nils-herrmann/clean_scirepeval huggingface dataset:
The Future Impact of ICTs on Environmental Sustainability

The mission of IPTS is to provide techno-economic analyses in support of the European policy-making process. IPTS' prime objectives are to monitor and analyse science and technology developments, their cross-sectoral impact, their interrelationship with the socioeconomic context and their implications for future policy development. IPTS operates international networks, pools the expertise of high-level advisors, and presents information in a timely and synthesised fashion to policy makers. The orientation and contents of this report cannot be taken as indicating the position of the European Commission or its services. The European Commission retains the copyright of this publication. Reproduction is authorised, except for commercial purposes, if the source is mentioned. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of the information in this report. Information and Communication Technologies (ICTs) not only constitute an industry in their own right but they also pervade all sectors of the economy, where they act as integrating and enabling technologies. ICTs have a profound impact on society, and their production and use have important consequences for development in economic, social and environmental areas. The extent to which ICTs also affect progress towards environmental sustainability in an economy is an issue that is still under debate. There is, however, increasing evidence that significant opportunities and threats are involved. These deserve more research and more attention in energy, climate change and technology policies. Commission) has commissioned a study entitled 'The Future Impact of ICTs on Environmental Sustainability', which aims to explore (qualitatively) and to assess (quantitatively) the way that ICTs will influence environmental sustainability between now and 2020. This study is the first quantitative projection to be carried out on how ICTs could affect the environment in the European Union. In order to estimate the effects of ICTs on a set of five environmental indicators, the project team adopted an innovative methodology combining qualitative scenario-building and quantitative modelling. The general conclusion was that ICTs can modify the value of these five indicators. ICTs could improve the situation, reinforcing positive effects in the environment, or they could worsen the situation. This suggests that environmental policies have to be designed to ensure that ICT applications make a beneficial contribution to environmental outcomes, and, at the same time, suppress rebound effects. There are significant opportunities for improving environmental sustainability through ICTs, which can rationalise energy management in housing …
ECONOMICS AND IDENTITY

This paper considers how identity, a person’s sense of self, affects economic outcomes. We incorporate the psychology and sociology of identity into an economic model of behavior. In the utility function we propose, identity is associated with different social categories and how people in these categories should behave. We then construct a simple game-theoretic model showing how identity can affect individual interactions. The paper adapts these models to gender discrimination in the workplace, the economics of poverty and social exclusion, and the household division of labor. In each case, the inclusion of identity substantively changes conclusions of previous economic analysis.
Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture

In neuroscience, all kinds of computation models were designed to answer the open question of how sensory stimuli are encoded by neurons and conversely, how sensory stimuli can be decoded from neuronal activities. Especially, functional Magnetic Resonance Imaging (fMRI) studies have made many great achievements with the rapid development of the deep network computation. However, comparing with the goal of decoding orientation, position and object category from activities in visual cortex, accurate reconstruction of image stimuli from human fMRI is a still challenging work. In this paper, the capsule network (CapsNet) architecture based visual reconstruction (CNAVR) method is developed to reconstruct image stimuli. The capsule means containing a group of neurons to perform the better organization of feature structure and representation, inspired by the structure of cortical mini column including several hundred neurons in primates. The high-level capsule features in the CapsNet includes diverse features of image stimuli such as semantic class, orientation, location and so on. We used these features to bridge between human fMRI and image stimuli. We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from highlevel capsule features to image stimuli again in an end-to-end manner. After estimating the serviceability of each voxel by encoding performance to accomplish the selecting of voxels, we secondly trained the nonlinear mapping from dimension-decreasing fMRI data to high-level capsule features. Finally, we can predict the high-level capsule features with fMRI data, and reconstruct image stimuli with the CapsNet. We evaluated the proposed CNAVR method on the dataset of handwritten digital images, and exceeded about 10% than the accuracy of all existing state-of-the-art methods on the structural similarity index (SSIM).
Discrete cuckoo search algorithm for the travelling salesman problem

In this paper, we present an improved and discrete version of the Cuckoo Search (CS) algorithm to solve the famous traveling salesman problem (TSP), an NP-hard combinatorial optimisation problem. CS is a metaheuristic search algorithm which was recently developed by Xin-She Yang and Suash Deb in 2009, inspired by the breeding behaviour of cuckoos. This new algorithm has proved to be very effective in solving continuous optimisation problems. We now extend and improve CS by reconstructing its population and introducing a new category of cuckoos so that it can solve combinatorial problems as well as continuous problems. The performance of the proposed discrete cuckoo search (DCS) is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that DCS is superior to some other metaheuristics.
Generating regular expression signatures for network traffic classification in trusted network management

Network traffic classification is a critical foundation for trusted network management and security systems. Matching application signatures in traffic payload is widely considered to be the most reliable classifying method. However, deriving accurate and efficient signatures for various applications is not a trivial task, for which current practice is mostly manual thus error-prone and of low efficiency. In this paper, we tackle the problem of automatic signature generation. In particular, we focus on generating regular expression signatures with a certain subset of standard syntax rules, which are of sufficient expressive power and compatible with most practical systems. We propose a novel approach that takes as input a labeled training data set and produces a set of signatures for matching the application classes presented in the data. The approach involves four procedures: pre-processing to extract application session payload, tokenization to find common substrings and incorporate position constraints, multiple sequence alignment to find common subsequences, and signature construction to transform the results into regular expressions. A real life full payload traffic trace is used to evaluate the proposed system, and signatures for a range of applications are automatically derived. The results indicate that the signatures are of high quality, and exhibit low false negatives and false positives.
A pose graph-based localization system for long-term navigation in CAD floor plans

Accurate localization is an essential technology for flexible automation. Industrial applications require mobile platforms to be precisely localized in complex environments, often subject to continuous changes and reconfiguration. Most of the approaches use precomputed maps both for localization and for interfacing robots with workers and operators. This results in increased deployment time and costs as mapping experts are required to setup the robotic systems in factory facilities. Moreover, such maps need to be updated whenever significant changes in the environment occur in order to be usable within commanding tools. To overcome those limitations, in this work we present a robust and highly accurate method for long-term LiDAR-based indoor localization that uses CAD-based architectural floor plans. The system leverages a combination of graph-based mapping techniques and Bayes filtering to maintain a sparse and up-to-date globally consistent map that represents the latest configuration of the environment. This map is aligned to the CAD drawing using prior constraints and is exploited for relative localization, thus allowing the robot to estimate its current pose with respect to the global reference frame of the floor plan. Furthermore, the map helps in limiting the disturbances caused by structures and clutter not represented in the drawing. Several long-term experiments in changing real-world environments show that our system outperforms common state-of-the-art localization methods in terms of accuracy and robustness while remaining memory and computationally efficient.
Outlier Detection using Granular Box Regression Methods

Granular computing (GrC) is an emerging computing paradigm of information processing. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of knowledge from information. Granular computing is more a theoretical perspective, it encourages an approach to data that recognizes and exploits the knowledge present in data at various levels of resolution or scales. Granular computing provides a rich variety of algorithms including methods derived from interval mathematics, fuzzy and rough sets and others. Within this framework granular box regression was proposed recently. The core idea of granular box regression is to determine a fuzzy graph by embedding a given dataset into a predefined number of “boxes”. Granular box regression utilizes intervals a challenge is the detection of outliers. In this paper, we propose borderline method and residual method to detect outliers in granular box regression. We also apply these methods to artificial as well as to real data of motor insurance.
Low-latency adaptive streaming over tcp

Media streaming over TCP has become increasingly popular because TCP's congestion control provides remarkable stability to the Internet. Streaming over TCP requires adapting to bandwidth availability, but unforunately, TCP can introduce significant latency at the application level, which causes unresponsive and poor adaptation. This article shows that this latency is not inherent in TCP but occurs as a result of throughput-optimized TCP implementations. We show that this latency can be minimized by dynamically tuning TCP's send buffer. Our evaluation shows that this approach leads to better application-level adaptation and it allows supporting interactive and other low-latency applications over TCP.
The Impact of the Cattell – Horn – Carroll Theory on Test Development and Interpretation of Cognitive and Academic Abilities

In recent years, the Cattell–Horn–Carroll (CHC) theory has had a significant impact on the measurement of cognitive abilities and the interpretation of intelligence test performance. The purpose of this chapter is to summarize the most salient ways in which CHC theory has influenced the field of intellectual assessment. The chapter begins with a brief summary of the evolution of CHC theory. Next, the specific ways in which current CHC theory and research have influenced test development are presented. Finally, the CHC cross-battery approach is described as one mechanism through which practitioners in the field of psychoeducational assessment have embraced CHC theory, particularly as it applies to test interpretation.
Heterogenous Uncertainty Sampling for Supervised Learning

Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.
Multifunction Cognitive Radar Task Scheduling Using Monte Carlo Tree Search and Policy Networks

A modern radar may be designed to perform multiple functions, such as surveillance, tracking, and fire control. Each function requires the radar to execute a number of transmit-receive tasks. A radar resource management (RRM) module makes decisions on parameter selection, prioritization, and scheduling of such tasks. RRM becomes especially challenging in overload situations, where some tasks may need to be delayed or even dropped. In general, task scheduling is an NP-hard problem. In this work, we develop the branch-and-bound (B&B) method which obtains the optimal solution but at exponential computational complexity. On the other hand, heuristic methods have low complexity but provide relatively poor performance. We resort to machine learning-based techniques to address this issue; specifically we propose an approximate algorithm based on the Monte Carlo tree search method. Along with using bound and dominance rules to eliminate nodes from the search tree, we use a policy network to help to reduce the width of the search. Such a network can be trained using solutions obtained by running the B&B method offline on problems with feasible complexity. We show that the proposed method provides near-optimal performance, but with computational complexity orders of magnitude smaller than the B&B algorithm.
A New Variable Step-Size NLMS Algorithm and Its Performance Analysis

Numerous variable step-size normalized least mean-square (VSS-NLMS) algorithms have been derived to solve the dilemma of fast convergence rate or low excess mean-square error in the past two decades. This paper proposes a new, easy to implement, nonparametric VSS-NLMS algorithm that employs the mean-square error and the estimated system noise power to control the step-size update. Theoretical analysis of its steady-state behavior shows that, when the input is zero-mean Gaussian distributed, the misadjustment depends only on a parameter β controlling the update of step size. Simulation experiments show that the proposed algorithm performs very well. Furthermore, the theoretical steady-state behavior is in very good agreement with the experimental results.
RC-RCD clamp circuit for ringing losses reduction in a flyback converter

An RCD clamp circuit is usually used in flyback converters, in order to limit the voltage spikes caused by leakage transformer inductance. Oscillation ringing appears due to the clamp diode, which deteriorates the converter's power rate. This brief describes this ringing phenomenon and the use of an RC-RCD clamp circuit for damping the clamp diode's oscillation. This clamp circuit is capable for improving a flyback converter's power ratio.
Topic Chronicle Forest for Topic Discovery and Tracking

To ease comprehension of given time-stamped corpora, we extend topic models to handle both the specificity and temporality of topics; this is a significant advance over previous models which fail to provide both views simultaneously. Our proposed model consists of the Topic Chronicle Forest(TCF) and Thematic Dirichlet Processes(TDP). TCF is a set of Topic Chronicle Trees, where each tree is a hierarchy of topics that becomes more specialized toward the leaves. Only one tree is defined in each time interval, a region, and is used for TDP to generate a document. The advantage of our approach lies in providing more compact topic organization, while preserving both the semantic of a given corpus and the thematic of each document. Experiments show that TCF is a useful extension for longitudinal topic discovery and tracking, and helps us to organize and digest data sets.
A Preliminary Taxonomy of Crowdsourcing

Many firms are now asking how they can benefit from the new form of outsourcing labelled “crowdsourcing”. Like many other forms of outsourcing, crowdsourcing is now being “talked up” by a somewhat credulous trade press. However, the term crowdsourcing has been used to describe several related, but different phenomena, and what might be successful with one form of crowdsourcing may not be with another. In this paper the notion of crowdsourcing is decomposed to create a taxonomy that expands our understanding of what is meant by the term. This taxonomy focuses on the different capability levels of crowdsourcing suppliers; different motivations; and different allocation of benefits. The management implications of these distinctions are then considered in light of what we know about other forms of outsourcing.
Data preprocessing algorithm for Web Structure Mining

World Wide Web is an extremely large collection of information, i.e. beyond our imagination. It provides enough information according to user's need. Web is rising dreadfully as approximately 70 million pages are added daily. Knowledge Discovery on web data is referred as Web Mining. Web Structure Mining based on the analysis of patterns from hyperlink structure in the web. Like as Data Mining, Web Mining has four stages i.e. Data Collection, Preprocessing, Knowledge Discovery and Knowledge Analysis. This paper based on the first two stages Data collection and Preprocessing. Data collection is to collect the data required for analysis. Data preprocessing is considered as an important stage of Web Structure mining because of data available on web is unstructured, heterogeneous and noisy.
Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this work, we introduce a new type of linear connections, named fast-forward connections, based on deep Long Short-Term Memory (LSTM) networks, and an interleaved bi-directional architecture for stacking the LSTM layers. Fast-forward connections play an essential role in propagating the gradients and building a deep topology of depth 16. On the WMT’14 English-to-French task, we achieve BLEU=37.7 with a single attention model, which outperforms the corresponding single shallow model by 6.2 BLEU points. This is the first time that a single NMT model achieves state-of-the-art performance and outperforms the best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3 even without using an attention mechanism. After special handling of unknown words and model ensembling, we obtain the best score reported to date on this task with BLEU=40.4. Our models are also validated on the more difficult WMT’14 English-to-German task.
Dual-polarized MIMO antenna system for WiFi and LTE wireless access point applications

In this paper, a dual-polarized multiple-input multiple-output (MIMO) antenna system suitable for indoor wireless access point is proposed. The presented MIMO antenna system consists of two coplanarwaveguide-fed monopole antennas with orthogonally polarized modes. According to the closely spaced structure of the MIMO antenna system, the mutual coupling between the ports is a big challenge. Therefore, a new structure of parasitic element is introduced in order to improve the mutual coupling between the ports. For the purpose of validating the simulated results, the antenna prototype has been fabricated and measured; the comparison of the results shows that there is an acceptable agreement between the measurement and simulation results. The proposed design covers the frequency bands of WiFi (2.4GHz), Worldwide Interoperability for Microwave Access (2.3 and 2.5GHz), and Long-Term Evolution (LTE; 1.5 and 2.6GHz) applications with a reflection coefficient less than 10 dB and a mutual coupling coefficient better than 15 dB. The MIMO antenna system provides an envelope correlation coefficient less than 0.15, polarization diversity gain more than 9.985 dB, and quasi-omnidirectional pattern within the expected frequency band. In addition, LTE downlink throughput measurements show that the proposed antenna system delivers data rates close to the theoretical maximum for quadrature phase shift keying, 16 quadrature amplitude modulation (QAM), and 64-QAM modulations.
Brain computer interface for gesture control of a social robot: An offline study

Brain computer interface (BCI) provides promising applications in neuroprosthesis and neurorehabilitation by controlling computers and robotic devices based on the patient's intentions. Here, we have developed a novel BCI platform that controls a personalized social robot using noninvasively acquired brain signals. Scalp electroencephalogram (EEG) signals are collected from a user in real-time during tasks of imaginary movements. The imagined body kinematics are decoded using a regression model to calculate the user-intended velocity. Then, the decoded kinematic information is mapped to control the gestures of a social robot. The platform here may be utilized as a human-robot-interaction framework by combining with neurofeedback mechanisms to enhance the cognitive capability of persons with dementia.
Structuring Use Cases with Goals

Use cases are wonderful but confusing. People, when asked to write them, do not know what to include or how to structure them. There is no published theory for them. This paper introduces a theory based on a small model of communication, distinguishing “goals” as a key element of use cases. The result is an easily used, scaleable, recursive model that provides benefits outside requirements gathering: goals and goal failures can be explicitly discussed and tracked; the goals structure is useful for project management, project tracking, staffing, and business process reengineering. The model and techniques described here have been applied and evaluated on a relatively large (50 work-year, 200 use-case) project.
Relational versus non-relational database systems for data warehousing

Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design has a close relationship with the relational model. Recently, there has been a surge of alternative technologies for large scale analytic processing, most of which are not based on the relational model. Out of these proposals, distributed file systems together with MapReduce have become strong competitors to relational database systems to analyze large data sets, exploiting parallel processing. Moreover, there is progress on using MapReduce to evaluate relational queries. With that motivation in mind, this panel will compare pros and cons of each technology for data warehousing and will identify research issues, considering practical aspects like ease of use, programming flexibility and cost; as well as technical aspects like data modeling, storage, hardware, scalability, query processing, fault tolerance and data mining.