nils-herrmann/clean_scirepeval huggingface dataset:
Ordinal Data Classification Using Kernel Discriminant Analysis: A Comparison of Three Approaches

Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order; however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression (KDLOR), is based on Linear Discriminant Analysis (LDA), a simple tool for classification. KDLOR brings LDA to the forefront in the ODC held, motivating further research. This paper compares three LDA based algorithms for ODC. The first method uses the generic framework of Frank and Hall for ODC instantiated with a kernel version of LDA. Similarly, the second method is based on the also generic Data Replication framework for ODC instantiated with the same kernel version of LDA. Both the Frank and Hall and Data Replication methods address the ODC problem by the use of a base binary classifier. Finally, the third method under comparison is KDLOR. The experiments are carried out on synthetic and real datasets. A comparison between the performances of the three systems is made based on tstatistics. The performance and running time complexity of the methods do not support any advantage of KDLOR over the other two methods.
A Hidden Markov Model for Vehicle Detection and Counting

To reduce roadway congestion and improve traffic safety, accurate traffic metrics, such as number of vehicles travelling through lane-ways, are required. Unfortunately most existing infrastructure, such as loop-detectors and many video detectors, do not feasibly provide accurate vehicle counts. Consequently, a novel method is proposed which models vehicle motion using hidden Markov models (HMM). The proposed method represents a specified small region of the roadway as 'empty', 'vehicle entering', 'vehicle inside', and 'vehicle exiting', and then applies a modified Viterbi algorithm to the HMM sequential estimation framework to initialize and track vehicles. Vehicle observations are obtained using an Adaboost trained Haar-like feature detector. When tested on 88 hours of video, from three distinct locations, the proposed method proved to be robust to changes in lighting conditions, moving shadows, and camera motion, and consistently out-performed Multiple Target Tracking (MTT) and Virtual Detection Line(VDL) implementations. The median vehicle count error of the proposed method is lower than MTT and VDL by 28%, and 70% respectively. As future work, this algorithm will be implemented to provide the traffic industry with improved automated vehicle counting, with the intent to eventually provide real-time counts.
A survey of decision tree classifier methodology

Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition, to name only a few. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. This paper presents a survey of current methods for DTC designs and the various existing issues. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed_ Finally, several remarks are made concerning possible future research directions.
Personalized POI Recommendation on Location-Based Social Networks Disseration Prospectus

The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people’s daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of “curse of choices”, resulting in the difficulty for a user to make a satisfied decision on “where to go” in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing. Developing POI recommender systems requires observation of human mobile behavior w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to “check in” at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobile behavior for personalized POI recommendation in spatial, temporal, social, and content aspects. Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobile behavior and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-temporal patterns, geo-social correlations, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this proposal, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. Disseration Prospectus 2 Arizona State University
Health Impacts of Yoga and Pranayama: A State-of-the-Art Review

Thousands of years ago yoga originated in India, and in present day and age, an alarming awareness was observed in health and natural remedies among people by yoga and pranayama which has been proven an effective method for improving health in addition to prevention and management of diseases. With increasing scientific research in yoga, its therapeutic aspects are also being explored. Yoga is reported to reduce stress and anxiety, improves autonomic functions by triggering neurohormonal mechanisms by the suppression of sympathetic activity, and even, now-a-days, several reports suggested yoga is beneficial for physical health of cancer patients. Such global recognition of yoga also testifies to India's growing cultural influence.
Apertium: a free/open-source platform for rule-based machine translation

Apertium is a free/open-source platform for rule-based machine translation. It is being widely used to build machine translation systems for a variety of language pairs, especially in those cases (mainly with related-language pairs) where shallow transfer suffices to produce good quality translations, although it has also proven useful in assimilation scenarios with more distant pairs involved. This article summarises the Apertium platform: the translation engine, the encoding of linguistic data, and the tools developed around the platform. The present limitations of the platform and the challenges posed for the coming years are also discussed. Finally, evaluation results for some of the most active language pairs are presented. An appendix describes Apertium as a free/open-source project.
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents. DESIRE achieves these in a single end-to-end trainable neural network model, while being computationally efficient. The model first obtains a diverse set of hypothetical future prediction samples employing a conditional variational auto-encoder, which are ranked and refined by the following RNN scoring-regression module. Samples are scored by accounting for accumulated future rewards, which enables better long-term strategic decisions similar to IOC frameworks. An RNN scene context fusion module jointly captures past motion histories, the semantic scene context and interactions among multiple agents. A feedback mechanism iterates over the ranking and refinement to further boost the prediction accuracy. We evaluate our model on two publicly available datasets: KITTI and Stanford Drone Dataset. Our experiments show that the proposed model significantly improves the prediction accuracy compared to other baseline methods.
Good genes, complementary genes and human mate preferences

The past decade has witnessed a rapidly growing interest in the biological basis of human mate choice. Here we review recent studies that demonstrate preferences for traits which might reveal genetic quality to prospective mates, with potential but still largely unknown influence on offspring fitness. These include studies assessing visual, olfactory and auditory preferences for potential good-gene indicator traits, such as dominance or bilateral symmetry. Individual differences in these robust preferences mainly arise through within and between individual variation in condition and reproductive status. Another set of studies have revealed preferences for traits indicating complementary genes, focussing on discrimination of dissimilarity at genes in the major histocompatibility complex (MHC). As in animal studies, we are only just beginning to understand how preferences for specific traits vary and inter-relate, how consideration of good and compatible genes can lead to substantial variability in individual mate choice decisions and how preferences expressed in one sensory modality may reflect those in another. Humans may be an ideal model species in which to explore these interesting complexities.
Lewin's Theory of Planned Change as a strategic resource.

This department highlights change management strategies that may be successful in strategically planning and executing organizational change initiatives. With the goal of presenting practical approaches helpful to nurse leaders advancing organizational change, content includes evidence-based projects, tools, and resources that mobilize and sustain organizational change initiatives. In this article, the author explores the use of the Lewin's Theory of Planned Change as a strategic resource to mobilize the people side of change. An overview of the theory is provided along with a discussion of its strengths, limitations, and targeted application.
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments

The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of initial network structure via the self-constructing network structure. ADL specifically addresses catastrophic forgetting by having a different-depth structure which is capable of achieving a trade-off between plasticity and stability. Network significance (NS) formula is proposed to drive the hidden nodes growing and pruning mechanism. Drift detection scenario (DDS) is put forward to signal distributional changes in data streams which induce the creation of a new hidden layer. Maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers. The efficacy of ADL is numerically validated under the prequential test-then-train procedure in lifelong environments using nine popular data stream problems. The numerical results demonstrate that ADL consistently outperforms recent continual learning methods while characterizing the automatic construction of network
The Sixth PASCAL Recognizing Textual Entailment Challenge

This paper presents the Fifth Recognizing Textual Entailment Challenge (RTE5). Following the positive experience of the last campaign, RTE-5 has been proposed for the second time as a track at the Text Analysis Conference (TAC). The structure of the RTE-5 Main Task remained unchanged, offering both the traditional two-way task and the threeway task introduced in the previous campaign. Moreover, a pilot Search Task was set up, consisting of finding all the sentences in a set of documents that entail a given hypothesis. 21 teams participated in the campaign, among which 20 in the Main Task (for a total of 54 runs) and 8 in the Pilot Task (for a total of 20 runs). Another important innovation introduced in this campaign was mandatory ablation tests that participants had to perform for all major knowledge resources employed by their systems.
Edge detection using constrained discrete particle swarm optimisation in noisy images

Edge detection algorithms often produce broken edges, especially in noisy images. We propose an algorithm based on discrete particle swarm optimisation (PSO) to detect continuous edges in noisy images. A constrained PSO-based algorithm with a new objective function is proposed to address noise and reduce broken edges. The localisation accuracy of the new algorithm is compared with that of a modified version of the Canny algorithm as a Gaussian-based edge detector, the robust rank order (RRO)-based algorithm as a statistical based edge detector, and our previously developed PSO-based algorithm.  Experimental results show that the performance of the new algorithm is higher than the Canny and RRO algorithms in the images corrupted by two different types of noise (impulsive and Gaussian noise). The new algorithm also detects edges more accurately and smoothly than our previously developed algorithm in noisy images.
A 0.4 V driving multi-touch capacitive sensor with the driving signal frequency set to (n+0.5) times the inverse of the LCD VCOM noise period

The time-periodic property of the LCD VCOM noise is utilized to reduce the effect of the VCOM noise on the mutual-capacitance measuring touch sensor placed on a LCD panel. The amplitude of the touch sensor driving signal (VSTM) can be reduced reliably down to 0.4 V with the reporting rate of 189 Hz, by using the following two methods. (1) The frequency of VSTM is set to (n+0.5)·fN, by synchronizing VSTM to a LCD gate driver signal with a referenceless CDR. fN is the inverse of the LCD VCOM noise period (TN) and n is a positive integer. (2) The reset period of the RX integrator is set to 2·TN.
Opinion Target Extraction Using Partially-Supervised Word Alignment Model

Mining opinion targets from online reviews is an important and challenging task in opinion mining. This paper proposes a novel approach to extract opinion targets by using partially-supervised word alignment model (PSWAM). At first, we apply PSWAM in a monolingual scenario to mine opinion relations in sentences and estimate the associations between words. Then, a graph-based algorithm is exploited to estimate the confidence of each candidate, and the candidates with higher confidence will be extracted as the opinion targets. Compared with existing syntax-based methods, PSWAM can effectively avoid parsing errors when dealing with informal sentences in online reviews. Compared with the methods using alignment model, PSWAM can capture opinion relations more precisely through partial supervision from partial alignment links. Moreover, when estimating candidate confidence, we make penalties on higherdegree vertices in our graph-based algorithm in order to decrease the probability of the random walk running into the unrelated regions in the graph. As a result, some errors can be avoided. The experimental results on three data sets with different sizes and languages show that our approach outperforms state-of-the-art methods.
Role of Social Media on Information Accessibility

Social media is the gathering place of a large pool of consumers. It is the repository of consumer information and acts as a means of spreading information to build market presence. Most literature states that organizational usage of social media enhances customer relations, but social media also acts as a medium for information acquisition. Not many previous studies have investigated the role of social media on information accessibility. Therefore this study examined the impact of social media on information accessibility. A total of 171 organization responded to the survey and the result of the survey showed that social media usage has a positive impact on information accessibility. Also it was found that factors such as interactivity, trust and institutional pressure positively influence social media usage in organizations. This study provided a clearer understanding on the real importance of social media and its benefits towards information acquisition. The results would motivate and guide organizations in the adoption of social media for information acquisition which is important for understanding the customers, competitors and the industry and to develop strategies for enhancing business performance.
An Efficient Edge-Preserving Algorithm for Removal of Salt-and-Pepper Noise

In this letter, a novel algorithm for removing salt-and-pepper noise from corrupted images is presented. We employ an efficient impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art impulse denoising techniques. Especially, the proposed method can preserve edges very well while removing impulse noise. Since our algorithm is algorithmically simple, it is very suitable to be applied to many real-time applications.
An FPGA-based processor for training convolutional neural networks

Convolutional neural networks (CNNs) have gained great success in various computer vision applications. However, training a CNN model is computation-intensive and time-consuming. Hence training is mainly processed on large clusters of high-performance processors like server CPUs and GPUs. In this paper, we propose an FPGA-based processor design to accelerate the training process of CNNs. We first analyze the operations in all types of CNN layers in the training process. A uniform computation engine design is proposed to efficiently carry out all kinds of operations based on the analysis. Then a scalable accelerator framework is presented that exploits the parallelism further by unrolling the loops in two levels. The proposed accelerator design is demonstrated by implementing a processor on the Xilinx ZU19EG FPGA working at 200 MHz. The evaluation results on a group of CNN models show that our processor is 5.7 to 10.7-fold faster than the software implementations on the Intel Core i5-4440 CPU(@3.10GHz).
ANCESTRY-CONSTRAINED PHYLOGENETIC ANALYSIS SUPPORTS THE INDO-EUROPEAN STEPPE HYPOTHESIS

Discussion of Indo-European origins and dispersal focuses on two hypotheses. Qualitative evidence from reconstructed vocabulary and correlations with archaeological data suggest that IndoEuropean languages originated in the Pontic-Caspian steppe and spread together with cultural innovations associated with pastoralism, beginning c. 6500–5500 bp. An alternative hypothesis, according to which Indo-European languages spread with the diffusion of farming from Anatolia, beginning c. 9500–8000 bp, is supported by statistical phylogenetic and phylogeographic analyses of lexical traits. The time and place of the Indo-European ancestor language therefore remain disputed. Here we present a phylogenetic analysis in which ancestry constraints permit more accurate inference of rates of change, based on observed changes between ancient or medieval languages and their modern descendants, and we show that the result strongly supports the steppe hypothesis. Positing ancestry constraints also reveals that homoplasy is common in lexical traits, contrary to the assumptions of previous work. We show that lexical traits undergo recurrent evolution due to recurring patterns of semantic and morphological change.
Active Sentiment Domain Adaptation

Domain adaptation is an important technology to handle domain dependence problem in sentiment analysis field. Existing methods usually rely on sentiment classifiers trained in source domains. However, their performance may heavily decline if the distributions of sentiment features in source and target domains have significant difference. In this paper, we propose an active sentiment domain adaptation approach to handle this problem. Instead of the source domain sentiment classifiers, our approach adapts the general-purpose sentiment lexicons to target domain with the help of a small number of labeled samples which are selected and annotated in an active learning mode, as well as the domain-specific sentiment similarities among words mined from unlabeled samples of target domain. A unified model is proposed to fuse different types of sentiment information and train sentiment classifier for target domain. Extensive experiments on benchmark datasets show that our approach can train accurate sentiment classifier with less labeled samples.
Causal diagrams for epidemiologic research.

Causal diagrams have a long history of informal use and, more recently, have undergone formal development for applications in expert systems and robotics. We provide an introduction to these developments and their use in epidemiologic research. Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates. They also provide a method for critical evaluation of traditional epidemiologic criteria for confounding. In particular, they reveal certain heretofore unnoticed shortcomings of those criteria when used in considering multiple potential confounders. We show how to modify the traditional criteria to correct those shortcomings.
A literature review on the vehicle routing problem with multiple depots

http://dx.doi.org/10.1016/j.cie.2014.10.029 0360-8352/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail addresses: jairo.montoya@unisabana.edu.co (J.R. Montoya-Torres), julian. lopez@eic-sas.com (J. López Franco), nietos@uninorte.edu.co (S. Nieto Isaza), healfelizzola@unisalle.edu.co (H. Felizzola Jiménez). Jairo R. Montoya-Torres a,⇑, Julián López Franco , Santiago Nieto Isaza , Heriberto Felizzola Jiménez , Nilson Herazo-Padilla e,