nils-herrmann/clean_scirepeval huggingface dataset:
Visual object recognition.

Visual object recognition is of fundamental importance to most animals. The diversity of tasks that any biological recognition system must solve suggests that object recognition is not a single, general purpose process. In this review, we consider evidence from the fields of psychology, neuropsychology, and neurophysiology, all of which supports the idea that there are multiple systems for recognition. Data from normal adults, infants, animals, and brain damaged patients reveal a major distinction between the classification of objects at a basic category level and the identification of individual objects from a homogeneous object class. An additional distinction between object representations used for visual perception and those used for visually guided movements provides further support for a multiplicity of visual recognition systems. Recent evidence from psychophysical and neurophysiological studies indicates that one system may represent objects by combinations of multiple views, or aspects, and another may represent objects by structural primitives and their spatial interrelationships.
Creativity in Higher Education : The use of Creative Cognition in Studying Book Section

There is consensus among educational researchers from differing theoretical perspectives that creativity is an ability essential to adaptation in a constantly changing work environment. Despite the growing popularity of creativity as a field of study and intervention in business and education in the last decade, Higher Education institutions have made surprisingly little progress in successfully teaching and developing students’ creative ability. The objective of this chapter is to disentangle the various facets of creativity in order to identify those psychological processes underlying creativity that are more amenable to intervention in Higher Education. The first section of this chapter reviews the main theoretical perspectives on creativity – the Big-C and little-c, the four Ps, and the creative process perspectives – and explores their potential for application in Higher Education. The second section reviews the evidence supporting a link between the use of creative cognition in studying, positive affect in studying, and academic performance, and argues that the use of creative cognition as a volitional, context-appropriate habit is the key target variable for interventions aiming at enhancing students’ creative ability.
Digital receivers and transmitters using polyphase filter banks for wireless communications

This paper provides a tutorial overview of multichannel wireless digital receivers and the relationships between channel bandwidth, channel separation, and channel sample rate. The overview makes liberal use of figures to support the underlying mathematics. A multichannel digital receiver simultaneously down-convert a set of frequency-division-multiplexed (FDM) channels residing in a single sampled data signal stream. In a similar way, a multichannel digital transmitter simultaneously up-converts a number of baseband signals to assemble a set of FDM channels in a single sampled data signal stream. The polyphase filter bank has become the architecture of choice to efficiently accomplish these tasks. This architecture uses three interacting processes to assemble or to disassemble the channelized signal set. In a receiver, these processes are an input commutator to effect spectral folding or aliasing due to a reduction in sample rate, a polyphase -path filter to time align the partitioned and resampled time series in each path, and a discrete Fourier transform to phase align and separate the multiple baseband aliases. In a transmitter, these same processes operate in a related manner to alias baseband signals to high order Nyquist zones while increasing the sample rate with the output commutator. This paper presents a sequence of simple modifications to sampled data structures based on analog prototype systems to obtain the basic polyphase structure. We further discuss ways to incorporate small modifications in the operation of the polyphase system to accommodate secondary performance requirements. MATLAB simulations of a 10-, 40-, and 50-channel resampling receiver are included in the electronic version of this paper.
Advances in dataflow programming languages

Many developments have taken place within dataflow programming languages in the past decade. In particular, there has been a great deal of activity and advancement in the field of dataflow visual programming languages. The motivation for this article is to review the content of these recent developments and how they came about. It is supported by an initial review of dataflow programming in the 1970s and 1980s that led to current topics of research. It then discusses how dataflow programming evolved toward a hybrid von Neumann dataflow formulation, and adopted a more coarse-grained approach. Recent trends toward dataflow visual programming languages are then discussed with reference to key graphical dataflow languages and their development environments. Finally, the article details four key open topics in dataflow programming languages.
The neural basis of economic decision-making in the Ultimatum Game.

The nascent field of neuroeconomics seeks to ground economic decision making in the biological substrate of the brain. We used functional magnetic resonance imaging of Ultimatum Game players to investigate neural substrates of cognitive and emotional processes involved in economic decision-making. In this game, two players split a sum of money;one player proposes a division and the other can accept or reject this. We scanned players as they responded to fair and unfair proposals. Unfair offers elicited activity in brain areas related to both emotion (anterior insula) and cognition (dorsolateral prefrontal cortex). Further, significantly heightened activity in anterior insula for rejected unfair offers suggests an important role for emotions in decision-making.
Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm

Bicycles that share the road with intelligent vehicles present particular challenges for automated perception systems. Bicycle detection is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision standpoint, bicycle detection is challenging as bicycle's appearance can change dramatically between viewpoints and a person riding on the bicycle is a non-rigid object. In this paper, we present a vision-based framework to detect and track bicycles that takes into account these issues. A mixture model of multiple viewpoints is defined and trained via a Support Vector Machine (SVM) to detect bicycles under a variety of circumstances. Each component of the model uses a part-based representation and known geometric context is used to improve overall detection efficiency.
A Regularization Method with Inference of Trust and Distrust in Recommender Systems

In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users’ preferences, accounting for the fact that users trust the recommendations of their friends, and they do not accept the recommendations of their foes. In addition, not only users’ preferences are sparse, but also users’ social relationships. So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users’ explicit trust and distrust relationships during the inference. We introduce a regularization method and design an objective function with a social regularization term to weigh the influence of friends’ trust and foes’ distrust degrees on users’ preferences. We formulate the objective function of our regularization method as a minimization problem with respect to the users’ and items’ latent features and then we solve our recommendation problem via gradient descent. Our experiments confirm that our approach preserves relatively high recommendation accuracy in the presence of sparsity in both the users’ preferences and social relationships, significantly outperforming several state-of-the-art methods.
Don't Get Caught in the Cold, Warm-up Your JVM: Understand and Eliminate JVM Warm-up Overhead in Data-Parallel Systems

Many widely used, latency sensitive, data-parallel distributed systems, such as HDFS, Hive, and Spark choose to use the Java Virtual Machine (JVM), despite debate on the overhead of doing so. This paper analyzes the extent and causes of the JVM performance overhead in the above mentioned systems. Surprisingly, we find that the warm-up overhead, i.e., class loading and interpretation of bytecode, is frequently the bottleneck. For example, even an I/O intensive, 1GB read on HDFS spends 33% of its execution time in JVM warm-up, and Spark queries spend an average of 21 seconds in warm-up. The findings on JVM warm-up overhead reveal a contradiction between the principle of parallelization, i.e., speeding up long running jobs by parallelizing them into short tasks, and amortizing JVM warm-up overhead through long tasks. We solve this problem by designing HotTub, a new JVM that amortizes the warm-up overhead over the lifetime of a cluster node instead of over a single job by reusing a pool of already warm JVMs across multiple applications. The speed-up is significant. For example, using HotTub results in up to 1.8X speedups for Spark queries, despite not adhering to the JVM specification in edge cases.
GORDIAN: Efficient and Scalable Discovery of Composite Keys

Identification of (composite) key attributes is of fundamental importance for many different data management tasks such as data modeling, data integration, anomaly detection, query formulation, query optimization, and indexing. However, information about keys is often missing or incomplete in many real-world database scenarios. Surprisingly, the fundamental problem of automatic key discovery has received little attention in the existing literature. Existing solutions ignore composite keys, due to the complexity associated with their discovery. Even for simple keys, current algorithms take a brute-force approach; the resulting exponential CPU and memory requirements limit the applicability of these methods to small datasets. In this paper, we describe GORDIAN, a scalable algorithm for automatic discovery of keys in large datasets, including composite keys. GORDIAN can provide exact results very efficiently for both real-world and synthetic datasets. GORDIAN can be used to find (composite) key attributes in any collection of entities, e.g., key column-groups in relational data, or key leaf-node sets in a collection of XML documents with a common schema. We show empirically that GORDIAN can be combined with sampling to efficiently obtain high quality sets of approximate keys even in very large datasets.
A systematic review of neurobiological and clinical features of mindfulness meditations.

Mindfulness meditation (MM) practices constitute an important group of meditative practices that have received growing attention. The aim of the present paper was to systematically review current evidence on the neurobiological changes and clinical benefits related to MM practice in psychiatric disorders, in physical illnesses and in healthy subjects.



A literature search was undertaken using Medline, ISI Web of Knowledge, the Cochrane collaboration database and references of retrieved articles. Controlled and cross-sectional studies with controls published in English up to November 2008 were included.



Electroencephalographic (EEG) studies have revealed a significant increase in alpha and theta activity during meditation. Neuroimaging studies showed that MM practice activates the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) and that long-term meditation practice is associated with an enhancement of cerebral areas related to attention. From a clinical viewpoint, Mindfulness-Based Stress Reduction (MBSR) has shown efficacy for many psychiatric and physical conditions and also for healthy subjects, Mindfulness-Based Cognitive Therapy (MBCT) is mainly efficacious in reducing relapses of depression in patients with three or more episodes, Zen meditation significantly reduces blood pressure and Vipassana meditation shows efficacy in reducing alcohol and substance abuse in prisoners. However, given the low-quality designs of current studies it is difficult to establish whether clinical outcomes are due to specific or non-specific effects of MM.



Despite encouraging findings, several limitations affect current studies. Suggestions are given for future research based on better designed methodology and for future directions of investigation.
Integration of Standardized and Non-Standardized Product Data

Smart products adapt to environments, process contexts, users and other products. Standardized product data such as BMECat, eCl@ss, and EPC global formats are designed to support the exploitation of product data and therefore contribute to more efficient supply chains. Non-standardized product data mainly target soft benefits which targets the exploration of product data [Su04]. We present an extended product data model (SPDO) that integrates standardized and non-standardized product data. The SPDO model consists of five product information types that receive product data and, if available, associated explicit semantics. Explicit semantics are used for advanced services, such as compatibility testing or product bundling. Exemplified by the GS1 approach, we present how standardized product data is retrieved via RFID-based Electronic Product Codes (EPC) and stored in SPDO instantiations.
A Hybrid Reverse Engineering Approach Combining Metrics and Program Visualization

Surprising as it may seem, many of the early adopters of the object-oriented paradigm already face a number of problems typically encountered in large-scale legacy systems. Consequently, reverse engineering techniques are relevant in an object-oriented context as well. This paper investigates a hybrid approach, combining the immediate appeal of visualisations with the scalability of metrics. We validate such a hybrid approach by showing how CodeCrawler —the experimental platform we built— allowed us to understand the program structure of, and identify potential design anomalies in a public domain software system.
Deep Scattering Spectrum with deep neural networks

State-of-the-art convolutional neural networks (CNNs) typically use a log-mel spectral representation of the speech signal. However, this representation is limited by the spectro-temporal resolution afforded by log-mel filter-banks. A novel technique known as Deep Scattering Spectrum (DSS) addresses this limitation and preserves higher resolution information, while ensuring time warp stability, through the cascaded application of the wavelet-modulus operator. The first order scatter is equivalent to log-mel features and standard CNN modeling techniques can directly be used with these features. However the higher order scatter, which preserves the higher resolution information, presents new challenges in modelling. This paper explores how to effectively use DSS features with CNN acoustic models. Specifically, we identify the effective normalization, neural network topology and regularization techniques to effectively model higher order scatter. The use of these higher order scatter features, in conjunction with CNNs, results in relative improvement of 7% compared to log-mel features on TIMIT, providing a phonetic error rate (PER) of 17.4%, one of the lowest reported PERs to date on this task.
Blockchain for IoT security and privacy: The case study of a smart home

Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT networks. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational overhead that is not suitable for most resource-constrained IoT devices. In our previous work, we presented a lightweight instantiation of a BC particularly geared for use in IoT by eliminating the Proof of Work (POW) and the concept of coins. Our approach was exemplified in a smart home setting and consists of three main tiers namely: cloud storage, overlay, and smart home. In this paper we delve deeper and outline the various core components and functions of the smart home tier. Each smart home is equipped with an always online, high resource device, known as “miner” that is responsible for handling all communication within and external to the home. The miner also preserves a private and secure BC, used for controlling and auditing communications. We show that our proposed BC-based smart home framework is secure by thoroughly analysing its security with respect to the fundamental security goals of confidentiality, integrity, and availability. Finally, we present simulation results to highlight that the overheads (in terms of traffic, processing time and energy consumption) introduced by our approach are insignificant relative to its security and privacy gains.
Kinematically Redundant Manipulators

This chapter focuses on redundancy resolution schemes, i. e., the techniques for exploiting the redundant degrees of freedom in the solution of the inverse kinematics problem. This is obviously an issue of major relevance for motion planning and control purposes. In particular, task-oriented kinematics and the basic methods for its inversion at the velocity (first-order differential) level are first recalled, with a discussion of the main techniques for handling kinematic singularities. Next, different first-order methods to solve kinematic redundancy are arranged in two main categories, namely those based on the optimization of suitable performance criteria and those relying on the augmentation of the task space. Redundancy resolution methods at the acceleration (second-order differential) level are then considered in order to take into account dynamics issues, e.g., torque minimization. Conditions under which a cyclic task motion results in a cyclic joint motion are also discussed; this is a major issue, e.g., for industrial applications in which a redundant manipulator is used to execute a repetitive task. The special class of hyperredundant manipulators is analyzed in detail. Suggestions for further reading are given in a final section. 11.1 Overview.............................................. 245
Interpreting random forest models using a feature contribution method

Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.
Fast Gated Neural Domain Adaptation: Language Model as a Case Study

Neural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a novel domain adaptation mechanism in neural network training. Instead of learning and adapting the neural network on millions of training sentences – which can be very timeconsuming or even infeasible in some cases – we design a domain adaptation gating mechanism which can be used in recurrent neural networks and quickly learn the out-of-domain knowledge directly from the word vector representations with little speed overhead. In our experiments, we use the recurrent neural network language model (LM) as a case study. We show that the neural LM perplexity can be reduced by 7.395 and 12.011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively. Furthermore, we show that using the domain-adapted neural LM to re-rank the statistical machine translation n-best list on the French-to-English language pair can significantly improve translation quality.
ON QUANTITATIVE MICROWAVE TOMOGRAPHY OF FEMALE BREAST

Microwave tomography deserves attention in biomedical imaging, owing to its potential capability of providing a morphological and functional assessment of the inspected tissues. However, such a goal requires the not trivial task of solving a non linear inverse scattering problem. In this paper, the factors affecting the complexity of the inverse problem are exploited to trace guidelines aimed at setting the matching fluid, the frequency range and the number of probes in such a way that the dielectric parameters of female breast tissues can be reliably retrieved. Examples, concerning 2D realistic numerical phantoms obtained by NMR images, are given to asses a posteriori the effectiveness of the proposed guidelines.  L. Crocco (crocco.l@irea.cnr.it). † Also with DIBET — Università degli Studi di Napol Federico II Via Claudio 21, Napoli
Timely Query Processing in Smart Electric Grids: Algorithms and Performance

Smart-grid applications have widely varying data needs as well as bandwidth and latency requirements. The usual approach to collecting the available data (e.g., from Phasor Measurement Units) at a centralized site continuously and executing all the applications there leads to large latencies and requirements for high communication bandwidth. This paper proposes and evaluates, using real data, techniques wherein the data packets are disseminated based on the applications' data needs and semantics. These techniques systematically filter data in the dissemination network and reduce bandwidth requirements while resulting in low latency solutions. For example, based on the results from our testbed for the Indian Electric Grid, we show that the processing overheads decrease by at least 50% for the large PMU data sizes compared to the traditional centralized approach.
A Bridging Framework for Model Optimization and Deep Propagation

Optimizing task-related mathematical model is one of the most fundamental methodologies in statistic and learning areas. However, generally designed schematic iterations may hard to investigate complex data distributions in real-world applications. Recently, training deep propagations (i.e., networks) has gained promising performance in some particular tasks. Unfortunately, existing networks are often built in heuristic manners, thus lack of principled interpretations and solid theoretical supports. In this work, we provide a new paradigm, named Propagation and Optimization based Deep Model (PODM), to bridge the gaps between these different mechanisms (i.e., model optimization and deep propagation). On the one hand, we utilize PODM as a deeply trained solver for model optimization. Different from these existing network based iterations, which often lack theoretical investigations, we provide strict convergence analysis for PODM in the challenging nonconvex and nonsmooth scenarios. On the other hand, by relaxing the model constraints and performing end-to-end training, we also develop a PODM based strategy to integrate domain knowledge (formulated as models) and real data distributions (learned by networks), resulting in a generic ensemble framework for challenging real-world applications. Extensive experiments verify our theoretical results and demonstrate the superiority of PODM against these state-of-the-art approaches.