nils-herrmann/clean_scirepeval huggingface dataset:
Visual abnormal behavior detection based on trajectory sparse reconstruction analysis

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  Abnormal behavior detection has been one of the most important research branches in intelligent video content 20 analysis. In this paper, we propose a novel abnormal behavior detection approach by introducing trajectory 21 sparse reconstruction analysis (SRA). Given a video scenario, we collect trajectories of normal behaviors and 22 extract the control point features of cubic B-spline curves to construct a normal dictionary set, which is further 23 divided into Route sets. On the dictionary set, sparse reconstruction coefficients and residuals of a test trajectory 24 to the Route sets can be calculated with SRA. The minimal residual is used to classify the test behavior into a 25 normal behavior or an abnormal one. SRA is solved by L1-norm minimization, leading to that a few of dictionary 26 samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is 27 valid even when the dictionary set is very small. Experimental results with comparisons show that the proposed 28 approach improves the state-of-the-art.
Desensitization to media violence: links with habitual media violence exposure, aggressive cognitions, and aggressive behavior.

This study examined the links between desensitization to violent media stimuli and habitual media violence exposure as a predictor and aggressive cognitions and behavior as outcome variables. Two weeks after completing measures of habitual media violence exposure, trait aggression, trait arousability, and normative beliefs about aggression, undergraduates (N = 303) saw a violent film clip and a sad or a funny comparison clip. Skin conductance level (SCL) was measured continuously, and ratings of anxious and pleasant arousal were obtained after each clip. Following the clips, participants completed a lexical decision task to measure accessibility of aggressive cognitions and a competitive reaction time task to measure aggressive behavior. Habitual media violence exposure correlated negatively with SCL during violent clips and positively with pleasant arousal, response times for aggressive words, and trait aggression, but it was unrelated to anxious arousal and aggressive responding during the reaction time task. In path analyses controlling for trait aggression, normative beliefs, and trait arousability, habitual media violence exposure predicted faster accessibility of aggressive cognitions, partly mediated by higher pleasant arousal. Unprovoked aggression during the reaction time task was predicted by lower anxious arousal. Neither habitual media violence usage nor anxious or pleasant arousal predicted provoked aggression during the laboratory task, and SCL was unrelated to aggressive cognitions and behavior. No relations were found between habitual media violence viewing and arousal in response to the sad and funny film clips, and arousal in response to the sad and funny clips did not predict aggressive cognitions or aggressive behavior on the laboratory task. This suggests that the observed desensitization effects are specific to violent content.
Performance Evaluation of Iris Based Recognition System Implementing PCA and ICA Encoding Techniques

In this paper, we describe and analyze the performance of two iris-encoding techniques. The first technique is based on Principle Component Analysis (PCA) encoding method while the second technique is a combination of Principal Component Analysis with Independent Component Analysis (ICA) following it. Both techniques are applied globally. PCA and ICA are two well known methods used to process a variety of data. Though PCA has been used as a preprocessing step that reduces dimensions for obtaining ICA components for iris, it has never been analyzed in depth as an individual encoding method. In practice PCA and ICA are known as methods that extract global and fine features, respectively. It is shown here that when PCA and ICA methods are used to encode iris images, one of the critical steps required to achieve a good performance is compensation for rotation effect. We further study the effect of varying the image resolution level on the performance of the two encoding methods. The major motivation for this study is the cases in practice where images of the same or different irises taken at different distances have to be compared. The performance of encoding techniques is analyzed using the CASIA dataset. The original images are non-ideal and thus require a sequence of preprocessing steps prior to application of encoding methods. We plot a series of Receiver Operating Characteristics (ROCs) to demonstrate various effects on the performance of the iris-based recognition system implementing PCA and ICA encoding techniques.
EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications

Brain Computer Interface (BCI) is the method of communicating the human brain with an external device. People who are incapable to communicate conventionally due to spinal cord injury are in need of Brain Computer Interface. Brain Computer Interface uses the brain signals to take actions, control, actuate and communicate with the world directly using brain integration with peripheral devices and systems. Brain waves are in necessitating to eradicate noises and to extract the valuable features. Artificial Neural Network (ANN) is a functional pattern classification technique which is trained all the way through the error Back-Propagation algorithm. In this paper in order to classify the mental tasks, the brain signals are trained using neural network and also using Principal Component Analysis with Artificial Neural Network. Principal Component Analysis (PCA) is a dominant tool for analyzing data and finding patterns in it. In Principal Component Analysis, data compression is possible and it projects higher dimensional data to lower dimensional data. By using Principal Component Analysis with Neural Network, the redundant data in the dataset is eliminated first and the obtained data is trained using Neural Network. EEG data for five cognitive tasks from five subjects are taken from the Colorado University database. Pattern classification is applied for the data of all tasks of one subject using Neural Network and also using Principal Component Analysis with Neural Network. Finally it is observed that the correctly classified percentage of data is better in Principal Component Analysis with Neural Network compared to Neural Network alone.
KVM, Xen and Docker: A performance analysis for ARM based NFV and cloud computing

Virtualization is a mature technology which has shown to provide computing resource and cost optimization while enabling consolidation, isolation and hardware abstraction through the concept of virtual machine. Recently, by sharing the operating system resources and simplifying the deployment of applications, containers are getting a more and more popular alternative to virtualization for specific use cases. As a result, today these two technologies are competing to provide virtual instances for cloud computing, Network Functions Virtualization (NFV), High Performance Computing (HPC), avionic and automotive platforms. In this paper, the performance of the most important open source hypervisor (KVM and Xen) and container (Docker) solutions are compared on the ARM architecture, which is rapidly emerging in the server world. The extensive system and Input/Output (I/O) performance measurements included in this paper show a slightly better performance for containers in CPU bound workloads and request/response networking; conversely, thanks to their caching mechanisms, hypervisors perform better in most disk I/O operations and TCP streaming benchmark.
Multimodal Emoji Prediction

Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication, that automatic systems are not used to deal with. In this paper we extend recent advances in emoji prediction by putting forward a multimodal approach that is able to predict emojis in Instagram posts. Instagram posts are composed of pictures together with texts which sometimes include emojis. We show that these emojis can be predicted by using the text, but also using the picture. Our main finding is that incorporating the two synergistic modalities, in a combined model, improves accuracy in an emoji prediction task. This result demonstrates that these two modalities (text and images) encode different information on the use of emojis and therefore can complement each other.
TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning

Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the planner. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games, with deeper trees often outperforming shallower ones. We also present a qualitative analysis that sheds light on the trees learned by TreeQN.
Determining the Training Window for Small Sample Size Classification with Concept Drift

We consider classification of sequential data in the presence of frequent and abrupt concept changes. The current practice is to use the data after the change to train a new classifier. However, if the window with the new data is too small, the classifier will be undertrained and hence less accurate that the "old'' classifier. Here we propose a method (called WR*) for resizing the training window after detecting a concept change. Experiments with synthetic and real data demonstrate the advantages of WR* over other window resizing methods.
Autoregressive Convolutional Neural Networks for Asynchronous Time Series

We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors, while the weights are datadependent functions learnt through a convolutional network. The architecture was designed for applications on asynchronous time series and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive series and UCI household electricity consumption dataset. The proposed architecture achieves promising results as compared to convolutional and recurrent neural networks.
A nonlinear model of information-seeking behavior

This paper offers a new, nonlinear model of informationseeking behavior, which contrasts with earlier stage models of information behavior and represents a potential cornerstone for a shift toward a new perspective for understanding user information behavior. The model is based on the findings of a study on interdisciplinary information-seeking behavior. The study followed a naturalistic inquiry approach using interviews of 45 academics. The interview results were inductively analyzed and an alternative framework for understanding information-seeking behavior was developed. This model illustrates three core processes and three levels of contextual interaction, each composed of several individual activities and attributes. These interact dynamically through time in a nonlinear manner. The behavioral patterns are analogous to an artist’s palette, in which activities remain available throughout the course of information-seeking. In viewing the processes in this way, neither start nor finish points are fixed, and each process may be repeated or lead to any other until either the query or context determine that information-seeking can end. The interactivity and shifts described by the model show information-seeking to be nonlinear, dynamic, holistic, and flowing. The paper offers four main implications of the model as it applies to existing theory and models, requirements for future research, and the development of information literacy curricula. Central to these implications is the creation of a new nonlinear perspective from which user information-seeking can be interpreted.
Automatic scoring of online discussion posts

Online discussions forums, known as forums for short, are conversational social cyberspaces constituting rich repositories of content and an important source of collaborative knowledge. However, most of this knowledge is buried inside the forum infrastructure and its extraction is both complex and difficult. The ability to automatically rate postings in online discussion forums, based on the value of their contribution, enhances the ability of users to find knowledge within this content. Several key online discussion forums have utilized collaborative intelligence to rate the value of postings made by users. However, a large percentage of posts go unattended and hence lack appropriate rating.
 In this paper, we focus on automatic rating of postings in online discussion forums. A set of features derived from the posting content and the threaded discussion structure are generated for each posting. These features are grouped into five categories, namely (i) relevance, (ii) originality, (iii) forum-specific features, (iv) surface features, and (v) posting-component features. Using a non-linear SVM classifier, the value of each posting is categorized into one of three levels High, Medium, or Low. This rating represents a seed value for each posting that is leveraged in filtering forum content. Experimental results have shown promising performance on forum data.
The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge

The Cancer Genome Atlas (TCGA) is a public funded project that aims to catalogue and discover major cancer-causing genomic alterations to create a comprehensive "atlas" of cancer genomic profiles. So far, TCGA researchers have analysed large cohorts of over 30 human tumours through large-scale genome sequencing and integrated multi-dimensional analyses. Studies of individual cancer types, as well as comprehensive pan-cancer analyses have extended current knowledge of tumorigenesis. A major goal of the project was to provide publicly available datasets to help improve diagnostic methods, treatment standards, and finally to prevent cancer. This review discusses the current status of TCGA Research Network structure, purpose, and achievements.
CNN-N-Gram for HandwritingWord Recognition

Given an image of a handwritten word, a CNN is employed to estimate its n-gram frequency profile, which is the set of n-grams contained in the word. Frequencies for unigrams, bigrams and trigrams are estimated for the entire word and for parts of it. Canonical Correlation Analysis is then used to match the estimated profile to the true profiles of all words in a large dictionary. The CNN that is used employs several novelties such as the use of multiple fully connected branches. Applied to all commonly used handwriting recognition benchmarks, our method outperforms, by a very large margin, all existing methods.
Selection of optimal supplier in supply chain management strategy with analytic network process and choquet integral

Please cite this article in press as: Tseng, M Industrial Engineering (2009), doi:10.1016/ Selection of appropriate suppliers in supply chain management strategy (SCMS) is a challenging issue because it requires battery of evaluation criteria/attributes, which are characterized with complexity, elusiveness, and uncertainty in nature. This paper proposes a novel hierarchical evaluation framework to assist the expert group to select the optimal supplier in SCMS. The rationales for the evaluation framework are based upon (i) multi-criteria decision making (MCDM) analysis that can select the most appropriate alternative from a finite set of alternatives with reference to multiple conflicting criteria, (ii) analytic network process (ANP) technique that can simultaneously take into account the relationships of feedback and dependence of criteria, and (iii) choquet integral—a non-additive fuzzy integral that can eliminate the interactivity of expert subjective judgment problems. A case PCB manufacturing firm is studied and the results indicated that the proposed evaluation framework is simple and reasonable to identify the primary criteria influencing the SCMS, and it is effective to determine the optimal supplier even with the interactive and interdependent criteria/attributes. This hierarchical evaluation framework provides a complete picture in SCMS contexts to both researchers and practitioners.
ZIGBEE BASED HOME AUTOMATION WIRELESS SENSOR NETWORK

Increased demands on implementation of wireless sensor networks in automation praxis result in relatively new wireless standard – ZigBee. The new workplace was established on the Department of Electronics and Multimedia Communications (DEMC) in order to keep up with ZigBee modern trend. This paper presents the first results and experiences associated with ZigBee based wireless sensor networking. The accent was put on suitable chipset platform selection for Home Automation wireless network purposes. Four popular microcontrollers was selected to investigate memory requirements and power consumption such as ARM, x51, HCS08, and Coldfire. Next objective was to test interoperability between various manufacturers’ platforms, what is important feature of ZigBee standard. A simple network based on ZigBee physical layer as well as ZigBee compliant network were made to confirm the basic ZigBee interoperability.
The Operational Analysis of Queueing Network Models

Queueing network models have proved to be cost effectwe tools for analyzing modern computer systems. This tutorial paper presents the basic results using the operational approach, a framework which allows the analyst to test whether each assumption is met in a given system. The early sections describe the nature of queueing network models and their apphcations for calculating and predicting performance quantitms The basic performance quantities--such as utilizations, mean queue lengths, and mean response tunes--are defined, and operatmnal relationships among them are derwed Following this, the concept of job flow balance is introduced and used to study asymptotic throughputs and response tunes. The concepts of state transition balance, one-step behavior, and homogeneity are then used to relate the proportions of time that each system state is occupied to the parameters of job demand and to dewce charactenstms Efficmnt methods for computing basic performance quantities are also described. Finally the concept of decomposition is used to stmphfy analyses by replacing subsystems with equivalent devices. All concepts are illustrated liberally with examples
Humans Are Easily Fooled by Digital Images

Digital images are ubiquitous in our modern lives, with uses ranging from social media to news, and even scientific papers. For this reason, it is crucial evaluate how accurate people are when performing the task of identify doctored images. In this paper, we performed an extensive user study evaluating subjects capacity to detect fake images. After observing an image, users have been asked if it had been altered or not. If the user answered the image has been altered, he had to provide evidence in the form of a click on the image. We collected 17,208 individual answers from 383 users, using 177 images selected from public forensic databases. Different from other previously studies, our method propose different ways to avoid lucky guess when evaluating users answers. Our results indicate that people show inaccurate skills at differentiating between altered and non-altered images, with an accuracy of 58%, and only identifying the modified images 46.5% of the time. We also track user features such as age, answering time, confidence, providing deep analysis of how such variables influence on the users’ performance.
Maternal Satisfaction on Delivery Service and Its Associated Factors among Mothers Who Gave Birth in Public Health Facilities of Debre Markos Town, Northwest Ethiopia

The existence of maternal health service does not guarantee its use by women; neither does the use of maternal health service guarantee optimal outcomes for women. The World Health Organization recommends monitoring and evaluation of maternal satisfaction to improve the quality and efficiency of health care during childbirth. Thus, this study aimed at assessing maternal satisfaction on delivery service and factors associated with it.



Community based cross-sectional study was conducted in Debre Markos town from March to April 2014. Systematic random sampling technique were used to select 398 mothers who gave birth within one year. The satisfaction of mothers was measured using 19 questions which were adopted from Donabedian quality assessment framework. Binary logistic regression was fitted to identify independent predictors.



Among mothers, the overall satisfaction on delivery service was found to be 318 (81.7%). Having plan to deliver at health institution (AOR = 3.30, 95% CI: 1.38-7.9) and laboring time of less than six hours (AOR = 4.03, 95% CI: 1.66-9.79) were positively associated with maternal satisfaction on delivery service. Those mothers who gave birth using spontaneous vaginal delivery (AOR = 0.11, 95% CI: 0.023-0.51) were inversely related to maternal satisfaction on delivery service.



This study revealed that the overall satisfaction of mothers on delivery service was found to be suboptimal. Reasons for delivery visit, duration of labor, and mode of delivery are independent predictors of maternal satisfaction. Thus, there is a need of an intervention on the independent predictors.