nils-herrmann/clean_scirepeval huggingface dataset:
A new ranking method for principal components analysis and its application to face image analysis

In this work, we investigate a new ranking method for principal component analysis (PCA). Instead of sorting the principal components in decreasing order of the corresponding eigenvalues, we propose the idea of using the discriminant weights given by separating hyperplanes to select among the principal components the most discriminant ones. The method is not restricted to any particular probability density function of the sample groups because it can be based on either a parametric or non-parametric separating hyperplane approach. In addition, the number of meaningful discriminant directions is not limited to the number of groups, providing additional information to understand group differences extracted from high-dimensional problems. To evaluate the discriminant principal components, separation tasks have been performed using face images and three different databases. Our experimental results have shown that the principal components selected by the separating hyperplanes allow robust reconstruction and interpretation of the data, as well as higher recognition rates using less linear features in situations where the differences between the sample groups are subtle and consequently most difficult for the standard and state-of-the-art PCA selection methods.
Survey of HDFS and MapReduce

Hadoop Distributed File System (HDFS) is the primary component of the Hadoop framework responsible for storage, which is designed for maintaining and processing huge datasets efficiently among clusters. Files are uploaded from a local file system to the HDFS so as to cooperate with MapReduce which is the computation infrastructure of Hadoop. There have been various system architectures that have been implemented for extensively large data analysis and computing applications which include distributed relational database management systems. However most of the data that is frequently growing is the data in unstructured form and thus new processing methods with more flexible data models were required. Hadoop has provided several solutions including the MapReduce architecture which was pioneered by Google and is now available in an open-source implementation.  Hadoop Distributed File System
Semantics + filtering + search = twitcident. exploring information in social web streams

Automatically filtering relevant information about a real-world incident from Social Web streams and making the information accessible and findable in the given context of the incident are non-trivial scientific challenges. In this paper, we engineer and evaluate solutions that analyze the semantics of Social Web data streams to solve these challenges. We introduce Twitcident, a framework and Web-based system for filtering, searching and analyzing information about real-world incidents or crises. Given an incident, our framework automatically starts tracking and filtering information that is relevant for the incident from Social Web streams and Twitter particularly. It enriches the semantics of streamed messages to profile incidents and to continuously improve and adapt the information filtering to the current temporal context. Faceted search and analytical tools allow people and emergency services to retrieve particular information fragments and overview and analyze the current situation as reported on the Social Web.
 We put our Twitcident system into practice by connecting it to emergency broadcasting services in the Netherlands to allow for the retrieval of relevant information from Twitter streams for any incident that is reported by those services. We conduct large-scale experiments in which we evaluate (i) strategies for filtering relevant information for a given incident and (ii) search strategies for finding particular information pieces. Our results prove that the semantic enrichment offered by our framework leads to major and significant improvements of both the filtering and the search performance. A demonstration is available via: http://wis.ewi.tudelft.nl/twitcident/
Invasive species in Southeast Asia: the knowledge so far

This review deals with alien species invasion in Southeast Asia, an important conservation and management concern in the region. I report on the current and potential future impacts of biological invasions on biodiversity in Southeast Asia. Current knowledge of the invasive species in Southeast Asia is mostly based on anecdotal observations. Nevertheless, I attempt to compile existing empirical evidence on the negative effects of the biological invaders found in the region. These impacts include displacement of native biota, modification of ecosystems, hybridization, environmental disturbance, and economic loss. Any effective counter-measure will need to involve a multi-national strategy, yet such measure is challenging due to a broad spectrum of political and economic development models among the Southeast Asian countries. An overview of the taxonomic structure of the invasive species in Southeast Asia shows that the invasive plant and fish are the most represented taxonomic groups in all countries. The current research effort in invasion ecology from Southeast Asia is not being up to international standard in comparison to other regions, and the absence of recent international journal articles on invasive plant species reveals the biases in biological invasion-related research. The lack of research capacity and financial support from governments, and the inability to disseminate scholarly data in international journals are the possible reasons for the dearth of research literature on biological invasions from the region. Finally, a forward-looking agenda for the region should include improving the quality and quantity of biological invasion research; adopting a tough approach to the illegal release of wildlife; and applying multi-national strategies that integrate data sharing, prioritization, public awareness, policy work, capacity building, conservation actions and surveillance.
Situation-aware multi-criteria recommender system: using criteria preferences as contexts

Recommender systems (RSs) have been successfully applied to alleviate the problem of information overload and assist users' decision makings. Multi-criteria recommender systems is one of the RSs which utilizes users' multiple ratings on different aspects of the items (i.e., multi-criteria ratings) to predict user preferences. Traditional approaches simply treat these multi-criteria ratings as addons, and aggregate them together to serve for item recommendations. In this paper, we propose the novel approaches which treat criteria preferences as contextual situations. More specifically, we believe that part of multiple criteria preferences can be viewed as contexts, while others can be treated in the traditional way in multi-criteria recommender systems. We compare the recommendation performance among three settings: using all the criteria ratings in the traditional way, treating all the criteria preferences as contexts, and utilizing selected criteria ratings as contexts. Our experiments based on two real-world rating data sets reveal that treating criteria preferences as contexts can improve the performance of item recommendations, but they should be carefully selected. The hybrid model of using selected criteria preferences as contexts and the remaining ones in the traditional way is finally demonstrated as the overall winner in our experiments.
A collaborative recommender system enhanced with particle swarm optimization technique

In a web environment, one of the most evolving application is those with recommendation system (RS). It is a subset of information filtering systems wherein, information about certain products or services or a person are categorized and are recommended for the concerned individual. Most of the authors designed collaborative movie recommendation system by using K-NN and K-means but due to a huge increase in movies and users quantity, the neighbour selection is getting more problematic. We propose a hybrid model based on movie recommender system which utilizes type division method and classified the types of the movie according to users which results reduce computation complexity. K-Means provides initial parameters to particle swarm optimization (PSO) so as to improve its performance. PSO provides initial seed and optimizes fuzzy c-means (FCM), for soft clustering of data items (users), instead of strict clustering behaviour in K-Means. For proposed model, we first adopted type division method to reduce the dense multidimensional data space. We looked up for techniques, which could give better results than K-Means and found FCM as the solution. Genetic algorithm (GA) has the limitation of unguided mutation. Hence, we used PSO. In this article experiment performed on Movielens dataset illustrated that the proposed model may deliver high performance related to veracity, and deliver more predictable and personalized recommendations. When compared to already existing methods and having 0.78 mean absolute error (MAE), our result is 3.503 % better with 0.75 as the MAE, showed that our approach gives improved results.
Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios

This paper analyzes how recommender systems can be applied to current e-learning systems to guide learners in personalized inclusive e-learning scenarios. Recommendations can be used to overcome current limitations of learning management systems in providing personalization and accessibility features. Recommenders can take advantage of standards-based solutions to provide inclusive support. To this end we have identified the need for developing semantic educational recommender systems, which are able to extend existing learning management systems with adaptive navigation support. In this paper we present three requirements to be considered in developing these semantic educational recommender systems, which are in line with the service-oriented approach of the third generation of learning management systems, namely: (i) a recommendation model; (ii) an open standards-based service-oriented architecture; and (iii) a usable and accessible graphical user interface to deliver the recommendations.
Collision Avoidance for Cooperative UAVs with Rolling Optimization Algorithm Based on Predictive State Space

Unmanned Aerial Vehicles (UAVs) have recently received notable attention because of their wide range of applications in urban civilian use and in warfare. With air traffic densities increasing, it is more and more important for UAVs to be able to predict and avoid collisions. The main goal of this research effort is to adjust real-time trajectories for cooperative UAVs to avoid collisions in three-dimensional airspace. To explore potential collisions, predictive state space is utilized to present the waypoints of UAVs in the upcoming situations, which makes the proposed method generate the initial collision-free trajectories satisfying the necessary constraints in a short time. Further, a rolling optimization algorithm (ROA) can improve the initial waypoints, minimizing its total distance. Several scenarios are illustrated to verify the proposed algorithm, and the results show that our algorithm can generate initial collision-free trajectories more efficiently than other methods in the common airspace.
Verb Classes Within and Across Languages

Verb classes are sets of semantically-related verbs sharing a range of linguistic properties, such as: — possible realizations of their arguments — interpretation associated with each possible argument realization A big question posed by the Valency Project: Which facets of verb classification are universal and which language particular?  Review my general perspective on verb classes. — Introduce a development in my work on verb classes. — Consider its implications for future crosslinguistic studies of verb classes.
EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition

Gesture is a natural interface in human–computer interaction, especially interacting with wearable devices, such as VR/AR helmet and glasses. However, in the gesture recognition community, it lacks of suitable datasets for developing egocentric (first-person view) gesture recognition methods, in particular in the deep learning era. In this paper, we introduce a new benchmark dataset named EgoGesture with sufficient size, variation, and reality to be able to train deep neural networks. This dataset contains more than 24 000 gesture samples and 3 000 000 frames for both color and depth modalities from 50 distinct subjects. We design 83 different static and dynamic gestures focused on interaction with wearable devices and collect them from six diverse indoor and outdoor scenes, respectively, with variation in background and illumination. We also consider the scenario when people perform gestures while they are walking. The performances of several representative approaches are systematically evaluated on two tasks: gesture classification in segmented data and gesture spotting and recognition in continuous data. Our empirical study also provides an in-depth analysis on input modality selection and domain adaptation between different scenes.
Beyond natives and immigrants: exploring types of net generation students

Previously assumed to be a homogenous and highly skilled group with respect to information and communications technology, the so-called Net Generation has instead been shown to possess a diverse range of technology skills and preferences. To better understand this diversity, we subjected data from 2096 students aged between 17 and 26 from three Australian universities to a cluster analysis. Through this analysis, we identified four distinct types of technology users: power users (14% of sample), ordinary users (27%), irregular users (14%) and basic users (45%). A series of exploratory chi-square analyses revealed significant associations between the different types of technology users and the university that students attended, their gender and age and whether the student was local or international. No associations were found for analyses related discipline area, socio-economic status or rurality of residence. The findings are discussed in light of the rhetoric associated with commentaries about the Net Generation, and suggestions about their implications for teaching and learning in universities are offered.
Auditory Expectation: The Information Dynamics of Music Perception and Cognition

Following in a psychological and musicological tradition beginning with Leonard Meyer, and continuing through David Huron, we present a functional, cognitive account of the phenomenon of expectation in music, grounded in computational, probabilistic modeling. We summarize a range of evidence for this approach, from psychology, neuroscience, musicology, linguistics, and creativity studies, and argue that simulating expectation is an important part of understanding a broad range of human faculties, in music and beyond.
Foldem: Heterogeneous Object Fabrication via Selective Ablation of Multi-Material Sheets

Foldem, a novel method of rapid fabrication of objects with multi-material properties is presented. Our specially formulated Foldem sheet allows users to fabricate and easily assemble objects with rigid, bendable, and flexible properties using a standard laser-cutter. The user begins by creating his designs in a vector graphics software package. A laser cutter is then used to fabricate the design by selectively ablating/vaporizing one or more layers of the Foldem sheet to achieve the desired physical properties for each joint. Herein the composition of the Foldem sheet, as well as various design considerations taken into account while building and designing the method, are described. Sample objects made with Foldem are demonstrated, each showcasing the unique attributes of Foldem. Additionally, a novel method for carefully calibrating a laser cutter for precise ablation is presented.
A Decision Support Framework to Assess Supply Chain Resilience

Our research is aimed at developing a quantitative approach for assessing supply chain resilience to disasters, a topic that has been discussed primarily in a qualitative manner in the literature. For this purpose, we propose a simulation-based framework that incorporates concepts of resilience into the process of supply chain design. In this context, resilience is defined as the ability of a supply chain system to reduce the probabilities of disruptions, to reduce the consequences of those disruptions, and to reduce the time to recover normal performance. The decision framework incorporates three determinants of supply chain resilience (density, complexity, and node criticality) and discusses their relationship to the occurrence of disruptions, to the impacts of those disruptions on the performance of a supply chain system and to the time needed for recovery. Different preliminary strategies for evaluating supply chain resilience to disasters are identified, and directions for future research are discussed.
Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition

In this paper, we propose using 3D Convolutional Neural Networks for large scale user-independent continuous gesture recognition. We have trained an end-to-end deep network for continuous gesture recognition (jointly learning both the feature representation and the classifier). The network performs three-dimensional (i.e. space-time) convolutions to extract features related to both the appearance and motion from volumes of color frames. Space-time invariance of the extracted features is encoded via pooling layers. The earlier stages of the network are partially initialized using the work of Tran et al. before being adapted to the task of gesture recognition.
Frequency effects on the scale and behavior of acoustic streaming.

Acoustic streaming underpins an exciting range of fluid manipulation phenomena of rapidly growing significance in microfluidics, where the streaming often assumes the form of a steady, laminar jet emanating from the device surface, driven by the attenuation of acoustic energy within the beam of sound propagating through the liquid. The frequencies used to drive such phenomena are often chosen ad hoc to accommodate fabrication and material issues. In this work, we seek a better understanding of the effects of sound frequency and power on acoustic streaming. We present and, using surface acoustic waves, experimentally verify a laminar jet model that is based on the turbulent jet model of Lighthill, which is appropriate for acoustic streaming seen at micro- to nanoscales, between 20 and 936 MHz and over a broad range of input power. Our model eliminates the critically problematic acoustic source singularity present in Lighthill's model, replacing it with a finite emission area and enabling determination of the streaming velocity close to the source. At high acoustic power P (and hence high jet Reynolds numbers ReJ associated with fast streaming), the laminar jet model predicts a one-half power dependence (U∼P1/2∼ ReJ) similar to the turbulent jet model. However, the laminar model may also be applied to jets produced at low powers-and hence low jet Reynolds numbers ReJ-where a linear relationship between the beam power and streaming velocity exists: U∼P∼ReJ2. The ability of the laminar jet model to predict the acoustic streaming behavior across a broad range of frequencies and power provides a useful tool in the analysis of microfluidics devices, explaining peculiar observations made by several researchers in the literature. In particular, by elucidating the effects of frequency on the scale of acoustically driven flows, we show that the choice of frequency is a vitally important consideration in the design of small-scale devices employing acoustic streaming for microfluidics.
Security analysis of an image encryption algorithm based on a DNA addition combining with chaotic maps

In this paper, we propose to cryptanalyse an encryption algorithm which combines a DNA addition and a chaotic map to encrypt a gray scale image. Our contribution consists on, at first, demonstrating that the algorithm, as it is described, is non-invertible, which means that the receiver cannot decrypt the ciphered image even if he posses the secret key. Then, a chosen plaintext attack on the invertible encryption block is described, where, the attacker can illegally decrypt the ciphered image by a temporary access to the encryption machinery.
Graph and model transformation tools for model migration

We describe the results of the Transformation Tool Contest 2010 workshop, in which nine graph and model transformation tools were compared for specifying model migration. The model migration problem—migration of UML activity diagrams from version 1.4 to version 2.2—is non-trivial and practically relevant. The solutions have been compared with respect to several criteria: correctness, conciseness, understandability, appropriateness, maturity and support for extensions to the core migration task. We describe in detail the comparison method, and discuss the strengths and weaknesses of the solutions with a special focus on the differences between graph and model transformation for model migration. The comparison results demonstrate tool and language features that strongly impact the efficacy of solutions, such as support for retyping of model elements. The results are used to motivate an agenda for future model migration research (including suggestions for areas in which the tools need to be further improved).
Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, whereas the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio estimation and modulation format classification, respectively. The proposed methods accurately evaluate optical signals employing up to 64 quadrature amplitude modulation, at 32 Gbd, using only directly detected data.
Wnt and GSK3 Signaling Pathways in Bipolar Disorder: Clinical and Therapeutic Implications

The neurobiology of bipolar disorder, a chronic and systemic ailment is not completely understood. The bipolar phenotype manifests in myriad ways, and psychopharmacological agents like lithium have long term beneficial effects. The enzyme glycogen synthase kinase 3 (GSK3) has come into focus, as lithium and several other mood stabilizing medications inhibit its activity. This kinase and its key upstream modulator, Wnt are dysregulated in mood disorders and there is a growing impetus to delineate the chief substrates involved in the development of these illnesses. In May 2016, a comprehensive literature search was undertaken which revealed that there is over activity of GSK3 in bipolar disorder with deleterious downstream effects like proinflammatory status, increased oxidative stress, and circadian dysregulation leading to declining neurotrophic support and enhanced apoptosis of neural elements. By developing specific GSK3 inhibitors the progressive worsening in bipolar disorder can be forestalled with improved prospects for the sufferers.