nils-herrmann/clean_scirepeval huggingface dataset:
Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.
On the Foundations of the Universal Relation Model

The universal relation model aims at achieving complete access-path independence in relational databases by relieving the user of the need for logical navigation among relations. We clarify the assumptions underlying it and explore the approaches suggested for implementing it.
The essential idea of the universal relation model is that access paths are embedded in attribute names. Thus attribute names must play unique “roles.” Furthermore, it assumes that for every set of attributes there is a basic relationship that the user has in mind. The user's queries refer to these basic relationships rather than to the underlying database.
Two fundamentally different approaches to the universal relation model have been taken. According to the first approach, the user's view of the database is a universal relation or many universal relations, about which the user poses queries. The second approach sees the model as having query-processing capabilities that relieve the user of the need to specify the logical access path. Thus, while the first approach gives a denotational semantics to query answering, the second approach gives it an operational semantics. We investigate the relationship between these two approaches.
Graph Clustering Bandits for Recommendation

We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users. Our algorithm dynamically groups users based on their observed behavioral similarity during a sequence of logged activities. In doing so, the algorithm reacts to the currently served user by shaping clusters around him/her but, at the same time, it explores the generation of clusters over users which are not currently engaged. We motivate the effectiveness of this clustering policy, and provide an extensive empirical analysis on real-world datasets, showing scalability and improved prediction performance over state-ofthe-art methods for sequential clustering of users in multi-armed bandit scenarios.
Solving NP-Hard Problems with Physarum-Based Ant Colony System

NP-hard problems exist in many real world applications. Ant colony optimization ACO algorithms can provide approximate solutions for those NP-hard problems, but the performance of ACO algorithms is significantly reduced due to premature convergence and weak robustness, etc. With these observations in mind, this paper proposes a  Physarum-based pheromone matrix optimization strategy in ant colony system ACS for solving NP-hard problems such as traveling salesman problem TSP and 0/1 knapsack problem 0/1 KP. In the Physarum-inspired mathematical model, one of the unique characteristics is that critical tubes can be reserved in the process of network evolution. The optimized updating strategy employs the unique feature and accelerates the positive feedback process in ACS, which contributes to the quick convergence of the optimal solution. Some experiments were conducted using both benchmark and real datasets. The experimental results show that the optimized ACS outperforms other meta-heuristic algorithms in accuracy and robustness for solving TSPs. Meanwhile, the convergence rate and robustness for solving 0/1 KPs are better than those of classical ACS.
Fusion of multi-sensor imagery for night vision: color visualization, target learning and search

1 This work was sponsored by the U.S. Defense Advanced Research Projects Agency, under Air Force Contract F19628-95-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and not necessarily endorsed by the U.S. Air Force. 2 Present Address: MCIS Department, Jacksonville State University, Jacksonville, AL 36265, U.S.A.  We present methods and results for fusion of imagery from multiple sensors to create a color night vision capability. The fusion system architectures are based on biological models of the spatial and opponentcolor processes in the human retina and visual cortex, implemented as shunting center-surround feed-forward neural networks. Real-time implementation of the dualsensor fusion system combines imagery from either a low-light CCD camera or a short-wave infrared camera, with thermal long-wave infrared imagery. Results are also shown for extensions of this fusion architecture to include imagery from all three of these sensors, Visible/SWIR/LWIR, as well as a four sensor system using Visible/SWIR/MWIR/LWIR cameras. We also demonstrate how results from these multi-sensor fusion systems are used as inputs to an interactive tool for target designation, learning, and search based on a Fuzzy ARTMAP neural network.
Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding

Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative and quantitative analysis demonstrate the high quality of the word, entity and multi-prototype mention embeddings. Using entity linking as a study case, we apply our disambiguation method as well as the multi-prototype mention embeddings on the benchmark dataset, and achieve state-of-the-art performance.
A review and analysis of churn prediction methods for customer retention in telecom industries

Customer churn prediction has gathered greater interest in business especially in telecommunications industries. Many authors have presented different versions of the churn prediction models greatly based on the data mining concepts employing the machine learning and meta-heuristic algorithms. This aim of this paper is to study some of the most important churn prediction techniques developed over the recent years. The primary objective is on the churn in telecom industries to accurately estimate the customer survival and customer hazard functions to gain the complete knowledge of churn over the customer tenure. Another objective is the identification of the customers who are at the blink of churn and approximating the time they will churn. This paper focuses on analyzing the churn prediction techniques to identify the churn behavior and validate the reasons for customer churn. This paper summarizes the churn prediction techniques in order to have a deeper understanding of the customer churn and it shows that most accurate churn prediction is given by the hybrid models rather than single algorithms so that telecom industries become aware of the needs of high risk customers and enhance their services to overturn the churn decision.
Four central questions about prediction in language processing

The notion that prediction is a fundamental principle of human information processing has been en vogue over recent years. The investigation of language processing may be particularly illuminating for testing this claim. Linguists traditionally have argued prediction plays only a minor role during language understanding because of the vast possibilities available to the language user as each word is encountered. In the present review I consider four central questions of anticipatory language processing: Why (i.e. what is the function of prediction in language processing)? What (i.e. what are the cues used to predict up-coming linguistic information and what type of representations are predicted)? How (what mechanisms are involved in predictive language processing and what is the role of possible mediating factors such as working memory)? When (i.e. do individuals always predict up-coming input during language processing)? I propose that prediction occurs via a set of diverse PACS (production-, association-, combinatorial-, and simulation-based prediction) mechanisms which are minimally required for a comprehensive account of predictive language processing. Models of anticipatory language processing must be revised to take multiple mechanisms, mediating factors, and situational context into account. Finally, I conjecture that the evidence considered here is consistent with the notion that prediction is an important aspect but not a fundamental principle of language processing. This article is part of a Special Issue entitled SI: Prediction and Attention.
Using flipped classroom approach to teach computer programming

Flipped classroom approach has been increasingly adopted in higher institutions. Although this approach has many advantages, there are also many challenges that should be considered. In this paper, we discuss the suitability of this approach to teach computer programming, and we report on our pilot experience of using this approach at Qatar University to teach one subject of computer programming course. It is found that students has positive attitude to this approach, it improves their learning. However, the main challenge was how to involve some of the students in online learning activities.
Attention and the detection of signals.

Detection of a visual signal requires information to reach a system capable of eliciting arbitrary responses required by the experimenter. Detection latencies are reduced when subjects receive a cue that indicates where in the visual field the signal will occur. This shift in efficiency appears to be due to an alignment (orienting) of the central attentional system with the pathways to be activated by the visual input. It would also be possible to describe these results as being due to a reduced criterion at the expected target position. However, this description ignores important constraints about the way in which expectancy improves performance. First, when subjects are cued on each trial, they show stronger expectancy effects than when a probable position is held constant for a block, indicating the active nature of the expectancy. Second, while information on spatial position improves performance, information on the form of the stimulus does not. Third, expectancy may lead to improvements in latency without a reduction in accuracy. Fourth, there appears to be little ability to lower the criterion at two positions that are not spatially contiguous. A framework involving the employment of a limited-capacity attentional mechanism seems to capture these constraints better than the more general language of criterion setting. Using this framework, we find that attention shifts are not closely related to the saccadic eye movement system. For luminance detection the retina appears to be equipotential with respect to attention shifts, since costs to unexpected stimuli are similar whether foveal or peripheral. These results appear to provide an important model system for the study of the relationship between attention and the structure of the visual system.
Fan-Beam Millimeter-Wave Antenna Design Based on the Cylindrical Luneberg Lens

A new design of two-dimensional cylindrical Luneberg lens is introduced based on TE10 mode propagation between parallel plates, with special focus on ease of manufacturing. The parallel plates are partially filled with low cost polymer material (Rexolite epsivr = 2.54) to match Luneberg's law. A planar linear tapered slot antenna (LTSA) is inserted into the air region between the parallel plates at the edge of the Luneberg lens as a feed antenna, with fine positioning to the focal point of the Luneberg lens to optimize the antenna system performance. A combined ray-optics/diffraction method is used to obtain the radiation pattern of the system and results are compared with predictions of a time domain numerical solver. Measurements done on a 10-cm Luneberg lens designed for operation at 30 GHz agree very well with predictions. For this prototype, 3-dB E- and if-plane beamwidths of 6.6deg and 54deg respectively were obtained, and the sidelobe level in the E-plane was -17.7-dB. Although the parallel plate configuration should lead to a narrow band design due to the dispersion characteristics of the TE10 mode, the measurement results demonstrate broadband characteristics with radiation efficiencies varying between 43% and 72% over the tested frequency band of 26.5-37 GHz. The designed cylindrical Luneberg lens can be used to launch multiple beams by implementing an arc array of planar LTSA elements at the periphery of the lens, and can be easily extended to higher mm-wave frequencies.
Autism , social cognition and superior temporal sulcus *

Results on brain imaging studies have led to a better understanding of the neural circuits involved in social cognition and its implication in autism spectrum disorders (ASD). It has been shown that the superior temporal sulcus (STS) is highly implicated in social processes, from perception of socially relevant information, such as body movements or eye gaze, to more complex social cognition processes. Furthermore, several multimodal brain imaging results point to anatomo-functional abnormalities in the STS in both children and adults with ASD. These results are highly consistent with social impairments in ASD, among which eye gaze perception is particularly relevant. Gaze abnormalities can now be objectively measured using eye-tracking methodology, leading to a better characterization of social perception impairments in autism. Moreover, these gaze abnormalities have been associated with STS abnormalities in ASD. Based on these results, our hypothesis is that anatomo-functional anomalies in the STS occurring early across brain development could constitute the first step in the cascade of neural dysfunction underlying autism. In the present work, we’ll review recent data of STS contribution to normal social cognition and it’s implication in autism.
Digital Image Authentication Model Based on Edge Adaptive Steganography

With the rapid growth of internet in the present world, social networking websites like Twitter, Facebook and Instagram are being increasingly used by people to share their life moments and events via images. But this vastly popular phenomenon of image sharing on internet is exposed to many risks. At present, these social networking websites are not bound by stringent privacy and copyright laws. Thus original image, once shared, can be easily downloaded and edited by the miscreants. Modified images can be then uploaded, morphed or shared further without the consent of the original owner of the image. Validation of the ownership of these images is practically impossible due to high degree of replication. This calls for an urgent requirement of an image authentication system. A digital image authentication system using adaptive steganography (Edge detection with variable LSB as the embedding technique) has been proposed in this paper. In the proposed technique the secret data related to person's login detail and current timestamp will be hidden in the original image using steganography without affecting the visual quality of the image. The information behind that image can be decoded only by the authorized party containing the original data of the person. This will validate the source and ownership of the shared image. It has been shown by the experimental results that the quality of image is not degraded even after the addition of secret data.
Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning

Pilot contamination posts a fundamental limit on the performance of massive multiple-input-multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.
Firefly Algorithm for Continuous Constrained Optimization Tasks

The paper provides an insight into the improved novel metaheuristics of the Firefly Algorithm for constrained continuous optimization tasks. The presented technique is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication. The first part of the paper is devoted to the detailed description of the existing algorithm. Then some suggestions for extending the simple scheme of the technique under consideration are presented. Subsequent sections concentrate on the performed experimental parameter studies and a comparison with existing Particle Swarm Optimization strategy based on existing benchmark instances. Finally some concluding remarks on possible algorithm extensions are given, as well as some properties of the presented approach and comments on its performance in the constrained continuous optimization tasks.
The brain circuitry of attention

From an operational perspective, attention is a matter of organizing multiple brain centres to act in concert on the task at hand. Taking focal visual attention as an example, recent anatomical findings suggest that the pulvinar might act as a remote hub for coordinating spatial activity within multiple cortical visual maps. The pulvinar can, in turn, be influenced by signals originating in the frontal and parietal eye fields, using common visuomotor neural circuitry, with the superior colliculus acting as an important link. By identifying a complex, real neural architecture ('RNA') model for attention, it is possible to integrate several different modes of operation - such as parallel or serial, bottom-up or top-down, preattentive or attentive - that characterize conflicting cognitive models of attention in visual search paradigms.
Organizational Use of Big Data and Competitive Advantage - Exploration of Antecedents

The use of Big Data can hold considerable benefits for organizations, but there is as yet little research on the impacts of Big Data use on key variables such as competitive advantage. The antecedents to Big Data use aiming at competitive advantage are also poorly understood. Drawing from prior research on the ICT-competitiveness link, this research examined the relationship of organizational use of Big Data and competitive advantage – as well as certain hypothesized antecedents to this relationship. Data was collected from a nationwide survey in Japan which was addressed to Big Data users in private companies. The result shows that the competitiveness is influenced by the systematic and extensive use of Big Data in the organization, and top management understanding for Big Data use. Antecedents of systematic and extensive Big Data use were found to be the cross-functional activities of analysts, proactive attitude of users, secured data management, and human resources. Future research should strive to verify these respondent opinions using longitudinal approaches based on objective financial data.
Nerve entrapment syndromes as a cause of pain in the hip, groin and buttock.

In sports medicine, chronic hip, groin and buttock pain is a common diagnostic problem. Because of the complex anatomy of this region and the many potential neurological causes for pain, few sports clinicians have a detailed understanding of this problem. This paper discusses the clinical aspects of nerve entrapment syndromes related to sport and takes a regional approach in order to provide a diagnostic framework for the general sports physician. The various neurological syndromes are discussed and the surgical management elaborated in detail. For some specific conditions, such as the so-called 'piriformis syndrome', the pathophysiological understanding has changed since the early descriptions and now this particular diagnosis is often ascribed to almost any cause of buttock and/or hamstring symptoms. We discuss the nature of the origin of local symptoms and note that the often described symptoms are more likely due to compression of structures other then the sciatic nerve. Furthermore, the role of piriformis hypertrophy or anatomical nerve variations in the genesis of this syndrome must be questioned. We suggest renaming this the 'deep gluteal syndrome' to account for all of the observed phenomena. As sports medicine continues to develop a scientific basis, the role of nerve entrapments as the basis for chronic symptomatology is undergoing a new understanding and clinicians need to be aware of the diagnostic possibilities and be able to advise patients accordingly on the basis of scientific fact not anecdotal fiction.
Mining HEXACO personality traits from Enterprise Social Media

In this paper we introduce a novel computational technique of extraction of personality traits (HEXACO) of employees from Enterprise Social Media posts. We deal with challenges such as not being able to use existing survey instruments for scoring and not being able to directly use existing psychological studies on written text due to lack of overlapping words between the existing dictionary and words used in Enterprise Social Media. Using our approach we are able to infer personality traits (HEXACO) from posts and find better coverage and usage of the extended dictionary.