nils-herrmann/clean_scirepeval huggingface dataset:
Alcohol-related problems and life satisfaction predict motivation to change among mandated college students.

The present study investigated the role specific types of alcohol-related problems and life satisfaction play in predicting motivation to change alcohol use. Participants were 548 college students mandated to complete a brief intervention following an alcohol-related policy violation. Using hierarchical multiple regression, we tested for the presence of interaction and quadratic effects on baseline data collected prior to the intervention. A significant interaction indicated that the relationship between a respondent's personal consequences and his/her motivation to change differs depending upon the level of concurrent social consequences. Additionally quadratic effects for abuse/dependence symptoms and life satisfaction were found. The quadratic probes suggest that abuse/dependence symptoms and poor life satisfaction are both positively associated with motivation to change for a majority of the sample; however, the nature of these relationships changes for participants with more extreme scores. Results support the utility of using a multidimensional measure of alcohol related problems and assessing non-linear relationships when assessing predictors of motivation to change. The results also suggest that the best strategies for increasing motivation may vary depending on the types of alcohol-related problems and level of life satisfaction the student is experiencing and highlight potential directions for future research.
DeepCPU: Serving RNN-based Deep Learning Models 10x Faster

Recurrent neural networks (RNNs) are an important class of deep learning (DL) models. Existing DL frameworks have unsatisfying performance for online serving: many RNN models suffer from long serving latency and high cost, preventing their deployment in production. This work characterizes RNN performance and identifies low data reuse as a root cause. We develop novel techniques and an efficient search strategy to squeeze more data reuse out of this intrinsically challenging workload. We build DeepCPU, a fast serving library on CPUs, to integrate these optimizations for efficient RNN computation. Our evaluation on various RNN models shows that DeepCPU improves latency and efficiency by an order of magnitude on CPUs compared with existing DL frameworks such as TensorFlow. It also empowers CPUs to beat GPUs on RNN serving. In production services of Microsoft, DeepCPU transforms many models from non-shippable (due to latency SLA violation) to shippable (well-fitting latency requirements) and saves millions of dollars of infrastructure costs.
Amygdala Microcircuits Controlling Learned Fear

We review recent work on the role of intrinsic amygdala networks in the regulation of classically conditioned defensive behaviors, commonly known as conditioned fear. These new developments highlight how conditioned fear depends on far more complex networks than initially envisioned. Indeed, multiple parallel inhibitory and excitatory circuits are differentially recruited during the expression versus extinction of conditioned fear. Moreover, shifts between expression and extinction circuits involve coordinated interactions with different regions of the medial prefrontal cortex. However, key areas of uncertainty remain, particularly with respect to the connectivity of the different cell types. Filling these gaps in our knowledge is important because much evidence indicates that human anxiety disorders results from an abnormal regulation of the networks supporting fear learning.
Reconfigurable Service−Oriented Architecture for Autonomic Computing

Almost a decade has passed since the objectives and benefits of autonomic computing were stated, yet even the latest system designs and deployments exhibit only limited and isolated elements of autonomic functionality. In previous work, we identified several of the key challenges behind this delay in the adoption of autonomic solutions, and proposed a generic framework for the development of autonomic computing systems that overcomes these challenges. In this article, we describe how existing technologies and standards can be used to realise our autonomic computing framework, and present its implementation as a service-oriented architecture. We show how this implementation employs a combination of automated code generation, model-based and object-oriented development techniques to ensure that the framework can be used to add autonomic capabilities to systems whose characteristics are unknown until runtime. We then use our framework to develop two autonomic solutions for the allocation of server capacity to services of different priorities and variable workloads, thus illustrating its application in the context of a typical data-centre resource management problem.
Relationships between human disturbance and wildlife land use in urban habitat fragments.

Habitat remnants in urbanized areas typically conserve biodiversity and serve the recreation and urban open-space needs of human populations. Nevertheless, these goals can be in conflict if human activity negatively affects wildlife. Hence, when considering habitat remnants as conservation refuges it is crucial to understand how human activities and land uses affect wildlife use of those and adjacent areas. We used tracking data (animal tracks and den or bed sites) on 10 animal species and information on human activity and environmental factors associated with anthropogenic disturbance in 12 habitat fragments across San Diego County, California, to examine the relationships among habitat fragment characteristics, human activity, and wildlife presence. There were no significant correlations of species presence and abundance with percent plant cover for all species or with different land-use intensities for all species, except the opossum (Didelphis virginiana), which preferred areas with intensive development. Woodrats (Neotoma spp.) and cougars (Puma concolor) were associated significantly and positively and significantly and negatively, respectively, with the presence and prominence of utilities. Woodrats were also negatively associated with the presence of horses. Raccoons (Procyon lotor) and coyotes (Canis latrans) were associated significantly and negatively and significantly and positively, respectively, with plant bulk and permanence. Cougars and gray foxes (Urocyon cinereoargenteus) were negatively associated with the presence of roads. Roadrunners (Geococcyx californianus) were positively associated with litter. The only species that had no significant correlations with any of the environmental variables were black-tailed jackrabbits (Lepus californicus) and mule deer (Odocoileus hemionus). Bobcat tracks were observed more often than gray foxes in the study area and bobcats correlated significantly only with water availability, contrasting with results from other studies. Our results appear to indicate that maintenance of habitat fragments in urban areas is of conservation benefit to some animal species, despite human activity and disturbance, as long as the fragments are large.
Feature engineering for crime hotspot detection

San-Francisco in the US and Natal in Brazil are two coastal cities which are known rather for its tech scene and natural beauty than for its criminal activities. We analyze characteristics of the urban environment in these two cities, deploying a machine learning model to detect categories and hotspots of criminal activities. We propose an extensive set of spatio-temporal & urban features which can significantly improve the accuracy of machine learning models for these tasks, one of which achieved Top 1% performance on a Crime Classification Competition by kaggle.com. Extensive evaluation on several years of crime records from both cities show how some features — such as the street network — carry important information about criminal activities.
eLSE Methodology: a Systematic Approach to the e-Learning Systems Evaluation

Quality of e-learning systems is one of the important topics that the researchers are investigating in the last years. This paper refines the concept of quality of e-learning systems and proposes a new framework, called TICS (Technology, Interaction, Content, Services), which focuses on the most important aspects to be considered when designing or evaluating an e-learning system. Our proposal emphasizes user-system interaction as one of such important aspects. Guidelines that address the TICS aspects and an evaluation methodology, called eLSE (e-Learning Systematic Evaluation) have been derived. eLSE methodology combines a specific inspection technique with user-testing. This inspection, called AT inspection, uses evaluation patterns, called Abstract Tasks (ATs), that precisely describe the activities to be performed during inspection. The results of an empirical validation of the AT inspection technique, carried out to validate this technique, have shown an advantage of the AT inspection over the other two usability evaluation methods, demonstrating that Abstract Tasks are effective and efficient tools to drive evaluators and improve their performance.
The Use Of Indirect Resin Composites In Clinical Practice: A Case Series

Since the first application of indirect composite resins, numerous advances in adhesive dentistry have been made. Furthermore, improvements in structure, composition and polymerization techniques led to the development of a second-generation of indirect resin composites (IRCs). IRCs have optimal esthetic performance, enhanced mechanical properties and reparability. Due to these characteristics they can be used for a wide range of clinical applications. IRCs can be used for inlays, onlays, crowns’ veneering material, fixed dentures prostheses and removable prostheses (teeth and soft tissue substitution), both on teeth and implants. The purpose of this article is to review the properties of these materials and describe a case series of patients treated with different type of restorations in various indications.  Aikaterini Petropoulou, Clinical Instructor, Department of Prosthodontics, School of Dentistry, National and Kapodistrian University of Athens, Greece, Tel: +306932989104; E-mail: aikatpetropoulou@gmail.com Received November 10, 2013;  2013;  2013  Petropoulou A, Pantzari F, Nomikos N, Chronopoulos V, Kourtis S (2013) The Use of Indirect Resin Composites in  A
Programmable self-assembly: constructing global shape using biologically-inspired local interactions and origami mathematics

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Customer Churn in Mobile Markets A Comparison of Techniques

The high increase in the number of companies competing in mature markets makes customer retention an important factor for any company to survive. Thus, many methodologies (e.g., data mining and statistics) have been proposed to analyse and study customer retention. The validity of such methods is not yet proved though. This paper tries to fill this gap by empirically comparing two techniques: Customer churn decision tree and logistic regression models. The paper proves the superiority of decision tree technique and stresses the needs for more advanced methods to churn modelling.
A Comparison Framework and Review of Service Brokerage Solutions for Cloud Architectures

Cloud service brokerage has been identified as a key concern for future cloud technology development and research. We compare service brokerage solutions. A range of specific concerns like architecture, programming and quality will be looked at. We apply a 2-pronged classification and comparison framework. We will identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. We will discuss complex cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks.
Distributed discrete-time optimization by exchanging one bit of information

This paper proposes a distributed discrete-time algorithm to solve an additive cost optimization problem over undirected deterministic or time-varying graphs. Different from most previous methods that require to exchange exact states between nodes, each node in our algorithm needs only the sign of the relative state between its neighbors, which is clearly one bit of information. Our analysis is based on optimization theory rather than Lyapunov theory or algebraic graph theory. The latter is commonly used in existing literature, especially in the continuous-time algorithm design, and is difficult to apply in our case. Besides, an optimization-theory-based analysis may make our results more extendible. In particular, our convergence proofs are based on the convergences of the subgradient method and the stochastic subgradient method. Moreover, the convergence rate of our algorithm can vary from $O(1/\ln(k))$ to $O(1/\sqrt{k})$, depending on the choice of the stepsize. A quantile regression problem is included to illustrate the performance of our algorithm using simulations.
Trie memory

Trie memory is a way of storing and retrieving information. ~ It is applicable to information that consists of function-argument (or item-term) pairs--information conventionally stored in unordered lists, ordered lists, or pigeonholes. The main advantages of trie memory over the other memoIw plans just mentioned are shorter access time, greater ease of addition or up-dating, greater convenience in handling arguments of diverse lengths, and the ability to take advantage of redundancies in the information stored. The main disadvantage is relative inefficiency in using storage space, but this inefficiency is not great when the store is large. In this paper several paradigms of trie memory are described and compared with other memory paradigms, their advantages and disadvantages are examined in detail, and applications are discussed. Many essential features of trie memory were mentioned by de la Briandais  in a paper presented to the Western Joint Computer Conference in 1959. The present development is essentially independent of his, having been described in memorandum form in January 1959 , and it is fuller in that it considers additional paradigms (finitedimensional trie memories) and includes experimental results bearing on the efficiency of utilization of storage space.
A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data

Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users’ efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. Our approach is validated using a real, large-scale dataset of 10,357 GPS-equipped taxis in the city of Beijing, China and showcases its effectiveness on the ridesharing task.
RoboCup: A Challenge Problem for AI and Robotics

Abs t rac t . RoboCup is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and exaznined. The first R o b o C u p competition was held at IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensorfllsion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a softwaxe platform for research on the software aspects of RoboCup. This paper describes technical chalhmges involw~d in , rules, and simulation environment.
Automated Vulnerability Detection in Source Code Using Deep Representation Learning

Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source code available to develop a largescale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source code. We evaluated our tool on code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source code is a promising approach for automated software vulnerability detection.
MOARSLAM: Multiple Operator Augmented RSLAM

To effectively act on the same physical space, robots must first communicate to share and fuse the map of the area in which they operate. For long-term online operation, the merging of maps from heterogeneous devices must be fast and allow for scalable growth in both the number of clients and the size of the map. This paper presents a system which allows multiple clients to share and merge maps built from a state-of-the-art relative SLAM system. Maps can also be augmented with virtual elements that are consistently shared by all the clients. The visual-inertial mapping framework which underlies this system is discussed, along with the server architecture and novel integrated multi-session loop closure system. We show quantitative results of the system. The map fusion benefits are demonstrated with an example augmented reality application.
Modeling Airfield Ground Lighting Systems for Narrowband Power-Line Communications

Airfield ground lighting (AGL) systems are responsible for providing visual reference to aircrafts in the airport neighborhood. In an AGL system, a large number of lamps are organized in serial circuits and connected to current regulators that supply energy to the lamps. Controlling and monitoring lamps (including detection and location of burnt-out lamps) are critical for cost-saving maintenance and operation of AGL systems. Power-line Communications (PLC) is an attractive technology to connect elements of the AGL, reusing the power distribution cable as a transmission medium. PLC technologies avoid the installation of new wires throughout the airport infrastructure. This paper proposes a new model for power-line communication links in AGL systems. Every element (isolation transformer, primary circuit cable, and lamps) has been analyzed in laboratory and modeled using SPICE. The resulting models have been integrated to build a complete power-line link model. Simulation results are compared to experimental results obtained in real conditions in the Airport of Seville (Spain).
On predicting the popularity of newly emerging hashtags in Twitter

Because of Twitter’s popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
Blur Insensitive Texture Classification Using Local Phase Quantization

In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.
Support vector machines/relevance vector machine for remote sensing classification: A review

Kernel-based machine learning algorithms are based on mapping data from the original input feature space to a kernel feature space of higher dimensionality to solve a linear problem in that space. Over the last decade, kernel based classification and regression approaches such as support vector machines have widely been used in remote sensing as well as in various civil engineering applications. In spite of their better performance with different datasets, support vector machines still suffer from shortcomings such as visualization/interpretation of model, choice of kernel and kernel specific parameter as well as the regularization parameter. Relevance vector machines are another kernel based approach being explored for classification and regression with in last few years. The advantages of the relevance vector machines over the support vector machines is the availability of probabilistic predictions, using arbitrary kernel functions and not requiring setting of the regularization parameter. This paper presents a state-ofthe-art review of SVM and RVM in remote sensing and provides some details of their use in other civil engineering application also.