nils-herrmann/clean_scirepeval huggingface dataset:
High Dimensional Bayesian Optimization with Elastic Gaussian Process

Bayesian optimization is an efficient way to optimize expensive black-box functions such as designing a new product with highest quality or tuning hyperparameter of a machine learning algorithm. However, it has a serious limitation when the parameter space is high-dimensional as Bayesian optimization crucially depends on solving a global optimization of a surrogate utility function in the same sized dimensions. The surrogate utility function, known commonly as acquisition function is a continuous function but can be extremely sharp at high dimension having only a few peaks marooned in a large terrain of almost flat surface. Global optimization algorithms such as DIRECT are infeasible at higher dimensions and gradient-dependent methods cannot move if initialized in the flat terrain. We propose an algorithm that enables local gradient-dependent algorithms to move through the flat terrain by using a sequence of gross-tofiner Gaussian process priors on the objective function as we leverage two underlying facts a) there exists a large enough length-scales for which the acquisition function can be made to have a significant gradient at any location in the parameter space, and b) the extrema of the consecutive acquisition functions are close although they are different only due to a small difference in the length-scales. Theoretical guarantees are provided and experiments clearly demonstrate the utility of the proposed method on both benchmark test functions and real-world case studies.
A Compromise Principle in Deep Monocular Depth Estimation

Monocular depth estimation, which plays a key role in understanding 3D scene geometry, is fundamentally an illposed problem. Existing methods based on deep convolutional neural networks (DCNNs) have examined this problem by learning convolutional networks to estimate continuous depth maps from monocular images. However, we find that training a network to predict a high spatial resolution continuous depth map often suffers from poor local solutions. In this paper, we hypothesize that achieving a compromise between spatial and depth resolutions can improve network training. Based on this “compromise principle”, we propose a regression-classification cascaded network (RCCN), which consists of a regression branch predicting a low spatial resolution continuous depth map and a classification branch predicting a high spatial resolution discrete depth map. The two branches form a cascaded structure allowing the main classification branch to benefit from the auxiliary regression branch. By leveraging large-scale raw training datasets and some data augmentation strategies, our network achieves competitive or state-of-the-art results on three challenging benchmarks, including NYU Depth V2 , KITTI , and Make3D .
Accelerating big data applications using lightweight virtualization framework on enterprise cloud

Hypervisor-based virtualization technology has been successfully used to deploy high-performance and scalable infrastructure for Hadoop, and now Spark applications. Container-based virtualization techniques are becoming an important option, which is increasingly used due to their lightweight operation and better scaling when compared to Virtual Machines (VM). With containerization techniques such as Docker becoming mature and promising better performance, we can use Docker to speed-up big data applications. However, as applications have different behaviors and resource requirements, before replacing traditional hypervisor-based virtual machines with Docker, it is important to analyze and compare performance of applications running in the cloud with VMs and Docker containers. VM provides distributed resource management for different virtual machines running with their own allocated resources, while Docker relies on shared pool of resources among all containers. Here, we investigate the performance of different Apache Spark applications using both Virtual Machines (VM) and Docker containers. While others have looked at Docker's performance, this is the first study that compares these different virtualization frameworks for a big data enterprise cloud environment using Apache Spark. In addition to makespan and execution time, we also analyze different resource utilization (CPU, disk, memory, etc.) by Spark applications. Our results show that Spark using Docker can obtain speed-up of over 10 times when compared to using VM. However, we observe that this may not apply to all applications due to different workload patterns and different resource management schemes performed by virtual machines and containers. Our work can guide application developers, system administrators and researchers to better design and deploy big data applications on their platforms to improve the overall performance.
A Temptation to Stalk: The Impact of Curiosity on User Acceptance of Social Networking Sites

Social networking sites occupy increasing fields of daily life and act as important communication channels today. But recent research also discusses the dark side of these sites, which expresses in form of stress, envy, addiction or even depression. Nevertheless, there must be a reason why people use social networking sites, even though they face related risks. One reason is human curiosity that tempts users to behave like this. The research on hand presents the impact of curiosity on user acceptance of social networking sites, which is theorized and empirically evaluated by using the technology acceptance model and a quantitative study among Facebook users. It further reveals that especially two types of human curiosity, epistemic and interpersonal curiosity, influence perceived usefulness and perceived enjoyment, and with it technology acceptance.
AutoTutor Improves Deep Learning of Computer Literacy : Is it the Dialog or the Talking Head ?

AutoTutor is a tutoring system that helps students construct answers to deep-reasoning questions by holding a conversation in natural language. AutoTutor delivers its dialog moves with an animated conversational agent whereas students type in their answers via keyboard. We conducted an experiment on 81 college students who learned topics on computer literacy (hardware, operating systems, internet) with AutoTutor or control conditions, and were assessed on learning gains. There was an experimental design that allowed us to assess the impact of learning condition (AutoTutor, read-text control, versus nothing) and the medium of presenting AutoTutor’s dialog moves (print only, speech only, talking head, versus talking head + print). All versions of AutoTutor improved performance in assessments of deep learning, but not shallow learning. Effects of the medium were more subtle, which suggests that it is the message (the dialog moves of AutoTutor) that is more important
Learning 6-DoF Grasping and Pick-Place Using Attention Focus

We address a class of manipulation problems where the robot perceives the scene with a depth sensor and can move its end effector in a space with six degrees of freedom – 3D position and orientation. Our approach is to formulate the problem as a Markov decision process (MDP) with abstract yet generally applicable state and action representations. Finding a good solution to the MDP requires adding constraints on the allowed actions. We develop a specific set of constraints called hierarchical SE(3) sampling (HSE3S) which causes the robot to learn a sequence of gazes to focus attention on the task-relevant parts of the scene. We demonstrate the effectiveness of our approach on three challenging pick-place tasks (with novel objects in clutter and nontrivial places) both in simulation and on a real robot, even though all training is done in simulation.
Full-Range Soft-Switching-Isolated Buck-Boost Converters With Integrated Interleaved Boost Converter and Phase-Shifted Control

A new method for deriving isolated buck-boost (IBB) converter with single-stage power conversion is proposed in this paper and novel IBB converters based on high-frequency bridgeless-interleaved boost rectifiers are presented. The semiconductors, conduction losses, and switching losses are reduced significantly by integrating the interleaved boost converters into the full-bridge diode-rectifier. Various high-frequency bridgeless boost rectifiers are harvested based on different types of interleaved boost converters, including the conventional boost converter and high step-up boost converters with voltage multiplier and coupled inductor. The full-bridge IBB converter with voltage multiplier is analyzed in detail. The voltage multiplier helps to enhance the voltage gain and reduce the voltage stresses of the semiconductors in the rectification circuit. Hence, a transformer with reduced turns ratio and parasitic parameters, and low-voltage rated MOSFETs and diodes with better switching and conduction performances can be applied to improve the efficiency. Moreover, optimized phase-shift modulation strategy is applied to the full-bridge IBB converter to achieve isolated buck and boost conversion. What's more, soft-switching performance of all of the active switches and diodes within the whole operating range is achieved. A 380-V output prototype is fabricated to verify the effectiveness of the proposed IBB converters and its control strategies.
25-Gb/s Operation of a Polymer Optical Waveguide on an Electrical Hybrid LSI Package Substrate With Optical Card Edge Connector

We propose a high-capacity polymer-based optical and electrical LSI package integrated with multimode Si photonic transmitters and receivers. We describe the fabrication and characteristics of the polymer-based hybrid LSI package substrate with a polymer optical waveguide, a mirror, and optical card edge connectors. We fabricated optical mirrors with several angles ranging from 40° to 45° for the Si photonic grating coupler by using a dicing blade at an angle. The dicing mirror changed the emission angle for the grating coupler. We also realized a large lateral misalignment tolerance (±11.5 μm) between the polymer waveguide and MMF for 1 dB of excess loss at 24 channels. We obtained 1-dB coupling loss using an optical card edge connector at 1.3 μm because of the large tolerance. We realized 25-Gb/s error-free transmission per channel at 1.3 μm. We also describe here the error penalty and jitter due to modal noise generated by coupling mismatch.
Security Policy Compliance: User Acceptance Perspective

Information security policy compliance is one of the key concerns that face organizations today. Although, technical and procedural security measures help improve information security, there is an increased need to accommodate human, social and organizational factors. While employees are considered the weakest link in information security domain, they also are assets that organizations need to leverage effectively. Employees' compliance with Information Security Policies (ISPs) is critical to the success of an information security program. The purpose of this research is to develop a measurement tool that provides better measures for predicting and explaining employees' compliance with ISPs by examining the role of information security awareness in enhancing employees' compliance with ISPs. The study is the first to address compliance intention from a users' perspective. Overall, analysis results indicate strong support for the proposed instrument and represent an early confirmation for the validation of the underlying theoretical model.
On the relationship between emotion and cognition

The current view of brain organization supports the notion that there is a considerable degree of functional specialization and that many regions can be conceptualized as either 'affective' or 'cognitive'. Popular examples are the amygdala in the domain of emotion and the lateral prefrontal cortex in the case of cognition. This prevalent view is problematic for a number of reasons. Here, I will argue that complex cognitive–emotional behaviours have their basis in dynamic coalitions of networks of brain areas, none of which should be conceptualized as specifically affective or cognitive. Central to cognitive–emotional interactions are brain areas with a high degree of connectivity, called hubs, which are critical for regulating the flow and integration of information between regions.
Glenohumeral range of motion deficits and posterior shoulder tightness in throwers with pathologic internal impingement.

Alterations in glenohumeral range of motion, including increased posterior shoulder tightness and glenohumeral internal rotation deficit that exceeds the accompanying external rotation gain, are suggested contributors to throwing-related shoulder injuries such as pathologic internal impingement. Yet these contributors have not been identified in throwers with internal impingement.



Throwers with pathologic internal impingement will exhibit significantly increased posterior shoulder tightness and glenohumeral internal rotation deficit without significantly increased external rotation gain.



Case control study; 
Eleven throwing athletes with pathologic internal impingement diagnosed using both clinical examination and a magnetic resonance arthrogram were demographically matched with 11 control throwers who had no history of upper extremity injury. Passive glenohumeral internal and external rotation were measured bilaterally with standard goniometry at 90 degrees of humeral abduction and elbow flexion. Bilateral differences in glenohumeral range of motion were used to calculate glenohumeral internal rotation deficit and external rotation gain. Posterior shoulder tightness was quantified as the bilateral difference in passive shoulder horizontal adduction with the scapula retracted and the shoulder at 90 degrees of elevation. Comparisons were made between groups with dependent t tests (P < .05).



The throwing athletes with internal impingement demonstrated significantly greater glenohumeral internal rotation deficit (P = .03) and posterior shoulder tightness (P = .03) compared with the control subjects. No significant differences were observed in external rotation gain between groups (P = .16).



These findings could indicate that a tightening of the posterior elements of the shoulder (capsule, rotator cuff) may contribute to impingement. The results suggest that management should include stretching to restore flexibility to the posterior shoulder.
Applying Ant Colony Optimization in software testing to generate prioritized optimal path and test data

Software testing is one of the most important parts of software development lifecycle. Among various types of software testing approaches structural testing is widely used. Structural testing can be improved largely by traversing all possible code paths of the software. Genetic algorithm is the most used search technique to automate path testing and test case generation. Recently, different novel search based optimization techniques such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Artificial Immune System (AIS), Particle Swarm Optimization (PSO) have been applied to generate optimal path to complete software coverage. In this paper, ant colony optimization (ACO) based algorithm has been proposed which will generate set of optimal paths and prioritize the paths. Additionally, the approach generates test data sequence within the domain to use as inputs of the generated paths. Proposed approach guarantees full software coverage with minimum redundancy. This paper also demonstrates the proposed approach applying it in a program module.
User Interfaces for Search by Marti A . Hearst

Much of this book describes the algorithms behind search engines and information retrieval systems. By contrast, this chapter focuses on the human users of search systems, and the window through which search systems are seen: the search user interface. The role of the search user interface is to aid in the searcher's understanding and expression of their information needs, and to help users formulate their queries, select among available information sources, understand search results, and keep track of the progress of their search. In the first edition of this book, very little was known about what makes for an effective search interface. In the intervening years, much has become understood about which ideas work from a usability perspective, and which do not. This chapter briefly summarizes the state of the art of search interface design, both in terms of developments in academic research as well as in deployment in commercial systems. The sections that follow discuss how people search, search interfaces today, visualization in search interfaces, and the design and evaluation of search user interfaces. Search tasks range from the relatively simple (e.g., looking up disputed facts or finding weather information) to the rich and complex (e.g., job seeking and planning vacations). Search interfaces should support a range of tasks, while taking into account how people think about searching for information. This section summarizes theoretical models about and empirical observations of the process of online information seeking. Information Lookup versus Exploratory Search User interaction with search interfaces differs depending on the type of task, the amount of time and effort available to invest in the process, and the domain expertise of the information seeker. The simple interaction dialogue used in Web search engines is most appropriate for finding answers to questions or to finding Web sites or other resources that act as search starting points. But, as Marchionini [89] notes, the " turn-taking " interface of Web search engines is inherently limited and is many cases is being supplanted by speciality search engines – such as for travel and health information – that offer richer interaction models. Marchionini [89] makes a distinction between information lookup and exploratory search. Lookup tasks are akin to fact retrieval or question answering, and are satisfied by short, discrete pieces of information: numbers, dates, names, or names of files or Web sites. Standard Web search interactions (as well as standard database management system queries) can …
ProcessProfiler 3 D : A visualisation framework for log-based process performance comparison

An organisation can significantly improve its performance by observing how their business operations are currently being carried out. A great way to derive evidence-based process improvement insights is to compare the behaviour and performance of processes for different process cohorts by utilising the information recorded in event logs. A process cohort is a coherent group of process instances that has one or more shared characteristics. Such process performance comparisons can highlight positive or negative variations that can be evident in a particular cohort, thus enabling a tailored approach to process improvement. Although existing process mining techniques can be used to calculate various statistics from event logs for performance analysis, most techniques calculate and display the statistics for each cohort separately. Furthermore, the numerical statistics and simple visualisations may not be intuitive enough to allow users to compare the performance of various cohorts efficiently and effectively. We developed a novel visualisation framework for log-based process performance comparison to address these issues. It enables analysts to quickly identify the performance differences between cohorts. The framework supports the selection of cohorts and a three-dimensional visualisation to compare the cohorts using a variety of performance metrics. The approach has been implemented as a set of plug-ins within the open source process mining framework ProM and has been evaluated using two real-life datasets from the insurance domain to assess the usefulness of such a tool. This paper also derives a set of design principles from our approach which provide guidance for the development of new approaches to process cohort performance comparison.
Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation

A key issue facing information systems researchers and practitioners has been the difficulty in creating favorable user reactions to new technologies. Insufficient or ineffective training has been identified as one of the key factors underlying this disappointing reality. Among the various enhancements to training being examined in research, the role of intrinsic motivation as a lever to create favorable user perceptions has not been sufficiently exploited. In this research, two studies were conducted to compare a traditional training method with a training method that included a component aimed at enhancing intrinsic motivation. The results strongly favored the use of an intrinsic motivator during training. Key implications for theory and practice are discussed. 1Allen Lee was the accepting senior editor for this paper. Sometimes when I am at my computer, I say to my wife, "1'11 be done in just a minute" and the next thing I know she's standing over me saying, "It's been an hour!" (Collins 1989, p. 11). Investment in emerging information technology applications can lead to productivity gains only if they are accepted and used. Several theoretical perspectives have emphasized the importance of user perceptions of ease of use as a key factor affecting acceptance of information technology. Favorable ease of use perceptions are necessary for initial acceptance (Davis et al. 1989), which of course is essential for adoption and continued use. During the early stages of learning and use, ease of use perceptions are significantly affected by training (e.g., Venkatesh and Davis 1996). Investments in training by organizations have been very high and have continued to grow rapidly. Kelly (1982) reported a figure of $100B, which doubled in about a decade (McKenna 1990). In spite of such large investments in training , only 10% of training leads to a change in behavior On trainees' jobs (Georgenson 1982). Therefore, it is important to understand the most effective training methods (e.g., Facteau et al. 1995) and to improve existing training methods in order to foster favorable perceptions among users about the ease of use of a technology, which in turn should lead to acceptance and usage. Prior research in psychology (e.g., Deci 1975) suggests that intrinsic motivation during training leads to beneficial outcomes. However, traditional training methods in information systems research have tended to emphasize imparting knowledge to potential users (e.g., Nelson and Cheney 1987) while not paying Sufficient attention to intrinsic motivation during training. The two field …
Exploiting Music Play Sequence for Music Recommendation

Users leave digital footprints when interacting with various music streaming services. Music play sequence, which contains rich information about personal music preference and song similarity, has been largely ignored in previous music recommender systems. In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems. Towards the goal, we propose to use word embedding techniques in music play sequences to estimate the similarity between songs. The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery. Furthermore, the proposed method only considers the knearest songs (e.g., k = 5) in the learning process and thus avoids the increase of time complexity. Experimental results on two public datasets demonstrate that our methods could significantly improve the performance on both rating prediction and topn recommendation tasks.