nils-herrmann/clean_scirepeval huggingface dataset:
Factors affecting ERP system adoption: A comparative analysis between SMEs and large companies

Proposes providing an insight about enterprise resource planning (ERP) adoption, highlighting contact points and significant differences between the way small to medium-sized enterprises (SMEs) and large companies approach such a task.  The research is based on a wide literature review, focused on the identification of a taxonomy of business and organizational factors influencing ERP adoption. The deriving research model was incorporated in a questionnaire that was preliminarily tested and finally provided to a sample of 366 companies of any size. Responses were collected through personal interviews made by a dedicated team to a top manager.  The analysis of the empirical data shows that business complexity, as a composed factor, is a weak predictor of ERP adoption, whereas just company size turns out to be a very good one. In other words, companies seem to be disregarding ERP systems as an answer to their business complexity. Unexpectedly, SMEs disregard financial constraints as the main cause for ERP system non-adoption, suggesting structural and organizational reasons as major ones. This pattern is partially different from what was observed in large organizations where the first reason for not adopting an ERP system is organizational. Moreover, the decision process regarding the adoption of ERP systems within SMEs is still more affected by exogenous reasons or “opportunity of the moment” than business-related factors, contrary to large companies that are more interested in managing process integration and data redundancy/inconsistency through ERP implementation.  The research model is based on the assumption that business complexity and organizational change are the most relevant variables influencing ERP adoption, and such variables are explained through a set of factors inherently limited by the results of the literature review.  The results of the empirical research provide indication to SMEs willing to take into consideration the adoption of an ERP system. The same outcomes could be incorporated into the development strategies of ERP software houses.  This paper contributes to enhancing the understanding of the factors influencing the evolution of information systems within SMEs with respect to large companies.
FHE over the integers and modular arithmetic circuits

Fully homomorphic encryption (FHE) over the integers, as proposed by van Dijk et al. in 2010 and developed in a number of papers afterwards, originally supported the evaluation of Boolean circuits (i.e. mod-2 arithmetic circuits) only. It is easy to generalize the somewhat homomorphic versions of the corresponding schemes to support arithmetic operations modulo Q for any Q > 2, but bootstrapping those generalized variants into fully homomorphic schemes is not easy. Thus, Nuida and Kurosawa settled a significant open problem in 2015 by showing that one could in fact construct FHE over the integers with message space Z/QZ for any constant prime Q. As a result of their work, we now have two different ways of homomorphically evaluating a mod-Q arithmetic circuit with an FHE scheme over the integers: one could either use their scheme with message space Z/QZ directly, or one could first convert the arithmetic circuit to a Boolean one, and evaluate that converted circuit using an FHE scheme with binary message space. In this paper, we compare both approaches and show that the latter is often preferable to the former.
Data-Driven Networking : Harnessing the “ Unreasonable Effectiveness of Data ” in Network Design

The last few years have witnessed the coming of age of data-driven paradigm in various aspects of computing (partly) empowered by advances in distributed system research (cloud computing, MapReduce, etc). In this paper, we observe that the benefits can flow the opposite direction: the design of distributed systems can be improved by data-driven paradigm. To this end, we present DDN, a new design framework for network protocols based on data-driven paradigm. We argue that DDN has the potential to significantly achieve better performance through harnessing more data than one single flow. Furthermore, we systematize existing instantiations of DDN by creating a unified framework for DDN, and use the framework to shed light on the common challenges and reusable design principles. We believe that by systematizing this paradigm as a broader community, we can unleash the unharnessed potential of DDN.
Android App Energy Efficiency: The Impact of Language, Runtime, Compiler, and Implementation

With millions of smart phones being purchased every year, the number of mobile applications that have been developed and installed keeps growing substantially. In 2016, Android-based phones dominate the market with 80.7% of market share and there are over 1.6 million apps in Google Play. The battery power limitation requires each app to reduce its impact on the battery life. Existing research and practices focus on how to optimize battery life by reducing network battery drain, judiciously turning off or dimming screens, dynamically adjusting CPU frequency of inactive apps, and changing app behaviors based on different battery states. These studies have greatly helped to improve the energy efficiency of Android apps. However, very few work has been conducted to analyze the impact of programming languages, compilers, runtime, and implementation choices on power consumption of Android apps, which is equally important for Android app developers to write more energy efficient code. This paper addresses the aforementioned problems by making the following contributions: 1) we develop the Android Energy Profiler (AEP) to measure the detailed power consumption of various Android apps, 2) we analyze the impact of different languages (C/C++/Java) and compiler optimization on energy consumption, 3) we study the impact of different Android system runtimes (ART vs. Dalvik) on energy efficiency, 4) we evaluate the impact of implementation choices (recursion vs. iteration and serial vs. parallel) on energy efficiency, and 5) we verify if thin client design can always lead to better energy efficiency.
Saliency-Guided Detection of Unknown Objects in RGB-D Indoor Scenes

This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object hypotheses using the 3D shape. We perform multi-class labeling on a Markov random field (MRF) over the voxels of the 3D scene, combining cues from object hypotheses and 3D shape. The results from MRF are further refined by merging the labeled objects, which are spatially connected and have high correlation between color histograms. Quantitative and qualitative evaluations on two benchmark RGB-D datasets illustrate the advantages of the proposed method. The experiments of object detection and manipulation performed on a mobile manipulator validate its effectiveness and practicability in robotic applications.
An inexpensive Arduino-based LED stimulator system for vision research

Light emitting diodes (LEDs) are being used increasingly as light sources in life sciences applications such as in vision research, fluorescence microscopy and in brain-computer interfacing. Here we present an inexpensive but effective visual stimulator based on light emitting diodes (LEDs) and open-source Arduino microcontroller prototyping platform. The main design goal of our system was to use off-the-shelf and open-source components as much as possible, and to reduce design complexity allowing use of the system to end-users without advanced electronics skills. The main core of the system is a USB-connected Arduino microcontroller platform designed initially with a specific emphasis on the ease-of-use creating interactive physical computing environments. The pulse-width modulation (PWM) signal of Arduino was used to drive LEDs allowing linear light intensity control. The visual stimulator was demonstrated in applications such as murine pupillometry, rodent models for cognitive research, and heterochromatic flicker photometry in human psychophysics. These examples illustrate some of the possible applications that can be easily implemented and that are advantageous for students, educational purposes and universities with limited resources. The LED stimulator system was developed as an open-source project. Software interface was developed using Python with simplified examples provided for Matlab and LabVIEW. Source code and hardware information are distributed under the GNU General Public Licence (GPL, version 3).
Applying High-Resolution Visible Imagery to Satellite Melt Pond Fraction Retrieval: A Neural Network Approach

During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multiperiod visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.
The effect of flow experience on mobile SNS users' loyalty

The purpose of this research is to examine the effect of flow experience on mobile social networking service (SNS) users’ loyalty.  Based on 305 valid responses collected from a survey questionnaire, structural equation modeling (SEM) technology was employed to examine the research model.  The results show that both information quality and system quality significantly affect users’ trust and flow experiences, which further determine their loyalty. The results indicate that flow experience is the strongest determinant of users’ loyalty.  Mobile SNS providers need to consider user experience when seeking users’ loyalty. They should enhance information quality and system quality in order to improve user trust and flow experience.  Although much research has been conducted to explore the effects of extrinsic motivations, such as perceived usefulness, on mobile commerce user behavior, the effect of intrinsic motivations, such as flow experience, is seldom tested. This research found the effect of flow experience on mobile SNS users’ loyalty to be significant.
Reducing the risk of query expansion via robust constrained optimization

We introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a robust constrained optimization problem. This framework provides a powerful method for modeling and solving complex expansion problems, by allowing multiple sources of domain knowledge or evidence to be encoded as simultaneous optimization constraints. Our robust optimization approach provides a clean theoretical way to model not only expansion benefit, but also expansion risk, by optimizing over uncertainty sets for the data. In addition, we introduce risk-reward curves to visualize expansion algorithm performance and analyze parameter sensitivity. We show that a robust approach significantly reduces the number and magnitude of expansion failures for a strong baseline algorithm, with no loss in average gain. Our approach is implemented as a highly efficient post-processing step that assumes little about the baseline expansion method used as input, making it easy to apply to existing expansion methods. We provide analysis showing that this approach is a natural and effective way to do selective expansion, automatically reducing or avoiding expansion in risky scenarios, and successfully attenuating noise in poor baseline methods.
Measuring McKibben actuator shrinkage using fiber sensor

This paper focuses on sensing of McKibben actuator shrinkage using a fiber sensor. Control of McKibben actuators requires sensing their shrinkage without preventing their deformation. We applied electro-conductive yarn, with resistance related to its extensional strain, suggesting that measuring the resistance of the yarn can determine its length. The yarn is also bendable and light in weight, thus not preventing the deformation of a McKibben actuator. A fiber sensor consisting of these electro-conductive yarns was attached onto the membrane of a McKibben actuator to measure the distance between both ends of the sensor along the membrane. This paper describes a fiber sensor model that can calculate McKibben actuator shrinkage from the fiber sensor measurement. Based on the model, we investigated the arrangement of a fiber sensor so that the fiber extensional stress is within its allowable range. We then show a prototype of the fiber sensor that was utilized to measure McKibben actuator shrinkage. Experimentally, we found that the shrinkage of an actuator could be measured within 20 % error of its length.
Prediction of 4-year college student performance using cognitive and noncognitive predictors and the impact on demographic status of admitted students.

This study was conducted to determine the validity of noncognitive and cognitive predictors of the performance of college students at the end of their 4th year in college. Results indicate that the primary predictors of cumulative college grade point average (GPA) were Scholastic Assessment Test/American College Testing Assessment (SAT/ACT) scores and high school GPA (HSGPA) though biographical data and situational judgment measures added incrementally to this prediction. SAT/ACT scores and HSGPA were collected and used in various ways by participating institutions in the admissions process while situational judgment measures and biodata were collected for research purposes only during the first few weeks of the participating students' freshman year. Alternative outcomes such as a self-report of performance on a range of student performance dimensions and a measure of organizational citizenship behavior, as well as class absenteeism, were best predicted by noncognitive measures. The racial composition of a student body selected with only cognitive measures or both cognitive and noncognitive measures under various levels of selectivity as well as the performance of students admitted under these scenarios is also reported. The authors concluded that both the biodata and situational judgment measures could be useful supplements to cognitive indexes of student potential in college admissions.
Complementary Skyrmion Racetrack Memory With Voltage Manipulation

Magnetic skyrmion holds promise as information carriers in the next-generation memory and logic devices, owing to the topological stability, small size, and extremely low current needed to drive it. One of the most potential applications of skyrmion is to design racetrack memory (RM), named Sk-RM, instead of utilizing domain wall. However, current studies face some key design challenges, e.g., skyrmion manipulation, data representation, and synchronization. To address these challenges, we propose here a complementary Sk-RM structure with voltage manipulation. Functionality and performance of the proposed design are investigated with micromagnetic simulations.
Linear cross-modal hashing for efficient multimedia search

Most existing cross-modal hashing methods suffer from the scalability issue in the training phase. In this paper, we propose a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals. Taking both the intra-similarity in each modal and the inter-similarity across different modals into consideration, the proposed approach aims at effectively learning hash functions from large-scale training datasets. More specifically, for each modal, we first partition the training data into $k$ clusters and then represent each training data point with its distances to $k$ centroids of the clusters. Interestingly, such a k-dimensional data representation can reduce the time complexity of the training phase from traditional O(n2) or higher to O(n), where $n$ is the training data size, leading to practical learning on large-scale datasets. We further prove that this new representation preserves the intra-similarity in each modal. To preserve the inter-similarity among data points across different modals, we transform the derived data representations into a common binary subspace in which binary codes from all the modals are "consistent" and comparable. nThe transformation simultaneously outputs the hash functions for all modals, which are used to convert unseen data into binary codes. Given a query of one modal, it is first mapped into the binary codes using the modal's hash functions, followed by matching the database binary codes of any other modals. Experimental results on two benchmark datasets confirm the scalability and the effectiveness of the proposed approach in comparison with the state of the art.
Juvenile gigantomastia , two cases treated by reduction mammoplasty with nipple-areola complex graft

Gigantomastia or “Gravidic macromastia” is a rare benign disorder of the breast, in which the breasts undergo a massive hypertrophy and increase in size. It may occur during marked hormonal changes as in puberty or pregnancy or after prolonged intake of certain medicine. The condition is manifested as a massive diffuse enlargement of the breast. It is usually associated with psychological effect on the patient and her family and physical disability due to excessive breast growth. This rare condition is of undetermined aetiology, which may be due to excess hormonal secretion, or hypersensitivity of the target organ to normal hormonal level. Histologically it is a glandular hyperplasia with an increase in connective tissue. This paper reports two case of gigantomastia due to hormonal changes during puberty. Ulceration and haemorrhage of the breasts complicated the picture and is the main cause of coming to surgery. Surgical procedure was subtotal mastectomy and free implantation of the nipple
A maritime inventory routing problem: Discrete time formulations and valid inequalities

4 A single product maritime inventory routing problem in which the production and con5 sumption rates vary over the planning horizon is studied. The problem includes a heteroge6 neous fleet and multiple production and consumption ports with limited storage capacity. 7 Two discrete time formulations are developed. One is a standard formulation and the other 8 is a fixed charge network flow reformulation. Mixed integer sets arising from the decomposition 9 of the formulations are identified. Several lot-sizing relaxations are derived for the network 10 formulation and used to establish valid inequalities to strengthen the proposed formulations. 11 Considering a set of instances based on real data, a computational study is conducted to 12 test the formulations and the effectiveness of the inclusion of valid inequalities. By using a 13 branch and bound scheme based on the strengthened network formulation a large majority of 14 the instances with up to sixty time periods are solved to optimality.
Flexible Piezoelectric Tactile Sensor Array for Dynamic Three-Axis Force Measurement

A new flexible piezoelectric tactile sensor array based on polyvinylidene fluoride (PVDF) film is proposed for measuring three-axis dynamic contact force distribution. The array consists of six tactile units arranged as a 3 × 2 matrix with spacing 8 mm between neighbor units. In each unit, a PVDF film is sandwiched between four square-shaped upper electrodes and one square-shaped lower electrode, forming four piezoelectric capacitors. A truncated pyramid bump is located above the four piezoelectric capacitors to improve force transmission. A three-axis contact force transmitted from the top of the bump will lead to the four piezoelectric capacitors underneath undergoing different charge changes, from which the normal and shear components of the force can be calculated. A series of dynamic tests have been carried out by exerting sinusoidal forces with amplitudes ranging from 0 to 0.5 N in the x-axis, 0 to 0.5 N in the y-axis, and 0 to 1.5 N in the z-axis, separately. The tactile units show good sensitivities with 14.93, 14.92, and 6.62 pC/N in the x-, y-, and z-axes, respectively. They can work with good linearity, relatively low coupling effect, high repeatability, and acceptable frequency response in the range of 5-400 Hz to both normal and shear load. In addition, dynamic three-axis force measurement has been conducted for all of the tactile units. The average errors between the applied and calculated forces are 10.68% ± 6.84%. Furthermore, the sensor array can be easily integrated onto a curved surface, such as robotic and prosthetic hands, due to its excellent flexibility.
The Mathematical Model of Contact Resistance and Injected Current in DRM Tests of SF6 Circuit Breaker

Dynamic contact resistance measurement (DRM) has been widely used in evaluating the contact condition of circuit breakers from 20 years ago. With the increasing of the injected current, the contact resistance is lower than it was. It is considered that the contact hardness, contact force, and specific resistivity are affected by the injected current. SF6 circuit breaker with self-elastic contact is studied in this paper because of its widely used in high-voltage power system. First, the parameters of the dynamic curves are discussed, and the contact resistance in arcing contact part is concerned because of its susceptibility. Second, the mathematical model of contact force is discussed and calculated, and the change of contact force under a different injected current can be ignored. Finally, the mathematical model of contact resistance and injected current is established, when the breaker contact speed is stable. The mathematical model is proven effective by DRM tests.
Effects of time-to-trigger parameter on handover performance in SON-based LTE systems

In this paper, we evaluate handover (HO) performance when we apply various time-to-trigger (TTT) methods in self-organizing network (SON)-based long term evolution (LTE) systems. Although TTT can mitigate the wasteful ping-pong HO effect, it also can cause undesirable radio link failure (RLF) due to delayed HO. The optimal HO timings that produce the lowest ping-pong rate within allowable RLF rate vary depending on user equipment (UE) speeds and neighboring cell configurations. To achieve efficient HO timings, we propose and investigate two methods: “adaptive” and “grouping.” In the “adaptive method,” we select the adaptive TTT value for each UE speed based on RLF rate of 2%. The “grouping method” classifies UE speeds into three ranges and assigns the proper TTT value to each range. To apply the LTE specification more effectively, we suggest the criteria of grouping, and propose the proper TTT value for each range. We consider HO in two neighboring cell configurations: from a macro cell to either a macro cell or a pico cell. Simulation results show that the HO performance of the “adaptive method” is greatly improved compared to that of applying fixed TTT values. The results also show that the performance of the “grouping method”, using proposed criteria and proper TTT values, is comparable to that of the “adaptive method.”