nils-herrmann/clean_scirepeval huggingface dataset:
Too much of a good thing: curvilinear relationships between personality traits and job performance.

The relationships between personality traits and performance are often assumed to be linear. This assumption has been challenged conceptually and empirically, but results to date have been inconclusive. In the current study, we took a theory-driven approach in systematically addressing this issue. Results based on two different samples generally supported our expectations of the curvilinear relationships between personality traits, including Conscientiousness and Emotional Stability, and job performance dimensions, including task performance, organizational citizenship behavior, and counterproductive work behaviors. We also hypothesized and found that job complexity moderated the curvilinear personality–performance relationships such that the inflection points after which the relationships disappear were lower for low-complexity jobs than they were for high-complexity jobs. This finding suggests that high levels of the two personality traits examined are more beneficial for performance in high- than low-complexity jobs. We conclude by discussing the implications of these findings for the use of personality in personnel selection.
Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

The purpose of this study is to give a detailed performance comparison about the feature detector and descriptor methods, particularly when their various combinations are used for image matching. As the case study, the localization experiments of a mobile robot in an indoor environment are given. In these experiments, 3090 query images and 127 dataset images are used. This study includes five methods for feature detectors such as features from accelerated segment test (FAST), oriented FAST and rotated binary robust independent elementary features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant feature transform (SIFT), binary robust invariant scalable keypoints (BRISK), and five other methods for feature descriptors which are BRIEF, BRISK, SIFT, SURF, and ORB. These methods are used in 23 different combinations and it was possible to obtain meaningful and consistent comparison results using some performance criteria defined in this study. All of these methods are used independently and separately from each other as being feature detector or descriptor. The performance analysis shows the discriminative power of various combinations of detector and descriptor methods. The analysis is completed using five parameters such as (i) accuracy, (ii) time, (iii) angle difference between keypoints, (iv) number of correct matches, and (v) distance between correctly matched keypoints. In a range of 60°, covering five rotational pose points for our system, “FAST-SURF” combination gave the best results with the lowest distance and angle difference values and highest number of matched keypoints. The combination “SIFT-SURF” is obtained as the most accurate combination with 98.41% of correct classification rate. The fastest algorithm is achieved with “ORB-BRIEF” combination with a total running time 21303.30 seconds in order to match 560 images captured during the motion with 127 dataset images.
Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding

We address the problem of dense visual-semantic embedding that maps not only full sentences and whole images but also phrases within sentences and salient regions within images into a multimodal embedding space. Such dense embeddings, when applied to the task of image captioning, enable us to produce several region-oriented and detailed phrases rather than just an overview sentence to describe an image. Specifically, we present a hierarchical structured recurrent neural network (RNN), namely Hierarchical Multimodal LSTM (HM-LSTM). Compared with chain structured RNN, our proposed model exploits the hierarchical relations between sentences and phrases, and between whole images and image regions, to jointly establish their representations. Without the need of any supervised labels, our proposed model automatically learns the fine-grained correspondences between phrases and image regions towards the dense embedding. Extensive experiments on several datasets validate the efficacy of our method, which compares favorably with the state-of-the-art methods.
Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities

We propose Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect of our design is to first view the structured data source as meaningful unstructured text, and then use the text to build an unsupervised neural network model using a Natural Language Processing (NLP) technique called word embedding. This model captures the hidden inter-/intra-column relationships between database tokens of different types. For each database token, the model includes a vector that encodes contextual semantic relationships. We seamlessly integrate the word embedding model into existing SQL query infrastructure and use it to enable a new class of SQL-based analytics queries called cognitive intelligence (CI) queries. CI queries use the model vectors to enable complex queries such as semantic matching, inductive reasoning queries such as analogies, predictive queries using entities not present in a database, and, more generally, using knowledge from external sources. We demonstrate unique capabilities of Cognitive Databases using an Apache Spark based prototype to execute inductive reasoning CI queries over a multi-modal database containing text and images. We believe our first-of-a-kind system exemplifies using AI functionality to endow relational databases with capabilities that were previously very hard to realize in practice.
Training-Free, Single-Image Super-Resolution Using a Dynamic Convolutional Network

The typical approach for solving the problem of single-image super-resolution (SR) is to learn a nonlinear mapping between the low-resolution (LR) and high-resolution (HR) representations of images in a training set. Training-based approaches can be tuned to give high accuracy on a given class of images, but they call for retraining if the HR <inline-formula><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> LR generative model deviates or if the test images belong to a different class, which limits their applicability. On the other hand, we propose a solution that does not require a training dataset. Our method relies on constructing a dynamic convolutional network (DCN) to learn the relation between the consecutive scales of Gaussian and Laplacian pyramids. The relation is in turn used to predict the detail at a finer scale, thus leading to SR. Comparisons with state-of-the-art techniques on standard datasets show that the proposed DCN approach results in about 0.8 and 0.3 dB gain in peak signal-to-noise ratio for <inline-formula><tex-math notation="LaTeX">$2\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$3\times$</tex-math></inline-formula> SR, respectively. The structural similarity index is on par with the competing techniques.
A Direct Brain-to-Brain Interface in Humans

We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the brain. We illustrate our method using a visuomotor task in which two humans must cooperate through direct brain-to-brain communication to achieve a desired goal in a computer game. The brain-to-brain interface detects motor imagery in EEG signals recorded from one subject (the "sender") and transmits this information over the internet to the motor cortex region of a second subject (the "receiver"). This allows the sender to cause a desired motor response in the receiver (a press on a touchpad) via TMS. We quantify the performance of the brain-to-brain interface in terms of the amount of information transmitted as well as the accuracies attained in (1) decoding the sender's signals, (2) generating a motor response from the receiver upon stimulation, and (3) achieving the overall goal in the cooperative visuomotor task. Our results provide evidence for a rudimentary form of direct information transmission from one human brain to another using non-invasive means.
Silhouette and Stereo Fusion for 3D Object Modeling

In this paper we present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both the 3D geometry and the texture. The core of the method is based on a deformable model, which defines the framework where texture and silhouette information can be fused. This is achieved by defining two external forces based on the images: a texture driven force and a silhouette driven force. The texture force is computed in two steps: a multi-stereo correlation voting approach and a gradient vector flow diffusion. Due to the high resolution of the voting approach, a multi-grid version of the gradient vector flow has been developed. Concerning the silhouette force, a new formulation of the silhouette constraint is derived. It provides a robust way to integrate the silhouettes in the evolution algorithm. As a consequence, we are able to recover the apparent contours of the model at the end of the iteration process. Finally, a texture map is computed from the original images for the reconstructed 3D model.
Trajectory Tracking Control for a 3-DOF Planar Parallel Manipulator Using the Convex Synchronized Control Method

In this paper, in order to satisfy multiple closed-loop performance specifications simultaneously while improving tracking accuracy during high-speed, high-acceleration tracking motions of a 3-degree-of-freedom (3-DOF) planar parallel manipulator, we propose a new control approach, termed convex synchronized (C-S) control. This control strategy is based on the so-called convex combination method, in which the synchronized control method is adopted. Through the adoption of a set of n synchronized controllers, each of which is tuned to satisfy at least one of a set of n closed-loop performance specifications, the resultant set of n closed-loop transfer functions are combined in a convex manner, from which a C-S controller is solved algebraically. Significantly, the resultant C-S controller simultaneously satisfies all n closed-loop performance specifications. Since each synchronized controller is only required to satisfy at least one of the n closed-loop performance specifications, the convex combination method is more efficient than trial-and-error methods, where the gains of a single controller are tuned to satisfy all n closed-loop performance specifications simultaneously. Furthermore, during the design of each synchronized controller, a feedback signal, termed the synchronization error, is employed. Different from the traditional tracking errors, this synchronization error represents the degree of coordination of the active joints in the parallel manipulator based on the manipulator kinematics. As a result, the trajectory tracking accuracy of each active joint and that of the manipulator end-effector is improved. Thus, possessing both the advantages of the convex combination method and synchronized control, the proposed C-S control method can satisfy multiple closed-loop performance specifications simultaneously while improving tracking accuracy. In addition, unavoidable dynamic modeling errors are addressed through the introduction of a robust performance specification, which ensures that all performance specifications are satisfied despite allowable variations in dynamic parameters, or modeling errors. Experiments conducted on a 3-DOF P-R-R-type planar parallel manipulator demonstrate the aforementioned claims.
An empirical investigation of employee portal success

Employee portals are utilized by many companies to improve companies’ information exchange, communication, and employee collaboration, as well as to better support their business processes. Owing to limited IT budgets and the need to justify investments in employee portals, assessing the benefits of these is an important field in research and practice. Thus, the purpose of this study is to gain a better understanding of employee portal success. We introduce a theoretical model for this that is based on the DeLone and McLean IS Success Model. Furthermore, we develop hypotheses regarding the associations between different models’ success dimensions and test them using more than 10,000 employees’ responses collected in a survey of 22 companies. Our results indicate that besides the factors contributing to IS success in general, other success dimensions – like the quality of the collaboration and process support – have to be considered when aiming for a successful employee portal. The study’s findings make it possible for practitioners to understand the levers with which to improve their employee portals. By empirically validating a comprehensive success model for employee portals, the study’s results advance theoretical development in the area of collaboration-centered systems and present a basis for further research in this field.
Tacit Knowledge and Knowledge Management : The Keys to Sustainable Competitive Advantage

B competitive advantage is increasingly found in knowing how to do things, rather than in having special access to resources and markets, knowledge and intellectual capital have become both the primary bases of core competencies and the key to superior performance. This article explores how companies can best grow their knowledge resources to create not simply competitive advantage, but sustainable competitive advantage. In recent years, the development of “hypercompetition” and shortened product lifecycles have reduced the degree to which much special knowledge can provide companies with sustained competitive advantage. Shrinking logistical and communication costs, along with new organizational designs, have enabled multinational corporations (MNCs) to function as truly global companies, rather than as conglomerations of national ones. Global companies introduce their newest products worldwide and effectively share knowledge across country units. Fueling hypercompetition, many industries now have several MNCs competing against each other on a worldwide basis, rather than a few local companies and only one MNC competing in each market. New product innovation has become the key to competing successfully. MNCs use their deep pockets to fund research and development (R&D), and they reverse engineer each other’s products and turn to consultants to learn about best practices in their industry. Companies develop or acquire new knowledge so rapidly that having special knowledge is no longer a basis for sustainable competitive advantage. To provide sustained competitive advantage, one needs knowledge that is difficult for outsiders to copy as well as the ability to rapidly develop new knowledge. In a graphic demonstration of the transient value of much knowledge, Michael Tushman began his courses at Columbia Business School by asking what the following list of products had in common: watches, cars, cameras, color TVs, hand tools, radial tires, industrial robots, machine tools, electric motors, financial services, food processors, microwave ovens, stereo equipment, athletic equipment, computer chips, optical equipment, medical equipment, and consulting services. The answer: each of these industries was initially dominated by a company with specialized knowledge, but that company rapidly lost its lead as other companies acquired the knowledge required to compete. Also illustrating the situation, fewer than 40% of the Fortune 500 companies of 1970 still existed in their original form by 1991. In today’s environment, much of the Organizational Dynamics, Vol. 29, No. 4, pp. 164–178, 2001 ISSN 0090-2616/01/$–see frontmatter
Symmetry and aesthetics in website design: It's a man's business

In recent years the aesthetic appearance of user-interfaces has become a topic of increasing interest in the field of human–computer interaction (HCI). At the same time, it is well known that there are genderspecific effects in HCI. The present study investigates the effect of web page symmetry by taking gender differences into account. A total of 60 people (30 male, 30 female) participated in a laboratory experiment, where 20 website startpages had to be rated regarding symmetry, intuitive beauty, classical and expressive aesthetics. Results show that vertical symmetry is an important factor in aesthetic website design. It has an impact on intuitive straightforward beauty appraisals and on classical and expressive aesthetics judgments. Asymmetrically designed web pages were considered to be less beautiful and achieved lower scores on the classical and expressive dimensions. Moreover, the study indicates that the symmetry effect only occurs among male participants: only men react unfavorably to asymmetrically designed websites, whereas women’s judgments are not influenced by symmetry. These findings should be taken into consideration by website designers, especially when they aim to design for target audience consisting of a majority of a certain gender.
BestConfig: tapping the performance potential of systems via automatic configuration tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise.
 To help users tap the performance potential of systems, we present Best Config, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment.
Cloud radio access network: Virtualizing wireless access for dense heterogeneous systems

Cloud radio access network (C-RAN) refers to the visualization of base station functionalities by means of cloud computing. This results in a novel cellular architecture in which low-cost wireless access points, known as radio units or remote radio heads, are centrally managed by a reconfigurable centralized "cloud", or central, unit. C-RAN allows operators to reduce the capital and operating expenses needed to deploy and maintain dense heterogeneous networks. This critical advantage, along with spectral efficiency, statistical multiplexing and load balancing gains, make C-RAN well positioned to be one of the key technologies in the development of 5G systems. In this paper, a succinct overview is presented regarding the state of the art on the research on C-RAN with emphasis on fronthaul compression, baseband processing, medium access control, resource allocation, system-level considerations and standardization efforts.
Leucocyte classification for leukaemia detection using image processing techniques

The counting and classification of blood cells allow for the evaluation and diagnosis of a vast number of diseases. The analysis of white blood cells (WBCs) allows for the detection of acute lymphoblastic leukaemia (ALL), a blood cancer that can be fatal if left untreated. Currently, the morphological analysis of blood cells is performed manually by skilled operators. However, this method has numerous drawbacks, such as slow analysis, non-standard accuracy, and dependences on the operator's skill. Few examples of automated systems that can analyse and classify blood cells have been reported in the literature, and most of these systems are only partially developed. This paper presents a complete and fully automated method for WBC identification and classification using microscopic images.



In contrast to other approaches that identify the nuclei first, which are more prominent than other components, the proposed approach isolates the whole leucocyte and then separates the nucleus and cytoplasm. This approach is necessary to analyse each cell component in detail. From each cell component, different features, such as shape, colour and texture, are extracted using a new approach for background pixel removal. This feature set was used to train different classification models in order to determine which one is most suitable for the detection of leukaemia.



Using our method, 245 of 267 total leucocytes were properly identified (92% accuracy) from 33 images taken with the same camera and under the same lighting conditions. Performing this evaluation using different classification models allowed us to establish that the support vector machine with a Gaussian radial basis kernel is the most suitable model for the identification of ALL, with an accuracy of 93% and a sensitivity of 98%. Furthermore, we evaluated the goodness of our new feature set, which displayed better performance with each evaluated classification model.



The proposed method permits the analysis of blood cells automatically via image processing techniques, and it represents a medical tool to avoid the numerous drawbacks associated with manual observation. This process could also be used for counting, as it provides excellent performance and allows for early diagnostic suspicion, which can then be confirmed by a haematologist through specialised techniques.
Accurate Measurement of the Air-Core Inductance of Iron-Core Transformers With a Non-Ideal Low-Power Rectifier

The air-core inductance of power transformers is measured using a nonideal low-power rectifier. Its dc output serves to drive the transformer into deep saturation, and its ripple provides low-amplitude variable excitation. The principal advantage of the proposed method is its simplicity. For validation, the experimental results are compared with 3-D finite-element simulations.
Complete Solution Classification for the Perspective-Three-Point Problem

In this paper, we use two approaches to solve the Perspective-Three-Point (P3P) problem: the algebraic approach and the geometric approach. In the algebraic approach, we use Wu-Ritt’s zero decomposition algorithm to give a complete triangular decomposition for the P3P equation system. This decomposition provides the first complete analytical solution to the P3P problem. We also give a complete solution classification for the P3P equation system, i.e., we give explicit criteria for the P3P problem to have one, two, three, and four solutions. Combining the analytical solutions with the criteria, we provide an algorithm, CASSC, which may be used to find complete and robust numerical solutions to the P3P problem. In the geometric approach, we give some pure geometric criteria for the number of real physical solutions.