nils-herrmann/clean_scirepeval huggingface dataset:
Dirichlet Process Mixture Model for Document Clustering with Feature Partition

Finding the appropriate number of clusters to which documents should be partitioned is crucial in document clustering. In this paper, we propose a novel approach, namely DPMFP, to discover the latent cluster structure based on the DPM model without requiring the number of clusters as input. Document features are automatically partitioned into two groups, in particular, discriminative words and nondiscriminative words, and contribute differently to document clustering. A variational inference algorithm is investigated to infer the document collection structure as well as the partition of document words at the same time. Our experiments indicate that our proposed approach performs well on the synthetic data set as well as real data sets. The comparison between our approach and state-of-the-art document clustering approaches shows that our approach is robust and effective for document clustering.
High-gain active composite right/left-handed leaky-wave antenna

A novel high-gain active composite right/left-handed (CRLH) metamaterial leaky-wave antenna (LWA) is presented. This antenna, which is designed to operate at broadside, is constituted by passive CRLH leaky-wave sections interconnected by amplifiers, which regenerate the power progressively leaked out of the structure in the radiation process in order to increase the effective aperture of the antenna and thereby its gain. The gain is further enhanced by a matching regeneration effect induced by the quasi-unilateral nature of the amplifiers. Both the cases of quasi-uniform and binomial field distributions, corresponding to maximum directivity and minimum side-lobe level, respectively, have been described. An active LWA prototype is demonstrated in transmission mode with a gain enhancement of 8.9 dB compared to its passive counterpart. The proposed antenna can attain an arbitrarily high gain by simple increase of the length of the structure, without penalty in terms of return loss and without requiring a complicated feeding network like conventional array antennas
Towards meaning change: experience goals driving design space expansion

Experience design is a relatively new approach to product design. While there are several possible starting points in designing for positive experiences, we start with experience goals that state a profound source for a meaningful experience. In this paper, we investigate three design cases that used experience goals as the starting point for both incremental and radical design, and analyse them from the perspective of their potential for design space expansion. Our work addresses the recent call for design research directed toward new interpretations of what could be meaningful to people, which is seen as the source for creating new meanings for products, and thereby, possibly leading to radical innovations. Based on this idea, we think about the design space as a set of possible concepts derived from deep meanings that experience goals help to communicate. According to our initial results from the small-scale touchpoint design cases, the type of experience goals we use seem to have the potential to generate not only incremental but also radical design ideas.
Dynamic Facial Expression Analysis and Synthesis With MPEG-4 Facial Animation Parameters

This paper describes a probabilistic framework for faithful reproduction of dynamic facial expressions on a synthetic face model with MPEG-4 facial animation parameters (FAPs) while achieving very low bitrate in data transmission. The framework consists of a coupled Bayesian network (BN) to unify the facial expression analysis and synthesis into one coherent structure. At the analysis end, we cast the FAPs and facial action coding system (FACS) into a dynamic Bayesian network (DBN) to account for uncertainties in FAP extraction and to model the dynamic evolution of facial expressions. At the synthesizer, a static BN reconstructs the FAPs and their intensity. The two BNs are connected statically through a data stream link. Using the coupled BN to analyze and synthesize the dynamic facial expressions is the major novelty of this work. The novelty brings about several benefits. First, very low bitrate (9 bytes per frame) in data transmission can be achieved. Second, a facial expression is inferred through both spatial and temporal inference so that the perceptual quality of animation is less affected by the misdetected FAPs. Third, more realistic looking facial expressions can be reproduced by modelling the dynamics of human expressions.
Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines

Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dimensionality of the specific trajectory states, lack of enough labeled data and so on. In this article, we propose a solution to solve this problem based on a novel deep learning model dubbed disjunctive factored four-way conditional restricted Boltzmann machine (DFFW-CRBM). Our method improves state-of-the-art deep learning techniques for high dimensional time-series modeling by introducing a novel tensor factorization capable of driving forth order Boltzmann machines to considerably lower energy levels, at no computational costs. DFFW-CRBMs are capable of accurately estimating, recognizing, and performing near-future prediction of three-dimensional trajectories from their 2D projections while requiring limited amount of labeled data. We evaluate our method on both simulated and real-world data, showing its effectiveness in predicting and classifying complex ball trajectories and human activities.
Polygon Mesh Processing

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Personalised gaming: a motivation and overview of literature

This article focuses on personalised games, which we define as games that utilise player models for the purpose of tailoring the game experience to the individual player. The main contribution of the article is a motivation for personalised gaming, supported by an extensive overview of scientific literature. The motivatin concerns (a) the psychological foundation, (b) the effect on player satisfaction, (c) the contribution to game development, and (d) the requirement for achieving ambitions. The provided overview of scientific literature goes into the subject of player modelling, as well as eight adaptive components: (1) space adaptation, (2) mission/task adaptation, (3) character adaptation, (4) game mechanics adaptation, (5) narrative adaptation, (6) music/sound adaptation, (7) player matching (multiplayer), and (8) difficulty scaling. In the concluding sections, the relationship to procedural content generation is discussed, as well as the generalisation to other domains.
Combinatorial Testing for Deep Learning Systems

Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications. However, the robustness of DL systems recently receives great concerns, such as adversarial examples against computer vision systems, which could potentially result in severe consequences. Adopting testing techniques could help to evaluate the robustness of a DL system and therefore detect vulnerabilities at an early stage. The main challenge of testing such systems is that its runtime state space is too large: if we view each neuron as a runtime state for DL, then a DL system often contains massive states, rendering testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to reduce the testing space while obtaining relatively high defect detection abilities. In this paper, we perform an exploratory study of CT on DL systems. We adapt the concept in CT and propose a set of coverage criteria for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems. We further pose several open questions and interesting directions for combinatorial testing of DL systems.
Full-Color Pixelated-Addressable Light Emitting Diode on Transparent Substrate (LEDoTS) Micro-Displays by CoB

In this paper, we report the design and fabrication of full-color pixelated-addressable light emitting diode on transparent substrate (LEDoTS) micro-displays. The LEDoTS micro-displays were realized by integrating monochromatic LEDs on a transparent quartz substrate using chip on board (CoB) technique. The full-color pixel has a pitch of 1 mm (P1) and includes red, green, and blue monochromatic LED chips. The pixelated-addressable driving circuit was designed on a custom PCB to provide constant current and individual addressing of each LED pixel with a certain frequency of row and column scanning. With a peripheral FPGA board, the LEDoTS micro-displays were programmed to display representative images and animations.
Regularization Theory and Neural Networks Architectures

We had previously shown that regularization principles lead to approximation schemes that are equivalent to networks with one layer of hidden units, called regularization networks. In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known radial basis functions approximation schemes. This paper shows that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models and some of the neural networks. In particular, we introduce new classes of smoothness functionals that lead to different classes of basis functions. Additive splines as well as some tensor product splines can be obtained from appropriate classes of smoothness functionals. Furthermore, the same generalization that extends radial basis functions (RBF) to hyper basis functions (HBF) also leads from additive models to ridge approximation models, containing as special cases Breiman's hinge functions, some forms of projection pursuit regression, and several types of neural networks. We propose to use the term generalized regularization networks for this broad class of approximation schemes that follow from an extension of regularization. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In summary, different multilayer networks with one hidden layer, which we collectively call generalized regularization networks, correspond to different classes of priors and associated smoothness functionals in a classical regularization principle. Three broad classes are (1) radial basis functions that can be generalized to hyper basis functions, (2) some tensor product splines, and (3) additive splines that can be generalized to schemes of the type of ridge approximation, hinge functions, and several perceptron-like neural networks with one hidden layer.
Author name disambiguation using a graph model with node splitting and merging based on bibliographic information

Author ambiguity mainly arises when several different authors express their names in the same way, generally known as the namesake problem, and also when the name of an author is expressed in many different ways, referred to as the heteronymous name problem. These author ambiguity problems have long been an obstacle to efficient information retrieval in digital libraries, causing incorrect identification of authors and impeding correct classification of their publications. It is a nontrivial task to distinguish those authors, especially when there is very limited information about them. In this paper, we propose a graph based approach to author name disambiguation, where a graph model is constructed using the co-author relations, and author ambiguity is resolved by graph operations such as vertex (or node) splitting and merging based on the co-authorship. In our framework, called a Graph Framework for Author Disambiguation (GFAD), the namesake problem is solved by splitting an author vertex involved in multiple cycles of co-authorship, and the heteronymous name problem is handled by merging multiple author vertices having similar names if those vertices are connected to a common vertex. Experiments were carried out with the real DBLP and Arnetminer collections and the performance of GFAD is compared with three representative unsupervised author name disambiguation systems. We confirm that GFAD shows better overall performance from the perspective of representative evaluation metrics. An additional contribution is that we released the refined DBLP collection to the public to facilitate organizing a performance benchmark for future systems on author disambiguation.
Effects of interparental violence on the psychological adjustment and competencies of young children.

Preschool children (N = 107) were divided into 4 groups on the basis of maternal report; home and shelter groups exposed to verbal and physical conflict, a home group exposed to verbal conflict only, and a home control group. Parental ratings of behavior problems and competencies and children's self-report data were collected. Results show that verbal conflict only was associated with a moderate level of conduct problems: verbal plus physical conflict was associated with clinical levels of conduct problems and moderate levels of emotional problems; and verbal plus physical conflict plus shelter residence was associated with clinical levels of conduct problems, higher level of emotional problems, and lower levels of social functioning and perceived maternal acceptance. Findings suggests a direct relationship between the nature of the conflict and residence and type and extent of adjustment problems.
VizClick visualizing clickstream data

Clickstream data is ubiquitous in today's web-connected world. Such data displays the salient features of big data, that is, volume, velocity and variety. As with any big data, visualizations can play a central role in making sense and generating hypotheses from such data. In this paper, we present a systematic approach of visualizing clickstream data. There are three basic questions we aim to address. First, we explore the inter-dependence between the large number of dimensions that are measured in clickstream data. Next, we analyze spatial aspects of data collected in web-analytics. Finally, the web designers might be interested in getting a deeper understanding of the website's topography and how browsers are interacting with it. Our approach is designed for business analysts, web designers and marketers; and helps them draw actionable insights in the management and refinement of large websites.
Graph Classification via Topological and Label Attributes

Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature-vectors constructed from different global topological attributes, as well as global label features. The main idea here is that the graphs from the same class should have similar topological and label attributes. Our method is simple and easy to implement, and via a detailed comparison on real benchmark datasets, we show that our topological and label feature-based approach delivers better or competitive classification accuracy, and is also substantially faster than other graph kernels. It is the most effective method for large unlabeled graphs.
A DCT-domain system for robust image watermarking

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data in a networked environment. It makes possible to tightly associate to a digital document a code allowing the identification of the data creator, owner, authorized consumer, and so on. In this paper a new watermarking algorithm for digital images is presented: the method, which operates in the frequency domain, embeds a pseudo-random sequence of real numbers in a selected set of DCT coefficients. After embedding, the watermark is adapted to the image by exploiting the masking characteristics of the human visual system, thus ensuring the watermark invisibility. By exploiting the statistical properties of the embedded sequence, the mark can be reliably extracted without resorting to the original uncorrupted image. Experimental results demonstrate that the watermark is robust to several signal processing techniques, including JPEG compression, low pass and median filtering, histogram equalization and stretching, dithering, addition of Gaussian noise, resizing, and multiple watermarking. ( 1998
Insights into Internet Memes

Internet memes are phenomena that rapidly gain popularity or notoriety on the Internet. Often, modifications or spoofs add to the profile of the original idea thus turning it into a phenomenon that transgresses social and cultural boundaries. It is commonly assumed that Internet memes spread virally but scientific evidence as to this assumption is scarce. In this paper, we address this issue and investigate the epidemic dynamics of 150 famous Internet memes. Our analysis is based on time series data that were collected from Google Insights, Delicious, Digg, and StumbleUpon. We find that differential equation models from mathematical epidemiology as well as simple log-normal distributions give a good account of the growth and decline of memes. We discuss the role of log-normal distributions in modeling Internet phenomena and touch on practical implications of our findings.
Joint Inference for Fine-grained Opinion Extraction

This paper addresses the task of finegrained opinion extraction – the identification of opinion-related entities: the opinion expressions, the opinion holders, and the targets of the opinions, and the relations between opinion expressions and their targets and holders. Most existing approaches tackle the extraction of opinion entities and opinion relations in a pipelined manner, where the interdependencies among different extraction stages are not captured. We propose a joint inference model that leverages knowledge from predictors that optimize subtasks of opinion extraction, and seeks a globally optimal solution. Experimental results demonstrate that our joint inference approach significantly outperforms traditional pipeline methods and baselines that tackle subtasks in isolation for the problem of opinion extraction.
Big Data Analytics for Emergency Communication Networks: A Survey

Disaster management is a crucial and urgent research issue. Emergency communication networks (ECNs) provide fundamental functions for disaster management, because communication service is generally unavailable due to large-scale damage and restrictions in communication services. Considering the features of a disaster (e.g., limited resources and dynamic changing of environment), it is always a key problem to use limited resources effectively to provide the best communication services. Big data analytics in the disaster area provides possible solutions to understand the situations happening in disaster areas, so that limited resources can be optimally deployed based on the analysis results. In this paper, we survey existing ECNs and big data analytics from both the content and the spatial points of view. From the content point of view, we survey existing data mining and analysis techniques, and further survey and analyze applications and the possibilities to enhance ECNs. From the spatial point of view, we survey and discuss the most popular methods and further discuss the possibility to enhance ECNs. Finally, we highlight the remaining challenging problems after a systematic survey and studies of the possibilities.
Linked data partitioning for RDF processing on Apache Spark

RDF models are widely used in the web of data due to their flexibility and similarity to graph patterns. Because of the growing use of RDFs, their volumes and contents are increasing. Therefore, processing of such massive amount of data on a single machine is not efficient enough, because of the response time and limited hardware resources. A common approach to overcome this limitation is cluster processing and huge datasets could benefit distributed cluster processing on Apache Hadoop. Because of using too much of hard disks, the processing time is usually inadequate. In this paper, we propose a partitiong approach based on Apache Spark for rapid processing of RDF data models. A key feature of Apache Spark is using main memory instead of hard disk, so the speed of data processing in our method is improved. We have evaluated the proposed method by runing SQL queris on RDF data which partitioned on the cluster and demonstrates improved performance.
Enterprise architecture management and its role in corporate strategic management

A considerable number of organizations continually face difficulties bringing strategy to execution, and suffer from a lack of structure and transparency in corporate strategic management. Yet, enterprise architecture as a fundamental exercise to achieve a structured description of the enterprise and its relationships appears far from being adopted in the strategic management arena. To move the adoption process along, this paper develops a comprehensive business architecture framework that assimilates and extends prior research and applies the framework to selected scenarios in corporate strategic management. This paper also presents the approach in practice, based on a qualitative appraisal of interviews with strategic directors across different industries. With its integrated conceptual guideline for using enterprise architecture to facilitate corporate strategic management and the insights gained from the interviews, this paper not only delves more deeply into the research but also offers advice for both researchers and practitioners.
Deep Reinforcement Learning That Matters

In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.