nils-herrmann/clean_scirepeval huggingface dataset:
Bagging Ensemble Selection for Regression

Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have shown that using random trees as base classifiers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by the promising results in classification, this paper examines the predictive performance of the BES-OOB strategy for regression problems. Our results show that the BES-OOB strategy outperforms Stochastic Gradient Boosting and Bagging when using regression trees as the base learners. Our results also suggest that the advantage of using a diverse model library becomes clear when the model library size is relatively large. We also present encouraging results indicating that the non-negative least squares algorithm is a viable approach for pruning an ensemble of ensembles.
On evaluating stream learning algorithms

Most streaming decision models evolve continuously over time, run in resource-aware environments, and detect and react to changes in the environment generating data. One important issue, not yet convincingly addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. This paper proposes a general framework for assessing predictive stream learning algorithms. We defend the use of prequential error with forgetting mechanisms to provide reliable error estimators. We prove that, in stationary data and for consistent learning algorithms, the holdout estimator, the prequential error and the prequential error estimated over a sliding window or using fading factors, all converge to the Bayes error. The use of prequential error with forgetting mechanisms reveals to be advantageous in assessing performance and in comparing stream learning algorithms. It is also worthwhile to use the proposed methods for hypothesis testing and for change detection. In a set of experiments in drift scenarios, we evaluate the ability of a standard change detection algorithm to detect change using three prequential error estimators. These experiments point out that the use of forgetting mechanisms (sliding windows or fading factors) are required for fast and efficient change detection. In comparison to sliding windows, fading factors are faster and memoryless, both important requirements for streaming applications. Overall, this paper is a contribution to a discussion on best practice for performance assessment when learning is a continuous process, and the decision models are dynamic and evolve over time.
Automatic Legal Text Summarisation: Experiments with Summary Structuring

We describe a set of experiments using machine learning techniques for the task of extractive summarisation. The research is part of a summarisation project for which we use a corpus of judgments of the UK House of Lords. We present classification results for naïve Bayes and maximum entropy and we explore methods for scoring the summary-worthiness of a sentence. We present sample output from the system, illustrating the utility of rhetorical status information, which provides a means for structuring summaries and tailoring them to different types of users.
Applications of Graph Labeling in Communication Networks

The field of Graph Theory plays vital role in various fields. One of the important areas in graph theory is Graph Labeling used in many applications like coding theory, x-ray crystallography, radar, astronomy, circuit design, communication network addressing, data base management. This paper gives an overview of labeling of graphs in heterogeneous fields to some extent but mainly focuses on the communication networks. Communication network has two types ‘Wired communication’ and ‘wireless communication’. Day by day wireless networks have been developed to ease communication between any two systems, results more efficient communication. This paper also explored role of labeling in expanding the utility of this channel assignment process in communication networks. Various papers based on graph labeling have been observed, and identified its usage towards communication networks. This paper addresses how the concept of graph labeling can be applied to network security, network addressing, channel assignment process, social networks. An overview and new ideas has been proposed here.
GOLOG: A Logic Programming Language for Dynamic Domains

This paper proposes a new logic programming language called GOLOG whose interpreter automatically maintains an explicit representation of the dynamic world being modeled, on the basis of user supplied axioms about the preconditions and eeects of actions and the initial state of the world. This allows programs to reason about the state of the world and consider the eeects of various possible courses of action before committing to a particular behavior. The net eeect is that programs may be written at a much higher level of abstraction than is usually possible. The language appears well suited for applications in high level control of robots and industrial processes, intelligent software agents, discrete event simulation, etc. It is based on a formal theory of action speciied in an extended version of the situation calculus. A prototype implementation in Prolog has been developed.
A 6–18 GHz GaAs multifunctional chip for Transmit/Receive Modules

In this contribution the development, design, fabrication and test of a highly integrated broadband multifunctional chip is presented. The MMIC covers the C-, X-and Ku- Band and it is suitable for applications in high performance Transmit/Receive Modules. In less than 26 mm2, the MMIC embeds several T/R switches, low noise/medium power amplifiers, a stepped phase shifter and analog/digital attenuators in order to perform the RF signal routing and phase/amplitude conditioning. Besides, an embedded serial-to-parallel converter drives the phase shifter and the digital attenuator leading to a reduction in complexity of the digital control interface.
CVSSearch: Searching through Source Code Using CVS Comments

CVSSearch is a tool that searches for fragments of source code by using CVS comments. CVS is a version control system that is widely used in the open source community . Our search tool takes advantage of the fact that a CVS comment typically describes the lines of code involved in the commit and this description will typically hold for many future versions. In other words, CVSSearch allows one to better search the most recent version of the code by looking at previous versions to better understand the current version.
Discrete Variational Autoencoders

Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We introduce a novel class of probabilistic models, comprising an undirected discrete component and a directed hierarchical continuous component, that can be trained efficiently using the variational autoencoder framework. The discrete component captures the distribution over the disconnected smooth manifolds induced by the continuous component. As a result, this class of models efficiently learns both the class of objects in an image, and their specific realization in pixels, from unsupervised data; and outperforms state-of-the-art methods on the permutation-invariant MNIST, OMNIGLOT, and Caltech-101 Silhouettes datasets.
Predicting the direction of stock market prices using random forest

Predicting trends in stock market prices has been an area of interest for researchers for many years due to its complex and dynamic nature. Intrinsic volatility in stock market across the globe makes the task of prediction challenging. Forecasting and diffusion modeling, although effective can’t be the panacea to the diverse range of problems encountered in prediction, short-term or otherwise. Market risk, strongly correlated with forecasting errors, needs to be minimized to ensure minimal risk in investment. The authors propose to minimize forecasting error by treating the forecasting problem as a classification problem, a popular suite of algorithms in Machine learning. In this paper, we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning. Some of the technical indicators such as Relative Strength Index (RSI), stochastic oscillator etc are used as inputs to train our model. The learning model used is an ensemble of multiple decision trees. The algorithm is shown to outperform existing algorithms found in the literature. Out of Bag (OOB) error estimates have been found to be encouraging.
DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that simultaneously exploits Apache Spark for large-scale distributed data management and Caffe for GPU-based acceleration. DeepSpark directly accepts Caffe input specifications, providing seamless compatibility with existing designs and network structures. To support parallel operations, DeepSpark automatically distributes workloads and parameters to Caffe-running nodes using Spark and iteratively aggregates training results by a novel lock-free asynchronous variant of the popular elastic averaging stochastic gradient descent update scheme, effectively complementing the synchronized processing capabilities of Spark. DeepSpark is an on-going project, and the current release is available at http://deepspark.snu.ac.kr. CCS Concepts •Networks → Cloud computing; •Theory of computation→Distributed computing models; •Computing methodologies → Neural networks;
Next-generation digital information storage in DNA.

Digital information is accumulating at an astounding rate, straining our ability to store and archive it. DNA is among the most dense and stable information media known. The development of new technologies in both DNA synthesis and sequencing make DNA an increasingly feasible digital storage medium. We developed a strategy to encode arbitrary digital information in DNA, wrote a 5.27-megabit book using DNA microchips, and read the book by using next-generation DNA sequencing.
Combining motion and appearance cues for anomaly detection

In this paper, we present a novel anomaly detection framework which integrates motion and appearance cues to detect abnormal objects and behaviors in video. For motion anomaly detection, we employ statistical histograms to model the normal motion distributions and propose a notion of “cut-bin” in histograms to distinguish unusual motions. For appearance anomaly detection, we develop a novel scheme based on Support Vector Data Description (SVDD), which obtains a spherically shaped boundary around the normal objects to exclude abnormal objects. The two complementary cues are finally combined to achieve more comprehensive detection results. Experimental results show that the proposed approach can effectively locate abnormal objects in multiple public video scenarios, achieving comparable performance to other state-of-the-art anomaly detection techniques. & 2015
Committee-Based Sample Selection for Probabilistic Classifiers

In many real-world learning tasks it is expensive to acquire a su cient number of labeled examples for training. This paper investigates methods for reducing annotation cost by sample selection. In this approach, during training the learning program examines many unlabeled examples and selects for labeling only those that are most informative at each stage. This avoids redundantly labeling examples that contribute little new information. Our work follows on previous research on Query By Committee, and extends the committee-based paradigm to the context of probabilistic classi cation. We describe a family of empirical methods for committee-based sample selection in probabilistic classi cation models, which evaluate the informativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn randomly from a probability distribution conditioned by the training set labeled so far. The method was applied to the real-world natural language processing task of stochastic part-of-speech tagging. We nd that all variants of the method achieve a signi cant reduction in annotation cost, although their computational e ciency di ers. In particular, the simplest variant, a two member committee with no parameters to tune, gives excellent results. We also show that sample selection yields a signi cant reduction in the size of the model used by the tagger.
Automated backward error analysis for numerical code

Numerical code uses floating-point arithmetic and necessarily suffers from roundoff and truncation errors. Error analysis is the process to quantify such uncertainty in the solution to a problem. Forward error analysis and backward error analysis are two popular paradigms of error analysis. Forward error analysis is more intuitive and has been explored and automated by the programming languages (PL) community. In contrast, although backward error analysis is more preferred by numerical analysts and the foundation for numerical stability, it is less known and unexplored by the PL community. To fill the gap, this paper presents an automated backward error analysis for numerical code to empower both numerical analysts and application developers. In addition, we use the computed backward error results to also compute the condition number, an important quantity recognized by numerical analysts for measuring how sensitive a function is to changes or errors in the input. Experimental results on Intel X87 FPU functions and widely-used GNU C Library functions demonstrate that our analysis is effective at analyzing the accuracy of floating-point programs.
Performance-influence models for highly configurable systems

Almost every complex software system today is configurable. While configurability has many benefits, it challenges performance prediction, optimization, and debugging. Often, the influences of individual configuration options on performance are unknown. Worse, configuration options may interact, giving rise to a configuration space of possibly exponential size. Addressing this challenge, we propose an approach that derives a performance-influence model for a given configurable system, describing all relevant influences of configuration options and their interactions. Our approach combines machine-learning and sampling heuristics in a novel way. It improves over standard techniques in that it (1) represents influences of options and their interactions explicitly (which eases debugging), (2) smoothly integrates binary and numeric configuration options for the first time, (3) incorporates domain knowledge, if available (which eases learning and increases accuracy), (4) considers complex constraints among options, and (5) systematically reduces the solution space to a tractable size. A series of experiments demonstrates the feasibility of our approach in terms of the accuracy of the models learned as well as the accuracy of the performance predictions one can make with them.
Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities

Best practice exemplars suggest that digital platforms play a critical role in managing supply chain activities and partnerships that generate perjormance gains for firms. However, there is Umited academic investigation on how and why information technology can create performance gains for firms in a supply chain management (SCM) context. Grant's (1996) theoretical notion of higher-order capabilities and a hierarchy of capabilities has been used in recent information systems research by Barua et al. (2004). Sambamurthy et al. (2003), and Mithas et al. (2004) to reframe the conversation from the direct performance impacts of IT resources and investments to how and why IT shapes higher-order proeess capabilities that ereate performance gains for firms. We draw on the emerging IT-enabled organizational capabilities perspective to suggest that firms that develop IT infrastrueture integration for SCM and leverage it to create a higher-order supply chain integration capability generate significant and sustainable performance gains. A research model is developed to investigate the hierarchy oflT-related capabilities and their impaet on firm performance. Data were collected from } 10 supply chain and logisties managers in manufacturing and retail organizations. Our results suggest that integrated IT infrastructures enable firms to develop the higher-order capability of supply chain process integration. This eapability enables firms to unbundle information flows from physical flows, and to share information with their supply chain partners to create information-based approaches for superior demand planning, for the staging and movement of physical products, and for streamlining voluminous and complex financial work processes. Furthermore. IT-enabled supply chain integration capability results in significant and sustained firm performance gains, especially in operational excellence and revenue growth.
Survey on Gesture Recognition for Hand Image Postures

One of the attractive methods for providing natural human-computer interaction is the use of the hand as an input device rather than the cumbersome devices such as keyboards and mice, which need the user to be located in a specific location to use these devices. Since human hand is an articulated object, it is an open issue to discuss. The most important thing in hand gesture recognition system is the input features, and the selection of good features representation. This paper presents a review study on the hand postures and gesture recognition methods, which is considered to be a challenging problem in the human-computer interaction context and promising as well. Many applications and techniques were discussed here with the explanation of system recognition framework and its main phases.
Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation

User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users’ interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user’s interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users’ ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines.
On Inferring Application Protocol Behaviors in Encrypted Network Traffic

Several fundamental security mechanisms for restricting access to network resources rely on the ability of a reference monitor to inspect the contents of traffic as it traverses the network. However, with the increasing popularity of cryptographic protocols, the traditional means of inspecting packet contents to enforce security policies is no longer a viable approach as message contents are concealed by encryption. In this paper, we investigate the extent to which common application protocols can be identified using only the features that remain intact after encryption—namely packet size, timing, and direction. We first present what we believe to be the first exploratory look at protocol identification in encrypted tunnels which carry traffic from many TCP connections simultaneously, using only post-encryption observable features. We then explore the problem of protocol identification in individual encrypted TCP connections, using much less data than in other recent approaches. The results of our evaluation show that our classifiers achieve accuracy greater than 90% for several protocols in aggregate traffic, and, for most protocols, greater than 80% when making fine-grained classifications on single connections. Moreover, perhaps most surprisingly, we show that one can even estimate the number of live connections in certain classes of encrypted tunnels to within, on average, better than 20%.
Neurobiological mechanisms involved in sleep bruxism.

Sleep bruxism (SB) is reported by 8% of the adult population and is mainly associated with rhythmic masticatory muscle activity (RMMA) characterized by repetitive jaw muscle contractions (3 bursts or more at a frequency of 1 Hz). The consequences of SB may include tooth destruction, jaw pain, headaches, or the limitation of mandibular movement, as well as tooth-grinding sounds that disrupt the sleep of bed partners. SB is probably an extreme manifestation of a masticatory muscle activity occurring during the sleep of most normal subjects, since RMMA is observed in 60% of normal sleepers in the absence of grinding sounds. The pathophysiology of SB is becoming clearer, and there is an abundance of evidence outlining the neurophysiology and neurochemistry of rhythmic jaw movements (RJM) in relation to chewing, swallowing, and breathing. The sleep literature provides much evidence describing the mechanisms involved in the reduction of muscle tone, from sleep onset to the atonia that characterizes rapid eye movement (REM) sleep. Several brainstem structures (e.g., reticular pontis oralis, pontis caudalis, parvocellularis) and neurochemicals (e.g., serotonin, dopamine, gamma aminobutyric acid [GABA], noradrenaline) are involved in both the genesis of RJM and the modulation of muscle tone during sleep. It remains unknown why a high percentage of normal subjects present RMMA during sleep and why this activity is three times more frequent and higher in amplitude in SB patients. It is also unclear why RMMA during sleep is characterized by co-activation of both jaw-opening and jaw-closing muscles instead of the alternating jaw-opening and jaw-closing muscle activity pattern typical of chewing. The final section of this review proposes that RMMA during sleep has a role in lubricating the upper alimentary tract and increasing airway patency. The review concludes with an outline of questions for future research.