nils-herrmann/clean_scirepeval huggingface dataset:
A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications

Over the last years significant efforts have been made to develop kernels that can be applied to sequence data such as DNA, text, speech, video and images. The Fisher Kernel and similar variants have been suggested as good ways to combine an underlying generative model in the feature space and discriminant classifiers such as SVM’s. In this paper we suggest an alternative procedure to the Fisher kernel for systematically finding kernel functions that naturally handle variable length sequence data in multimedia domains. In particular for domains such as speech and images we explore the use of kernel functions that take full advantage of well known probabilistic models such as Gaussian Mixtures and single full covariance Gaussian models. We derive a kernel distance based on the Kullback-Leibler (KL) divergence between generative models. In effect our approach combines the best of both generative and discriminative methods and replaces the standard SVM kernels. We perform experiments on speaker identification/verification and image classification tasks and show that these new kernels have the best performance in speaker verification and mostly outperform the Fisher kernel based SVM’s and the generative classifiers in speaker identification and image classification.
Reading patterns and usability in visualizations of electronic documents

We present an exploration of reading patterns and usability in visualizations of electronic documents. Twenty subjects wrote essays and answered questions about scientific documents using an overview+detail, a fisheye, and a linear interface. We study reading patterns by progression maps that visualize the progression of subjects' reading activity, and by visibility maps that show for how long different parts of the document are visible. The reading patterns help explain differences in usability between the interfaces and show how interfaces affect the way subjects read. With the overview+detail interface, subjects get higher grades for their essays. All but one of the subjects prefer this interface. With the fisheye interface, subjects use more time on gaining an overview of the document and less time on reading the details. Thus, they read the documents faster, but display lower incidental learning. We also show how subjects only briefly have visible the parts of the document that are not initially readable in the fisheye interface, even though they express a lack of trust in the algorithm underlying the fisheye interface. When answering questions, the overview is used for jumping directly to answers in the document and to already-visited parts of the document. However, subjects are slower at answering questions with the overview+detail interface. From the visualizations of the reading activity, we find that subjects using the overview+detail interface often explore the document further even when a satisfactory answer to the given question has already been read. Thus, overviews may grab subjects' attention and possibly distract them.
An improved MPPT technique for high gain DC-DC converter using model predictive control for photovoltaic applications

This paper presents an enhanced Maximum Power Point Tracking (MPPT) of Photovoltaic (PV) systems by means of Model Predictive Control (MPC) techniques. The PV array can feed power to the load through a DC/DC converter boosting the output voltage. Due to stochastic behavior of solar energy, MPPT control technique of PV arrays is required to operate at maximum power point. Extracting the maximum power from PV systems has been widely investigated within the literature. The main contribution of this paper is enhancement of the Incremental Conductance (INC) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the selected high gain multilevel DC-DC converter. Comparing the developed technique to the conventional INC method shows significant improvement in PV system performance. Experimental validation is presented using the dSpace CP 1103 to implement the proposed MPC-MPPT.
Skip graphs

Skip graphs are a novel distributed data structure, based on skip lists, that provide the full functionality of a balanced tree in a distributed system where elements are stored in separate nodes that may fail at any time. They are designed for use in searching peer-to-peer networks, and by providing the ability to perform queries based on key ordering, they improve on existing search tools that provide only hash table functionality. Unlike skip lists or other tree data structures, skip graphs are highly resilient, tolerating a large fraction of failed nodes without losing connectivity. In addition, constructing, inserting new elements into, searching a skip graph and detecting and repairing errors in the data structure introduced by node failures can be done using simple and straight-forward algorithms.
Learning STRIPS Operators from Noisy and Incomplete Observations

Agents learning to act autonomously in realworld domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-world domains. We propose a method which learns STRIPS action models in such domains, by decomposing the problem into first learning a transition function between states in the form of a set of classifiers, and then deriving explicit STRIPS rules from the classifiers’ parameters. We evaluate our approach on simulated standard planning domains from the International Planning Competition, and show that it learns useful domain descriptions from noisy, incomplete observations.
The Streaming Capacity of Sparsely Connected P2P Systems With Distributed Control

Peer-to-peer (P2P) streaming technologies can take advantage of the upload capacity of clients, and hence can scale to large content distribution networks with lower cost. A fundamental question for P2P streaming systems is the maximum streaming rate that all users can sustain. Prior works have studied the optimal streaming rate for a complete network, where every peer is assumed to be able to communicate with all other peers. This is, however, an impractical assumption in real systems. In this paper, we are interested in the achievable streaming rate when each peer can only connect to a small number of neighbors. We show that even with a random peer-selection algorithm and uniform rate allocation, as long as each peer maintains Ω(lon N) downstream neighbors, where N is the total number of peers in the system, the system can asymptotically achieve a streaming rate that is close to the optimal streaming rate of a complete network. These results reveal a number of important insights into the dynamics of the system, based on which we then design simple improved algorithms that can reduce the constant factor in front of the Ω(lon N) term, yet can achieve the same level of performance guarantee. Simulation results are provided to verify our analysis.
Large-Step Markov Chains for the Traveling Salesman Problem

We introduce a new class of Markov chain Monte Carlo search procedures, leading to more powerful optimizat~on methods than simulated annealing. The main idea is to embed deterministic local search techniques into stochastic algorithms. The Monte Carlo explores only local optima, and it is able to make large, global changes, even at low temperatures, thus overcoming large barriers in configuration space. We test these procedures in the case of the Traveling Salesman Problem. The embedded local searches we use are 3-opt and Lin-Kernighan. The large change or step consists of a special kind of 4-change followed by local-opt minimization. We test this algorithm on a number of instances. The power of the method is illustrated by solving to optimality some large problems such M the LIN318, the ATbT532, and the RAT783 problems. For even larger instances with randomly distributed cities, the Markov chain procedure improves %opt by over 1.6%~~ and Lin-Kernighan by 1.3%, leading to a new best heuristic.
Classification of fruits using Probabilistic Neural Networks - Improvement using color features

This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image Processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits; i.e., apples, bananas, carrots, mangoes and oranges were analysed and seventeen features were extracted based on the fruits' morphological and colour characteristics. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB/SIMULINK environment. The results obtained were a significant improvement over a previous study.
Recommender Systems

The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the particular characteristics of the data available. For example, movie watchers on Netflix frequently provide ratings on a scale of 1 (disliked) to 5 (liked). Such a data source records the quality of interactions between users and items. Additionally, the system may have access to user-specific and item-specific profile attributes such as demographics and product descriptions respectively. Recommender systems differ in the way they analyze these data sources to develop notions of affinity between users and items which can be used to identify well-matched pairs. Collaborative Filtering systems analyze historical interactions alone, while Content-based Filtering systems are based on profile attributes; and Hybrid techniques attempt to combine both of these designs. The architecture of recommender systems and their evaluation on real-world problems is an active area of research.
Optimizing query execution for variable-aligned length compression of bitmap indices

Indexing is a fundamental mechanism for efficient data access. Recently, we proposed the Variable-Aligned Length (VAL) bitmap index encoding framework, which generalizes the commonly used word-aligned compression techniques. VAL presented a variable-aligned compression framework, which allows columns of a bitmap to be compressed using different encoding lengths. This flexibility creates a tunable compression that balances the trade-off between space and query processing time. The variable format of VAL presents several unique opportunities for query optimization.
 In this paper we explore multiple algorithms to optimize both point queries and range queries in VAL. In particular, we propose a dynamic encoding-length translation heuristic to process point queries. For range queries, we propose several column orderings based on the bitmap's metadata: largest segment length first (lsf), column size (size), and weighted size (ws). In our empirical study over both real and synthetic data sets, we show that our dynamic translation selection scheme produces query execution times only 3.5% below the optimal. We also found that the weighted size column ordering significantly and consistently out-performs other ordering techniques. Finally, we show that algorithms scale to data sets that are row-ordered.
Learning on the border: active learning in imbalanced data classification

This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly less number of observations of the target concept. Various real-world classification tasks, such as medical diagnosis, text categorization and fraud detection suffer from this phenomenon. The standard machine learning algorithms yield better prediction performance with balanced datasets. In this paper, we demonstrate that active learning is capable of solving the class imbalance problem by providing the learner more balanced classes. We also propose an efficient way of selecting informative instances from a smaller pool of samples for active learning which does not necessitate a search through the entire dataset. The proposed method yields an efficient querying system and allows active learning to be applied to very large datasets. Our experimental results show that with an early stopping criteria, active learning achieves a fast solution with competitive prediction performance in imbalanced data classification.
A Discontinuity Adaptive Prior for Image Denoising

The presence of noise in digital images degrades the visual quality by corrupting the information associated with the image. The aim of denoising is to restore an image from its noisy version by preserving signal information. In this paper, we are considering an image corrupted by additive Gaussian noise. The image is modeled as Markov random field (MRF) and an estimation of maximum-a-posteriori (MAP) is obtained using graduated non-convexity. The results are compared with other spatial domain filtering methods. The discontinuity adaptive prior helps in preserving edge information. The results suggest that proposed method has an improved performance.
Confidence sets for persistence diagrams

Persistent homology is a method for probing topological properties of point clouds and functions. The method involves tracking the birth and death of topological features as one varies a tuning parameter. Features with short lifetimes are informally considered to be “topological noise,” and those with a long lifetime are considered to be “topological signal.” In this paper, we bring some statistical ideas to persistent homology. In particular, we derive confidence sets that allow us to separate topological signal from topological noise.
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics

We construct multi-modal concept representations by concatenating a skip-gram linguistic representation vector with a visual concept representation vector computed using the feature extraction layers of a deep convolutional neural network (CNN) trained on a large labeled object recognition dataset. This transfer learning approach brings a clear performance gain over features based on the traditional bag-of-visual-word approach. Experimental results are reported on the WordSim353 and MEN semantic relatedness evaluation tasks. We use visual features computed using either ImageNet or ESP Game images.
Unsupervised Meta-Learning for Reinforcement Learning

Meta-learning is a powerful tool that builds on multi-task learning to learn how to quickly adapt a model to new tasks. In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by meta-learning prior tasks. The performance of meta-learning algorithms critically depends on the tasks available for meta-training: in the same way that supervised learning algorithms generalize best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks. In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design. If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated. In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning. We describe a general recipe for unsupervised meta-reinforcement learning, and describe an effective instantiation of this approach based on a recently proposed unsupervised exploration technique and model-agnostic meta-learning. We also discuss practical and conceptual considerations for developing unsupervised meta-learning methods. Our experimental results demonstrate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design, significantly exceeds the performance of learning from scratch, and even matches performance of meta-learning methods that use hand-specified task distributions.
Unsupervised word sense disambiguation using WordNet relatives

Hee-Cheol Seo *, Hoojung Chung , Hae-Chang Rim *, Sung Hyon Myaeng , Soo-Hong Kim c a Department of Computer Science and Engineering, Korea University 1, 5-ka, Anam-dong, Seongbuk-Gu, Seoul 136-701, Republic of Korea b Information and Communications University, 58-4 Hwaam-dong, Yuseong-gu, Daejeon 305-732, Republic of Korea c Department of Computer Software Engineering, College of Engineering, Sangmyung University, San 98-20, Anso-Dong, Chonan, Chungnam, Republic of Korea
Genome plasticity a key factor in the success of polyploid wheat under domestication.

Wheat was domesticated about 10,000 years ago and has since spread worldwide to become one of the major crops. Its adaptability to diverse environments and end uses is surprising given the diversity bottlenecks expected from recent domestication and polyploid speciation events. Wheat compensates for these bottlenecks by capturing part of the genetic diversity of its progenitors and by generating new diversity at a relatively fast pace. Frequent gene deletions and disruptions generated by a fast replacement rate of repetitive sequences are buffered by the polyploid nature of wheat, resulting in subtle dosage effects on which selection can operate.
CTC in the Context of Generalized Full-Sum HMM Training

We formulate a generalized hybrid HMM-NN training procedure using the full-sum over the hidden state-sequence and identify CTC as a special case of it. We present an analysis of the alignment behavior of such a training procedure and explain the strong localization of label output behavior of full-sum training (also referred to as peaky or spiky behavior). We show how to avoid that behavior by using a state prior. We discuss the temporal decoupling between output label position/time-frame, and the corresponding evidence in the input observations when this is trained with BLSTM models. We also show a way how to overcome this by jointly training a FFNN. We implemented the Baum-Welch alignment algorithm in CUDA to be able to do fast soft realignments on GPU. We have published this code along with some of our experiments as part of RETURNN, RWTH’s extensible training framework for universal recurrent neural networks. We finish with experimental validation of our study on WSJ and Switchboard.
Client-Driven Network-level QoE fairness for Encrypted 'DASH-S'

Adaptive video streams, when competing behind a bottleneck link, generate flows that lead to instability, under-utilization, and unfairness. Recent studies suggest there is also a negative impact on users' perceived quality of experience as a consequence. Two general classes of solution exist. Client-side bitrate adaptation algorithms can improve stability and may achieve flow-rate equality. However, operating in isolation, bitrate adaptation has no ability to establish QoE fairness. Conversely, network services have been shown to achieve stability and quality of experience by managing bottleneck resources. However, the widespread use of HTTPS renders these services ineffective.
 In this paper we show that QoE can only be achieved when both network and client interact. We do so by a constructive argument, and then architect client-Driven Video Delivery (cDVD) in response. Our cDVD implementation provides a client-level API into the network and builds on software-defined principles. cDVD measurements reinforce our argument and raise new opportunities for exploration.
Sockeye: A Toolkit for Neural Machine Translation

We describe SOCKEYE,1 an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). SOCKEYE is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNET, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks. SOCKEYE also supports a wide range of optimizers, normalization and regularization techniques, and inference improvements from current NMT literature. Users can easily run standard training recipes, explore different model settings, and incorporate new ideas. In this paper, we highlight SOCKEYE’s features and benchmark it against other NMT toolkits on two language arcs from the 2017 Conference on Machine Translation (WMT): English–German and Latvian–English. We report competitive BLEU scores across all three architectures, including an overall best score for SOCKEYE’s transformer implementation. To facilitate further comparison, we release all system outputs and training scripts used in our experiments. The SOCKEYE toolkit is free software released under the Apache 2.0 license.
10, 000 gold for 20 dollars: an exploratory study of World of Warcraft gold buyers

Buying virtual currencies with real money from a third-party often violates the terms of use of online games. This study quantitatively investigates players who buy in-game gold from a third-party in World of Warcraft. A cross-cultural survey dataset of 2865 players reveals that differences between Asian and Western players are negligible compared to differences across genders, job categories, and play motivations. Our findings have implications for the design and study of interactions between players and virtual currencies.
Short Text Similarity with Word Embeddings

Determining semantic similarity between texts is important in many tasks in information retrieval such as search, query suggestion, automatic summarization and image finding. Many approaches have been suggested, based on lexical matching, handcrafted patterns, syntactic parse trees, external sources of structured semantic knowledge and distributional semantics. However, lexical features, like string matching, do not capture semantic similarity beyond a trivial level. Furthermore, handcrafted patterns and external sources of structured semantic knowledge cannot be assumed to be available in all circumstances and for all domains. Lastly, approaches depending on parse trees are restricted to syntactically well-formed texts, typically of one sentence in length.
 We investigate whether determining short text similarity is possible using only semantic features---where by semantic we mean, pertaining to a representation of meaning---rather than relying on similarity in lexical or syntactic representations. We use word embeddings, vector representations of terms, computed from unlabelled data, that represent terms in a semantic space in which proximity of vectors can be interpreted as semantic similarity.
 We propose to go from word-level to text-level semantics by combining insights from methods based on external sources of semantic knowledge with word embeddings. A novel feature of our approach is that an arbitrary number of word embedding sets can be incorporated. We derive multiple types of meta-features from the comparison of the word vectors for short text pairs, and from the vector means of their respective word embeddings. The features representing labelled short text pairs are used to train a supervised learning algorithm. We use the trained model at testing time to predict the semantic similarity of new, unlabelled pairs of short texts 
 We show on a publicly available evaluation set commonly used for the task of semantic similarity that our method outperforms baseline methods that work under the same conditions.