nils-herrmann/clean_scirepeval huggingface dataset:
A Simple Language Model based on PMI Matrix Approximations

In this study, we introduce a new approach for learning language models by training them to estimate word-context pointwise mutual information (PMI), and then deriving the desired conditional probabilities from PMI at test time. Specifically, we show that with minor modifications to word2vec’s algorithm, we get principled language models that are closely related to the well-established Noise Contrastive Estimation (NCE) based language models. A compelling aspect of our approach is that our models are trained with the same simple negative sampling objective function that is commonly used in word2vec to learn word embeddings.
PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions

Given a set D of tuples defined on a domain Omega, we study differentially private algorithms for constructing a histogram over Omega to approximate the tuple distribution in D. Existing solutions for the problem mostly adopt a hierarchical decomposition approach, which recursively splits Omega into sub-domains and computes a noisy tuple count for each sub-domain, until all noisy counts are below a certain threshold. This approach, however, requires that we (i) impose a limit h on the recursion depth in the splitting of Omega and (ii) set the noise in each count to be proportional to h. The choice of h is a serious dilemma: a small h makes the resulting histogram too coarse-grained, while a large h leads to excessive noise in the tuple counts used in deciding whether sub-domains should be split. Furthermore, h cannot be directly tuned based on D; otherwise, the choice of h itself reveals private information and violates differential privacy. To remedy the deficiency of existing solutions, we present PrivTree, a histogram construction algorithm that adopts hierarchical decomposition but completely eliminates the dependency on a pre-defined h. The core of PrivTree is a novel mechanism that (i) exploits a new analysis on the Laplace distribution and (ii) enables us to use only a constant amount of noise in deciding whether a sub-domain should be split, without worrying about the recursion depth of splitting. We demonstrate the application of PrivTree in modelling spatial data, and show that it can be extended to handle sequence data (where the decision in sub-domain splitting is not based on tuple counts but a more sophisticated measure). Our experiments on a variety of real datasets show that PrivTree considerably outperforms the states of the art in terms of data utility.
Effectiveness of virtual reality exposure therapy for active duty soldiers in a military mental health clinic.

Exposure therapy is an evidence-based treatment for posttraumatic stress disorder (PTSD), but research evaluating its effectiveness with active duty service members is limited. This report examines the effectiveness of virtual reality exposure therapy (VRE) for active duty soldiers (N = 24) seeking treatment following a deployment to Iraq or Afghanistan. Relative to their pretreatment self-reported symptoms on the PTSD Checklist, Military Version (M = 60.92; SD = 11.03), patients reported a significant reduction at posttreatment (M = 47.08; SD = 12.70; p < .001). Sixty-two percent of patients (n = 15) reported a reliable change of 11 points or more. This study supports the effectiveness of exposure therapy for active duty soldiers and extends previous research on VRE to this population.
Diabetes Prediction by using Bacterial Foraging Optimization Algorithm And Artificial Neural Network

Diagnosis of any diseases earlier is always preferable .diabetes is one such diseases.it reaches to the epidemic proportional in many developing countries and also reaching to industrialized nations. In this study, we investigate a programmable method to analyze diabetes diseases based on BFO and ANN.ANN is a powerful tool and solves the problems such as classification and prediction. Its performance is highly dependent on its architecture and weights. To gain high efficiency in ANN, an appropriate Architecture and learning Algorithm is preferable. The experiments on dataset occupied from undertaking repository to verify the potential of BFO algorithm and showed that BFO-ANN is more accurate than traditional ANN in terms of classification. General Terms-Artificial Neural Network, BacterialForaging Optimization Algorithm.
Neural nets versus conventional techniques in credit scoring in Egyptian banking

Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank’s personal loans’ data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques.
A comprehensive physical model for light reflection

A new general reflectance model for computer graphics is presented. The model is based on physical optics and describes specular, directional diffuse, and uniform diffuse reflection by a surface. The reflected light pattern depends on wavelength, incidence angle, two surface roughness parameters, and surface refractive index. The formulation is self consistent in terms of polarization, surface roughness, masking/shadowing, and energy. The model applies to a wide range of materials and surface finishes and provides a smooth transition from diffuse-like to specular reflection as the wavelength and incidence angle are increased or the surface roughness is decreased. The model is analytic and suitable for Computer Graphics applications. Predicted reflectance distributions compare favorably with experiment. The model is applied to metallic, nonmetallic, and plastic materials, with smooth and rough surfaces.
Dynamic Adaptive Streaming over HTTP/2.0

MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard that has been recently ratified as an international standard (IS). In comparison to other streaming systems, e.g., HTTP progressive download, DASH is able to handle varying bandwidth conditions providing smooth streaming. Furthermore, it enables NAT and Firewall traversal, flexible and scalable deployment as well as reduced infrastructure costs due to the reuse of existing Internet infrastructure components, e.g., proxies, caches, and Content Distribution Networks (CDN). Recently, the Hypertext Transfer Protocol Bis (httpbis) working group of the IETF has officially started the development of HTTP 2.0. Initially three major proposals have been submitted to the IETF i.e., Googles' SPDY, Microsofts' HTTP Speed+Mobility and Network-Friendly HTTP Upgrade, but SPDY has been chosen as working draft for HTTP 2.0. In this paper we implemented MPEG-DASH over HTTP 2.0 (i.e., SPDY), demonstrating its potential benefits and drawbacks. Moreover, several experimental evaluations have been performed that compare HTTP 2.0 with HTTP 1.1 and HTTP 1.0 in the context of DASH. In particular, the protocol overhead, the performance for different round trip times, and DASH with HTTP 2.0 in a lab test scenario has been evaluated in detail.
Detection and Modeling of Buildings from Multiple Aerial Images

Automatic detection and description of cultural features, such as buildings, from aerial images is becoming increasingly important for a number of applications. This task also offers an excellent domain for studying the general problems of scene segmentation, 3-D inference and shape description under highly challenging conditions. We describe a system that detects and constructs 3-D models for rectilinear buildings with either flat or symmetric gable roofs from multiple aerial images; the multiple images, however, need not be stereo pairs (i.e. they may be acquired at different times). Hypotheses for rectangular roof components are generated by grouping lines in the images hierarchically, the hypotheses are verified by searching for presence of predicted walls and shadows. The hypothesis generation process combines the tasks of hierarchical grouping with matching at successive stages. Overlap and containment relations between 3-D structures are analyzed to resolve conflicts. This system has been tested on a large number of real examples with good result, some of which, and their evaluation, are included in the paper. Detection and Modeling of Buildings from Multiple Aerial Images
Brain-computer interfacing in disorders of consciousness.

Recent neuroimaging research has strikingly demonstrated the existence of covert awareness in some patients with disorders of consciousness (DoC). These findings have highlighted the potential for the development of simple brain-computer interfaces (BCI) as a diagnosis in behaviourally unresponsive patients.



This study here reviews current EEG-based BCIs that hold potential for assessing and eventually assisting patients with DoC. It highlights key areas for further development that might eventually make their application feasible in this challenging patient group.



The major types of BCIs proposed in the literature are considered, namely those based on the P3 potential, sensorimotor rhythms, steady state oscillations and slow cortical potentials. In each case, a brief overview of the relevant literature is provided and then their relative merits for BCI applications in DoC are considered.



A range of BCI designs have been proposed and tested for enabling communication in fully conscious, paralysed patients. Although many of these have potential applicability for patients with DoC, they share some key challenges that need to be overcome, including limitations of stimulation modality, feedback, user training and consistency.



Future work will need to address the technical and practical challenges facing reliable implementation at the patient's bedside.
Very-Heavy Sled Training for Improving Horizontal-Force Output in Soccer Players.

Sprint running acceleration is a key feature of physical performance in team sports, and recent literature shows that the ability to generate large magnitudes of horizontal ground-reaction force and mechanical effectiveness of force application are paramount. The authors tested the hypothesis that very-heavy loaded sled sprint training would induce an improvement in horizontal-force production, via an increased effectiveness of application.



Training-induced changes in sprint performance and mechanical outputs were computed using a field method based on velocity-time data, before and after an 8-wk protocol (16 sessions of 10- × 20-m sprints). Sixteen male amateur soccer players were assigned to either a very-heavy sled (80% body mass sled load) or a control group (unresisted sprints).



The main outcome of this pilot study is that very-heavy sled-resisted sprint training, using much greater loads than traditionally recommended, clearly increased maximal horizontal-force production compared with standard unloaded sprint training (effect size of 0.80 vs 0.20 for controls, unclear between-groups difference) and mechanical effectiveness (ie, more horizontally applied force; effect size of 0.95 vs -0.11, moderate between-groups difference). In addition, 5-m and 20-m sprint performance improvements were moderate and small for the very-heavy sled group and small and trivial for the control group, respectively.  This brief report highlights the usefulness of very-heavy sled (80% body mass) training, which may suggest value for practical improvement of mechanical effectiveness and maximal horizontal-force capabilities in soccer players and other team-sport athletes.



This study may encourage further research to confirm the usefulness of very-heavy sled in this context.
Compact and Tunable Slot-Loop Antenna

A compact and tunable slot-loop antenna is studied in this communication. By periodically loading the slot line with varactor diodes, the slow-wave loop is capable of operating at frequencies from 2.34 GHz to 4 GHz with the measured input reflection coefficient better than -7.5 dB while occupying a small footprint of 0.072 λg × 0.072 λg (8.1 mm × 8.1 mm), where λg is the guided wavelength of a slot line at 2.34 GHz. The slot-loop in the ground plane is excited by a simple microstrip line, which enables the impedance matching from 3.2 GHz to 4 GHz for VSWR ≤ 2. A LC low-pass circuit is configured into the bias network to isolate the RF signal from the DC power path and consistent radiation patterns are experimentally obtained across the entire tunable frequencies of interest. The slot loop combined with the feed line and the bias circuitry is only 40 mm × 38.97 mm. Experimental results validate the feasibility of the presented antenna and agree well with simulation data.
SETAP: Software engineering teamwork assessment and prediction using machine learning

Effective teaching of teamwork skills in local and globally distributed Software Engineering (SE) teams is recognized as an important part of the education of current and future software engineers. Effective methods for assessment and early prediction of learning effectiveness in SE teamwork are not only a critical part of teaching but also of value in industrial training and project management. This paper presents a novel analytical approach to the assessment and, most importantly, the prediction of learning outcomes in SE teamwork based on data from our joint software engineering class concurrently taught at San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda). Our approach focuses on assessment and prediction of SE teamwork in terms of ability of student teams to apply best SE processes and develop SE products. It differs from existing work in the following aspects: a) it develops and uses only objective and quantitative measures of team activity from multiple sources, such as statistics of student time use, software engineering tool use, and instructor observations; b) it leverages powerful machine learning (ML) techniques applied to team activity measurements to identify quantitative and objective factors which can assess and predict learning of software engineering teamwork skills at the team level. In this paper we provide the following contributions: a) we present in detail for the first time the full team activity measurement data set we developed, consisting of over 40 objective and quantitative measures extracted from student teams working on class projects; b) we present a ML framework which applies the Random Forest (RF) algorithm to the team activity measurements and team outcomes, focusing on predicting teams that are likely to fail; c) we describe in detail our now fully implemented and operational data processing pipeline, consisting of data collection methods from multiple sources, ML training database creation, and ML analysis subsystems; and finally d) we present very preliminary results of ML analysis results based on the data from our joint software engineering classes in Fall 2012, and Spring 2013, with the data from 17 student teams. While our ML training database is currently small, it continuously grows. Our preliminary results, verified with two independent accuracy measures, show that RF is able to predict SE Process and SE Product team performance in intuitively explainable manner.
repository copy of Transformational Leadership and Burnout : The Role of Thriving and Followers ' Openness to Experience

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Enriching Morphologically Poor Languages for Statistical Machine Translation

We address the problem of translating from morphologically poor to morphologically rich languages by adding per-word linguistic information to the source language. We use the syntax of the source sentence to extract information for noun cases and verb persons and annotate the corresponding words accordingly. In experiments, we show improved performance for translating from English into Greek and Czech. For English–Greek, we reduce the error on the verb conjugation from 19% to 5.4% and noun case agreement from 9% to 6%.
Semantic Attachments for Domain-Independent Planning Systems

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attach-
A comparative study of classification algorithms for forecasting rainfall

India is an agricultural country which largely depends on monsoon for irrigation purpose. A large amount of water is consumed for industrial production, crop yield and domestic use. Rainfall forecasting is thus very important and necessary for growth of the country. Weather factors including mean temperature, dew point temperature, humidity, pressure of sea and speed of wind and have been used to forecasts the rainfall. The dataset of 2245 samples of New Delhi from June to September (rainfall period) from 1996 to 2014 has been collected from a website named Weather Underground. The training dataset is used to train the classifier using Classification and Regression Tree algorithm, Naive Bayes approach, K nearest Neighbour and 5-10-1 Pattern Recognition Neural Network and its accuracy is tested on a test dataset. Pattern Recognition networks has given 82.1% accurate results, KNN with 80.7% correct forecasts ranks second, Classification and Regression Tree(CART) gives 80.3% while Naive Bayes provides 78.9% correctly classified samples.
Navigating in the new competitive landscape : Building strategic flexibility and competitive advantage in the 21 st century

Executive Overview A new competitive landscape is developing largely based on the technological revolution and increasing globalization. The strategic discontinuities encountered by firms are transforming the nature of competition. To navigate effectively in this new competitive landscape, to build and maintain competitive advantage, requires a new type of organization. Success in the 21st century organization will depend first on building strategic flexibility. To develop strategic flexibility and competitive advantage, requires exercising strategic leadership, building dynamic core competences, focusing and developing human capital, effectively using new manufacturing and information technologies, employing valuable strategies (exploiting global markets and cooperative strategies) and implementing new organization structures and culture (horizontal organization, learning and innovative culture, managing firm as bundles of assets). Thus, the new competitive landscape will require new types of organization and leaders for survival and global market leadership.
Intelligent Tutoring Systems and Learning Outcomes : A Meta-Analysis

Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer

Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto’s service for marketers to track their brands in user-generated images, and LogoGrab’s mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.
Self-Paced Learning for Matrix Factorization

Matrix factorization (MF) has been attracting much attention due to its wide applications. However, since MF models are generally non-convex, most of the existing methods are easily stuck into bad local minima, especially in the presence of outliers and missing data. To alleviate this deficiency, in this study we present a new MF learning methodology by gradually including matrix elements into MF training from easy to complex. This corresponds to a recently proposed learning fashion called self-paced learning (SPL), which has been demonstrated to be beneficial in avoiding bad local minima. We also generalize the conventional binary (hard) weighting scheme for SPL to a more effective realvalued (soft) weighting manner. The effectiveness of the proposed self-paced MF method is substantiated by a series of experiments on synthetic, structure from motion and background subtraction data.