nils-herrmann/clean_scirepeval huggingface dataset:
Model-based diagnosis of an automotive engine using several types of fault models

Automotive engines is an important application for model-based diagnosis because of legislative regulations. A diagnosis system for the air-intake system of a turbo-charged engine is constructed. The design is made in a systematic way and follows a framework of hypothesis testing. Different types of sensor faults and leakages are considered. It is shown how many different types of fault models, e.g., additive and multiplicative faults, can be used within one common diagnosis system, and using the same underlying design principle. The diagnosis system is experimentally validated on a real engine using industry-standard dynamic test-cycles.
Characterizing Facebook's Memcached Workload

Memcached is one of the world's largest key-value deployments. This article analyzes the Memcached workload at Facebook, looking at server-side performance, request composition, caching efficacy, and key locality. The observations presented here lead to several design insights and new research directions for key-value caches, such as the relative inadequacy of the least recently used (LRU) replacement policy.
An Inconvenient Trust: User Attitudes toward Security and Usability Tradeoffs for Key-Directory Encryption Systems

Many critical communications now take place digitally, but recent revelations demonstrate that these communications can often be intercepted. To achieve true message privacy, users need end-to-end message encryption, in which the communi­ cations service is not able to decrypt the content. Historically, end-to-end encryption has proven extremely difficult for peo­ ple to use correctly, but recently tools like Apple’s iMes­ sage and Google’s End-to-End have made it more broadly accessible by using key-directory services. These tools (and others like them) sacrifice some security properties for con­ venience, which alarms some security experts, but little is known about how average users evaluate these tradeoffs. In a 40-person interview study, we asked participants to complete encryption tasks using both a traditional key-exchange model and a key-directory-based registration model. We then de­ scribed the security properties of each and asked participants for their opinions. We found that participants understood the two models well and made coherent assessments about when different tradeoffs might be appropriate. Overall, our partici­ pants found the security of the registration model to be “good enough” for many everyday purposes.
New Dynamic Battery Model for Hybrid Vehicles

Battery storage (BS) systems are static energy conversion units that convert the chemical energy directly into electrical energy. They exist in our cars, laptops, electronic appliances, micro electricity generation systems and in many other mobile to stationary power supply systems. The economic advantages, partial sustainability and the portability of these units pose promising substitutes for backup power systems for hybrid vehicles and hybrid electricity generation systems. Dynamic behaviour of these systems can be analysed by using mathematical modeling and simulation software programs. Though, there have been many mathematical models presented in the literature and proved to be successful, dynamic simulation of these systems are still very exhaustive and time consuming as they do not behave according to specific mathematical models or functions. The charging and discharging of battery functions are a combination of exponential and non-linear nature. The aim of this research paper is to present a suitable convenient, dynamic battery model that can be used to model a general BS system. Proposed model is a new modified dynamic Lead-Acid battery model considering the effect of temperature and cyclic charging and discharging effects. Simulink has been used to study the characteristics of the system and the proposed system has proved to be very successful as the simulation results have been very good. Simulink Matlab, Battery Model, Simulation, BS Lead-Acid, Dynamic modeling, Temperature effect, Hybrid Vehicles.
Online End-of-Turn Detection from Speech Based on Stacked Time-Asynchronous Sequential Networks

This paper presents a novel modeling called stacked timeasynchronous sequential networks (STASNs) for online endof-turn detection. An online end-of-turn detection that determines turn-taking points in a real-time manner is an essential component for human-computer interaction systems. In this study, we use long-range sequential information of multiple time-asynchronous sequential features, such as prosodic, phonetic, and lexical sequential features, to enhance online end-ofturn detection performance. Our key idea is to embed individual sequential features in a fixed-length continuous representation by using sequential networks. This enables us to simultaneously handle multiple time-asynchronous sequential features for end-of-turn detection. STASNs can embed all of the sequential information between a start-of-conversation and the current end-of-utterance in a fixed-length continuous representation that can be directly used for classification by stacking multiple sequential networks. Experiments show that STASNs outperforms conventional modeling with limited sequential information. Furthermore, STASNs with senone bottleneck features extracted using senone-based deep neural networks have superior performance without requiring lexical features decoded by an automatic speech recognition process.
An active-clamping current-fed push-pull converter for vehicle inverter application and resonance analysis

A resonant current-fed push-pull converter with active clamping is investigated for vehicle inverter application in this paper. A communal clamping capacitor and a voltage doubler topology are employed. Moreover, two kinds of resonances to realize ZVS ON of MOSFETs or to remove reverse-recovery problem of secondary diodes are analyzed and an optimized resonance is utilized for volume restriction. At last, a 150W prototype with 10V to 16V input voltage, 360V output voltage is implemented to verify the design.
ROBUST SOLUTIONS TO LEAST-SQUARES PROBLEMS WITH UNCERTAIN DATA ∗

We consider least-squares problems where the coefficient matrices A, b are unknown but bounded. We minimize the worst-case residual error using (convex) second-order cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can be interpreted as a Tikhonov regularization procedure, with the advantage that it provides an exact bound on the robustness of solution and a rigorous way to compute the regularization parameter. When the perturbation has a known (e.g., Toeplitz) structure, the same problem can be solved in polynomial-time using semidefinite programming (SDP). We also consider the case when A, b are rational functions of an unknown-but-bounded perturbation vector. We show how to minimize (via SDP) upper bounds on the optimal worst-case residual. We provide numerical examples, including one from robust identification and one from robust interpolation.
A generalized surface appearance representation for computer graphics

(Under the direction of Anselmo Lastra) For image synthesis in computer graphics, two major approaches for representing a surface's appearance are texture mapping, which provides spatial detail, such as wallpaper, or wood grain; and the 4D bi-directional reflectance distribution function (BRDF) which provides angular detail, telling how light reflects off surfaces. I combine these two modes of variation to form the 6D spatial bi-directional reflectance distribution function (SBRDF). My compact SBRDF representation simply stores BRDF coefficients at each pixel of a map. I propose SBRDFs as a surface appearance representation for computer graphics and present a complete system for their use. I acquire SBRDFs of real surfaces using a device that simultaneously measures the BRDF of every point on a material. The system has the novel ability to measure anisotropy (direction of threads, scratches, or grain) uniquely at each surface point. I fit BRDF parameters using an efficient nonlinear optimization approach specific to BRDFs. SBRDFs can be rendered using graphics hardware. My approach yields significantly more detailed, general surface appearance than existing techniques for a competitive rendering cost. I also propose an SBRDF rendering method for global illumination using prefiltered environment maps. This improves on existing prefiltered environment map techniques by decoupling the BRDF from the environment maps, so a single set of maps may be used to illuminate the unique BRDFs at each surface point. I demonstrate my results using measured surfaces including gilded wallpaper, plant leaves, upholstery fabrics, wrinkled gift-wrapping paper and glossy book covers. iv To Tiffany, who has worked harder and sacrificed more for this than have I. ACKNOWLEDGMENTS I appreciate the time, guidance and example of Anselmo Lastra, my advisor. I'm grateful to Steve Molnar for being my mentor throughout graduate school. I'm grateful to the other members of my committee, Henry Fuchs, Gary Bishop, and Lars Nyland for helping and teaching me and creating an environment that allows research to be done successfully and pleasantly. I am grateful for the effort and collaboration of Ben Cloward, who masterfully modeled the Carolina Inn lobby, patiently worked with my software, and taught me much of how artists use computer graphics. I appreciate the collaboration of Wolfgang Heidrich, who worked hard on this project and helped me get up to speed on shading with graphics hardware. I'm thankful to Steve Westin, for patiently teaching me a great deal about surface appearance and light measurement. I'm grateful for …
Towards Text-based Emotion Detection A Survey and Possible Improvements

This paper presents an overview of the emerging field of emotion detection from text and describes the current generation of detection methods that are usually divided into the following three main categories: keyword-based, learning-based, and hybrid recommendation approaches. Limitations of current detection methods are examined, and possible solutions are suggested to improve emotion detection capabilities in practical systems, which emphasize on human-computer interactions. These solutions include extracting keywords with semantic analysis, and ontology design with emotion theory of appraisal. Furthermore, a case-based reasoning architecture is proposed to combine these solutions.
ActionFlowNet: Learning Motion Representation for Action Recognition

We present a data-efficient representation learning approach to learn video representation with small amount of labeled data. We propose a multitask learning model ActionFlowNet to train a single stream network directly from raw pixels to jointly estimate optical flow while recognizing actions with convolutional neural networks, capturing both appearance and motion in a single model. Our model effectively learns video representation from motion information on unlabeled videos. Our model significantly improves action recognition accuracy by a large margin (23.6%) compared to state-of-the-art CNN-based unsupervised representation learning methods trained without external large scale data and additional optical flow input. Without pretraining on large external labeled datasets, our model, by well exploiting the motion information, achieves competitive recognition accuracy to the models trained with large labeled datasets such as ImageNet and Sport-1M.
Human Error and Commercial Aviation Accidents: An Analysis Using the Human Factors Analysis and Classification System

The aim of this study was to extend previous examinations of aviation accidents to include specific aircrew, environmental, supervisory, and organizational factors associated with two types of commercial aviation (air carrier and commuter/ on-demand) accidents using the Human Factors Analysis and Classification System (HFACS).



HFACS is a theoretically based tool for investigating and analyzing human error associated with accidents and incidents. Previous research has shown that HFACS can be reliably used to identify human factors trends associated with military and general aviation accidents.



Using data obtained from both the National Transportation Safety Board and the Federal Aviation Administration, 6 pilot-raters classified aircrew, supervisory, organizational, and environmental causal factors associated with 1020 commercial aviation accidents that occurred over a 13-year period.



The majority of accident causal factors were attributed to aircrew and the environment, with decidedly fewer associated with supervisory and organizational causes. Comparisons were made between HFACS causal categories and traditional situational variables such as visual conditions, injury severity, and regional differences.



These data will provide support for the continuation, modification, and/or development of interventions aimed at commercial aviation safety.



HFACS provides a tool for assessing human factors associated with accidents and incidents.
A Benchmark Dataset to Study the Representation of Food Images

It is well-known that people love food. However, an insane diet can cause problems in the general health of the people. Since health is strictly linked to the diet, advanced computer vision tools to recognize food images (e.g. acquired with mobile/wearable cameras), as well as their properties (e.g., calories), can help the diet monitoring by providing useful information to the experts (e.g., nutritionists) to assess the food intake of patients (e.g., to combat obesity). The food recognition is a challenging task since the food is intrinsically deformable and presents high variability in appearance. Image representation plays a fundamental role. To properly study the peculiarities of the image representation in the food application context, a benchmark dataset is needed. These facts motivate the work presented in this paper. In this work we introduce the UNICT-FD889 dataset. It is the first food image dataset composed by over 800 distinct plates of food which can be used as benchmark to design and compare representation models of food images. We exploit the UNICT-FD889 dataset for Near Duplicate Image Retrieval (NDIR) purposes by comparing three standard state-of-the-art image descriptors: Bag of Textons, PRICoLBP and SIFT. Results confirm that both textures and colors are fundamental properties in food representation. Moreover the experiments point out that the Bag of Textons representation obtained considering the color domain is more accurate than the other two approaches for NDIR.
Stem cells, cancer, and cancer stem cells

Stem cell biology has come of age. Unequivocal proof that stem cells exist in the haematopoietic system has given way to the prospective isolation of several tissue-specific stem and progenitor cells, the initial delineation of their properties and expressed genetic programmes, and the beginnings of their utility in regenerative medicine. Perhaps the most important and useful property of stem cells is that of self-renewal. Through this property, striking parallels can be found between stem cells and cancer cells: tumours may often originate from the transformation of normal stem cells, similar signalling pathways may regulate self-renewal in stem cells and cancer cells, and cancer cells may include 'cancer stem cells' — rare cells with indefinite potential for self-renewal that drive tumorigenesis.
VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text

The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a goldstandard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.
User personality and resistance to mandatory information systems in organizations: a theoretical model and empirical test of dispositional resistance to change

This research is driven by the assumption made in several user resistance studies that employees are generally resistant to change. It investigates the extent to which employees’ resistance to IT-induced change is caused by individuals’ predisposition to resist change. We develop a model of user resistance that assumes the influence of dispositional resistance to change on perceptual resistance to change, perceived ease of use, and usefulness, which in turn influence user resistance behavior. Using an empirical study of 106 HR employees forced to use a new human resources information system, the analysis reveals that 17.0–22.1 percent of the variance in perceived ease of use, usefulness, and perceptual resistance to change can be explained by the dispositional inclination to change initiatives. The four dimensions of dispositional resistance to change – routine seeking, emotional reaction, short-term focus and cognitive rigidity – have an even stronger effect than other common individual variables, such as age, gender, or working experiences. We conclude that dispositional resistance to change is an example of an individual difference that is instrumental in explaining a large proportion of the variance in beliefs about and user resistance to mandatory IS in organizations, which has implications for theory, practice, and future research.
Measurement of sub-bandage pressure during venous compression therapy using flexible force sensors

Compression bandaging is the most common therapy used to treat venous ulceration. The bandages must be applied so that they generate a specific pressure profile in order for the treatment to be effective. No method currently exists to monitor the pressure applied by the bandage over a number of days outside of a laboratory setting. A portable device was developed that is capable of monitoring sub-bandage pressure as the user goes about their daily routine. The device consists of four Tekscan FlexiForce A401-series force sensors connected to an excitation circuit and PIC microcontroller circuit. It is capable of measuring pressures in the range of 0 - 96 mmHg. These sensors were chosen because they are cheap, thin, flexible and durable. Both circuits are housed in a protective case that attaches to the users leg. Preliminary results correspond with the pressure values stated in the literature and the device is capable of generating accurate sub-bandage pressure data.
Extending Body Space in Immersive Virtual Reality: A Very Long Arm Illusion

Recent studies have shown that a fake body part can be incorporated into human body representation through synchronous multisensory stimulation on the fake and corresponding real body part - the most famous example being the Rubber Hand Illusion. However, the extent to which gross asymmetries in the fake body can be assimilated remains unknown. Participants experienced, through a head-tracked stereo head-mounted display a virtual body coincident with their real body. There were 5 conditions in a between-groups experiment, with 10 participants per condition. In all conditions there was visuo-motor congruence between the real and virtual dominant arm. In an Incongruent condition (I), where the virtual arm length was equal to the real length, there was visuo-tactile incongruence. In four Congruent conditions there was visuo-tactile congruence, but the virtual arm lengths were either equal to (C1), double (C2), triple (C3) or quadruple (C4) the real ones. Questionnaire scores and defensive withdrawal movements in response to a threat showed that the overall level of ownership was high in both C1 and I, and there was no significant difference between these conditions. Additionally, participants experienced ownership over the virtual arm up to three times the length of the real one, and less strongly at four times the length. The illusion did decline, however, with the length of the virtual arm. In the C2-C4 conditions although a measure of proprioceptive drift positively correlated with virtual arm length, there was no correlation between the drift and ownership of the virtual arm, suggesting different underlying mechanisms between ownership and drift. Overall, these findings extend and enrich previous results that multisensory and sensorimotor information can reconstruct our perception of the body shape, size and symmetry even when this is not consistent with normal body proportions.
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots

We study response selection for multi-turn conversation in retrieval based chatbots. Existing works either ignores relationships among utterances, or misses important information in context when matching a response with a highly abstract context vector finally. We propose a new session based matching model to address both problems. The model first matches a response with each utterance on multiple granularities, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models the relationships among the utterances. The final matching score is calculated with the hidden states of the RNN. Empirical study on two public data sets shows that our model can significantly outperform the state-of-the-art methods for response selection in multi-turn conversation.
A Categorisation of Cloud Computing Business Models

This paper reviews current cloud computing business models and presents proposals on how organisations can achieve sustainability by adopting appropriate models. We classify cloud computing business models into eight types: (1) Service Provider and Service Orientation; (2) Support and Services Contracts; (3) In- House Private Clouds; (4) All-In-One Enterprise Cloud; (5) One-Stop Resources and Services; (6) Government funding; (7) Venture Capitals; and (8) Entertainment and Social Networking. Using the Jericho Forum’s ‘Cloud Cube Model’ (CCM), the paper presents a summary of the eight business models. We discuss how the CCM fits into each business model, and then based on this discuss each business model’s strengths and weaknesses. We hope adopting an appropriate cloud computing business model will help organisations investing in this technology to stand firm in the economic downturn.