nils-herrmann/clean_scirepeval huggingface dataset:
Analogy-driven 3D style transfer

Style transfer aims to apply the style of an exemplar model to a target one, while retaining the target’s structure. The main challenge in this process is to algorithmically distinguish style from structure, a high-level, potentially ill-posed cognitive task. Inspired by cognitive science research we recast style transfer in terms of shape analogies. In IQ testing, shape analogy queries present the subject with three shapes: source, target and exemplar, and ask them to select an output such that the transformation, or analogy, from the exemplar to the output is similar to that from the source to the target. The logical process involved in identifying the source-to-target analogies implicitly detects the structural differences between the source and target and can be used effectively to facilitate style transfer. Since the exemplar has a similar structure to the source, applying the analogy to the exemplar will provide the output we seek. The main technical challenge we address is to compute the source to target analogies, consistent with human logic. We observe that the typical analogies we look for consist of a small set of simple transformations, which when applied to the exemplar generate a continuous, seamless output model. To assemble a shape analogy, we compute an optimal set of source-to-target transformations, such that the assembled analogy best fits these criteria. The assembled analogy is then applied to the exemplar shape to produce the desired output model. We use the proposed framework to seamlessly transfer a variety of style properties between 2D and 3D objects and demonstrate significant improvements over the state of the art in style transfer. We further show that our framework can be used to successfully complete partial scans with the help of a user provided structural template, coherently propagating scan style across the completed surfaces.
Low level Metrics to High level SLAs - LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments

Cloud computing represents a novel on-demand computing approach where resources are provided in compliance to a set of predefined non-functional properties specified and negotiated by means of Service Level Agreements (SLAs). In order to avoid costly SLA violations and to timely react to failures and environmental changes, advanced SLA enactment strategies are necessary, which include appropriate resource-monitoring concepts. Currently, Cloud providers tend to adopt existing monitoring tools, as for example those from Grid environments. However, those tools are usually restricted to locality and homogeneity of monitored objects, are not scalable, and do not support mapping of low-level resource metrics e.g., system up and down time to high-level application specific SLA parameters e.g., system availability. In this paper we present a novel framework for managing the mappings of the Low-level resource Metrics to High-level SLAs (LoM2HiS framework). The LoM2HiS framework is embedded into FoSII infrastructure, which facilitates autonomic SLA management and enforcement. Thus, the LoM2HiS framework detects future SLA violation threats and can notify the enactor component to act so as to avert the threats. We discuss the conceptual model of the LoM2HiS framework, followed by the implementation details. Finally, we present the first experimental results and a proof of concept of the LoM2HiS framework.
Efficient musical noise suppression for speech enhancement system

Noise reduction techniques that are relying on spectral weighting rules often generate annoying musical noise artifacts in the processed signal. In this paper, we present a postfilter (PF) for the spectral weighting gains that is capable of reducing musical noise in a simple but efficient way. It includes a robust detector for speech pauses and low SNR conditions and adaptively smoothes the weighting gains over frequency based on soft-decisions. Objective and subjective measurements show consistent improvements if the postfilter is applied to conventional noise reduction techniques.
RT to Win! Predicting Message Propagation in Twitter

Twitter is a very popular way for people to share information on a bewildering multitude of topics. Tweets are propagated using a variety of channels: by following users or lists, by searching or by retweeting. Of these vectors, retweeting is arguably the most effective, as it can potentially reach the most people, given its viral nature. A key task is predicting if a tweet will be retweeted, and solving this problem furthers our understanding of message propagation within large user communities. We carry out a human experiment on the task of deciding whether a tweet will be retweeted which shows that the task is possible, as human performance levels are much above chance. Using a machine learning approach based on the passive-aggressive algorithm, we are able to automatically predict retweets as well as humans. Analyzing the learned model, we find that performance is dominated by social features, but that tweet features add a substantial boost.
Linear-time computability of combinatorial problems on series-parallel graphs

A series-parallel graph can be constructed from a certain graph by recurslvely applying "series" and "parallel" connections The class of such graphs, which Is a well-known model of series-parallel electrical networks, is a subclass of planar graphs It is shown in a umfied manner that there exist hnearume algorithms for many combinatorial problems ff an input graph is restricted to the class of series-parallel graphs. These include 0) the decision problem with respect to a property characterized by a finite number of forbidden graphs, (u) the mlmmum edge (vertex) deletion problem with respect to the same property as above, and (Ul) the generalized matching problem Consequently, the following problems, among others, prove to be hnear-tlme computable for the class of series-parallel graphs. (I) the minimum vertex cover problem, (2) the maximum outerplanar (reduced) subgraph problem, (3) the minimum feedback vertex set problem, (4) the maximum (induced) hne-subgraph problem, (5) the maximum matching problem, and (6) the maximum disjoint triangle problem.
Large Scale Sentiment Learning with Limited Labels

Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. Although there is a lot of work on sentiment analysis, there are no many datasets available which one can use for developing new methods and for evaluation. To the best of our knowledge, the largest dataset for sentiment analysis is TSentiment [8], a 1.6 millions machine-annotated tweets dataset covering a period of about 3 months in 2009. This dataset however is too short and therefore insufficient to study heterogeneous, fast evolving streams. Therefore, we annotated the Twitter dataset of 2015 (228 million tweets without retweets and 275 million with retweets) and we make it publicly available for research. For the annotation we leverage the power of unlabeled data, together with labeled data using semi-supervised learning and in particular, Self-Learning and Co-Training. Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes a batch and a stream-processing of the data. In the former, all labeled and unlabeled data are available to the algorithms from the beginning, whereas in the later, they are revealed gradually based on their arrival time in the stream.
LidarBoost: Depth superresolution for ToF 3D shape scanning

Depth maps captured with time-of-flight cameras have very low data quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature.
Frictionless Commerce ? A Comparison of Internet and Conventional Retailers

There have been many claims that the Internet represents a new “frictionless market.” Our research empirically analyzes the characteristics of the Internet as a channel for two categories of homogeneous products — books and CDs. Using a data set of over 8,500 price observations collected over a period of 15 months, we compare pricing behavior at 41 Internet and conventional retail outlets. We find that prices on the Internet are 9-16% lower than prices in conventional outlets, depending on whether taxes, shipping and shopping costs are included in the price. Additionally, we find that Internet retailers’ price adjustments over time are up to 100 times smaller than conventional retailers’ price adjustments — presumably reflecting lower menu costs in Internet channels. We also find that levels of price dispersion depend importantly on the measures employed. When we simply compare the prices posted by different Internet retailers we find substantial dispersion. Internet retailer prices differ by an average of 33% for books and 25% for CDs. However, when we weight these prices by proxies for market share, we find dispersion is lower in Internet channels than in conventional channels, reflecting the dominance of certain heavily branded retailers. We conclude that while there is lower friction in many dimensions of Internet competition, branding, awareness, and trust remain important sources of heterogeneity among Internet retailers.
Can Saliency Information Benefit Image Captioning Models?

To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the agreement between bottom-up saliency-based visual attention and object referrals in scene description constructs. We investigate the properties of human-written descriptions and machine-generated ones. We then propose a saliency-boosted image captioning model in order to investigate benefits from low-level cues in language models. We learn that (1) humans mention more salient objects earlier than less salient ones in their descriptions, (2) the better a captioning model performs, the better attention agreement it has with human descriptions, (3) the proposed saliencyboosted model, compared to its baseline form, does not improve significantly on the MS COCO database, indicating explicit bottom-up boosting does not help when the task is well learnt and tuned on a data, (4) a better generalization ability is, however, observed for the saliency-boosted model on unseen data.
Layered segmentation and optical flow estimation over time

Layered models provide a compelling approach for estimating image motion and segmenting moving scenes. Previous methods, however, have failed to capture the structure of complex scenes, provide precise object boundaries, effectively estimate the number of layers in a scene, or robustly determine the depth order of the layers. Furthermore, previous methods have focused on optical flow between pairs of frames rather than longer sequences. We show that image sequences with more frames are needed to resolve ambiguities in depth ordering at occlusion boundaries; temporal layer constancy makes this feasible. Our generative model of image sequences is rich but difficult to optimize with traditional gradient descent methods. We propose a novel discrete approximation of the continuous objective in terms of a sequence of depth-ordered MRFs and extend graph-cut optimization methods with new “moves” that make joint layer segmentation and motion estimation feasible. Our optimizer, which mixes discrete and continuous optimization, automatically determines the number of layers and reasons about their depth ordering. We demonstrate the value of layered models, our optimization strategy, and the use of more than two frames on both the Middlebury optical flow benchmark and the MIT layer segmentation benchmark.
Multiple documents summarization based on evolutionary optimization algorithm

This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization.
Applied cryptography - protocols, algorithms, and source code in C, 2nd Edition

Cryptographic techniques have applications far beyond the obvious uses of encoding and decoding information. For Internet developers who need to know about capabilities, such as digital signatures, that depend on cryptographic techniques, theres no better overview than Applied Cryptography, the definitive book on the subject. Bruce Schneier covers general classes of cryptographic protocols and then specific techniques, detailing the inner workings of real-world cryptographic algorithms including the Data Encryption Standard and RSA public-key cryptosystems. The book includes source-code listings and extensive advice on the practical aspects of cryptography implementation, such as the importance of generating truly random numbers and of keeping keys secure.
A Note on Auto-tuning GEMM for GPUs

The development of high performance dense linear algebra (DLA) critically depends on highly optimized BLAS, and especially on the matrix multiplication routine (GEMM). This is especially true for Graphics Processing Units (GPUs), as evidenced by recently published results on DLA for GPUs that rely on highly optimized GEMM [13, 11]. However, the current best GEMM performance, e.g. of up to 375 GFlop/s in single precision and of up to 75 GFlop/s in double precision arithmetic on NVIDIA’s GTX 280, is difficult to achieve. The development involves extensive GPU knowledge and even backward engineering to understand some undocumented insides about the architecture that have been of key importance in the development . In this paper, we describe some GPU GEMM auto-tuning optimization techniques that allow us to keep up with changing hardware by rapidly reusing, rather than reinventing, the existing ideas. Auto-tuning, as we show in this paper, is a very practical solution where in addition to getting an easy portability, we can often get substantial speedups even on current GPUs (e.g. up to 27% in certain cases for both single and double precision GEMMs on the GTX 280).
Terrain Synthesis By-Example

Synthesizing terrain or adding detail to terrains manually is a long and tedious process. With procedural synthesis methods this process is faster but more difficult to control. This paper presents a new technique of terrain synthesis that uses an existing terrain to synthesize new terrain. To do this we use multi-resolution analysis to extract the high-resolution details from existing models and apply them to increase the resolution of terrain. Our synthesized terrains are more heterogeneous than procedural results, are superior to terrains created by texture transfer, and retain the large-scale characteristics of the original terrain.
Distributed Clustering of Linear Bandits in Peer to Peer Networks

We provide two distributed confidence ball algorithms for solving linear bandit problems in peer to peer networks with limited communication capabilities. For the first, we assume that all the peers are solving the same linear bandit problem, and prove that our algorithm achieves the optimal asymptotic regret rate of any centralised algorithm that can instantly communicate information between the peers. For the second, we assume that there are clusters of peers solving the same bandit problem within each cluster, and we prove that our algorithm discovers these clusters, while achieving the optimal asymptotic regret rate within each one. Through experiments on several real-world datasets, we demonstrate the performance of proposed algorithms compared to the state-of-the-art.
Beyond Self-Tracking and Reminders: Designing Smartphone Apps That Support Habit Formation

Habit formation is an important part of behavior change interventions: to ensure an intervention has long-term effects, the new behavior has to turn into a habit and become automatic. Smartphone apps could help with this process by supporting habit formation. To better understand how, we conducted a 4-week study exploring the influence of different types of cues and positive reinforcement on habit formation and reviewed the functionality of 115 habit formation apps. We discovered that relying on reminders supported repetition but hindered habit development, while the use of event-based cues led to increased automaticity; positive reinforcement was ineffective. The functionality review revealed that existing apps focus on self-tracking and reminders, and do not support event-based cues. We argue that apps, and technology-based interventions in general, have the potential to provide real habit support, and present design guidelines for interventions that could support habit formation through contextual cues and implementation intentions.
Scalable sparse tensor decompositions in distributed memory systems

We investigate an efficient parallelization of the most common iterative sparse tensor decomposition algorithms on distributed memory systems. A key operation in each iteration of these algorithms is the matricized tensor times Khatri-Rao product (MTTKRP). This operation amounts to element-wise vector multiplication and reduction depending on the sparsity of the tensor. We investigate a fine and a coarse-grain task definition for this operation, and propose hypergraph partitioning-based methods for these task definitions to achieve the load balance as well as reduce the communication requirements. We also design a distributed memory sparse tensor library, HyperTensor, which implements a well-known algorithm for the CANDECOMP-/PARAFAC (CP) tensor decomposition using the task definitions and the associated partitioning methods. We use this library to test the proposed implementation of MTTKRP in CP decomposition context, and report scalability results up to 1024 MPI ranks. We observed up to 194 fold speedups using 512 MPI processes on a well-known real world data, and significantly better performance results with respect to a state of the art implementation.
Automated Extension of Narrative Planning Domains with Antonymic Operators

AI Planning has been widely used for narrative generation and the control of virtual actors in interactive storytelling. Planning models for such dynamic environments must include alternative actions which enable deviation away from a baseline storyline in order to generate multiple story variants and to be able to respond to changes that might be made to the story world. However, the actual creation of these domain models has been a largely empirical process with a lack of principled approaches to the definition of alternative actions. Our work has addressed this problem and in the paper we present a novel automated method for the generation of interactive narrative domain models from existing non-interactive versions. Central to this is the use of actions that are contrary to those forming the baseline plot within a principled mechanism for their semi-automatic production. It is important that such newly created domain content should still be human-readable and to this end labels for new actions and predicates are generated automatically using antonyms selected from a range of on-line lexical resources. Our approach is fully implemented in a prototype system and its potential demonstrated via both formal experimental evaluation and user evaluation of the generated action labels.
Multi-class SVM classifier for english handwritten digit recognition using manual class segmentation

A new method for recognition of isolated handwritten English digits is presented here. This method is based on Support Vector Machines (SVMs). Mean and standard deviation of each digit is considered as the features. Using these features, multiple SVM classifiers are trained to separate different classes of digits. Support vector machine are based on the concept of decision planes that defines the decision boundaries. The decision plane is one that separates between the set of digits having different class membership. The approach works in four steps
 1) Preprocessing 2) Feature extraction
 3) Classification 4) detection. A database of 100 different representation of each digit is constructed for the training database. The digits are first manually segmented into 5 classes to minimize the time required to obtain the hyperplane. Then the input is again check against the two classes by 2-class SVM classifier. Experiments show that the proposed features can provide a very good recognition result using Support Vector Machines at a recognition rate 97%, compared with 91.25% obtained by MLP neural network classifier using the same features and test set.
Improving Data Association in Vision-based SLAM

This paper presents an approach to vision-based simultaneous localization and mapping (SLAM). Our approach uses the scale invariant feature transform (SIFT) as features and applies a rejection technique to concentrate on a reduced set of distinguishable, stable features. We track detected SIFT features over consecutive frames obtained by a stereo camera and select only those features that appear to be stable from different views. Whenever a feature is selected, we compute a representative feature given the previous observations. This approach is applied within a Rao-Blackwellized particle filter to make the data association easier and furthermore to reduce the number of landmarks that need to be maintained in the map. Our system has been implemented and tested on data gathered with a mobile robot in a typical office environment. Experiments presented in this paper demonstrate that our method improves the data association and in this way leads to more accurate maps