nils-herrmann/clean_scirepeval huggingface dataset:
Using Big Data to Estimate Consumer Surplus: The Case of Uber

Estimating consumer surplus is challenging because it requires identification of the entire demand curve. We rely on Uber's "surge" pricing algorithm and the richness of its individual level data to first estimate demand elasticities at several points along the demand curve. We then use these elasticity estimates to estimate consumer surplus. Using almost 50 million individual level observations and a regression discontinuity design, we estimate that in 2015 the UberX service generated about $2.9 billion in consumer surplus in the four U.S. cities included in our analysis. For each dollar spent by consumers, about $1.60 of consumer surplus is generated. Back of the envelope calculations suggest that the overall consumer surplus generated by the UberX service in the United States in 2015 was $6.8 billion.
Scalable Algorithms for Data and Network Analysis
Venue: Found. Trends Theor. Comput. Sci.

In the age of Big Data, efficient algorithms are now in higher demandmore than ever before. While Big Data takes us into the asymptoticworld envisioned by our pioneers, it also challenges the classical notionof efficient algorithms: Algorithms that used to be considered efficient,according to polynomial time characterization, may no longer be adequatefor solving today's problems. It is not just desirable, but essential,that efficient algorithms should be scalable. In other words, their complexityshould be nearly linear or sub linear with respect to the problemsize. Thus, scalability, not just polynomial time computability, shouldbe elevated as the central complexity notion for characterizing efficientcomputation.In this tutorial, I will survey a family of algorithmic techniques forthe design of provably good scalable algorithms. These techniques includelocal network exploration, advanced sampling, sparsification, andgeometric partitioning. They also include spectral graph theoreticalmethods, such as those used for computing electrical flows and samplingfrom Gaussian Markov random fields. These methods exemplifythe fusion of combinatorial, numerical, and statistical thinking in networkanalysis. I will illustrate the use of these techniques by a few basicproblems that are fundamental in network analysis, particularly for theidentification of significant nodes and coherent clusters/communities insocial and information networks. I also take this opportunity to discusssome frameworks beyond graph theoretical models for studying conceptualquestions to understand multifaceted network data that arisein social influence, network dynamics, and Internet economics.
Recurrent Polynomial Network for Dialogue State Tracking
Venue: Dialogue Discourse

Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework recurrent polynomial network (RPN) RPN's unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC 2/3) Experiments showed that RPN can significantly outperform both traditional rule based approaches and statistical approaches with similar feature set. Compared with the state of the art statistical DST approaches with a lot richer features, RPN is also competitive.
Complex log file synthesis for rapid sandbox benchmarking of security and computer network analysis tools
Venue: Inf. Syst.

Today Information and Communications Technology (ICT) networks are a dominating component of our daily life. Centralized logging allows keeping track of events occurring in ICT networks. Therefore a central log store is essential for timely detection of problems such as service quality degradations, performance issues or especially security relevant cyber attacks. There exist various software tools such as security information and event management (SIEM) systems, log analysis tools and anomaly detection systems, which exploit log data to achieve this. While there are many products on the market, based on different approaches, the identification of the most efficient solution for a specific infrastructure, and the optimal configuration is still an unsolved problem. Today's general test environments do not sufficiently account for the specific properties of individual infrastructure setups. Thus, tests in these environments are usually not representative. However, testing on the real running productive systems exposes the network infrastructure to dangerous or unstable situations. The solution to this dilemma is the design and implementation of a highly realistic test environment, i.e. sandbox solution, that follows a different novel approach. The idea is to generate realistic network event sequence (NES) data that reflects the actual system behavior and which is then used to challenge network analysis software tools with varying configurations safely and realistically offline. In this paper we define a model, based on log line clustering and Markov chain simulation to create this synthetic log data. The presented model requires only a small set of real network data as an input to understand the complex real system behavior. Based on the input's characteristics highly realistic customer specified NES data is generated. To prove the applicability of the concept developed in this work, we conclude the paper with an illustrative example of evaluation and test of an existing anomaly detection system by using generated NES data. HighlightsGenerating log data that reflects realistic network behavior.Log data modeling, based on log line clustering and Markov chain simulation.Rate, analyze and improve software tools, which exploit log data.Detailed evaluation of the model and presentation of an illustrative application.Cornerstones to improve the selection, deployment and operation of IDSs.
A 3D printing path optimizer based on Christofides algorithm
Venue: 2016 IEEE International Conference on Consumer Electronics Taiwan (ICCE TW)

Rapid prototyping and product customization have become more convenient with the emergence of 3D printing technologies. In extrusion deposition based 3D printing, objects are built by connecting many lines of filament, layer by layer. The efficiency of the printing process can be improved by optimizing motion paths of the printing nozzle. In this paper, a 3D printing path optimizer based on Christofides algorithm is proposed. Experiment results show that the proposed optimizer can significantly reduce the length of motion paths compared to a nearest neighbor based optimizer using in consumer 3D printers.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Venue: ICML

Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and non linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log likelihood and RMSE compared to existing state of the art methods, and finish by using dropout's uncertainty in deep reinforcement learning.
A Survey on One Time Password

A password is a sequence of characters used for user authentication to prove identity or access authorization to gain access to the resources which should be kept secret. Passwords are the powerful equipment's that tend to keep all data and information digitally safe. Text based words are most well liked style of user authentication on websites due to its convenience and easiness. The easier the password is for the owner to remember usually means it would be easier for an attacker to guess. And also the security of system can be reduced by passwords that are difficult to remember. However, users' passwords are at risk to be exploited and compromised underneath different threats and vulnerabilities. Security of Password is very important for user authentication on networking system. Passwords are prone to various types of attacks like password stealing attack, password reuse attack, password cracking attack, brute force attack etc. Passwords can be protected by various methods introduced by different researchers. To decrease the harm caused by phishing and other attacks, banks, governments, and other industries are deploying One Time Password systems. In this paper, we proposed a survey about One Time Password.
Secure authentication for data protection in cloud computing using color schemes
Venue: 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

Due increase in the usage of cloud based systems there is an increase in the amount of information on the cloud and as a result there is need for confidentiality. Most common method used for authentication is textual password. But these passwords are susceptible to shoulder surfing, dictionary attack, eavesdropping. Generally the passwords tend to follow patterns that are easier for attackers to guess. A literature survey shows that text based password suffer this security problem. Pictographic passwords are provided as replacement to text based passwords. Pictographic passwords may prone to shoulder surfing. Pictographic passwords may suffer with the usability issue. This paper uses color code authentication which provides two step authentication to the user. Each time user logged in with generated one time password. This scheme is tested with different kinds of security attacks. User has to memorize only the sequence of three colors and three shades selected at the time of registration. This scheme is useful for secure authentication method for data protection on cloud.
Provable User Authentication Scheme in Telecare Medicine Information System Using Elliptic Curve Cryptosystem
Venue: 2016 11th International Conference on Availability, Reliability and Security (ARES)

Recently, the telecare medicine information system (TMIS) is one of the most convenient health care deliveries. It helps the patient and doctor keep frequent connection, so the quality of medical treatment is enhanced. Two main problems needed to be considered are the security and privacy of patient. Many schemes proposed to satisfy such requirements are not suitable for public medical environment because of their some limitations. For example, the patients identity and password are not protected, time consuming computations in such schemes take a lot of time in authentication phase. In this paper, we survey some typical previous results in this area to inherit some advantages. Afterward, we propose a provable lightweight dedicated scheme appropriate for TMIS in insecure channel.
Structural priming during sentence comprehension in Chinese English bilinguals
Venue: Applied Psycholinguistics

Cross linguistic priming in comprehension is understudied, and it remains unclear whether bilinguals have shared abstract syntactic representations during sentence comprehension. This article reports a self paced reading experiment investigating the influence of Chinese passive relative clauses on the interpretation of English sentences that are temporarily ambiguous between an active main clause and a passive reduced relative (dispreferred) structure. The results showed that reading Chinese passive relative primes reduced processing difficulty in English targets at the dispreferred disambiguation. Chinese to English priming in comprehension occurred without lexical and word order equivalence between primes and targets. In addition, translation equivalent verbs did not boost cross linguistic structural priming. The findings support an account under which bilingual sentence processing involves abstract, unordered syntactic representations that are integrated between languages.
What are our students doing? Workload, time allocation and time management in PBL instruction. A case study in Science Education

What are our students doing: studying, working with classmates, looking for information at the library or Internet? During 2 years, 131 preservice teachers were monitored using a survey, obtaining hours they employed in different tasks. Within the second year, problems found in the first were managed. Results reveal that, in Problem Based Learning, working in groups is the greatest time consumer although is distributed relatively homogeneously. Furthermore, students spend too much time on almost all activities, leading to a general overload properly handled in the second part of the study. Reflections on the consequences of time allocation research offers valuable opportunities to improve the quality of teaching learning processes.
Three Dimensional Printing in Plastic and Reconstructive Surgery: A Systematic Review
Venue: Annals of plastic surgery

BackgroundIncreasingly affordable three dimensional (3D) printing technologies now make it possible for surgeons to create highly customizable patient tailored products. This process provides the potential to produce individualized artificial and biologic implants, regenerative scaffolds, and cell specific replacement tissue and organs. The combination of accurate volumetric analysis and production of 3D printed biologic materials are evolving techniques that demonstrate great promise in achieving an accurate and naturally appearing anthropomorphic reconstruction. This systematic review summarizes the current published literature and known ongoing research on 3D printing in the field of plastic and reconstructive surgery (PRS) MethodsThree medical databases (PubMed, Ovid MEDLINE, and Google Scholar) as well as recent news articles and university websites were searched using PRS and industry related search terms. Inclusion criteria consisted of any publication or reputable news or academic article in electronic or printed media directly studying or commenting on the use of 3D printing technology in relation to PRS. The current literature was critically appraised, and quality of selected articles was assessed and manually filtered for relevance by 2 reviewers.  total of 1092 articles were identified from the aforementioned sources discussing 3D printing in medicine. The 3D printing in relation to biologic and surgical applications was discussed in 226 articles. Within this subset, 103 articles were included in the review. Of those selected, 5 were pertinent to surgical planning, training, and patient education; 4 to upper extremity and hand prosthetics; 24 to bone and craniomaxillofacial (CMF) reconstruction; 10 to breast reconstruction; 20 to nose, ear, and cartilage reconstruction; 20 to skin; and finally 20 involving overlapping general topics in 3D printing and PRS. ConclusionsThe 3D printing provides the ability to construct complex individualized implants that not only improve patient outcomes but also increase economic feasibility. The technology offers a potential level of accessibility that is paramount for remote and resource limited locations where health care is most often limited. The 3D printing based technologies will have an immense impact on the reconstruction of traumatic injuries, facial and limb prosthetic development, as well as advancements in biologic and synthetic implants.
A Discriminative Feature Learning Approach for Deep Face Recognition
Venue: ECCV

Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state of the art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for face recognition task. Specifically, the center loss simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers. More importantly, we prove that the proposed center loss function is trainable and easy to optimize in the CNNs. With the joint supervision of softmax loss and center loss, we can train a robust CNNs to obtain the deep features with the two key learning objectives, inter class dispension and intra class compactness as much as possible, which are very essential to face recognition. It is encouraging to see that our CNNs (with such joint supervision) achieve the state of the art accuracy on several important face recognition benchmarks, Labeled Faces in the Wild (LFW) YouTube Faces (YTF) and MegaFace Challenge. Especially, our new approach achieves the best results on MegaFace (the largest public domain face benchmark) under the protocol of small training set (contains under 500000 images and under 20000 persons) significantly improving the previous results and setting new state of the art for both face recognition and face verification tasks.
New Communication Technologies: Women's Rights Violations, Limits on Freedom of Expression, and Alternative ways to Promote Human Rights

New technologies are changing the way men and women live their lives. As a new communication technology, the Internet can be used to both harm and promote human rights. When it comes to gender relationships, social inequalities might be reflected online, regarding access and the ways technologies are used. Hate speech, porn revenge and other ways to offend women's rights online are discussed, leading to questions related to possible limitations on freedom of expression. This chapter examines legal solutions that have been proposed in Brazil regarding electronic devices and Internet regulation and considers how restrictions on freedom of expression can be addressed in a human rights perspective. Tamara Amoroso Goncalves Concordia University, Canada. Daniela Rosendo Universidade Federal de Santa Catarina, Brazil New Communication Technologies
Linear identification of nonlinear systems: A lifting technique based on the Koopman operator
Venue: 2016 IEEE 55th Conference on Decision and Control (CDC)

We exploit the key idea that nonlinear system identification is equivalent to linear identification of the so called Koopman operator. Instead of considering nonlinear system identification in the state space, we obtain a novel linear identification technique by recasting the problem in the infinite dimensional space of observables. This technique can be described in two main steps. In the first step, similar to a component of the Extended Dynamic Mode Decomposition algorithm, the data are lifted to the infinite dimensional space and used for linear identification of the Koopman operator. In the second step, the obtained Koopman operator is "projected back" to the finite dimensional state space, and identified to the nonlinear vector field through a linear least squares problem. The proposed technique is efficient to recover (polynomial) vector fields of different classes of systems, including unstable, chaotic, and open systems. In addition, it is robust to noise, well suited to model low sampling rate datasets, and able to infer network topology and dynamics.
Assessing the effects of transformational leadership: A study on Egyptian hotel employees

This study examines the effects of transformational leadership on hotel employees for some aspects such as innovation climate, creativity, creative self efficacy and satisfaction. Direct guest contact employees of thirty eight, 5 star hotels in Egypt and their immediate supervisors were targeted. A total of (500) employees and (150) supervisors provided the collected data. The findings indicated that transformational leadership is experienced in Egyptian hotels; in addition, it helps fostering a climate for innovation and promotes employee's creativity and satisfaction. An insignificant relationship was found between transformational leadership and employee's creative self efficacy. Some practical implication is delivered such as the crucial need for hotel leaders to take a sound understanding of appropriate strategy for promoting employee's creative self efficacy.
Fostering a Climate for Innovation, Creativity and Self efficacy through Transformational Leadership in the Egyptian Hospitality Industry

The purpose of this study is to examine the linkage between transformational leadership and the organizational climate for innovation, creativity and creative self efficacy.  Direct guest contact employees and their immediate supervisors from thirtyeight 5 star hotels in Egypt was targeted. A total of (500) employees and (150) supervisors provided the collected data. A series of statistical analyses were adapted to: (1) Identify the elements of transformational leadership style using a multi factor leadership questionnaire (MLQ 5X) (2) Examine the consequence of transformational leadership style in fostering a climate for innovation, creativity and creative self efficacy.  The study revealed that transformational leaders foster an organizational climate for innovation and creativity.  The results pointed to mechanisms by which organizations may positively shape such employees' creative self efficacy views. Leaders are likely to foster favorable conditions for the creative self efficacy such as: verbally persuade employees that could be creative and personally demonstrate and instruct their employees on creativity relevant skills. This could be achieved by provision of hands on opportunities to apply these skills. Hospitality leaders need to organize effective training initiatives to develop their subordinates' creative selfefficacy and provide employees with the relevant details of the tasks assigned to them. A larger and more diverse sample is recommended for future research.  This study will add to the existing literature and managerial practices in several ways. The main contribution of the present research is investigating the applicability of the transformational leadership within the Egyptian hotels. Also, this research introduces a positive expectation of the transformational leadership on the innovation and creative climate in Egyptian hotels. Third, this research present transformational leadership style as a mechanism to promote innovation and creativity climate among employees.