nils-herrmann/clean_scirepeval huggingface dataset:
Pixel-Oriented Visualization Techniques for Exploring Very Large Data Bases

An important goal of visualization technology is to support the e xploration and analysis of very large amounts of data. In this paper , we describe a set of pix eloriented visualization techniques which use each pix el of the display to visualize one data v alue and therefore allo w the visualization of the lar gest amount of data possible. Most of the techniques ha ve been specifically designed for visualizing and querying lar ge databases. The techniques may be di vided into query-independent techniques which directly visualize the data (or a certain portion of it) and query-dependent techniques which visualize the data in the conte xt f a specific query. Examples for the class of query-independent techniques are the screen-filling curve and recursi ve pattern techniques. The screen-filling curv e techniques are based on the well-kno wn Morton and Peano-Hilbert curv e algorithms, and the recursive pattern technique is based on a generic recursi ve scheme which generalizes a wide range of pix el-oriented arrangements for visualizing lar ge data sets. Examples for the class of query-dependent techniques are the snak piral and snak eaxes techniques, which visualize the distances with respect to a database query and arrange the most rele vant data items in the center of the display . Beside describing the basic ideas of our techniques, we pro vide example visualizations generated by the various techniques, which demonstrate the usefulness of our techniques and show some of their adv antages and disadv antages.
Coverage-based Neural Machine Translation

Attention mechanism advanced state-of-the-art neural machine translation (NMT) by jointly learning to align and translate. However, attentional NMT ignores past alignment information, which leads to over-translation and undertranslation problems. In response to this problem, we maintain a coverage vector to keep track of the attention history. The coverage vector is fed to the attention model to help adjust the future attention, which guides NMT to pay more attention to the untranslated source words. Experiments show that coverage-based NMT significantly improves both alignment and translation quality over NMT without coverage.
Stress and the brain: from adaptation to disease

In response to stress, the brain activates several neuropeptide-secreting systems. This eventually leads to the release of adrenal corticosteroid hormones, which subsequently feed back on the brain and bind to two types of nuclear receptor that act as transcriptional regulators. By targeting many genes, corticosteroids function in a binary fashion, and serve as a master switch in the control of neuronal and network responses that underlie behavioural adaptation. In genetically predisposed individuals, an imbalance in this binary control mechanism can introduce a bias towards stress-related brain disease after adverse experiences. New candidate susceptibility genes that serve as markers for the prediction of vulnerable phenotypes are now being identified.
A Survey on the Contributions of Software-Defined Networking to Traffic Engineering

Since the appearance of OpenFlow back in 2008, software-defined networking (SDN) has gained momentum. Although there are some discrepancies between the standards developing organizations working with SDN about what SDN is and how it is defined, they all outline traffic engineering (TE) as a key application. One of the most common objectives of TE is the congestion minimization, where techniques such as traffic splitting among multiple paths or advanced reservation systems are used. In such a scenario, this manuscript surveys the role of a comprehensive list of SDN protocols in TE solutions, in order to assess how these protocols can benefit TE. The SDN protocols have been categorized using the SDN architecture proposed by the open networking foundation, which differentiates among data-controller plane interfaces, application-controller plane interfaces, and management interfaces, in order to state how the interface type in which they operate influences TE. In addition, the impact of the SDN protocols on TE has been evaluated by comparing them with the path computation element (PCE)-based architecture. The PCE-based architecture has been selected to measure the impact of SDN on TE because it is the most novel TE architecture until the date, and because it already defines a set of metrics to measure the performance of TE solutions. We conclude that using the three types of interfaces simultaneously will result in more powerful and enhanced TE solutions, since they benefit TE in complementary ways.
Image denoising via group sparsity residual constraint

Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group sparsity residual. To reduce the residual, we first obtain some good estimation of the group sparse coefficients of the original image by the first-pass estimation of noisy image, and then centralize the group sparse coefficients of noisy image to the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but results in a faster speed.
Review of Fall Detection Techniques: A Data Availability Perspective

A fall is an abnormal activity that occurs rarely; however, missing to identify falls can have serious health and safety implications on an individual. Due to the rarity of occurrence of falls, there may be insufficient or no training data available for them. Therefore, standard supervised machine learning methods may not be directly applied to handle this problem. In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data. The proposed taxonomy is independent of the type of sensors used and specific feature extraction/selection methods. The taxonomy identifies different categories of classification methods for the study of fall detection based on the availability of their data during training the classifiers. Then, we present a comprehensive literature review within those categories and identify the approach of treating a fall as an abnormal activity to be a plausible research direction. We conclude our paper by discussing several open research problems in the field and pointers for future research.
Believable Bot Navigation via Playback of Human Traces

Imitation is a powerful and pervasive primitive underlying examples of intelligent behavior in nature. Can we use it as a tool to help build artificial agents that behave like humans do? This question is studied in the context of the BotPrize competition, a Turing-like test where computer game bots compete by attempting to fool human judges into thinking they are just another human player. One problem faced by such bots is that of human-like navigation within the virtual world. This chapter describes the Human Trace Controller, a component of the UTˆ2 bot which took second place in the BotPrize 2010 competition. The controller uses a database of recorded human games in order to quickly retrieve and play back relevant segments of human navigation behavior. Empirical evidence suggests that the method of direct imitation allows the bot to effectively solve several navigation problems while moving in a human-like fashion.
Structure-from-Motion Revisited

Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections. While incremental reconstruction systems have tremendously advanced in all regards, robustness, accuracy, completeness, and scalability remain the key problems towards building a truly general-purpose pipeline. We propose a new SfM technique that improves upon the state of the art to make a further step towards this ultimate goal. The full reconstruction pipeline is released to the public as an open-source implementation.
Collective Stance Classification of Posts in Online Debate Forums

Online debate sites are a large source of informal and opinion-sharing dialogue on current socio-political issues. Inferring users’ stance (PRO or CON) towards discussion topics in domains such as politics or news is an important problem, and is of utility to researchers, government organizations, and companies. Predicting users’ stance supports identification of social and political groups, building of better recommender systems, and personalization of users’ information preferences to their ideological beliefs. In this paper, we develop a novel collective classification approach to stance classification, which makes use of both structural and linguistic features, and which collectively labels the posts’ stance across a network of the users’ posts. We identify both linguistic features of the posts and features that capture the underlying relationships between posts and users. We use probabilistic soft logic (PSL) (Bach et al., 2013) to model post stance by leveraging both these local linguistic features as well as the observed network structure of the posts to reason over the dataset. We evaluate our approach on 4FORUMS (Walker et al., 2012b), a collection of discussions from an online debate site on issues ranging from gun control to gay marriage. We show that our collective classification model is able to easily incorporate rich, relational information and outperforms a local model which uses only linguistic information.
Constructing and Evaluating a Novel Crowdsourcing-based Paraphrased Opinion Spam Dataset

Opinion spam, intentionally written by spammers who do not have actual experience with services or products, has recently become a factor that undermines the credibility of information online. In recent years, studies have attempted to detect opinion spam using machine learning algorithms. However, limitations of goldstandard spam datasets still prove to be a major obstacle in opinion spam research. In this paper, we introduce a novel dataset called Paraphrased OPinion Spam (POPS), which contains a new type of review spam that imitates real human opinions using crowdsourcing. To create such a seemingly truthful review spam dataset, we asked task participants to paraphrase truthful reviews, and include factual information and domain knowledge in their reviews. The classification experiments and semantic analysis results show that our POPS dataset most linguistically and semantically resembles truthful reviews. We believe that our new deceptive opinion spam dataset will help advance opinion spam research.
A modular compliant actuator for emerging high performance and fall-resilient humanoids

The application of humanoids in real world environments necessarily requires robots that can demonstrate physical resilience against strong physical interactions with the environment and impacts, that may occur during falling incidents, that are unavoidable. This paper introduces a modular high performance actuation unit designed to be robust against impacts and strong physical perturbations. The protection against impacts is achieved with the use of elastic transmission combined with soft cover elements on the link side. The paper introduce the details of the actuator design and implementation and discuss the effects of the soft cover and series elastic transmission on the reduction of the impact forces which reach the reduction drive of the actuator during impacts. The model of prototype joint, including the actuator unit, its elastic transmission and the driving link soft cover, is introduced and simulations were performed to study the effect of the elastic properties of the transmission and the soft cover on the reduction of the impact forces transmitted to the reduction drive. The results from the simulations are confirmed by experimental measurements on the real system under induced experimental impact trials, demonstrating the significant effect of the soft cover in the further reduction of impact forces. The performance of the proposed actuator unit in terms of physical robustness makes it ideal for the development of emerging humanoids robots that will be capable of surviving falls and recovers from them.
Automatic Panoramic Image Stitching using Invariant Features

This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.
Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection

Detection of object of a known class is a fundamental problem of computer vision. The appearance of objects can change greatly due to illumination, view point, and articulation. For object classes with large intra-class variation, some divide-and-conquer strategy is necessary. Tree structured classifier models have been used for multi-view multi- pose object detection in previous work. This paper proposes a boosting based learning method, called Cluster Boosted Tree (CBT), to automatically construct tree structured object detectors. Instead of using predefined intra-class sub- categorization based on domain knowledge, we divide the sample space by unsupervised clustering based on discriminative image features selected by boosting algorithm. The sub-categorization information of the leaf nodes is sent back to refine their ancestors' classification functions. We compare our approach with previous related methods on several public data sets. The results show that our approach outperforms the state-of-the-art methods.
Social Media ? Get serious ! Understanding the functional building blocks of social media

Traditionally, consumers used the Internet to simply expend content: they read it, they watched it, and they used it to buy products and services. Increasingly, however, consumers are utilizing platforms–—such as content sharing sites, blogs, social networking, and wikis–—to create, modify, share, and discuss Internet content. This represents the social media phenomenon, which can now significantly impact a firm’s reputation, sales, and even survival. Yet, many executives eschew or ignore this form of media because they don’t understand what it is, the various forms it can take, and how to engage with it and learn. In response, we present a framework that defines social media by using seven functional building blocks: identity, conversations, sharing, presence, relationships, reputation, and groups. As different social media activities are defined by the extent to which they focus on some or all of these blocks, we explain the implications that each block can have for how firms should engage with social media. To conclude, we present a number of recommendations regarding how firms should develop strategies for monitoring, understanding, and responding to different social media activities. final version published in Business Horizons (2011) v. 54 pp. 241-251. doi: 10.106/j.bushor.2011.01.005 1. Welcome to the jungle: The social media ecology Social media employ mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-
Short-term electricity demand forecasting using double seasonal exponential smoothing

This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the demand on the corresponding day of adjacent weeks. There is strong appeal in using a forecasting method that is able to capture both seasonalities. The multiplicative seasonal ARIMA model has been adapted for this purpose. In this paper, we adapt the Holt-Winters exponential smoothing formulation so that it can accommodate two seasonalities. We correct for residual autocorrelation using a simple autoregressive model. The forecasts produced by the new double seasonal Holt-Winters method outperform those from traditional Holt-Winters and from a well-specified multiplicative double seasonal ARIMA model.
Chunking parallel loops in the presence of synchronization

Modern languages for shared-memory parallelism are moving from a bulk-synchronous Single Program Multiple Data (SPMD) execution model to lightweight Task Parallel execution models for improved productivity. This shift is intended to encourage programmers to express the ideal parallelism in an application at a fine granularity that is natural for the underlying domain, while delegating to the compiler and runtime system the job of extracting coarser-grained useful parallelism for a given target system. A simple and important example of this separation of concerns between ideal and useful parallelism can be found in chunking of parallel loops, where the programmer expresses ideal parallelism by declaring all iterations of a loop to be parallel and the implementation exploits useful parallelism by executing iterations of the loop in sequential chunks.
 Though chunking of parallel loops has been used as a standard transformation for several years, it poses some interesting challenges when the parallel loop may directly or indirectly (via procedure calls) perform synchronization operations such as barrier, signal or wait statements. In such cases, a straightforward transformation that attempts to execute a chunk of loops in sequence in a single thread may violate the semantics of the original parallel program. In this paper, we address the problem of chunking parallel loops that may contain synchronization operations. We present a transformation framework that uses a combination of transformations from past work (e.g., loop strip-mining, interchange, distribution, unswitching) to obtain an equivalent set of parallel loops that chunk together statements from multiple iterations while preserving the semantics of the original parallel program. These transformations result in reduced synchronization and scheduling overheads, thereby improving performance and scalability. Our experimental results for 11 benchmark programs on an UltraSPARC II multicore processor showed a geometric mean speedup of 0.52x for the unchunked case and 9.59x for automatic chunking using the techniques described in this paper. This wide gap underscores the importance of using these techniques in future compiler and runtime systems for programming models with lightweight parallelism.
A Hybrid Approach for Near-Range Video Stabilization

Near-range videos contain objects that are close to the camera. These videos often contain discontinuous depth variation (DDV), which is the main challenge to the existing video stabilization methods. Traditionally, 2D methods are robust to various camera motions (e.g., quick rotation and zooming) under scenes with continuous depth variation (CDV). However, in the presence of DDV, they often generate wobbled results due to the limited ability of their 2D motion models. Alternatively, 3D methods are more robust in handling near-range videos. We show that, by compensating rotational motions and ignoring translational motions, near-range videos can be successfully stabilized by 3D methods without sacrificing the stability too much. However, it is time-consuming to reconstruct the 3D structures for the entire video and sometimes even impossible due to rapid camera motions. In this paper, we combine the advantages of 2D and 3D methods, yielding a hybrid approach that is robust to various camera motions and can handle the near-range scenarios well. To this end, we automatically partition the input video into CDV and DDV segments. Then, the 2D and 3D approaches are adopted for CDV and DDV clips, respectively. Finally, these segments are stitched seamlessly via a constrained optimization. We validate our method on a large variety of consumer videos.
Examining the Impact of Feature Selection Methods on Text Classification

Feature selection that aims to determine and select the distinctive terms representing a best document is one of the most important steps of classification. With the feature selection, dimension of document vectors are reduced and consequently duration of the process is shortened. In this study, feature selection methods were studied in terms of dimension reduction rates, classification success rates, and dimension reductionclassification success relation. As classifiers, kNN (k-Nearest Neighbors) and SVM (Support Vector Machines) were used. 5 standard (Odds Ratio-OR, Mutual Information-MI, Information Gain-IG, Chi-Square-CHI and Document Frequency-DF), 2 combined (Union of Feature Selections-UFS and Correlation of Union of Feature Selections-CUFS) and 1 new (Sum of Term Frequency-STF) feature selection methods were tested. The application was performed by selecting 100 to 1000 terms (with an increment of 100 terms) from each class. It was seen that kNN produces much better results than SVM. STF was found out to be the most successful feature selection considering the average values in both datasets. It was also found out that CUFS, a combined model, is the one that reduces the dimension the most, accordingly, it was seen that CUFS classify the documents more successfully with less terms and in short period compared to many of the standard methods. Feature selection; text classification; text mining; k-Nearest Neighbors; support vector machines
Detection of Relation Assertion Errors in Knowledge Graphs

Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. We perform an extensive evaluation on a variety of datasets, backed by a manual evaluation on DBpedia and NELL, and we propose and evaluate heuristics for the selection of relevant graph paths to be used as features in our method.
Type-Safe Delegation for Run-Time Component Adaptation

The aim of component technology is the replacement of large monolithic applications with sets of smaller software components, whose particular functionality and interoperation can be adapted to users’ needs. However, the adaptation mechanisms of component software are still limited. Most proposals concentrate on adaptations that can be achieved either at compile time or at link time. Current support for dynamic component adaptation, i.e. unanticipated, incremental modifications of a component system at run-time, is not sufficient. This paper proposes object-based inheritance (also known as delegation) as a complement to purely forwarding-based object composition. It presents a typesafe integration of delegation into a class-based object model and shows how it overcomes the problems faced by forwarding-based component interaction, how it supports independent extensibility of components and unanticipated, dynamic component adaptation.