nils-herrmann/clean_scirepeval huggingface dataset:
Probability Models for Customer-Base Analysis

As more firms begin to collect (and seek value from) richer customer-level datasets, a focus on the emerging concept of customer-base analysis is becoming increasingly common and critical. Such analyses include forward-looking projections ranging from aggregate-level sales trajectories to individual-level conditional expectations (which, in turn, can be used to derive estimates of customer lifetime value). We provide an overview of a class of parsimonious models (called probability models) that are well-suited to meet these rising challenges. We first present a taxonomy that captures some of the key distinctions across different kinds of business settings and customer relationships, and identify some of the unique modeling and measurement issues that arise across them. We then provide deeper coverage of these modeling issues, first for noncontractual settings (i.e., situations in which customer “death” is unobservable), then contractual ones (i.e., situations in which customer “death” can be observed). We review recent literature in these areas, highlighting substantive insights that arise from the research as well as the methods used to capture them. We focus on practical applications that use appropriately chosen data summaries (such as recency and frequency) and rely on commonly available software packages (such as Microsoft Excel). n 2009 Direct Marketing Educational Foundation, Inc.
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs

Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two entities connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query
Asset Pricing When Returns Are Nonnormal : Fama-French Factors vs . Higher-order Systematic Co-Moments *

A growing literature contends that, since returns are not normal, higher-order co-moments matter to risk-averse investors. Fama and French (1993, 1995) find that non-market risk factors based on size and book-to-market ratio are priced by investors. We test the hypothesis that the Fama-French factors simply proxy for the pricing of higher-order co-moments. Using portfolio returns over various time horizons, we show that adding a set of systematic comoments (but not standard moments) of order 3 through 10 reduces the explanatory power of the Fama-French factors to insignificance in almost every case.  We are grateful for helpful comments and suggestions from Warren Bailey, Javier Estrada, Wayne Ferson, Campbell Harvey, Raymond Kan, Tae-Hwy Lee, Bruce Lehmann, David Mayers, Mark Rubinstein, Sergei Sarkissian, Mike Stutzer, and an anonymous referee. Chung acknowledges financial support from an A. Gary Anderson Graduate School of Management Summer Research Grant.
A Survey on Facial Expression Recognition Techniques

These Human facial expressions convey a lot of information visually rather than articulately. Facial expression recognition plays a crucial role in the area of human-machine interaction. Recognition of facial expression by computer with high recognition rate is still a challenging task. Facial Expression Recognition usually performed in three-stages consisting of face detection, feature extraction, and expression classification. This paper presents a survey of the current work done in the field of facial expression recognition techniques with various face detection, feature extraction and classification methods used by them and their performance.
What is a sports injury?

Current sports injury reporting systems lack a common conceptual basis. We propose a conceptual foundation as a basis for the recording of health problems associated with participation in sports, based on the notion of impairment used by the World Health Organization. We provide definitions of sports impairment concepts to represent the perspectives of health services, the participants in sports and physical exercise themselves, and sports institutions. For each perspective, the duration of the causative event is used as the norm for separating concepts into those denoting impairment conditions sustained instantly and those developing gradually over time. Regarding sports impairment sustained in isolated events, 'sports injury' denotes the loss of bodily function or structure that is the object of observations in clinical examinations; 'sports trauma' is defined as an immediate sensation of pain, discomfort or loss of functioning that is the object of athlete self-evaluations; and 'sports incapacity' is the sidelining of an athlete because of a health evaluation made by a legitimate sports authority that is the object of time loss observations. Correspondingly, sports impairment caused by excessive bouts of physical exercise is denoted as 'sports disease' (overuse syndrome) when observed by health service professionals during clinical examinations, 'sports illness' when observed by the athlete in self-evaluations, and 'sports sickness' when recorded as time loss from sports participation by a sports body representative. We propose a concerted development effort in this area that takes advantage of concurrent ontology management resources and involves the international sporting community in building terminology systems that have broad relevance.
Dynamic Practical Byzantine Fault Tolerance

This paper describes a novel Byzantine fault tolerant protocol that allows replicas to join and exit dynamically. With the astonishing success of cryptocurrencies, people attach great importance in “blockchain” and robust Byzantine fault tolerant (BFT) protocols for consensus. Among the conventional wisdom, the Practical Byzantine Fault Tolerance (PBFT), proposed by Miguel and Liskov in 1999, occupies an important position. Although PBFT has many advantages, it has fatal disadvantages. Firstly, it works in a completely enclosed environment, where users who want to add or take out any node must stop the whole system. Secondly, although PBFT guarantees liveness and safety if at most $\left\lfloor {\frac{{{\rm{n}} - 1}}{3}} \right\rfloor$ out of a total n replicas are faulty, it takes no measure to deal with these ineffective or malicious replicas, which is harmful to the system and will cause system crash finally. These drawbacks are unbearable in practice. In order to solve them, we present an alternative, Dynamic PBFT.
Circadian and Circalunar Clock Interactions in a Marine Annelid

Life is controlled by multiple rhythms. Although the interaction of the daily (circadian) clock with environmental stimuli, such as light, is well documented, its relationship to endogenous clocks with other periods is little understood. We establish that the marine worm Platynereis dumerilii possesses endogenous circadian and circalunar (monthly) clocks and characterize their interactions. The RNAs of likely core circadian oscillator genes localize to a distinct nucleus of the worm's forebrain. The worm's forebrain also harbors a circalunar clock entrained by nocturnal light. This monthly clock regulates maturation and persists even when circadian clock oscillations are disrupted by the inhibition of casein kinase 1δ/ε. Both circadian and circalunar clocks converge on the regulation of transcript levels. Furthermore, the circalunar clock changes the period and power of circadian behavior, although the period length of the daily transcriptional oscillations remains unaltered. We conclude that a second endogenous noncircadian clock can influence circadian clock function.
Realization of embedded speech recognition module based on STM32

Speech recognition is the key to realize man-machine interface technology. In order to improve the accuracy of speech recognition and implement the module on embedded system, an embedded speaker-independent isolated word speech recognition system based on ARM is designed after analyzing speech recognition theory. The system uses DTW algorithm and improves the algorithm using a parallelogram to extract characteristic parameters and identify the results. To finish the speech recognition independently, the system uses the STM32 series chip combined with the other external circuitry. The results of speech recognition test can achieve 90%, and which meets the real-time requirements of recognition.
Flexoelectricity in soft materials and biological membranes

Flexoelectricity and the concomitant emergence of electromechanical size-effects at the nanoscale have been recently exploited to propose tantalizing concepts such as the creation of “apparently piezoelectric” materials without piezoelectric materials, e.g. graphene, emergence of “giant” piezoelectricity at the nanoscale, enhanced energy harvesting, among others. The aforementioned developments pertain primarily to hard ceramic crystals. In this work, we develop a nonlinear theoretical framework for flexoelectricity in soft materials. Using the concept of soft electret materials, we illustrate an interesting nonlinear interplay between the so-called Maxwell stress effect and flexoelectricity, and propose the design of a novel class of apparently piezoelectric materials whose constituents are intrinsically non-piezoelectric. In particular, we show that the electret-Maxwell stress based mechanism can be combined with flexoelectricity to achieve unprecedentedly high values of electromechanical coupling. Flexoelectricity is also important for a special class of soft materials: biological membranes. In this context, flexoelectricity manifests itself as the development of polarization upon changes in curvature. Flexoelectricity is found to be important in a number of biological functions including hearing, ion transport and in some situations where mechanotransduction is necessary. In this work, we present a simple linearized theory of flexoelectricity in biological membranes and some illustrative examples. & 2013 Elsevier Ltd. All rights reserved.
Local Contrast Learning

Learning a deep model from small data is yet an opening and challenging problem. We focus on one-shot classification by deep learning approach based on a small quantity of training samples. We proposed a novel deep learning approach named Local Contrast Learning (LCL) based on the key insight about a human cognitive behavior that human recognizes the objects in a specific context by contrasting the objects in the context or in her/his memory. LCL is used to train a deep model that can contrast the recognizing sample with a couple of contrastive samples randomly drawn and shuffled. On one-shot classification task on Omniglot, the deep model based LCL with 122 layers and 1.94 millions of parameters, which was trained on a tiny dataset with only 60 classes and 20 samples per class, achieved the accuracy 97.99% that outperforms human and state-of-the-art established by Bayesian Program Learning (BPL) trained on 964 classes. LCL is a fundamental idea which can be applied to alleviate parametric model’s overfitting resulted by lack of training samples.
A Wireless Emergency Telemedicine System for Patients Monitoring and Diagnosis

Recently, remote healthcare systems have received increasing attention in the last decade, explaining why intelligent systems with physiology signal monitoring for e-health care are an emerging area of development. Therefore, this study adopts a system which includes continuous collection and evaluation of multiple vital signs, long-term healthcare, and a cellular connection to a medical center in emergency case and it transfers all acquired raw data by the internet in normal case. The proposed system can continuously acquire four different physiological signs, for example, ECG, SpO2, temperature, and blood pressure and further relayed them to an intelligent data analysis scheme to diagnose abnormal pulses for exploring potential chronic diseases. The proposed system also has a friendly web-based interface for medical staff to observe immediate pulse signals for remote treatment. Once abnormal event happened or the request to real-time display vital signs is confirmed, all physiological signs will be immediately transmitted to remote medical server through both cellular networks and internet. Also data can be transmitted to a family member's mobile phone or doctor's phone through GPRS. A prototype of such system has been successfully developed and implemented, which will offer high standard of healthcare with a major reduction in cost for our society.
A Risk Assessment Framework for Automotive Embedded Systems

The automotive industry is experiencing a paradigm shift towards autonomous and connected vehicles. Coupled with the increasing usage and complexity of electrical and/or electronic systems, this introduces new safety and security risks. Encouragingly, the automotive industry has relatively well-known and standardised safety risk management practices, but security risk management is still in its infancy. In order to facilitate the derivation of security requirements and security measures for automotive embedded systems, we propose a specifically tailored risk assessment framework, and we demonstrate its viability with an industry use-case. Some of the key features are alignment with existing processes for functional safety, and usability for non-security specialists.
 The framework begins with a threat analysis to identify the assets, and threats to those assets. The following risk assessment process consists of an estimation of the threat level and of the impact level. This step utilises several existing standards and methodologies, with changes where necessary. Finally, a security level is estimated which is used to formulate high-level security requirements.
 The strong alignment with existing standards and processes should make this framework well-suited for the needs in the automotive industry.
Incremental Dynamic Analysis for Estimating Seismic Performance Sensitivity and Uncertainty ‡

Incremental Dynamic Analysis (IDA) is presented as a powerful tool to evaluate the variability in the seismic demand and capacity of non-deterministic structural models, building upon existing methodologies of Monte Carlo simulation and approximate moment-estimation. A nine-story steel moment-resisting frame is used as a testbed, employing parameterized moment-rotation relationships with non-deterministic quadrilinear backbones for the beam plastic-hinges. The uncertain properties of the backbones include the yield moment, the post-yield hardening ratio, the end-of-hardening rotation, the slope of the descending branch, the residual moment capacity and the ultimate rotation reached. IDA is employed to accurately assess the seismic performance of the model for any combination of the parameters by performing multiple nonlinear timehistory analyses for a suite of ground motion records. Sensitivity analyses on both the IDA and the static pushover level reveal the yield moment and the two rotational-ductility parameters to be the most influential for the frame behavior. To propagate the parametric uncertainty to the actual seismic performance we employ a) Monte Carlo simulation with latin hypercube sampling, b) point-estimate and c) first-order second-moment techniques, thus offering competing methods that represent different compromises between speed and accuracy. The final results provide firm ground for challenging current assumptions in seismic guidelines on using a median-parameter model to estimate the median seismic performance and employing the well-known square-root-sum-of-squares rule to combine aleatory randomness and epistemic uncertainty.
Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment

Rotation estimation is a fundamental step for various robotic applications such as automatic control of ground/aerial vehicles, motion estimation and 3D reconstruction. However it is now well established that traditional navigation equipments, such as global positioning systems (GPSs) or inertial measurement units (IMUs), suffer from several disadvantages. Hence, some vision-based works have been proposed recently. Whereas interesting results can be obtained, the existing methods have non-negligible limitations such as a difficult feature matching (e.g. repeated textures, blur or illumination changes) and a high computational cost (e.g. analyze in the frequency domain). Moreover, most of them utilize conventional perspective cameras and thus have a limited field of view. In order to overcome these limitations, in this paper we present a novel rotation estimation approach based on the extraction of vanishing points in omnidirectional images. The first advantage is that our rotation estimation is decoupled from the translation computation, which accelerates the execution time and results in a better control solution. This is made possible by our complete framework dedicated to omnidirectional vision, whereas conventional vision has a rotation/translation ambiguity. Second, we propose a top-down approach which maintains the important constraint of vanishing point orthogonality by inverting the problem: instead of performing a difficult line clustering preliminary step, we directly search for the orthogonal vanishing points. Finally, experimental results on various data sets for diverse robotic applications have demonstrated that our novel framework is accurate, robust, maintains the orthogonality of the vanishing points and can run in real-time.
Brain Tumor Detection and Classification using Segmentation in MRI

In this paper, a attempt has been made to summarize segmentation techniques which are useful for separation of tumor region from brain tumor MRI images. By selecting a proper segmentation technique, it is possible to segment tumor region accurately, which helps in measuring the area of tumor region from brain tumor MRI image. This is possible by using digital image processing tool. Digital image processing is useful for CT scan, MRI, and Ultrasound type of medical images. Digital image processing improves the quality of these medical images using various enhancement techniques. From this enhanced image the radiologist can easily identify infected region and its location. Digital image processing also able to separate out infected region from MRI or CT scan images easily which helps radiologist for diagnoses of the disease at earlier stage. It has several advantages over other imaging techniques, providing high contrast between soft tissues. However, the amount of data is far too much for manual analysis, which has been one of the biggest obstacles in the effective use of MRI.
Automated Short Answer Scoring using Weighted Cosine Coefficient

Scoring short answer question (consisting of at most 100 words) requires a special treatment, particularly on the term weighting process. With such limitation, it is therefore infeasible to have term weighting with term frequency model because the frequency of term appearance is very rare. This paper proposes a new method, named Weighted Cosine Coefficient. The experiment result shows that Weighted Cosine Coefficient method can improve the correlation value on Automated Short Answer Scoring (ASAS) system compared to that of Jaccard Coefficient, Dice Coefficient, Cosine Coefficient, Weighted Jaccard Coefficient, and Weighted Dice Coefficient. The average improvement of the correlation value on the tested data set is 0.56.
Do Airbnb properties affect house prices ?

The growth of peer-to-peer markets has provided a mechanism through which private individuals can enter a market as small scale, often temporary, suppliers of a good or service. Companies that facilitate this type of supply have attracted controversy in cities around the world, with concerns regarding Uber and Airbnb in particular. Both companies have been subject to criticism for failing to pay taxes to local authorities and for avoiding regulatory oversight that constrains more traditional suppliers of these services. It is not surprising that these companies have been criticized by more traditional suppliers of short term accommodation or transportation as presenting unfair competition, and attempts have been made to ban provision of these services in several cities around the world. A central complaint concerning Airbnb focuses on the impact of short-term rental properties on the value of residential property. A report prepared by the Office of the Attorney General of the State of New York lists these impacts among a number of concerns: do Airbnb rentals provide a black market in unsafe hotels? Do short-term rentals make New York City less affordable? Is the influx of out-of-town visitors upsetting the quiet of longstanding residential neighborhoods? These concerns pose difficulties because they imply different impacts on the values of residential properties. If short-term rentals provided via Airbnb create a concentration of what are effectively unsafe hotels, upsetting quiet residential neighborhoods, they would generate a local concentration of externalities that might be expected to depress property values rather than make housing less affordable. Alternatively, if these externality effects are modest relative to the impacts of space diverted from providing housing for residents to providing short-term accommodation for visitors, then local concentration of Airbnb properties may increase house prices. Perhaps because of this confusion, it is possible to find divergent viewpoints expressed about the impacts of Airbnb in the popular press and in consultant reports, but (to our knowledge) no careful study that estimates the direct impact of Airbnb rental availability on house prices. This paper presents such a study in the context of New York City.
Consumer Power : Evolution in the Digital Age

The predictions of growing consumer power in the digital age that predated the turn of the century were fueled by the rise of the Internet, then reignited by social media. This article explores the intersection of consumer behavior and digital media by clearly defining consumer power and empowerment in Internet and social media contexts and by presenting a theoretical framework of four distinct consumer power sources: demand-, information-, network-, and crowd-based power. Furthermore, we highlight technology's evolutionary role in the development of these power sources and discuss the nature of shifts in power from marketers to consumers in terms of each source. The framework organizes prior marketing literature on Internet-enabled consumer empowerment and highlights gaps in current research. Specific research questions are elaborated for each source of power outlining the agenda for future research areas.