nils-herrmann/clean_scirepeval huggingface dataset:
The Biber Cognitive Estimation Test.

Normative data from 113 participants, and cross-validation data from 49 additional participants, are presented for the Biber Cognitive Estimation Test (BCET), a 20-item test with five estimation questions in each of four categories: time/duration, quantity, weight, and distance. In Study 1, the range of normal answers is provided for each item, and a cut-off for impaired performance is suggested. Although very low IQ or education levels would be expected to invalidate this test as a measure of estimation skills, participants in the current sample made few errors. In Study 2, a cross-validation suggested a slightly more conservative cut-off score for abnormality. Study 3 examined cognitive estimation in demented (dementia of the Alzheimer's type and dementia syndrome of Parkinson's disease) versus intact elderly participants. Results indicated that the BCET was able to distinguish between demented and intact elderly participants.
Finding the face in the crowd: an anger superiority effect.

Facial gestures have been given an increasingly critical role in models of emotion. The biological significance of interindividual transmission of emotional signals is a pivotal assumption for placing the face in a central position in these models. This assumption invited a logical corollary, examined in this article: Face-processing should be highly efficient. Three experiments documented an asymmetry in the processing of emotionally discrepant faces embedded in crowds. The results suggested that threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.
Capabilites and Reliability of LEDs and Laser Diodes

In general, high temperature testing is used to determine LED and laser diode lifetimes, even though laser diode failure mechanisms are more sensitive to increases in current density. As a measured parameter of degradation, the current density is of great significance when searching for failure modes in a laser diode. Raising the current density however, is not really indicative of lifetime since it is more likely a situation to be avoided than one that simulates normal lifetime degradation. The reliability of semiconductor sources is very dependent on the degradation modes. This report intends to summarize some of the degradation modes and capabilities of typical LEDs and laser diodes currently used in many communication and sensing systems.
Embedding Factorization Models for Jointly Recommending Items and User Generated Lists

Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (e.g., playlists and booklists). On one hand, user generated lists contain rich signal about item co-occurrence, as items within a list are usually gathered based on a specific theme. On the other hand, a user's preference over a list also indicate her preference over items within the list. We believe that 1) if the rich relevance signal within user generated lists can be properly leveraged, an enhanced recommendation for individual items can be provided, and 2) if user-item and user-list interactions are properly utilized, and the relationship between a list and its contained items is discovered, the performance of user-item and user-list recommendations can be mutually reinforced.
 Towards this end, we devise embedding factorization models, which extend traditional factorization method by incorporating item-item (item-item-list) co-occurrence with embedding-based algorithms. Specifically, we employ factorization model to capture users' preferences over items and lists, and utilize embedding-based models to discover the co-occurrence information among items and lists. The gap between the two types of models is bridged by sharing items' latent factors. Remarkably, our proposed framework is capable of solving the new-item cold-start problem, where items have never been consumed by users but exist in user generated lists. Overall performance comparisons and micro-level analyses demonstrate the promising performance of our proposed approaches.
Optimal selection of wavelet basis function applied to ECG signal denoising

Over the years ElectroCardioGram (ECG) signal has been used to assess the cardiovascular condition of humans. In practice, real time acquisition and transmission of the ECG may contain noise signals superimposed on it. In general, the signal processing algorithms employed for denoising provide optimal performance and eliminate the high frequency noise between any two beats contained in a continuous ECG signal. Despite their optimal performance, the signal processing algorithms significantly attenuate the peaks of characteristics wave of the ECG signal. This paper presents a selection procedure of mother wavelet basis functions applied for denoising of the ECG signal in wavelet domain while retaining the signal peaks close to their full amplitude. The obtained wavelet based denoised ECG signals retain the necessary diagnostics information contained in the original ECG signal.
A robust segmentation approach for noisy medical images using fuzzy clustering with spatial probability

Image segmentation plays a major role in medical imaging applications. During last decades, developing robust and efficient algorithms for medical image segmentation has been a demanding area of growing research interest. The renowned unsupervised clustering method, Fuzzy C-Means (FCM) algorithm is extensively used in medical image segmentation. Despite its pervasive use, conventional FCM is highly sensitive to noise because it segments images on the basis of intensity values. In this paper, for the segmentation of noisy medical images, an effective approach is presented. The proposed approach utilizes histogram based Fuzzy C-Means clustering algorithm for the segmentation of medical images. To improve the robustness against noise, the spatial probability of the neighboring pixels is integrated in the objective function of FCM. The noisy medical images are denoised, with the help of an effective denoising algorithm, prior to segmentation, to increase further the approach’s robustness. A comparative analysis is done between the conventional FCM and the proposed approach. The results obtained from the experimentation show that the proposed approach attains reliable segmentation accuracy despite of noise levels. From the experimental results, it is also clear that the proposed approach is more efficient and robust against noise when compared to that of the FCM.
Content-based image retrieval by integrating color and texture features

Content-based image retrieval (CBIR) has been an active research topic in the last decade. Feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a content-based image retrieval method based on an efficient integration of color and texture features. As its color features, pseudo-Zernike chromaticity distribution moments in opponent chromaticity space are used. As its texture features, rotation-invariant and scale-invariant image descriptor in steerable pyramid domain are adopted, which offers an efficient and flexible approximation of early processing in the human visual system. The integration of color and texture information provides a robust feature set for color image retrieval. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for different test DBs.
(MP)2T: Multiple People Multiple Parts Tracker

We present a method for multi-target tracking that exploits the persistence in detection of object parts. While the implicit representation and detection of body parts have recently been leveraged for improved human detection, ours is the first method that attempts to temporally constrain the location of human body parts with the express purpose of improving pedestrian tracking. We pose the problem of simultaneous tracking of multiple targets and their parts in a network flow optimization framework and show that parts of this network need to be optimized separately and iteratively, due to inter-dependencies of node and edge costs. Given potential detections of humans and their parts separately, an initial set of pedestrian tracklets is first obtained, followed by explicit tracking of human parts as constrained by initial human tracking. A merging step is then performed whereby we attempt to include part-only detections for which the entire human is not observable. This step employs a selective appearance model, which allows us to skip occluded parts in description of positive training samples. The result is high confidence, robust trajectories of pedestrians as well as their parts, which essentially constrain each other’s locations and associations, thus improving human tracking and parts detection. We test our algorithm on multiple real datasets and show that the proposed algorithm is an improvement over the state-of-the-art.
Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model

We show how to consider similarity between features for calculation of similarity of objects in the Vector Space Model (VSM) for machine learning algorithms and other classes of methods that involve similarity between objects. Unlike LSA, we assume that similarity between features is known (say, from a synonym dictionary) and does not need to be learned from the data. We call the proposed similarity measure soft similarity. Similarity between features is common, for example, in natural language processing: words, n-grams, or syntactic n-grams can be somewhat different (which makes them different features) but still have much in common: for example, words “play” and “game” are different but related. When there is no similarity between features then our soft similarity measure is equal to the standard similarity. For this, we generalize the well-known cosine similarity measure in VSM by introducing what we call “soft cosine measure”. We propose various formulas for exact or approximate calculation of the soft cosine measure. For example, in one of them we consider for VSM a new feature space consisting of pairs of the original features weighted by their similarity. Again, for features that bear no similarity to each other, our formulas reduce to the standard cosine measure. Our experiments show that our soft cosine measure provides better performance in our case study: entrance exams question answering task at CLEF. In these experiments, we use syntactic n-grams as features and Levenshtein distance as the similarity between n-grams, measured either in characters or in elements of n-grams.
Applying multi-agent technique in multi-section flexible manufacturing system

In the highly competitive market, cooperative multi-agent transaction and negotiation mechanism have become an important research topic. This paper uses multi-agent technology to construct a multi-section flexible manufacturing system (FMS) model, and utilizes simulation to build a manufacturing environment based on JADE framework for multi-agent to combine with dispatching rules, such as shortest imminent processing time (SIPT), first come first serve (FCFS) earliest due date (EDD), and Buffer Sequence. This paper finds that using multi-agent technique for multi-section FMS model can enhance the production efficiency in practice. Meanwhile, in this study, multi-agent systems combined with dynamic dispatching can be used to identify the best dispatching rules combination for achieving largest throughput, and thus it can provide the reference for production scheduling in advance.
Multi-input bidirectional DC-DC converter combining DC-link and magnetic-coupling for fuel cell systems

This paper presents a new multi-input bidirectional dc-dc converter which connects a fuel cell, storage and load by a combination of a dc-link and magnetic-coupling. A boost-dual-half-bridge and a bidirectional direct-connected switching cell are used. The topology is simple and only needs six power switches. The load and the sources are galvanically isolated. Furthermore, the proposed converter draws/injects smooth current from the fuel cell and the supercapacitor. The system is suitable for medium-power applications where simple topology, autonomous operation, compact packaging, and low cost are required. Different control schemes to manage power flow are proposed and compared. Simulation and experimental results that verify the effectiveness of the topology and its control scheme are presented. Moreover, it is shown that the idea of combining the dc-link and magnetic-coupling can be further developed to a general topology for multi-port bidirectional dc-dc converters.
A Dictionary Approach to EBSD Indexing

We propose a framework for indexing of grain and sub-grain structures in electron backscatter diffraction (EBSD) images of polycrystalline materials. The framework is based on a previously introduced physics-based forward model by Callahan and De Graef (2013) relating measured patterns to grain orientations (Euler angle). The forward model is tuned to the microscope and the sample symmetry group. We discretize the domain of the forward model onto a dense grid of Euler angles and for each measured pattern we identify the most similar patterns in the dictionary. These patterns are used to identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixels 3× 3 neighborhood. Indexing is accomplished by computing the mean orientation of the closest dictionary matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von MisesFisher distributions over the quaternionic 3-sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification. We demonstrate the proposed dictionary-based approach on a Ni-base IN100 alloy. c1 c1 Part of this work was reported in the Proceedings of the IEEE International Conference on Image Processing (ICIP), Melbourne Australia, Sept 2013.
Integrated staffing and scheduling for an aircraft line maintenance problem

This paper presents a method for constructing the workforce schedules of an aircraft maintenance company. The method integrates both the staffing and the scheduling decision. We formulate the optimization problem using a mixed integer linear programming approach and solve it heuristically using a branch-and-bound enumeration framework. Each node of the tree represents a combination of teams and team sizes. An extensive computational experiment is added to illustrate the algorithmic performance using a set of test instances that are derived from a real-life setting. We conclude this paper by discussing some results and by addressing interesting future extensions to the model.
Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule

The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.
Piriformis syndrome, diagnosis and treatment.

Piriformis syndrome (PS) is an uncommon cause of sciatica that involves buttock pain referred to the leg. Diagnosis is often difficult, and it is one of exclusion due to few validated and standardized diagnostic tests. Treatment for PS has historically focused on stretching and physical therapy modalities, with refractory patients also receiving anesthetic and corticosteroid injections into the piriformis muscle origin, belly, muscle sheath, or sciatic nerve sheath. Recently, the use of botulinum toxin (BTX) to treat PS has gained popularity. Its use is aimed at relieving sciatic nerve compression and inherent muscle pain from a tight piriformis. BTX is being used increasingly for myofascial pain syndromes, and some studies have demonstrated superior efficacy to corticosteroid injection. The success of BTX in treating PS supports the prevailing pathoanatomic etiology of the condition and suggests a promising future for BTX in the treatment of other myofascial pain syndromes.
An analytical framework for evaluating e-commerce business models and strategies

Electronic commerce or business is more than just another way to sustain or enhance existing business practices. Rather, e-commerce is a paradigm shift. It is a `̀ disruptive’’ innovation that is radically changing the traditional way of doing business. The industry is moving so fast because it operates under totally different principles and work rules in the digital economy. A general rule in e-commerce is that there is no simple prescription and almost no such thing as an established business or revenue model for companies even within the same industry. Under such conditions, an analytical framework is needed to assist e-commerce planners and strategic managers in assessing the critical success factors when formulating e-commerce business models and strategies. This research develops an analytical framework based on the theories of transaction costs and switching costs. Both demand-side and supply-side economies of scale and scope are also applied to the development of this framework. In addition, e-commerce revenue models and strategies are also discussed. Based on the analytical framework developed by this research, this paper discusses the five essential steps for e-commerce success. They are: redefine the competitive advantage; rethink business strategy; re-examine traditional business and revenue models, re-engineer the corporation and Web site; and re-invent customer service. E-commerce planners and strategic managers will be able to use the framework to analyze and evaluate the critical successful factors for e-commerce success.
Music Education and Its Impact on Students with Special Needs

Part of the Curriculum and Instruction Commons, Curriculum and Social Inquiry Commons, Disability and Equity in Education Commons,  Commons, Elementary Education and Teaching Commons, Higher Education and Teaching Commons, Junior High, Intermediate, Middle School Education and Teaching Commons, Other Educational Administration and Supervision Commons, Other Teacher Education and Professional Development Commons, Pre-Elementary, Early Childhood, Kindergarten Teacher Education Commons, Scholarship of Teaching and Learning Commons, and the Special Education Administration Commons Survey: Let us know how this paper benefits you.
Machine Learning to Balance the Load in Parallel Branch-and-Bound

We describe in this paper a new approach to parallelize branchand-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several subproblems that can be optimized separately with the goal that the amount of work that each processor carries out is balanced between the processors, while achieving interesting speedups. The main innovation of our approach consists in the use of machine learning to create a function able to estimate the difficulty (number of nodes) of a subproblem of the original problem. We also present a set of features that we developed in order to characterize the encountered subproblems. These features are used as input of the function learned with machine learning in order to estimate the difficulty of a subproblem. The estimates of the numbers of nodes are then used to decide how to partition the original optimization tree into a given number of subproblems, and to decide how to distribute them among the available processors. The experiments that we carry out show that our approach succeeds in balancing the amount of work between the processors, and that interesting speedups can be achieved with little effort.
In-Store Gamification: Testing a Location-Based Treasure Hunt App in a Real Retailing Environment

Traditional retailers are facing strong competition from e-commerce. One way to meet this challenge is to follow the marketing movement of focusing on customer experiences. This transformation is based on the notion of engaging customers and one way to drive this engagement is through gamification to support value creation. In this study, we have identified variables affecting intentions to use gamified services and in what ways. For this purpose, we developed an app that generated different levels of gamification by varying the number of game elements. The data from a survey distributed during a field experiment indicates that an increasing level of gamification and technology experience have direct positive associations with intrinsic motivation. Furthermore, intrinsic motivation has a positive direct association with satisfaction, although this is partly mediated by mood. Finally, satisfaction has a positive direct relation with intention to use.
NIGERIAN YOUTHS AND SOCIAL MEDIA : HARNESSING THE POTENTIALS FOR ACADEMIC EXCELLENCE

One of the breakthroughs in information and communication technology in the 21st century was the discovery and emergence of the new media which have facilitated the creation of the different platforms for social interaction. The potentials of the new media are seamless and boundless in terms of interactions, interrelationships, and information sharing and exchanges. The only limitation to the utilization of the potentials of the platforms is illiteracy in terms of the hardware, software and usage. The question is, to what extent has the Nigerian youths converted the potentials and possibilities of the social media to platforms for networking academic materials for scholarship and academic excellence? The study examined the extent of usage of one of the most popular social media platforms, facebook, by the Nigerian youths in their academic pursuit. It found that about two-thirds of the Nigerian youths that use Facebook platform use it for other reasons than exchange of academic materials. And, recommends that the youths should be encouraged to harness the potentials of the platform for scholarship.