nils-herrmann/clean_scirepeval huggingface dataset:
Training Bit Fully Convolutional Network for Fast Semantic Segmentation

Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation. However, a typical FCN usually requires lots of floating point computation and large run-time memory, which effectively limits its usability. We propose a method to train Bit Fully Convolution Network (BFCN), a fully convolutional neural network that has low bit-width weights and activations. Because most of its computation-intensive convolutions are accomplished between low bit-width numbers, a BFCN can be accelerated by an efficient bit-convolution implementation. On CPU, the dot product operation between two bit vectors can be reduced to bitwise operations and popcounts, which can offer much higher throughput than 32-bit multiplications and additions. To validate the effectiveness of BFCN, we conduct experiments on the PASCAL VOC 2012 semantic segmentation task and Cityscapes. Our BFCN with 1-bit weights and 2-bit activations, which runs 7.8x faster on CPU or requires less than 1% resources on FPGA, can achieve comparable performance as the 32-bit counterpart.  Deep convolutional neural networks (DCNN), with its recent progress, has considerably changed the landscape of computer vision (Krizhevsky, Sutskever, and Hinton 2012) and many other fields. To achieve close to state-of-the-art performance, a DCNN usually has a lot of parameters and high computational complexity, which may easily overwhelm resource capability of embedded devices. Substantial research efforts have been invested in speeding up DCNNs on both general-purpose (Vanhoucke, Senior, and Mao 2011; Gong et al. 2014; Han et al. 2015) and specialized computer hardware (Farabet et al. 2009; Farabet et al. 2011; Pham et al. 2012; Chen et al. 2014b; Chen et al. 2014c; Zhang et al. 2015a). Recent progress in using low bit-width networks has considerably reduced parameter storage size and computation burden by using 1-bit weight and low bit-width activations. In particular, in BNN (Kim and Smaragdis 2016) and XNOR-net (Rastegari et al. 2016), during the forward pass the most computationally expensive convolutions can  Summary results of our BFCNs. Performance measure in mean IoU. be done by combining xnor and popcount operations, thanks to the following equivalence when x and y are bit vectors:
Bayesian Learning of Recursively Factored Environments

Model-based reinforcement learning techniques have historically encountered a number of difficulties scaling up to large observation spaces. One promising approach has been to decompose the model learning task into a number of smaller, more manageable sub-problems by factoring the observation space. Typically, many different factorizations are possible, which can make it difficult to select an appropriate factorization without extensive testing. In this paper we introduce the class of recursively decomposable factorizations, and show how exact Bayesian inference can be used to efficiently guarantee predictive performance close to the best factorization in this class. We demonstrate the strength of this approach by presenting a collection of empirical results for 20 different Atari 2600 games.
Generalized space shift keying modulation for MIMO channels

A fundamental component of spatial modulation (SM), termed generalized space shift keying (GSSK), is presented. GSSK modulation inherently exploits fading in wireless communication to provide better performance over conventional amplitude/phase modulation (APM) techniques. In GSSK, only the antenna indices, and not the symbols themselves (as in the case of SM and APM), relay information. We exploit GSSKpsilas degrees of freedom to achieve better performance, which is done by formulating its constellation in an optimal manner. To support our results, we also derive upper bounds on GSSKpsilas bit error probability, where the source of GSSKpsilas strength is made clear. Analytical and simulation results show performance gains (1.5-3 dB) over popular multiple antenna APM systems (including Bell Laboratories layered space time (BLAST) and maximum ratio combining (MRC) schemes), making GSSK an excellent candidate for future wireless applications.
Symbolic Representation of Neural Networks

Although backpropagation neural networks generally predict better than decision trees do for pattern classiication problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, more often than not, explicit knowledge is needed by human experts. This work drives a symbolic representation for neural networks to make explicit each prediction of a neural network. An algorithm is proposed and implemented to extract symbolic rules from neural networks. Explicitness of the extracted rules is supported by comparing the symbolic rules generated by decision trees methods. Empirical study demonstrates that the proposed algorithm generates high quality rules from neural networks comparable with those of decision trees in terms of predictive accuracy, number of rules and average number of conditions for a rule. The symbolic rules from nerual networks preserve high predictive accuracy of original networks. An early and shorter version of this paper has been accepted for presentation at IJCAI'95.
A pattern language for developing privacy enhancing technologies

A Privacy Enhancement Technology (PET) is an application or a mechanism which allows users to protect the privacy of their personally identifiable information. Early PETs were about enabling anonymous mailing and anonymous browsing, but lately there have been active research and development efforts in many other problem domains. This paper describes the first pattern language for developing privacy enhancement technologies. Currently, it contains 12 patterns. These privacy patterns are not limited to a specific problem domain; they can be applied to design anonymity systems for various types of online communication, online data sharing, location monitoring, voting and electronic cash management. The pattern language guides a developer when he or she is designing a PET for an existing problem, or innovating a solution for a new problem.
Discrimination of focal and non-focal EEG signals using entropy-based features in EEMD and CEEMDAN domains

In this paper, a comprehensive analysis for the discrimination of the focal and non-focal electroencephalography (EEG) signals is carried out in the ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) domains. A number of spectral entropy-based features such as the Shannon entropy, log-energy entropy, Renyi entropy and Tsallis entropy are calculated in the EEMD and CEEMDAN domain of the EEG signals. In lieu of using the signals from the EEG channels, the differences between two adjacent EEG channels are used due to its robustness to noise and interference. The ability of the entropy-based features in separating the focal and non-focal EEG signals is explored utilizing the one-way ANOVA analysis and the box-whisker plots. The results reveal that among the entropy measures computed in the EEMD and CEEMDAN domains, the quadratic Renyi entropy and log-energy entropy measures are most promising in discriminating the focal and non-focal EEG signals. The analysis may encourage the researchers to develop improved algorithms to classify these signals and would be helpful in locating the epileptogenic zones.
Pushing the Limits of Design Fiction: The Case For Fictional Research Papers

This paper considers how design fictions in the form of 'imaginary abstracts' can be extended into complete 'fictional papers'. Imaginary abstracts are a type of design fiction that are usually included within the content of 'real' research papers, they comprise brief accounts of fictional problem frames, prototypes, user studies and findings. Design fiction abstracts have been proposed as a means to move beyond solutionism to explore the potential societal value and consequences of new HCI concepts. In this paper we contrast the properties of imaginary abstracts, with the properties of a published paper that presents fictional research, Game of Drones. Extending the notion of imaginary abstracts so that rather than including fictional abstracts within a 'non-fiction' research paper, Game of Drones is fiction from start to finish (except for the concluding paragraph where the fictional nature of the paper is revealed). In this paper we review the scope of design fiction in HCI research before contrasting the properties of imaginary abstracts with the properties of our example fictional research paper. We argue that there are clear merits and weaknesses to both approaches, but when used tactfully and carefully fictional research papers may further empower HCI's burgeoning design discourse with compelling new methods.
Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks

We propose a method for recognizing such event causalities as “smoke cigarettes” → “die of lung cancer” using background knowledge taken from web texts as well as original sentences from which candidates for the causalities were extracted. We retrieve texts related to our event causality candidates from four billion web pages by three distinct methods, including a why-question answering system, and feed them to our multi-column convolutional neural networks. This allows us to identify the useful background knowledge scattered in web texts and effectively exploit the identified knowledge to recognize event causalities. We empirically show that the combination of our neural network architecture and background knowledge significantly improves average precision, while the previous state-of-the-art method gains just a small benefit from such background knowledge.
An ontology approach to object-based image retrieval

In this paper, an image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions. Low-level features describing the color, position, size and shape of the resulting regions are extracted and are automatically mapped to appropriate intermediatelevel descriptors forming a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) in a human-centered fashion. When querying, clearly irrelevant image regions are rejected using the intermediate-level descriptors; following that, a relevance feedback mechanism employing the low-level features is invoked to produce the final query results. The proposed approach bridges the gap between keyword-based approaches, which assume the existence of rich image captions or require manual evaluation and annotation of every image of the collection, and query-by-example approaches, which assume that the user queries for images similar to one that already is at his disposal.
A comparison of linear genetic programming and neural networks in medical data mining

We introduce a new form of linear genetic programming (GP). Two methods of acceleration of our GP approach are discussed: 1) an efficient algorithm that eliminates intron code and 2) a demetic approach to virtually parallelize the system on a single processor. Acceleration of runtime is especially important when operating with complex data sets, because they are occuring in real-world applications. We compare GP performance on medical classification problems from a benchmark database with results obtained by neural networks. Our results show that GP performs comparable in classification and generalization.
Business Intelligence in the Cloud

Business Intelligence (BI) deals with integrated approaches to management support. Currently, there are constraints to BI adoption and a new era of analytic data management for business intelligence these constraints are the integrated infrastructures that are subject to BI have become complex, costly, and inflexible, the effort required consolidating and cleansing enterprise data and Performance impact on existing infrastructure / inadequate IT infrastructure. So, in this paper Cloud computing will be used as a possible remedy for these issues. We will represent a new environment atmosphere for the business intelligence to make the ability to shorten BI implementation windows, reduced cost for BI programs compared with traditional on-premise BI software, Ability to add environments for testing, proof-of-concepts and upgrades, offer users the potential for faster deployments and increased flexibility. Also, Cloud computing enables organizations to analyze terabytes of data faster and more economically than ever before. Business intelligence (BI) in the cloud can be like a big puzzle. Users can jump in and put together small pieces of the puzzle but until the whole thing is complete the user will lack an overall view of the big picture. In this paper reading each section will fill in a piece of the puzzle.
Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches.
Modeling Reportable Events as Turning Points in Narrative

We present novel experiments in modeling the rise and fall of story characteristics within narrative, leading up to the Most Reportable Event (MRE), the compelling event that is the nucleus of the story. We construct a corpus of personal narratives from the bulletin board website Reddit, using the organization of Reddit content into topic-specific communities to automatically identify narratives. Leveraging the structure of Reddit comment threads, we automatically label a large dataset of narratives. We present a change-based model of narrative that tracks changes in formality, affect, and other characteristics over the course of a story, and we use this model in distant supervision and selftraining experiments that achieve significant improvements over the baselines at the task of identifying MREs.
Spatial Cognition: The Role of Landmark, Route, and Survey Knowledge in Human and Robot Navigation

The paper gives a brief overview of the interdisciplinary DFG priority program on spatial cognition and presents one specific theme which was the topic of a recent workshop in Göttingen in some more detail. A taxonomy of landmark, route, and survey knowledge for navigation tasks proposed at the workshop is presented. Different ways of acquiring route knowledge are discussed. The importance of employing different spatial reference systems for carrying out navigation tasks is emphasized. Basic mechanisms of spatial memory in human and animal navigation are presented. After outlining the fundamental representational issues, methodological issues in robot and human navigation are discussed. Three applications of spatial cognition research in navigation tasks are given to exemplify both technological relevance and human impact of basic research
Empirical evaluation of a dynamic and distributed taxi-sharing system

Modern societies rely on efficient transportation systems for sustainable mobility. In this paper, we perform a large-scale and empirical evaluation of a dynamic and distributed taxi-sharing system. The novel system takes advantage of nowadays widespread availability of communication and computation to convey a cost-efficient, door-to-door and flexible system, offering a quality of service similar to traditional taxis. The shared taxi service is assessed in a real-city scenario using a highly realistic simulation platform. Simulation results have shown the system's advantages for both passengers and taxi drivers, and that trade-offs need to be considered. Compared with the current taxi operation model, results show a increase of 48% on the average occupancy per traveled kilometer with a full deployment of the taxi-sharing system.
Silicon carbide JFET resonant inverter for induction heating home appliances

This paper presents a one switch silicon carbide JFET normally-ON resonant inverter applied to induction heating for consumer home cookers. The promising characteristics of silicon carbide (SiC) devices need to be verified in practical applications; therefore, the objective of this work is to compare Si IGBTs and normally-ON commercially available JFET in similar operating conditions, with two similar boards. The paper describes the gate circuit implemented, the design of the basic converter in ideal operation, namely Zero Voltage Switching (ZVS) and Zero Derivative Voltage Switching (ZVDS), as well as some preliminary comparative results for 700W and 2 kW output power delivered to an induction heating coil and load.
Modeling of ground-penetrating Radar for accurate characterization of subsurface electric properties

The possibility to estimate accurately the subsurface electric properties from ground-penetrating radar (GPR) signals using inverse modeling is obstructed by the appropriateness of the forward model describing the GPR subsurface system. In this paper, we improved the recently developed approach of Lambot et al. whose success relies on a stepped-frequency continuous-wave (SFCW) radar combined with an off-ground monostatic transverse electromagnetic horn antenna. This radar configuration enables realistic and efficient forward modeling. We included in the initial model: 1) the multiple reflections occurring between the antenna and the soil surface using a positive feedback loop in the antenna block diagram and 2) the frequency dependence of the electric properties using a local linear approximation of the Debye model. The model was validated in laboratory conditions on a tank filled with a two-layered sand subject to different water contents. Results showed remarkable agreement between the measured and modeled Green's functions. Model inversion for the dielectric permittivity further demonstrated the accuracy of the method. Inversion for the electric conductivity led to less satisfactory results. However, a sensitivity analysis demonstrated the good stability properties of the inverse solution and put forward the necessity to reduce the remaining clutter by a factor 10. This may partly be achieved through a better characterization of the antenna transfer functions and by performing measurements in an environment without close extraneous scatterers.
Stability Enhancement Based on Virtual Impedance for DC Microgrids With Constant Power Loads

In this paper, a converter-based dc microgrid is studied. By considering the impact of each component in dc microgrids on system stability, a multistage configuration is employed, which includes the source stage, interface converter stage between buses, and common load stage. In order to study the overall stability of the above dc microgrid with constant power loads (CPLs), a comprehensive small-signal model is derived by analyzing the interface converters in each stage. The instability issue induced by the CPLs is revealed by using the criteria of impedance matching. Meanwhile, virtual-impedance-based stabilizers are proposed in order to enhance the damping of dc microgrids with CPLs and guarantee the stable operation. Since droop control is commonly used to reach proper load power sharing in dc microgrids, its impact is taken into account when testing the proposed stabilizers. By using the proposed stabilizers, virtual impedances are employed in the output filters of the interface converters in the second stage of the multistage configuration. In particular, one of the virtual impedances is connected in series with the filter capacitor, and the other one is connected at the output path of the converter. It can be seen that by using the proposed stabilizers, the unstable poles induced by the CPLs are forced to move into the stable region. The proposed method is verified by the MATLAB/Simulink model of multistage dc microgrids with three distributed power generation units.
Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach

This study analyzes the political agenda of the European Parliament (EP) plenary, how it has evolved over time, and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making plenary speeches. To unveil the plenary agenda and detect latent themes in legislative speeches over time, MEP speech content is analyzed using a new dynamic topic modeling method based on two layers of Non-negative Matrix Factorization (NMF). This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999-2014. Our findings suggest that two-layer NMF is a valuable alternative to existing dynamic topic modeling approaches found in the literature, and can unveil niche topics and associated vocabularies not captured by existing methods. Substantively, our findings suggest that the political agenda of the EP evolves significantly over time and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro-crisis. MEP contributions to the plenary agenda are also found to be impacted upon by voting behaviour and the committee structure of the Parliament. ∗Insight Centre for Data Analytics & School of Computer Science, University College Dublin, Ireland (derek.greene@ucd.ie) †School of Politics & International Relations, University College Dublin, Ireland (james.cross@ucd.ie).