uncertain Data

We study the problem of clustering data objects whose locations are uncertain. A data object is represented by an uncertainty region over which a probability density function (pdf) is defined. One method to cluster uncertain objects of this sort is to apply the UK-means algorithm, which is based on the traditional K-means algorithm. In UK-means, an object is assigned to the cluster whose representative has the smallest expected distance to the object. For arbitrary pdf, calculating the expected distance between an object and a cluster representative requires expensive integration computation. We study various pruning methods to avoid such expensive expected distance calculation.