nils-herrmann/clean_scirepeval huggingface dataset:
A personalized recommender system for pervasive social networks

The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. On the one hand, users tend to be always connected for sharing experiences and conducting their social interactions with friends and acquaintances, through so-called Mobile Social Networks, further improving their social inclusion. On the other hand, the pervasiveness of communication infrastructures spreading data (cellular networks, direct device-to-device contacts, interactions with ambient devices as in the Internet-of-Things) makes compulsory the deployment of solutions able to filter off undesired information and to select what content should be addressed to which users, for both (i) better user experience, and (ii) resource saving of both devices and network. In this work, we propose a novel framework for pervasive social networks, called Pervasive PLIERS (p-PLIERS), able to discover and select, in a highly personalized way, contents of interest for single mobile users. p-PLIERS exploits the recently proposed PLIERS tag-based recommender system  as context a reasoning tool able to adapt recommendations to heterogeneous interest profiles of different users. p-PLIERS effectively operates also when limited knowledge about the network is maintained. It is implemented in a completely decentralized environment, in which new contents are continuously generated and diffused through the network, and it relies only on the exchange of single nodes’ knowledge during proximity contacts and through device-to-device communications. We evaluated p-PLIERS by simulating its behavior in three different scenarios: a big event (Expo 2015), a conference venue (ACM KDD’15), and a working day in the city of Helsinki. For each scenario, we used real or synthetic mobility traces and we extracted real datasets from Twitter interactions to characterize the generation and sharing of user contents.
CAMSHIFT Tracker Design Experiments With Intel OpenCV and SAI

When humans interact with computer systems, they expect the experience to meet human standards of reactiveness, robustness and, if possible, non-intrusiveness. In order for computer vision techniques to have a significant impact in human-computer interaction, the development of efficient and robust algorithms, as well as their integration and operation as part of complex (including multi-modal) systems, must be specifically addressed. This report describes design and implementation experiments for CAMSHIFT-based tracking systems using Intel’s Open Computer Vision library and SAI (Software Architecture for Immersipresence), a software architecture model created specifically to address the integration of different solutions to technical challenges, developed independently in separate fields, into working systems, that operate under hard performance constraints. Results show that the SAI formalism is an enabling tool for designing, describing and implementing robust systems of efficient algorithms.
Multi-task Deep Reinforcement Learning with PopArt

The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequentialdecision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent’s updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy with a single set of weights that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.
Microfabricated adhesive mimicking gecko foot-hair.

The amazing climbing ability of geckos has attracted the interest of philosophers and scientists alike for centuries. However, only in the past few years has progress been made in understanding the mechanism behind this ability, which relies on submicrometre keratin hairs covering the soles of geckos. Each hair produces a miniscule force approximately 10(-7) N (due to van der Waals and/or capillary interactions) but millions of hairs acting together create a formidable adhesion of approximately 10 N x cm(-2): sufficient to keep geckos firmly on their feet, even when upside down on a glass ceiling. It is very tempting to create a new type of adhesive by mimicking the gecko mechanism. Here we report on a prototype of such 'gecko tape' made by microfabrication of dense arrays of flexible plastic pillars, the geometry of which is optimized to ensure their collective adhesion. Our approach shows a way to manufacture self-cleaning, re-attachable dry adhesives, although problems related to their durability and mass production are yet to be resolved.
Behavioral dynamics of steering, obstacle avoidance, and route selection.

The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obstacles behave as attractors and repellers of heading, respectively, whose strengths depend on distance. The observed behavior was modeled as a dynamical system in which angular acceleration is a function of goal and obstacle angle and distance. By linearly combining terms for goals and obstacles, one could predict whether participants adopt a route to the left or right of an obstacle to reach a go (Experiment 3). Route selection may emerge from on-line steering dynamics, making explicit path planning unnecessary.
Customer churn analysis : A case study on the telecommunication industry of Thailand

Customer churn creates a huge anxiety in highly competitive service sectors especially the telecommunications sector. The objective of this research was to develop a predictive churn model to predict the customers that will be to churn; this is the first step to construct a retention management plan. The dataset was extracted from the data warehouse of the mobile telecommunication company in Thailand. The system generated the customer list, to implement a retention campaign to manage the customers with tendency to leave the company. WEKA software was used to implement the followings techniques: C4.5 decision trees algorithm, the logistic regression algorithm and the neural network algorithm. The C4.5 algorithm of decision trees proved optimal among the models. The findings are unequivocally beneficial to industry and other partners.
Detection of Diabetic Retinopathy in Retinal Images Using MLP Classifier

The rising situation in the developing world suggests diabetic retinopathy may soon be a major problem in the clinical world . Hence, detection of diabetic retinopathy is important. This paper focuses on Multi Layer Perception Neural Network (MLPNN) to detect diabetic retinopathy in retinal images. In this paper the MLPNN classifier is presented to classify retinal images as normal and abnormal. A feature vector is formed with 64-point Discrete Cosine Transform (DCT) with different 09 statistical parameters namely Entropy, mean, standard deviation, average, Euler number, contrast, correlation, energy and homogeneity. The Train N Times method was used to train the MLPNN to find best feature subset. The training and cross validation rates by the MLP NN are 100% for detection of normal and abnormal retinal images.
Moringa oleifera: a food plant with multiple medicinal uses.

Moringa oleifera Lam (Moringaceae) is a highly valued plant, distributed in many countries of the tropics and subtropics. It has an impressive range of medicinal uses with high nutritional value. Different parts of this plant contain a profile of important minerals, and are a good source of protein, vitamins, beta-carotene, amino acids and various phenolics. The Moringa plant provides a rich and rare combination of zeatin, quercetin, beta-sitosterol, caffeoylquinic acid and kaempferol. In addition to its compelling water purifying powers and high nutritional value, M. oleifera is very important for its medicinal value. Various parts of this plant such as the leaves, roots, seed, bark, fruit, flowers and immature pods act as cardiac and circulatory stimulants, possess antitumor, antipyretic, antiepileptic, antiinflammatory, antiulcer, antispasmodic, diuretic, antihypertensive, cholesterol lowering, antioxidant, antidiabetic, hepatoprotective, antibacterial and antifungal activities, and are being employed for the treatment of different ailments in the indigenous system of medicine, particularly in South Asia. This review focuses on the detailed phytochemical composition, medicinal uses, along with pharmacological properties of different parts of this multipurpose tree.
Wide & Deep Learning for Recommender Systems

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.
Epigenetics and depression: return of the repressed.

Epigenetics has recently emerged as a potential mechanism by which adverse environmental stimuli can result in persistent changes in gene expression. Epigenetic mechanisms function alongside the DNA sequence to modulate gene expression and ultimately influence protein production. The current review provides an introduction and overview of epigenetics with a particular focus on preclinical and clinical studies relevant to major depressive disorder (MDD).



PubMed and Web of Science databases were interrogated from January 1995 up to December 2012 using combinations of search terms, including "epigenetic", "microRNA" and "DNA methylation" cross referenced with "depression", "early life stress" and "antidepressant".



There is an association between adverse environmental stimuli, such as early life stress, and epigenetic modification of gene expression. Epigenetic changes have been reported in humans with MDD and may serve as biomarkers to improve diagnosis. Antidepressant treatments appear to reverse or initiate compensatory epigenetic alterations that may be relevant to their mechanism of action.



As a narrative review, the current report was interpretive and qualitative in nature.



Epigenetic modification of gene expression provides a mechanism for understanding the link between long-term effects of adverse life events and the changes in gene expression that are associated with depression. Although still a developing field, in the future, epigenetic modifications of gene expression may provide novel biomarkers to predict future susceptibility and/or onset of MDD, improve diagnosis, and aid in the development of epigenetics-based therapies for depression.
Discriminant Analysis for Recognition of Human Face Images (Invited Paper)

The discrimination power of various human facial features is studied and a new scheme for automatic face recognition (AFR) is proposed. The first part of the paper focuses on the linear discriminant analysis (LDA) of different aspects of human faces in the spatial as well as in the wavelet domain. This analysis allows objective evaluation of the significance of visual information in different parts (features) of the face for identifying the human subject. The LDA of faces also provides us with a small set of features that carry the most relevant information for classification purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class variations and minimizing within-class variations. The result is an efficient projection-based feature-extraction and classification scheme for AFR. Each projection creates a decision axis with a certain level of discrimination power or reliability. Soft decisions made based on each of the projections are combined, and probabilistic or evidential approaches to multisource data analysis are used to provide more reliable recognition results. For a medium-sized database of human faces, excellent classification accuracy is achieved with the use of very-low-dimensional feature vectors. Moreover, the method used is general and is applicable to many other image-recognition tasks.
A neurophilosophical slant on consciousness research.

Explaining the nature and mechanisms of conscious experience in neurobiological terms seems to be an attainable, if yet unattained, goal. Research at many levels is important, including research at the cellular level that explores the role of recurrent pathways between thalamic nuclei and the cortex, and research that explores consciousness from the perspective of action. Conceptually, a clearer understanding of the logic of expressions such as "causes" and "correlates", and about what to expect from a theory of consciousness are required. The logic of some terms, such as "qualia" and "reductionism", continues to generate misunderstandings about the scientific possibilities and limits. Experimentally, a deeper understanding of the role of the thalamus in coordinating activity across cortical levels, and a readiness to reconsider the orthodox approach to thalamocortical organization are also required.
Multi-criteria decision making with skyline computation

Multi-criteria decision making is one of the most critical and yet most challenging components in modern enterprise business intelligence. It is well known that complex business decisions are often made based on multi-dimensional criteria. The competitiveness of optimal business decision making typically resorts to finding a good trade-off among many different and possibly contradicting criteria, e.g., maximum profit, minimum price, minimum resource consumption. A skyline query operator is by design to find the set of interesting data points (objects) over a large dimensional data collection, satisfying a set of possibly contradicting conditions. In this paper, we provide an in-depth coverage of skyline computation models, algorithms and optimization techniques for improving both efficiency and quality of multi-criteria decision making. By reviewing and revising the state of art research in multi-criteria decision making using skyline operations, we describe the essential concepts, the alternative models, and the suite of techniques for providing scalable and elastic skyline computation in massively distributed computing environments. The paper consists of four parts. First, we provide an overview of skyline query operators in terms of concepts, basic processing algorithms and representative application scenarios. Second, we review the state of art literature in skyline query processing research and development, outline the most representative classes of skyline query processing and optimization techniques and discuss the pros and cons of existing approaches. Third, we provide a comprehensive analysis on the inherent limitations of some existing skyline models and algorithms and discuss why scaling skyline query processing over large high dimensional datasets continues to pose daunting challenges. Finally, we present optimization techniques for designing parallel skyline query processing algorithms and how to utilize GPUs to support and scale parallel skyline computations over high dimensional large datasets. We also introduce a novel dominance test technique, called GNL (GPU-based Nested Loop), which can drastically reduce the cost of dominance tests by leveraging GPUs, and outperform CPU-based dominance tests.
Runtime-behavior based malware classification using online machine learning

Identification of malware's family is an intricate process whose success and accuracy depends on different factors. These factors are mainly related to the process of extracting of meaningful and distinctive features from a set of malware samples, modeling malware via its static or dynamic features and particularly techniques used to classify malware samples. In this paper, we propose a new malware classification method based on behavioral features. File system, network, registry activities observed during the execution traces of the malware samples are used to represent behavior based features. Existing classification schemes apply machine-learning algorithms to the stored data, i.e., they are off-line. In this study, we use on-line machine learning algorithms that can provide instantaneous update about the new malware sample by following its introduction to the classification scheme. To validate the effectiveness and scalability of our method, we have evaluated our method by using 18,000 recent malicious files. Experimental results show that our method classifies malware with an accuracy of 92.
SAX-EFG: an evolutionary feature generation framework for time series classification

A variety of real world applications fit into the broad definition of time series classification. Using traditional machine learning approaches such as treating the time series sequences as high dimensional vectors have faced the well known "curse of dimensionality" problem. Recently, the field of time series classification has seen success by using preprocessing steps that discretize the time series using a Symbolic Aggregate ApproXimation technique (SAX) and using recurring subsequences ("motifs") as features.
 In this paper we explore a feature construction algorithm based on genetic programming that uses SAX-generated motifs as the building blocks for the construction of more complex features. The research shows that the constructed complex features improve the classification accuracy in a statistically significant manner for many applications.
Model-Integrating Microservices: A Vision Paper

Model-integrating development is a novel approach that aims to provide a comprehensive conceptual framework for the engineering of flexible software systems. The atomic building blocks for architecting model-integrating software are modelintegrating components which support the modular cooperation of flexible models and efficient code at runtime. Model-integrating components achieve flexibility by using models at runtime and operations on them like querying, transforming and interpreting. Microservices achieve flexibility by upgrading whole components at runtime. In this short paper, we sketch the vision of Model-Integrating Microservices (MIMs) that combine the strengths of model-integrating components with microservices to support continuous software engineering. With this early work, we intent to initiate a fruitful discussion about architectural design considerations in the community. 1 Background and Motivation Model-Integrating Development (MID) [Der15] is a novel approach that aims to provide a comprehensive conceptual framework for the design and development of flexible software that can be adapted easily and – in parts – even autonomously [ST09]. Moreover, being able to make changes quickly and to evolve software easily also supports longevity [GRG15]. We observe that these capabilities are similarly useful in the emerging trend of Continuous Software Engineering (CSE). The building blocks for MID are Model-Integrating Components (MoCos) which we have presented and implemented in previous research [DEIE14]. MoCos result from the symbiosis of Component-Based Development (CBD) [SGM02] and Model-Driven Development (MDD) [BCW12] concepts. A MoCo is a non-redundant, reusable and executable combination of logically related models and code in an integrated form where both parts are stored together in one component. In traditional MDD, models (e.g., UML activity models, feature models, component models) are used at design time. In contrast, in MID models are used directly at runtime, too. As a result, the internals of MoCos can be easily monitored and analyzed using model queries, as well as systematically modified using repeatable model transformations. The same techniques can be used for evolving components using an editor during development and maintenance, and adapting them at runtime using an administration panel or an autonomic adaptation manager component. By combining the strengths of modeling languages (e.g., abstraction, separation of conCopyright © 2016 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors. M. Derakhshanmanesh, M. Grieger: Model-Integrating Microservices
A Brushless DC Motor Drive With Power Factor Correction Using Isolated Zeta Converter

This paper presents a brushless dc (BLDC) motor drive with power factor correction (PFC) for low-power applications. In this work, the speed of the BLDC motor is controlled by adjusting the dc link voltage of the voltage source inverter (VSI) feeding a BLDC motor. Therefore, VSI is used for achieving only an electronic commutation of the BLDC motor and operates in a low frequency switching for reduced switching losses. A PFC-based isolated zeta converter operating in discontinuous conduction mode (DCM) is used for controlling the dc link voltage of the VSI with inherent PFC at ac mains using single voltage sensor. The proposed drive is implemented to achieve a unity power factor at ac mains for a wide range of speed control and supply voltage fluctuations. An improved power quality is achieved with power quality indices within limits of IEC 61000-3-2 standard.