nils-herrmann/clean_scirepeval huggingface dataset:
A declarative approach to procedural modeling of virtual worlds

With the ever increasing costs of manual content creation for virtual worlds, the potential of creating it automatically becomes too attractive to ignore. However, for most designers, traditional procedural content generation methods are complex and unintuitive to use, hard to control, and generated results are not easily integrated into a complete and consistent virtual world. We introduce a novel declarative modeling approach that enables designers to concentrate on stating what they want to create instead of on describing how they should model it. It aims at reducing the complexity of virtual world modeling by combining the strengths of semantics-based modeling with manual and procedural approaches. This article describes two of its main contributions to procedural modeling of virtual worlds: interactive procedural sketching and virtual world consistency maintenance. We discuss how these techniques, integrated in our modeling framework SketchaWorld, build up to enable designers to create a complete 3D virtual world in minutes. Procedural sketching provides a fast and more intuitive way to model virtual worlds, by letting designers interactively sketch their virtual world using high-level terrain features, which are then procedurally expanded using a variety of integrated procedural methods. Consistency maintenance guarantees that the semantics of all terrain features is preserved throughout the modeling process. In particular, it automatically solves conflicts possibly emerging from interactions between terrain features. We believe that these contributions together represent a significant step towards providing more user control and flexibility in procedural modeling of virtual worlds. It can therefore be expected that by further reducing its complexity, virtual world modeling will become accessible to an increasingly broad
Stop-word removal algorithm for Arabic language

We have designed and implemented an efficient stop-word removal algorithm for Arabic language based on a finite state machine (FSM). An efficient stop-word removal technique is needed in many natural language processing application such as: spelling normalization, stemming and stem weighting, Question answering systems and in information retrieval systems (IR). Most of the existing stop-word removal techniques bases on a dictionary that contains a list of stop-word, it is very expensive, it takes too much time for searching process and required too much space to store these stop-words. The new Arabic removal stop-word technique has been tested using a set of 242 Arabic abstracts chosen from the Proceedings of the Saudi Arabian National Computer conferences, and another set of data chosen from the holy Q'uran, and it gives impressive results that reached approximately to 98%.
Free-rider Episode Screening via Dual Partition Model

One of the drawbacks of frequent episode mining is that overwhelmingly many of the discovered patterns are redundant. Free-rider episode, as a typical example, consists of a real pattern doped with some additional noise events. Because of the possible high support of the inside noise events, such free-rider episodes may have abnormally high support that they cannot be filtered by frequency based framework. An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently. In this paper, we take more complex subepisodes into consideration and develop a novel partition model named EDP for free-rider episode filtering from a given set of episodes. It combines (1) a dual partition strategy which divides an episode to an underlying real pattern and potential noises; (2) a novel definition of the expected support of a free-rider episode based on the proposed partition strategy. We can deem the episode interesting if the observed support is substantially higher than the expected support estimated by our model. The experiments on synthetic and real-world datasets demonstrate EDP can effectively filter free-rider episodes compared with existing state-of-the-arts.
A Survey on Information Diffusion in Online Social Networks: Models and Methods

By now, personal life has been invaded by online social networks (OSNs) everywhere. They intend to move more and more offline lives to online social networks. Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks. From this diffusion process, lots of latent information can be mined. It can be used for market predicting, rumor controlling, and opinion monitoring among other things. However, the research of these applications depends on the diffusion models and methods. For this reason, we survey various information diffusion models from recent decades. From a research process view, we divide the diffusion models into two categories—explanatory models and predictive models—in which the former includes epidemics and influence models and the latter includes independent cascade, linear threshold, and game theory models. The purpose of this paper is to investigate the research methods and techniques, and compare them according to the above categories. The whole research structure of the information diffusion models based on our view is given. There is a discussion at the end of each section, detailing related models that are mentioned in the literature. We conclude that these two models are not independent, they always complement each other. Finally, the issues of the social networks research are discussed and summarized, and directions for future study are proposed.
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks

Video classification problem has been studied many years. The success of Convolutional Neural Networks (CNN) in image recognition tasks gives a powerful incentive for researchers to create more advanced video classification approaches. As video has a temporal content Long Short Term Memory (LSTM) networks become handy tool allowing to model long-term temporal clues. Both approaches need a large dataset of input data. In this paper three models provided to address video classification using recently announced YouTube-8M large-scale dataset. The first model is based on frame pooling approach. Two other models based on LSTM networks. Mixture of Experts intermediate layer is used in third model allowing to increase model capacity without dramatically increasing computations. The set of experiments for handling imbalanced training data has been conducted.
Debugging the Internet of Things: The Case of Wireless Sensor Networks

The Internet of Things (IoT) has the strong potential to support a human society interacting more symbiotically with its physical environment. Indeed, the emergence of tiny devices that sense environmental cues and trigger actuators after consulting logic and human preferences promises a more environmentally aware and less wasteful society. However, the IoT inherently challenges software development processes, particularly techniques for ensuring software reliability. Researchers have developed debugging tools for wireless sensor networks (WSNs), which can be viewed as the enablers of perception in the IoT. These tools gather run-time information on individual sensor node executions and node interactions and then compress that information.
Memory, navigation and theta rhythm in the hippocampal-entorhinal system

Theories on the functions of the hippocampal system are based largely on two fundamental discoveries: the amnestic consequences of removing the hippocampus and associated structures in the famous patient H.M. and the observation that spiking activity of hippocampal neurons is associated with the spatial position of the rat. In the footsteps of these discoveries, many attempts were made to reconcile these seemingly disparate functions. Here we propose that mechanisms of memory and planning have evolved from mechanisms of navigation in the physical world and hypothesize that the neuronal algorithms underlying navigation in real and mental space are fundamentally the same. We review experimental data in support of this hypothesis and discuss how specific firing patterns and oscillatory dynamics in the entorhinal cortex and hippocampus can support both navigation and memory.
Analysis and measurement of 3D torque and forces for permanent magnet motors with slotless windings

Slotless windings, skewed and rhombic, are widely used in industry. Beside the drive torque, possibly undesired transverse torques and forces are generated, which have not been analyzed previously. An analytical derivation of the torque and force components in all three directions is detailed in this paper for the skewed winding. It is shown that for some winding configurations alternating transverse torque components are generated, which may compromise stable operation in applications where for example magnetic or gas bearings are involved. Moreover the windings are analyzed with regard to a potential use as active magnetic radial bearings in high-speed applications. Finally, measurements are presented to verify the theoretical results.
The Impact of Information Technology Infrastructure Flexibility on Strategic Alignment and Application Implementations

IT infrastructure flexibility is now being viewed as an organizational core competency that is necessary for organizations to survive and prosper in rapidly-changing, competitive, business environments. Using data from 200 U.S. and Canadian companies, this study examines the impact of the four components of IT infrastructure flexibility (compatibility, connectivity, modularity, and IT personnel) on strategic IT-business alignment and the extent to which various applications are implemented within an organization. The “extent” of implementation refers to the the organization’s experience with the particular application and the degree to which the application is implemented and used throughout the organization. The findings from analysis of a structural model provide evidence that connectivity, modularity, and IT personnel (among other considerations that we discuss in the paper) make significant, positive impacts on strategic alignment and that all four components result in significant, positive impacts on the applications implementation. The study reinforces the importance of IT infrastructure flexibility to organizations as one source for sustainable competitive advantage.
A Systematic Gap Analysis of Social Engineering Defence Mechanisms Considering Social Psychology

Social engineering is the acquisition of information about computer systems by methods that deeply include non-technical means. While technical security of most critical systems is high, the systems remain vulnerable to attacks from social engineers. Social engineering is a technique that: (i) does not require any (advanced) technical tools, (ii) can be used by anyone, (iii) is cheap. Traditional penetration testing approaches often focus on vulnerabilities in network or software systems. Few approaches even consider the exploitation of humans via social engineering. While the amount of social engineering attacks and the damage they cause rise every year, the defences against social engineering do not evolve accordingly. Hence, the security awareness of these attacks by employees remains low. We examined the psychological principles of social engineering and which psychological techniques induce resistance to persuasion applicable for social engineering. The techniques examined are an enhancement of persuasion knowledge, attitude bolstering and influencing the decision making. While research exists elaborating on security awareness, the integration of resistance against persuasion has not been done. Therefore, we analysed current defence mechanisms and provide a gap analysis based on research in social psychology. Based on our findings we provide guidelines of how to improve social engineering defence mechanisms such as security awareness programs.
Word similarity computation based on HowNet

Word similarity computation has important applications in many areas, such as natural language processing, intelligence information retrieval, document clustering, automatic answering, word sense disambiguation, machine translation etc. This article makes an intensive study of word similarity computation based on HowNet and the word similarity is computed in three steps: (1) computes sememes similarity, (2) computes concepts similarity using the weight sum method of sememes similarity, (3) takes the maximum similarity of concepts as words similarity. This article mainly introduces numbering the sememes and presents a more accurate method of decomposing the concept description type. The experiment shows that the algorithm of word similarity presented in this article is simple and feasible, and the computed results are relatively consistent with awareness.
Large-scale machine learning at twitter

The success of data-driven solutions to difficult problems, along with the dropping costs of storing and processing massive amounts of data, has led to growing interest in large-scale machine learning. This paper presents a case study of Twitter's integration of machine learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin with an overview of this platform, which handles "traditional" data warehousing and business intelligence tasks for the organization. The core of this work lies in recent Pig extensions to provide predictive analytics capabilities that incorporate machine learning, focused specifically on supervised classification. In particular, we have identified stochastic gradient descent techniques for online learning and ensemble methods as being highly amenable to scaling out to large amounts of data. In our deployed solution, common machine learning tasks such as data sampling, feature generation, training, and testing can be accomplished directly in Pig, via carefully crafted loaders, storage functions, and user-defined functions. This means that machine learning is just another Pig script, which allows seamless integration with existing infrastructure for data management, scheduling, and monitoring in a production environment, as well as access to rich libraries of user-defined functions and the materialized output of other scripts.
A scriptable rapid application deployment framework for sensor networks

Wireless sensor networks are finding application in heterogeneous scenarios with varied deployment constraints and consumer requirements. Effective instrumentation, visualization, archival, analysis, and third-party notification are the cornerstones of any sensor network deployment. Unfortunately, the manifestation of these generalized needs can differ widely between deployments leading to creation of multiple specialized applications. We present RAPIDSNAP-a flexible, generic and scriptable rapid sensor network application deployment framework that can be quickly customized to realize end-user centric sensor network deployments. Algorithms for routing, clustering and localization have been implemented on sensor nodes and integrated with RAPIDSNAP. Along with standard components for control and visualization, RAPIDSNAP allows each deployment specific extension to be captured in a JavaScript based script called a Rapidapp. RAPIDSNAP acts as an execution platform for Rapidapps and an abstraction layer for them to transparently communicate with nodes in the network.
Transference of kettlebell training to strength, power, and endurance.

Kettlebells are a popular implement in many strength and conditioning programs, and their benefits are touted in popular literature, books, and videos. However, clinical data on their efficacy are limited. The purpose of this study was to examine whether kettlebell training transfers strength and power to weightlifting and powerlifting exercises and improves muscular endurance. Thirty-seven subjects were assigned to an experimental (EXP, n = 23; mean age = 40.9 ± 12.9 years) or a control group (CON; n = 14; mean age = 39.6 ± 15.8 years), range 18-72 years. The participants were required to perform assessments including a barbell clean and jerk, barbell bench press, maximal vertical jump, and 45° back extensions to volitional fatigue before and after a 10-week kettlebell training program. Training was structured in a group setting for 2 d·wk(-1) for 10 weeks. A repeated measures analysis of variance was conducted to determine group × time interactions and main effects. Post hoc pairwise comparisons were conducted when appropriate. Bench press revealed a time × group interaction and a main effect (p < 0.05). Clean and jerk and back extension demonstrated a trend toward a time × group interaction, but it did not reach significance (p = 0.053). However, clean and jerk did reveal a main effect for time (p < 0.05). No significant findings were reported for maximal vertical jump. The results demonstrate a transfer of power and strength in response to 10 weeks of training with kettlebells. Traditional training methods may not be convenient or accessible for strength and conditioning specialists, athletes, coaches, and recreational exercisers. The current data suggest that kettlebells may be an effective alternative tool to improve performance in weightlifting and powerlifting.
Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience

In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remains largely mysterious. For example in the context of artificial perception, this issue is usually circumvented by having engineers pre-define the spatial structure of the problem the agent has to face. We here show that the structure of space can be autonomously discovered by a naive agent in the form of sensorimotor regularities, that correspond to so called compensable sensory experiences: these are experiences that can be generated either by the agent or its environment. By detecting such compensable experiences the agent can infer the topological and metric structure of the external space in which its body is moving. We propose a theoretical description of the nature of these regularities and illustrate the approach on a simulated robotic arm equipped with an eye-like sensor, and which interacts with an object. Finally we show how these regularities can be used to build an internal representation of the sensor's external spatial configuration.
IJB – S : IARPA Janus Surveillance Video Benchmark ∗

We present IJB–S dataset, an open-source IARPA Janus Surveillance Video Benchmark and associated protocols. The dataset consists of images and surveillance video collected from 202 subjects at a Department of Defense (DoD) training facility. Surveillance video was captured across multiple vignettes representative of a variety of real-world surveillance use cases that are particularly of interest to law enforcement and national security communities. Each video was annotated by human subject matter experts in order to generate ground truth identity and bounding box face labels. In total, over 10 million annotations were collected for the dataset. We present benchmark results utilizing state of the art deep learning approaches such as FaceNet. Our results illustrate and characterize the difficulty of the dataset.
Learning Across Scales - Multiscale Methods for Convolution Neural Networks

In this work, we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs). We show that the forward propagation in CNNs can be interpreted as a time-dependent nonlinear differential equation and learning can be seen as controlling the parameters of the differential equation such that the network approximates the data-label relation for given training data. Using this continuous interpretation, we derive two new methods to scale CNNs with respect to two different dimensions. The first class of multiscale methods connects low-resolution and high-resolution data using prolongation and restriction of CNN parameters inspired by algebraic multigrid techniques. We demonstrate that our method enables classifying highresolution images using CNNs trained with low-resolution images and vice versa and warm-starting the learning process. The second class of multiscale methods connects shallow and deep networks and leads to new training strategies that gradually increase the depths of the CNN while re-using parameters for initializations.
Bacteriocins: developing innate immunity for food.

Bacteriocins are bacterially produced antimicrobial peptides with narrow or broad host ranges. Many bacteriocins are produced by food-grade lactic acid bacteria, a phenomenon which offers food scientists the possibility of directing or preventing the development of specific bacterial species in food. This can be particularly useful in preservation or food safety applications, but also has implications for the development of desirable flora in fermented food. In this sense, bacteriocins can be used to confer a rudimentary form of innate immunity to foodstuffs, helping processors extend their control over the food flora long after manufacture.
Automatic Query Expansion for Patent Passage Retrieval using Paradigmatic and Syntagmatic Information

Patent text is a mixture of legal terms and domain specific terms. Patent writers tend to paraphrase standard terminology with hypernym, hyponym and synonym substitutions in order to avoid narrowing the scope of the patent invention. The practice of paraphrasing affects the exact match retrieval function negatively. There have been many success stories addressing vocabulary mismatching using pseudo-relevance feedback and distributional semantics. However, in the patent text genre these techniques have not yielded the same level of performance as in other text genres. In this paper we propose a combination of automatic query expansion methods to identify strong domain specific lexical-semantic relations. With our method we avoid a decrease in performance and we report an improvement in recall-oriented evaluation measurements for the CLEP-IP 2013 test collection.
Emotion processing and the amygdala: from a 'low road' to 'many roads' of evaluating biological significance

A subcortical pathway through the superior colliculus and pulvinar to the amygdala is commonly assumed to mediate the non-conscious processing of affective visual stimuli. We review anatomical and physiological data that argue against the notion that such a pathway plays a prominent part in processing affective visual stimuli in humans. Instead, we propose that the primary role of the amygdala in visual processing, like that of the pulvinar, is to coordinate the function of cortical networks during evaluation of the biological significance of affective visual stimuli. Under this revised framework, the cortex has a more important role in emotion processing than is traditionally assumed.
Selective Encoding for Abstractive Sentence Summarization

We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-ofthe-art baseline models.