nils-herrmann/clean_scirepeval huggingface dataset:
Teachable robots: Understanding human teaching behavior to build more effective robot learners

While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present an analysis of real-time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. We report three main observations on how people administer feedback when teaching a robot a task through Reinforcement Learning: (a) they use the reward channel not only for feedback, but also for future-directed guidance; (b) they have a positive bias to their feedback — possibly using the signal as a motivational channel; and (c) they change their behavior as they develop a mental model of the robotic learner. Given this, we made specific modifications to the simulated RL robot, and analyzed and evaluated its learning behavior in four additional experiments with human trainers. We report significant improvements on several learning measures. This work demonstrates the importance of understanding the human-teacher/robot-learner partnership in order to design algorithms that support how people want to teach while simultaneously improving the robot’s learning behavior.
Experimental kinematics modeling estimation for wheeled skid-steering mobile robots

Skid-steering mobile robots, both wheels and tracked vehicles, are widely used because of their simple mechanism and robust. However, due to the complex wheel-ground interactions and the kinematics constraints, it is a challenge to understand the kinematics and dynamics of such a robotic platform. In this paper, we develop a kinematics modeling estimation scheme to analyze the skid-steering wheeled mobile robot based on the boundedness of the Instantaneous Centers of Rotation (ICRs) of treads on the 2D motion plane, and the parameters of this model are determined based on experimental analysis. We study the relationship between the ICRs of the robot treads and two physical factors, i.e., the radius of the path curvature and the robot speed. Moreover, an ICRs coefficient and a nondimensional variable are introduced. An approximating function is used to describe the relationship. To validate the obtained results, the proposed model has been applied to a popular research robotic platform, Pioneer P3-AT. It is empirically demonstrated that the developed model improves dead-reckoning performance of this skid-steering robot.
A Framework for Robust Subspace Learning

Many computer vision, signal processing and statistical problems can be posed as problems of learning low dimensional linear or multi-linear models. These models have been widely used for the representation of shape, appearance, motion, etc., in computer vision applications. Methods for learning linear models can be seen as a special case of subspace fitting. One draw-back of previous learning methods is that they are based on least squares estimation techniques and hence fail to account for “outliers” which are common in realistic training sets. We review previous approaches for making linear learning methods robust to outliers and present a new method that uses an intra-sample outlier process to account for pixel outliers. We develop the theory of Robust Subspace Learning (RSL) for linear models within a continuous optimization framework based on robust M-estimation. The framework applies to a variety of linear learning problems in computer vision including eigen-analysis and structure from motion. Several synthetic and natural examples are used to develop and illustrate the theory and applications of robust subspace learning in computer vision.
A Trusted Safety Verifier for Process Controller Code

Attackers can leverage security vulnerabilities in control systems to make physical processes behave unsafely. Currently, the safe behavior of a control system relies on a Trusted Computing Base (TCB) of commodity machines, firewalls, networks, and embedded systems. These large TCBs, often containing known vulnerabilities, expose many attack vectors which can impact process safety. In this paper, we present the Trusted Safety Verifier (TSV), a minimal TCB for the verification of safety-critical code executed on programmable controllers. No controller code is allowed to be executed before it passes physical safety checks by TSV. If a safety violation is found, TSV provides a demonstrative test case to system operators. TSV works by first translating assembly-level controller code into an intermediate language, ILIL. ILIL allows us to check code containing more instructions and features than previous controller code safety verification techniques. TSV efficiently mixes symbolic execution and model checking by transforming an ILIL program into a novel temporal execution graph that lumps together safetyequivalent controller states. We implemented TSV on a Raspberry Pi computer as a bump-in-the-wire that intercepts all controllerbound code. Our evaluation shows that it can test a variety of programs for common safety properties in an average of less than three minutes, and under six minutes in the worst case—a small one-time addition to the process engineering life cycle.
Cloud-Native, Event-Based Programming for Mobile Applications

Creating mobile applications often requires both client and server-side code development, each requiring vastly different skills. Recently, cloud providers like Amazon and Google introduced "server-less" programming models that abstract away many infrastructure concerns and allow developers to focus on their application logic. In this demonstration, we introduce OpenWhisk, our system for constructing cloud native actions, within the context of mobile application development process. We demonstrate how OpenWhisk is used in mobile application development, allows cloud API customizations for mobile, and simplifies mobile application architectures.
What happens when HTTP adaptive streaming players compete for bandwidth?

With an increasing demand for high-quality video content over the Internet, it is becoming more likely that two or more adaptive streaming players share the same network bottleneck and compete for available bandwidth. This competition can lead to three performance problems: player instability, unfairness between players, and bandwidth underutilization. However, the dynamics of such competition and the root cause for the previous three problems are not yet well understood. In this paper, we focus on the problem of competing video players and describe how the typical behavior of an adaptive streaming player in its Steady-State, which includes periods of activity followed by periods of inactivity (ON-OFF periods), is the main root cause behind the problems listed above. We use two adaptive players to experimentally showcase these issues. Then, focusing on the issue of player instability, we test how several factors (the ON-OFF durations, the available bandwidth and its relation to available bitrates, and the number of competing players) affect stability.
Diversity in smartphone usage

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.
E-commerce customer churn prediction based on improved SMOTE and AdaBoost

E-commerce customer churn rate is high and the customer churn dataset is seriously imbalanced. In order to improve the prediction accuracy of churn customers as well as strengthen to identify non-churn customers, this paper presents e-commerce customer churn prediction model based on improved SMOTE and AdaBoost. First, processing the churn data with improved SMOTE, which combines oversampling and undersampling methods to address the imbalance problem and subsequently integrates AdaBoost algorithm to predict. Finally, the empirical study on B2C E-commerce platform proves that this model has better efficiency and accuracy compared with the mature customer churn prediction algorithms.
Transformational leadership , job satisfaction , and team performance : A multilevel mediation model of trust ☆

Article history: Received 2 December 2011 Received in revised 31 October 2012  In spite of calls for deliberate differentiation between individual and team levels of analysis, leadership research based on well-grounded theory referring to multiple levels is scarce. We seek to fill this gap by analyzing the relations between transformational leadership, trust in supervisor and team, job satisfaction, and team performance via multilevel analysis. Results are based on a sample of 360 employees from 39 academic teams. Transformational leadership was positively related to followers' job satisfaction at individual as well as team levels of analysis and to objective team performance. The relation between individual perceptions of supervisors' transformational leadership and job satisfaction was mediated by trust in the supervisor as well as trust in the team. Yet, trust in the team did not mediate the relationship between team perceptions of supervisors' transformational leadership and team performance. Implications for theory and research of leadership at multiple levels as well as for practice are discussed.
Operational Flexibility and Financial Hedging: Complements or Substitutes?

W consider a firm that invests in capacity under demand uncertainty and thus faces two related but distinct types of risk: mismatch between capacity and demand and profit variability. Whereas mismatch risk can be mitigated with greater operational flexibility, profit variability can be reduced through financial hedging. We show that the relationship between these two risk mitigating strategies depends on the type of flexibility: Product flexibility and financial hedging tend to be complements (substitutes)—i.e., product flexibility tends to increase (decrease) the value of financial hedging, and, vice versa, financial hedging tends to increase (decrease) the value of product flexibility—when product demands are positively (negatively) correlated. In contrast to product flexibility, postponement flexibility is a substitute to financial hedging as intuitively expected. Although our analytical results assume perfect flexibility and perfect hedging and rely on a linear approximation of the value of hedging, we validate their robustness in an extensive numerical study.
Deep Learning for steganalysis is better than a Rich Model with an Ensemble Classifier, and is natively robust to the cover source-mismatch

Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, is very promising for the steganalysis. In this paper, we follow-up the study of Qian et al., and show that, due to intrinsic joint minimization, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments were conducted in order to find the best ”shape” of the CNN. Second, experiments were carried out in the clairvoyant scenario in order to compare the CNN and FNN to an RM with an EC. The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. The results show that the CNN and FNN are naturally robust to the mismatch problem. In Addition to the experiments, we provide discussions on the internal mechanisms of a CNN, and weave links with some previously stated ideas, in order to understand the impressive results we obtained.
DataMill: rigorous performance evaluation made easy

Empirical systems research is facing a dilemma. Minor aspects of an experimental setup can have a significant impact on its associated performance measurements and potentially invalidate conclusions drawn from them. Examples of such influences, often called hidden factors, include binary link order, process environment size, compiler generated randomized symbol names, or group scheduler assignments. The growth in complexity and size of modern systems will further aggravate this dilemma, especially with the given time pressure of producing results. So how can one trust any reported empirical analysis of a new idea or concept in computer science?
 This paper introduces DataMill, a community-based easy-to-use services-oriented open benchmarking infrastructure for performance evaluation. DataMill facilitates producing robust, reliable, and reproducible results. The infrastructure incorporates the latest results on hidden factors and automates the variation of these factors. Multiple research groups already participate in DataMill.
 DataMill is also of interest for research on performance evaluation. The infrastructure supports quantifying the effect of hidden factors, disseminating the research results beyond mere reporting. It provides a platform for investigating interactions and composition of hidden factors.
Minimal Narrative Annotation Schemes and Their Applications

The increased use of large corpora in narrative research has created new opportunities for empirical research and intelligent narrative technologies. To best exploit the value of these corpora, several research groups are eschewing complex discourse analysis techniques in favor of high-level minimalist narrative annotation schemes that can be quickly applied, achieve high inter-rater agreement, and are amenable to automation using machine-learning techniques. In this paper we compare different annotation schemes that have been employed by two groups of researchers to annotate large corpora of narrative text. Using a dualannotation methodology, we investigate the correlation between narrative clauses distinguished by their structural role (orientation, action, evaluation), their subjectivity, and their narrative level within the discourse. We find that each simple narrative annotation scheme captures a structurally distinct characteristic of real-world narratives, and each combination of labels is evident in a corpus of 19 weblog narratives (951 narrative clauses). We discuss several potential applications of minimalist narrative annotation schemes, noting the combination of label across these two annotation schemes that best support each task.
How to Use the SOINN Software: User's Guide (Version 1.0)

The Self-Organizing Incremental Neural Network (SOINN) is an unsupervised classifier that is capable of online incremental learning. Studies have been performed not only for improving the SOINN, but also for applying it to various problems. Furthermore, using the SOINN, more intelligent functions are achieved, such as association, reasoning, and so on. In this paper, we show how to use the SOINN software and to apply it to the above problems.
Embassies: Radically Refactoring the Web

Web browsers ostensibly provide strong isolation for the client-side components of web applications. Unfortunately, this isolation is weak in practice; as browsers add increasingly rich APIs to please developers, these complex interfaces bloat the trusted computing base and erode cross-app isolation boundaries. We reenvision the web interface based on the notion of a pico-datacenter, the client-side version of a shared server datacenter. Mutually untrusting vendors run their code on the user’s computer in low-level native code containers that communicate with the outside world only via IP. Just as in the cloud datacenter, the simple semantics makes isolation tractable, yet native code gives vendors the freedom to run any software stack. Since the datacenter model is designed to be robust to malicious tenants, it is never dangerous for the user to click a link and invite a possibly-hostile party onto the client.
Why should Incumbent Firms jump on the Start-up Bandwagon in the Digital Era? - A Qualitative Study

Due to ongoing digitalization, the traditional business models of incumbent firms are threatened by the innovation performance of start-ups. Therefore, a few incumbent firms have established programs to collaborate with start-ups in order to receive relevant impulses from them. However, empirically, there has barely been any insight into the specific role that start-ups play for incumbent firms. For this purpose, we present the key findings of our qualitative study, which has been built on interviews (n=35) with experts. Our results reveal that incumbent firms and start-ups have equally environmental factors affecting their decision-making in pursuit of digitalization. However, they differ in their technological and organizational factors. According to our findings, we have emphasized that collaboration between incumbent firms and start-ups could be an opportunity to meet the challenges of the digital era. They can build on these identified enabling factors of the partner and overcome their own inhibiting factors.
Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a “picture” not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining “vague” when there is not enough “evidence” in the data or standard modeling constructs do not “fit”. Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.
Phone classification using HMM/SVM system and normalization technique

Support vector machines (SVM) were originally developed for binary classification and extended for multi-class classification. Due to their powerfulness and adaptation to hard classification problems, we have chosen them for automatic speech recognition (ASR). The aim of this paper is to investigate the use of SVM multi-class classification coupled with HMM for TIMIT phones. SVM requires that all data samples for training and test to have the same features vector size. Due to the variability in length of phone signals even for the same phone, we have used a normalization technique: zero padding and resampling on all data samples to get them have features vector with the same size. After mapping the 61 TIMIT phones in 46 phones and conducting tests using LibSVM and HTK, we have obtained a classification accuracy rate of 91.26% with the hybrid HMM/SVM system and 71.41% with the HMM-based system. These results show that the hybrid HMM/SVM system using the normalization technique overcomes an HMM-based system and improves the recognition accuracy by 19.8%. Therefore, our experiments result encouraged us to use this hybrid system and normalization technique for the next work in the context of spoken dialogue system.
K-Automorphism: A General Framework For Privacy Preserving Network Publication

The growing popularity of social networks has generated interesting data management and data mining problems. An important concern in the release of these data for study is their privacy, since social networks usually contain personal information. Simply removing all identifiable personal information (such as names and social security number) before releasing the data is insufficient. It is easy for an attacker to identify the target by performing different structural queries. In this paper we propose k-automorphism to protect against multiple structural attacks and develop an algorithm (called KM) that ensures k-automorphism. We also discuss an extension of KM to handle “dynamic” releases of the data. Extensive experiments show that the algorithm performs well in terms of protection it provides.
Human microbiomes and their roles in dysbiosis, common diseases, and novel therapeutic approaches

The human body is the residence of a large number of commensal (non-pathogenic) and pathogenic microbial species that have co-evolved with the human genome, adaptive immune system, and diet. With recent advances in DNA-based technologies, we initiated the exploration of bacterial gene functions and their role in human health. The main goal of the human microbiome project is to characterize the abundance, diversity and functionality of the genes present in all microorganisms that permanently live in different sites of the human body. The gut microbiota expresses over 3.3 million bacterial genes, while the human genome expresses only 20 thousand genes. Microbe gene-products exert pivotal functions via the regulation of food digestion and immune system development. Studies are confirming that manipulation of non-pathogenic bacterial strains in the host can stimulate the recovery of the immune response to pathogenic bacteria causing diseases. Different approaches, including the use of nutraceutics (prebiotics and probiotics) as well as phages engineered with CRISPR/Cas systems and quorum sensing systems have been developed as new therapies for controlling dysbiosis (alterations in microbial community) and common diseases (e.g., diabetes and obesity). The designing and production of pharmaceuticals based on our own body's microbiome is an emerging field and is rapidly growing to be fully explored in the near future. This review provides an outlook on recent findings on the human microbiomes, their impact on health and diseases, and on the development of targeted therapies.