nils-herrmann/clean_scirepeval huggingface dataset:
Krylov Subspace Descent for Deep Learning

In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In our method, we construct on each iteratio n a Krylov subspace formed by the gradient and an approximation to the Hess ian matrix, and then use a subset of the training data samples to optimize ove r this subspace. As with the Hessian Free (HF) method of [6], the Hessian matrix i s never explicitly constructed, and is computed using a subset of data. In p ractice, as in HF, we typically use a positive definite substitute for the Hessi an matrix such as the Gauss-Newton matrix. We investigate the effectiveness of o ur proposed method on learning the parameters of deep neural networks, and comp are its performance to widely used methods such as stochastic gradient descent, conjugate gradient descent and L-BFGS, and also to HF. Our method leads to faster convergence than either L-BFGS or HF, and generally performs better than either of them in cross-validation accuracy. It is also simpler and more gene ral than HF, as it does not require a positive semi-definite approximation of the He ssian matrix to work well nor the setting of a damping parameter. The chief drawba ck versus HF is the need for memory to store a basis for the Krylov subspace.
ThemeDelta: Dynamic Segmentations over Temporal Topic Models

We present ThemeDelta, a visual analytics system for extracting and visualizing temporal trends, clustering, and reorganization in time-indexed textual datasets. ThemeDelta is supported by a dynamic temporal segmentation algorithm that integrates with topic modeling algorithms to identify change points where significant shifts in topics occur. This algorithm detects not only the clustering and associations of keywords in a time period, but also their convergence into topics (groups of keywords) that may later diverge into new groups. The visual representation of ThemeDelta uses sinuous, variable-width lines to show this evolution on a timeline, utilizing color for categories, and line width for keyword strength. We demonstrate how interaction with ThemeDelta helps capture the rise and fall of topics by analyzing archives of historical newspapers, of U.S. presidential campaign speeches, and of social messages collected through iNeighbors, a web-based social website. ThemeDelta is evaluated using a qualitative expert user study involving three researchers from rhetoric and history using the historical newspapers corpus.
Named Entity Disambiguation for Noisy Text

We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing newsbased datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-ofthe-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.
Sentence Alignment for Monolingual Comparable Corpora

We address the problem of sentence alignment for monolingual corpora, a phenomenon distinct from alignment in parallel corpora. Aligning large comparable corpora automatically would provide a valuable resource for learning of text-totext rewriting rules. We incorporate context into the search for an optimal alignment in two complementary ways: learning rules for matching paragraphs using topic structure and further refining the matching through local alignment to find good sentence pairs. Evaluation shows that our alignment method outperforms state-of-the-art systems developed for the same task.
Statistically Significant Detection of Linguistic Change

We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track its linguistic displacement over time.
 We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book Ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.
Why to decouple the uplink and downlink in cellular networks and how to do it

Ever since the inception of mobile telephony, the downlink and uplink of cellular networks have been coupled, that is, mobile terminals have been constrained to associate with the same base station in both the downlink and uplink directions. New trends in network densification and mobile data usage increase the drawbacks of this constraint, and suggest that it should be revisited. In this article we identify and explain five key arguments in favor of downlink/uplink decoupling based on a blend of theoretical, experimental, and architectural insights. We then overview the changes needed in current LTE-A mobile systems to enable this decoupling, and then look ahead to fifth generation cellular standards. We demonstrate that decoupling can lead to significant gains in network throughput, outage, and power consumption at a much lower cost compared to other solutions that provide comparable or lower gains.
A Complexity-Invariant Distance Measure for Time Series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While there is a plethora of classification algorithms that can be applied to time series, all of the current empirical evidence suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping. In this work we make a surprising claim. There is an invariance that the community has missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where complex objects are incorrectly assigned to a simpler class. We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series classification experiments ever attempted, and show that complexity-invariant distance measures can produce improvements in accuracy in the vast majority of cases.
A systematic mapping on gamification applied to education

Gamification is a term that refers to the use of game elements in non-game contexts with the goal of engaging people in a variety of tasks. There is a growing interest in gamification as well as its applications and implications in the field of Education since it provides an alternative to engage and motivate students during the process of learning. Despite this increasing interest, to the best of our knowledge, there are no studies that cover and classify the types of research being published and the most investigated topics in the area. As a first step towards bridging this gap, we carried out a systematic mapping to synthesize an overview of the area. We went through 357 papers on gamification. Among them, 48 were related to education and only 26 met the criteria for inclusion and exclusion of articles defined in this study. These 26 papers were selected and categorized according to their contribution. As a result, we provide an overview of the area. Such an overview suggests that most studies focus on investigating how gamification can be used to motivate students, improve their skills, and maximize learning.
Survey on security threats and protection mechanisms in embedded automotive networks

Embedded electronic components, so-called ECU (Electronic Controls Units), are nowadays a prominent part of a car's architecture. These ECUs, monitoring and controlling the different subsystems of a car, are interconnected through several gateways and compose the global internal network of the car. Moreover, modern cars are now able to communicate with other devices through wired or wireless interfaces such as USB, Bluetooth, WiFi or even 3G. Such interfaces may expose the internal network to the outside world and can be seen as entry points for cyber attacks. In this paper, we present a survey on security threats and protection mechanisms in embedded automotive networks. After introducing the different protocols being used in the embedded networks of current vehicles, we then analyze the potential threats targeting these networks and describe how the attackers' opportunities can be enhanced by the new communication abilities of modern cars. Finally, we present the security solutions currently being devised to address these problems.
A Bayesian Foundation for Individual Learning Under Uncertainty

Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty). The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next highest level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean-field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i) are analytical and extremely efficient, enabling real-time learning, (ii) have a natural interpretation in terms of RL, and (iii) contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty). These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory.
Mindfulness meditation and substance use in an incarcerated population.

Despite the availability of various substance abuse treatments, alcohol and drug misuse and related negative consequences remain prevalent. Vipassana meditation (VM), a Buddhist mindfulness-based practice, provides an alternative for individuals who do not wish to attend or have not succeeded with traditional addiction treatments. In this study, the authors evaluated the effectiveness of a VM course on substance use and psychosocial outcomes in an incarcerated population. Results indicate that after release from jail, participants in the VM course, as compared with those in a treatment-as-usual control condition, showed significant reductions in alcohol, marijuana, and crack cocaine use. VM participants showed decreases in alcohol-related problems and psychiatric symptoms as well as increases in positive psychosocial outcomes. The utility of mindfulness-based treatments for substance use is discussed.
Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems

Recommender systems increasingly use contextual and demographical data as a basis for recommendations. Users, however, often feel uncomfortable providing such information. In a privacy-minded design of recommenders, users are free to decide for themselves what data they want to disclose about themselves. But this decision is often complex and burdensome, because the consequences of disclosing personal information are uncertain or even unknown. Although a number of researchers have tried to analyze and facilitate such information disclosure decisions, their research results are fragmented, and they often do not hold up well across studies. This article describes a unified approach to privacy decision research that describes the cognitive processes involved in users’ “privacy calculus” in terms of system-related perceptions and experiences that act as mediating factors to information disclosure. The approach is applied in an online experiment with 493 participants using a mock-up of a context-aware recommender system. Analyzing the results with a structural linear model, we demonstrate that personal privacy concerns and disclosure justification messages affect the perception of and experience with a system, which in turn drive information disclosure decisions. Overall, disclosure justification messages do not increase disclosure. Although they are perceived to be valuable, they decrease users’ trust and satisfaction. Another result is that manipulating the order of the requests increases the disclosure of items requested early but decreases the disclosure of items requested later.
A lambda calculus of objects and method specialization

This paper presents an untyped lambda calculus, extended with object primitives that reflect the capabilities of so-called delegation-based object-oriented languages. A type inference system allows static detection of errors, such as message not understood, while at the same time allowing the type of an inherited method to be specialized to the type of the inheriting object. Type soundness is proved using operational semantics and examples illustrating the expressiveness of the pure calculus are presented.  F.3.1, D.3.3, F.4.1
Virtual worlds in competitive contexts: Analyzing eSports consumer needs

More recently, 3D graphical environments on the Internet, that is virtual worlds, have moved to the center of scientific interest. Since virtual worlds are suggested to mold social computing, research has predominately focused on collaborative virtual worlds. Yet, virtual worlds increasingly move to competitive environments leaving operating businesses with the question as to what to offer in order to fulfill customers’ needs. To close this knowledge gap, we examine competitive virtual worlds in terms of eSports services intrinsically tying cooperation and competition; we illuminate competitive and hedonic need gratifications of continuous eSports use. We apply Uses and Gratifications theory reporting on ten in-depth expert interviews as well as survey data collected from 360 eSports players. We reveal that both competitive (competition and challenge) and hedonic need gratifications (escapism) drive continuous eSports use.
Evaluation of Traditional, Orthogonal, and Radial Tree Diagrams by an Eye Tracking Study

Node-link diagrams are an effective and popular visualization approach for depicting hierarchical structures and for showing parent-child relationships. In this paper, we present the results of an eye tracking experiment investigating traditional, orthogonal, and radial node-link tree layouts as a piece of empirical basis for choosing between those layouts. Eye tracking was used to identify visual exploration behaviors of participants that were asked to solve a typical hierarchy exploration task by inspecting a static tree diagram: finding the least common ancestor of a given set of marked leaf nodes. To uncover exploration strategies, we examined fixation points, duration, and saccades of participants' gaze trajectories. For the non-radial diagrams, we additionally investigated the effect of diagram orientation by switching the position of the root node to each of the four main orientations. We also recorded and analyzed correctness of answers as well as completion times in addition to the eye movement data. We found out that traditional and orthogonal tree layouts significantly outperform radial tree layouts for the given task. Furthermore, by applying trajectory analysis techniques we uncovered that participants cross-checked their task solution more often in the radial than in the non-radial layouts.
A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee

Sometimes, one needs to control different emotional situations which can lead the person suffering them to dangerous situations, in both the medium and short term. There are studies which indicate that stress increases the risk of cardiac problems. In this study we have designed and built a stress sensor based on Galvanic Skin Response (GSR), and controlled by ZigBee. In order to check the device's performance, we have used 16 adults (eight women and eight men) who completed different tests requiring a certain degree of effort, such as mathematical operations or breathing deeply. On completion, we appreciated that GSR is able to detect the different states of each user with a success rate of 76.56%. In the future, we plan to create an algorithm which is able to differentiate between each state.
Verification of Rogowski current transducer's ability to measure fast switching transients

Rogowski current transducers combine a high bandwidth, an easy to use thin flexible coil, and low insertion impedance making them an ideal device for measuring pulsed currents in power electronic applications. Practical verification of a Rogowski transducer's ability to measure current transients due to the fastest MOSFET and IGBT switching requires a calibrated test facility capable of generating a pulse with a rise time of the order of a few 10's ns. A flexible 8-module system has been built which gives a 2000A peak current with a rise time of 40ns. The modular approach enables verification for a range of transducer coil sizes and ratings.
Clustering product features for opinion mining

In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product features/attributes (also called aspects). However, for the same feature, people can express it with many different words or phrases. To produce a useful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. Although several methods have been proposed to extract product features from reviews, limited work has been done on clustering or grouping of synonym features. This paper focuses on this task. Classic methods for solving this problem are based on unsupervised learning using some forms of distributional similarity. However, we found that these methods do not do well. We then model it as a semi-supervised learning problem. Lexical characteristics of the problem are exploited to automatically identify some labeled examples. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods by a large margin.
Using Image Flow to Detect Eye Blinks in Color Videos

Static computer vision techniques enable non-intrusive observation and analysis of biometrics such as eye blinks. However, ambiguous eye behaviors such as partial blinks and asymmetric eyelid movements present problems that computer vision techniques relying on static appearance alone cannot solve reliably. Image flow analysis enables reliable and efficient interpretation of these ambiguous eye blink behaviors. In this paper we present a method for using image flow analysis to compute problematic eye blink parameters. The flow analysis produces the magnitude and direction of the eyelid movement. A deterministic finite state machine uses the eyelid movement data to compute blink parameters (e.g., blink count, blink rate, and other transitional statistics) for use in human computer interaction applications across a wide range of disciplines. We conducted extensive experiments employing this method on approximately 750K color video frames of five subjects
Discriminative Unsupervised Alignment of Natural Language Instructions with Corresponding Video Segments

We address the problem of automatically aligning natural language sentences with corresponding video segments without any direct supervision. Most existing algorithms for integrating language with videos rely on handaligned parallel data, where each natural language sentence is manually aligned with its corresponding image or video segment. Recently, fully unsupervised alignment of text with video has been shown to be feasible using hierarchical generative models. In contrast to the previous generative models, we propose three latent-variable discriminative models for the unsupervised alignment task. The proposed discriminative models are capable of incorporating domain knowledge, by adding diverse and overlapping features. The results show that discriminative models outperform the generative models in terms of alignment accuracy.
Shaping HTTP adaptive streams for a better user experience

With increasing audio/video service consumption through unmanaged IP networks, HTTP adaptive streaming techniques have emerged to handle bandwidth limitations and variations. But while it is becoming common to serve multiple clients in one home network, these solutions do not adequately address fine tuned quality arbitration between the multiple streams. While clients compete for bandwidth, the video suffers unstable conditions and/or inappropriate bit-rate levels.
 We hereby experiment a mechanism based on traffic chapping that allow bandwidth arbitration to be implemented in the home gateway, first determining desirable target bit-rates to be reached by each stream and then constraining the clients to stay within their limits. This enables the delivery of optimal quality of experience to the maximum number of users. This approach is validated through experimentation, and results are shown through a set of objective measurement criteria.