nils-herrmann/clean_scirepeval huggingface dataset:
Vertical Integration of CDN and Network Operator: Model and Analysis

Content Delivery Network (CDN) services largely affect the delivery quality perceived by users. While those services were initially offered by independent entities, some large ISP now develop their own CDN activities to control costs and delivery quality. But this new activity is also a new source of revenues for those vertically integrated ISP-CDNs, which can sell those services to content providers. In this paper, we investigate the impact of having an ISP and a vertically-integrated CDN, on the main actors of the ecosystem (users, competing ISPs). Our approach is based on an economic model of revenues and costs, and a multilevel game-theoretic formulation of the interactions among actors. Our model incorporates the possibility for the vertically-integrated ISP to partially offer CDN services to competitors in order to optimize the trade-off between CDN revenue (if fully offered) and competitive advantage on subscriptions at the ISP level (if not offered to competitors). Our results highlight two counterintuitive phenomena: an ISP may prefer an independent CDN over controlling (integrating) a CDN, and from the user point of view vertical integration is preferable to an independent CDN or a no-CDN configuration. Hence, a regulator may want to elicit such CDN-ISP vertical integrations rather than prevent them.
Person Following by Autonomous Robots: A Categorical Overview

A wide range of human-robot collaborative applications in diverse domains such as manufacturing, search-andrescue, health care, the entertainment industry, and social interactions, require an autonomous robot to follow its human companion. Different working environments and applications pose diverse challenges by adding constraints on the choice of sensors, the degree of autonomy, and dynamics of the person-following robot. Researchers have addressed these challenges in many ways and contributed to the development of a large body of literature. This paper provides a comprehensive overview of the literature by categorizing different aspects of person-following by autonomous robots. Also, the corresponding operational challenges are identified based on various design choices for ground, underwater, and aerial scenarios. In addition, state-of-the-art methods for perception, planning, control, and interaction are elaborately discussed and their applicability in varied operational scenarios are presented. Then, qualitative evaluations of some of the prominent methods are performed, corresponding practicalities are illustrated, and their feasibility is analyzed in terms of standard metrics. Furthermore, several prospective application areas are identified, and open problems are highlighted for future research.
Large-scale analysis of frequency modulation in birdsong databases

Birdsong often contains large amounts of rapid frequency modulation (FM). It is believed that the use or otherwise of FM is adaptive to the acoustic environment, and also that there are specific social uses of FM such as trills in aggressive territorial encounters. Yet temporal fine detail of FM is often absent or obscured in standard audio signal analysis methods such as Fourier analysis or linear prediction. Hence it is important to consider high resolution signal processing techniques for analysis of FM in bird vocalisations. If such methods can be applied at big data scales, this offers a further advantage as large datasets become available. We introduce methods from the signal processing literature which can go beyond spectrogram representations to analyse the fine modulations present in a signal at very short timescales. Focusing primarily on the genus Phylloscopus, we investigate which of a set of four analysis methods most strongly captures the species signal encoded in birdsong. In order to find tools useful in practical analysis of large databases, we also study the computational time taken by the methods, and their robustness to additive noise and MP3 compression. We find three methods which can robustly represent species-correlated FM attributes, and that the simplest method tested also appears to perform the best. We find that features representing the extremes of FM encode species identity supplementary to that captured in frequency features, whereas bandwidth features do not encode additional information. Large-scale FM analysis can efficiently extract information useful for bioacoustic studies, in addition to measures more commonly used to characterise vocalisations.
A Personalized Time-Sequence-Based Book Recommendation Algorithm for Digital Libraries

Book recommendations are of great significance in colleges and universities. Although current recommendation approaches have made significant achievements, these approaches do not consider college students’ similar learning trajectories in the same major. In order to recommend books more accurately, mining the knowledge system is very crucial for college students in the same major. This paper proposes a personalized book recommendation algorithm that is based on the time sequential collaborative filtering recommendation, combined with students’ learning trajectories. In order to recommend books effectively, our algorithm leverages space distance. In this algorithm, we consider two important characteristics: the time sequence information of borrowing books and the circulation times of books. Our experimental results demonstrate that our book recommendation algorithm is in accordance with the college students’ demand for professional learning.
Flower menus: a new type of marking menu with large menu breadth, within groups and efficient expert mode memorization

This paper presents Flower menu, a new type of Marking menu that does not only support straight, but also curved gestures for any of the 8 usual orientations. Flower menus make it possible to put many commands at each menu level and thus to create as large a hierarchy as needed for common applications. Indeed our informal analysis of menu breadth in popular applications shows that a quarter of them have more than 16 items. Flower menus can easily contain 20 items and even more (theoretical maximum of 56 items). Flower menus also support within groups as well as hierarchical groups. They can thus favor breadth organization (within groups) or depth organization (hierarchical groups): as a result, the designers can lay out items in a very flexible way in order to reveal meaningful item groupings. We also investigate the learning performance of the expert mode of Flower menus. A user experiment is presented that compares linear menus (baseline condition), Flower menus and Polygon menus, a variant of Marking menus that supports a breadth of 16 items. Our experiment shows that Flower menus are more efficient than both Polygon and Linear menus for memorizing command activation in expert mode.
Customer retention-Case studies of agencies in the professional service sector

Acknowledgements i ACKNOWLEDGEMENTS This master thesis was written at the division of Industrial Marketing and E-commerce at Luleå University of Technology. The time frame for this thesis was ten weeks within which I have gained increased knowledge on the concept of customer retention within professional service firms but foremost, I have gained increased knowledge on how to conduct a thesis on my own. First and foremost, I would like to thank my supervisor and mentor professor Manucher Farhang for his guidance, patience and support during this ten weeks timeframe. I would also like to thank Lowe Brindfors in Stockholm, Favör Reklambyrå in Luleå, Holy Diver in Stockholm and Vinter Reklambyrå in Luleå for taking time to participate in this study and for providing valuable information about their agencies. Last but not least, I would like to thank my family for their understanding of my absence and to thank my closest friends for their never ending support and encouragements.  In recent years, customer retention has gained increased value among both goods and service providing firms. However although extensive research exist on the concept of customer retention and its measures and instruments, studies and research on how professional service firms retain their customers remain limited. Hence, in this thesis how professional service providers retain their targeted customers over time will be investigated through four case studies in the professional service industry, more specifically in the advertising sector. The empirical data was collected through interviews with four Swedish advertising agencies: Lowe Brindfors, Favör Reklambyrå, Holy Diver and Vinter Reklambyrå. The findings of this study first and foremost indicate that professional service providers do not have any formal nor standardized procedure which they follow when it comes to retaining their customers. The strategies employed by the firms are highly customized to each individual customer. Further the findings from this study indicate that in order to retain customers over time professional service providers need to place more efforts on the creation of personal relationships with the clients, as it is a strong bond tying customers to the firm. The findings further imply that the creation of customer satisfaction and the creation of switching barriers are the main strategies employed by firms, for retaining customers. Other factors affecting professional service firms " retention strategies are the firms " ability to convey confidence, to get the customers involved, and to be able to deliver …
Understanding the Influence of Social Media in the Workplace: An Integration of Media Synchronicity and Social Capital Theories

Social media has deeply penetrated workplace, which has affected multiple aspects of employees' lives. This paper aims to investigate the influence of social media on employees' work performance and the underlying mechanism for how they create value at work. Based on media synchronicity and social capital theories, we propose that social media can promote work performance by stimulating trust among employees and offering a communication channel where explicit and implicit knowledge can be effectively transferred. The research model is tested using data collected from 105 Chinese software professionals. The results reveal that social media can enhance trust among employees. The direct effect on knowledge transfer was not significant but the impact was mediated by trust. Trust enhances knowledge exchange in the workplace with a stronger influence on the transfer of implicit knowledge rather than explicit knowledge. Implicit knowledge transfer was significantly related to work performance, while explicit knowledge transfer did not significantly influence work performance. The theoretical and practical contributions of this study are discussed.
Dynamic trajectory planning for mobile robot navigation in crowded environments

This paper describes a trajectory planning algorithm for mobile robot navigation in crowded environments; the aim is to solve the problem of planning a valid path through moving people. The proposed solution relies on an algorithm based on the Informed Optimal Rapidly-exploring Random Tree (InformedRRT*), where the planner continuously computes a valid path to navigate in crowded environments. While the robot executes the trajectory of the current path, this re-planning method always allows a feasible and optimal solution to be obtained. Compared to other state-of-the-art algorithms, this solution does not compute the entire path each time an obstacle is detected, instead it evaluating the current solution validity, i.e., the presence of moving obstacles on the current path; in this case the algorithm tries to repair the current solution. Only if the current path is completely unacceptable is a new path computed from scratch. Thanks to its reactivity, our solution always guarantees a valid path that brings the robot to the desired goal position. This dynamic approach is validated in a real case scenario where a mobile robot moves through a human crowd in a safe and reliable way.
Convex Channel Design for Improved Capacitorless DRAM Retention Time

A convex channel surface with Si0.8Ge0.2 is proposed to enhance the retention time of a capacitorless DRAM Generation 2 type of capacitorless DRAM cell. This structure provides a physical well together with an electrostatic barrier to more effectively store holes and thereby achieve larger sensing margin as well as retention time. The advantages of this new cell design as compared with the planar cell design are assessed via twodimensional device simulations. The results indicate that the convex heterojunction channel design is very promising for future capacitorless DRAM.  Time; Convex Channel; Silicon Germanium;
The Family Diet Study: a cross-sectional study into the associations between diet, food habits and body weight status in Malay families.

Childhood obesity is becoming more common as Malaysia experiences rapid nutrition transition. Current evidence related to parental influences on child dietary intake and body weight status is limited. The present study aimed to report, among Malay families, the prevalence of energy mis-reporting and dietary relationships within family dyads.



The cross-sectional Family Diet Study (n = 236) was conducted at five primary schools in central of Peninsular Malaysia. Each family consisted of a Malay child, aged 8-12 years, and their main caregiver(s). Information on socio-demographics, dietary intake and anthropometry were collected. Correlations and regression analyses were used to assess dietary relationships within family dyads.



Approximately 29.6% of the children and 75.0% parents were categorised as being overweight or obese. Intakes of nutrients and food groups were below the national recommended targets for majority of children and adults. A large proportion of energy intake mis-reporters were identified: mothers (55.5%), fathers (40.2%) and children (40.2%). Children's body mass index (BMI) was positively associated with parental BMI (fathers, r = 0.37; mothers, r = 0.34; P < 0.01). For dietary intakes, moderate-to-strong (0.35-0.72) and weak-to-moderate (0.16-0.35) correlations were found between mother-father and child-parent dyads, respectively. Multiple regression revealed that maternal percentage energy from fat (β = 0.09, P < 0.01) explained 81% of the variation in children's fat intake.



Clear parental dietary relationships, especially child-mother dyads, were found. Despite a significant proportion of families with members who were overweight or obese, the majority reported dietary intakes below recommended levels, distorted by energy mis-reporting. The findings of the present study can inform interventions targeting parent-child relationships to improve family dietary patterns in Malaysia.
On predicting the time taken to correct bug reports in open source projects

Existing studies on the maintenance of open source projects focus primarily on the analyses of the overall maintenance of the projects and less on specific categories like the corrective maintenance. This paper presents results from an empirical study of bug reports from an open source project, identifies user participation in the corrective maintenance process through bug reports, and constructs a model to predict the corrective maintenance effort for the project in terms of the time taken to correct faults. Our study focuses on 72482 bug reports from over nine releases of Ubuntu, a popular Linux distribution. We present three main results 1) 95% of the bug reports are corrected by people participating in groups of size ranging from 1 to 8 people, 2) there is a strong linear relationship (about 92%) between the the number of people participating in a bug report and the time taken to correct it, 3) a linear model can be used to predict the time taken to correct bug reports.
Benchmarking modern distributed streaming platforms

The prevalence of big data technology has generated increasing demands in large-scale streaming data processing. However, for certain tasks it is still challenging to appropriately select a platform due to the diversity of choices and the complexity of configurations. This paper focuses on benchmarking some principal streaming platforms. We achieve our goals on StreamBench, a streaming benchmark tool based on which we introduce proper modifications and extensions. We then accomplish performance comparisons among different big data platforms, including Apache Spark, Apache Storm and Apache Samza. In terms of performance criteria, we consider both computational capability and fault-tolerance ability. Finally, we give a summary on some key knobs for performance tuning as well as on hardware utilization.
Multi-body Motion Estimation from Monocular Vehicle-Mounted Cameras

This paper addresses the problem of simultaneous estimation of a vehicle's ego motion and motions of multiple moving objects in the scene-called eoru motions-through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g., visual odometry and simultaneous localization and mapping) can only estimate the ego motion of the vehicle. The capability of a robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego motion. First, the framework for general unconstrained motion is introduced and then it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix. First, the ego motion is segmented and then several hypotheses are generated for the eoru motions. All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with a constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (the KITTI dataset), including several relative ego-motion and eoru-motion scenarios. A benchmark dataset (Hopkins 155) is used to evaluate this method with a general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as multibody structure from motion in the computer vision community.
Just Join for Parallel Ordered Sets

Ordered sets (and maps when data is associated with each key) are one of the most important and useful data types. The set-set functions union, intersection and difference are particularly useful in certain applications. Brown and Tarjan first described an algorithm for these functions, based on 2-3 trees, that meet the optimal Θ(m log (n/m+1)) time bounds in the comparison model (n and m ≤ n are the input sizes). Later Adams showed very elegant algorithms for the functions, and others, based on weight-balanced trees. They only require a single function that is specific to the balancing scheme---a function that joins two balanced trees---and hence can be applied to other balancing schemes. Furthermore the algorithms are naturally parallel. However, in the twenty-four years since, no one has shown that the algorithms, sequential or parallel are asymptotically work optimal. In this paper we show that Adams' algorithms are both work efficient and highly parallel (polylog span) across four different balancing schemes---AVL trees, red-black trees, weight balanced trees and treaps. To do this we use careful, but simple, algorithms for Join that maintain certain invariants, and our proof is (mostly) generic across the schemes.
 To understand how the algorithms perform in practice we have also implemented them (all code except Join is generic across the balancing schemes). Interestingly the implementations on all four balancing schemes and three set functions perform similarly in time and speedup (more than 45x on 64 cores). We also compare the performance of our implementation to other existing libraries and algorithms.
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning

This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupancy map of the environment, to targets whose relative locations can be easily acquired through low-cost solutions (e.g., visible light localization, Wi-Fi signal localization). We validate that the intrinsic motivation is crucial for improving DRL performance in tasks with challenging exploration requirements. Our experimental results show that our proposed method is able to more effectively learn navigation policies, and has better generalization capabilities in previously unseen environments. A video of our experimental results can be found at https://goo.gl/pWbpcF.
Asymmetry and Ambiguity in Newsvendor Models

The traditional decision-making framework for newsvendor models is to assume a distribution of the underlying demand. However, the resulting optimal policy is typically sensitive to the choice of the distribution. A more conservative approach is to assume that the distribution belongs to a set parameterized by a few known moments. An ambiguity-averse newsvendor would choose to maximize the worst-case profit. Most models of this type assume that only the mean and the variance are known, but do not attempt to include asymmetry properties of the distribution. Other recent models address asymmetry by including skewness and kurtosis. However, closed-form expressions on the optimal bounds are difficult to find for such models. In this paper, we propose a framework under which the expectation of a piecewise linear objective function is optimized over a set of distributions with known asymmetry properties. This asymmetry is represented by the first two moments of multiple random variables that result from partitioning the original distribution. In the simplest case, this reduces to semivariance. The optimal bounds can be solved through a second-order cone programming (SOCP) problem. This framework can be applied to the risk-averse and risk-neutral newsvendor problems and option pricing. We provide a closed-form expression for the worst-case newsvendor profit with only mean, variance and semivariance information.
Implementation of BCH Code ( n , k ) Encoder and Decoder for Multiple Error Correction Control

In this paper we have designed and implemented (15, k) a BCH Encoder and decoder using VHDL for reliable data transfer in AWGN channel with multiple error correction control. The digital logic implementation of binary encoding of multiple error correcting BCH code (15, k) of length n=15 over GF (2 4 ) with irreducible primitive polynomial x 4 +x+1 is organized into shift register circuits. Using the cyclic codes, the reminder b(x) can be obtained in a linear (15-k) stage shift register with feedback connections corresponding to the coefficients of the generated polynomial. Three encoders are designed using VHDL to encode the single, double and triple error correcting BCH code (15, k) corresponding to the coefficient of generated polynomial. Information bit is transmitted in unchanged form up to K clock cycles and during this period parity bits are calculated in the LFSR then the parity bits are transmitted from k+1 to 15 clock cycles. Total 15-k numbers of parity bits with k information bits are transmitted in 15 code word. In multiple error correction method, we have implemented (15, 5 ,3 ) ,(15,7, 2) and (15, 11, 1) BCH encoder and decoder using VHDL and the simulation is done using Xilinx ISE 14.2. KeywordsBCH, BER, SNR, BCH Encoder, Decoder VHDL, Error Correction, AWGN, LFSR