nils-herrmann/clean_scirepeval huggingface dataset:
Attribute-Guided Face Generation Using Conditional CycleGAN

We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a highres face image for the low-res input that satisfies the given attributes. To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the input attributes. We demonstrate high-quality results on the attribute-guided conditional CycleGAN, which can synthesize realistic face images with appearance easily controlled by user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network, the attribute-guided network becomes the identity-guided conditional CycleGAN which produces high-quality and interesting results on identity transfer. We demonstrate three applications on identityguided conditional CycleGAN: identity-preserving face superresolution, face swapping, and frontal face generation, which consistently show the advantage of our new method.
X Goes First: Teaching Simple Games through Multimodal Interaction

What would it take to teach a computer to play a game entirely through language and sketching? In this paper, we present an implemented program through which an instructor can teach the rules of simple board games using such input. We describe the architecture, information flow, and vocabulary of instructional events and walk through an annotated example. In our approach, the instructional and communication events guide abductive reasoning for language interpretation and help to integrate information from sketching and language. Having a general target representation enables the learning process to be viewed more as translation and problem solving than as induction. Lastly, learning by demonstration complements and extends instruction, resulting in concrete, operational rules.
Modified Caesar Cipher for Better Security Enhancement

Encryption is the process of scrambling a message so that only the intended recipient can read it. With the fast progression of digital data exchange in electronic way, Information Security is becoming much more important in data storage and transmission. Caesar cipher is a mono alphabetic cipher. It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter. In this paper, author modified the traditional Caesar cipher and fixed the key size as one. Another thing alphabet index is checked if the alphabet index is even then increase the value by one else alphabet index is odd decrease the key value by one. Encryption and scrambling of the letters in the Cipher Text.
Dueling Network Architectures for Deep Reinforcement Learning

In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. In this paper, we present a new neural network architecture for model-free reinforcement learning. Our dueling network represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. Our results show that this architecture leads to better policy evaluation in the presence of many similar-valued actions. Moreover, the dueling architecture enables our RL agent to outperform the state-of-the-art on the Atari 2600 domain.
Semi-productive Polysemy and Sense Extension

In this paper we discuss various aspects of systematic or conventional polysemy and their formal treatment within an implemented constraint based approach to linguistic representation. We distinguish between two classes of systematic polysemy: constructional polysemy, where a single sense assigned to a lexical entry is contextually specialised, and sense extension, which predictably relates two or more senses. Formally the first case is treated as instantiation of an underspecified lexical entry and the second by use of lexical rules. The problems of distinguishing between these two classes are discussed in detail. We illustrate how lexical rules can be used both to relate fully conventionalised senses and also applied productively to recognise novel usages and how this process can be controlled to account for semi-productivity by utilising probabilities.
Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora

We introduce (1) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs, and (2) the concept of bilingual parsing with a variety of parallel corpus analysis applications. Aside from the bilingual orientation, three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics: it skips directly to a context-free rather than finite-state base, it permits a minimal extra degree of ordering flexibility, and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm. A convenient normal form is shown to exist. Analysis of the formalism's expressiveness suggests that it is particularly well suited to modeling ordering shifts between languages, balancing needed flexibility against complexity constraints. We discuss a number of examples of how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks such as segmentation, bracketing, phrasal alignment, and parsing.
Design and Implementation of the LogicBlox System

The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a ``self-service'' model. Our perspective in this area is informed by over twenty years of experience building dozens of mission-critical enterprise applications that are in use by hundreds of large enterprises across industries such as retail, telecommunications, banking, and government. We designed and built LogicBlox to be the system we wished we had when developing those applications.
 In this paper, we discuss the design considerations behind the LogicBlox system and give an overview of its implementation, highlighting innovative aspects. These include: LogiQL, a unified and declarative language based on Datalog; the use of purely functional data structures; novel join processing strategies; advanced incremental maintenance and live programming facilities; a novel concurrency control scheme; and built-in support for prescriptive and predictive analytics.
Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

In this paper, we present an automatic tool for estimating geometrical parameters from 3-D human scans independent on pose and robustly against the topological noise. It is based on an automatic segmentation of body parts exploiting curve skeleton processing and ad hoc heuristics able to remove problems due to different acquisition poses and body types. The software is able to locate body trunk and limbs, detect their directions, and compute parameters like volumes, areas, girths, and lengths. Experimental results demonstrate that measurements provided by our system on 3-D body scans of normal and overweight subjects acquired in different poses are highly correlated with the body fat estimates obtained on the same subjects with dual-energy X-rays absorptiometry (DXA) scanning. In particular, maximal lengths and girths, not requiring precise localization of anatomical landmarks, demonstrate a good correlation (up to 96%) with the body fat and trunk fat. Regression models based on our automatic measurements can be used to predict body fat values reasonably well.
Learning Noun Phrase Query Segmentation

Query segmentation is the process of taking a user’s search-engine query and dividing the tokens into individual phrases or semantic units. Identification of these query segments can potentially improve both document-retrieval precision, by first returning pages which contain the exact query segments, and document-retrieval recall, by allowing query expansion or substitution via the segmented units. We train and evaluate a machine-learned query segmentation system that achieves 86% segmentationdecision accuracy on a gold standard set of segmented noun phrase queries, well above recently published approaches. Key enablers of this high performance are features derived from previous natural language processing work in noun compound bracketing. For example, token association features beyond simple N-gram counts provide powerful indicators of segmentation.
Internet inter-domain traffic

In this paper, we examine changes in Internet inter-domain traffic demands and interconnection policies. We analyze more than 200 Exabytes of commercial Internet traffic over a two year period through the instrumentation of 110 large and geographically diverse cable operators, international transit backbones, regional networks and content providers. Our analysis shows significant changes in inter-AS traffic patterns and an evolution of provider peering strategies. Specifically, we find the majority of inter-domain traffic by volume now flows directly between large content providers, data center / CDNs and consumer networks. We also show significant changes in Internet application usage, including a global decline of P2P and a significant rise in video traffic. We conclude with estimates of the current size of the Internet by inter-domain traffic volume and rate of annualized inter-domain traffic growth.
Mid-level deep pattern mining

Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activation extracted from the first fully-connected layer of a CNN have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern , surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.
Big Data Classification using Fuzzy K-Nearest Neighbor

Because of the massive increase in the size of the data it becomes troublesome to perform effective analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition a need for a means to meet the computational requirements to process such huge volume of data. The objective of this paper is to classify big data using Fuzzy K-Nearest Neighbor classifier, and to provide a comparative study between the results of the proposed systems and the method reviewed in the literature. In this paper we implemented the Fuzzy K-Nearest Neighbor method using the MapReduce paradigm to process on big data. Results on different data sets show that the proposed Fuzzy K-Nearest Neighbor method outperforms a better performance than the method reviewed in the literature.
Client Puzzles: A Cryptographic Countermeasure Against Connection Depletion Attacks

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Effects of Weighted Vests on the Engagement of Children With Developmental Delays and Autism

The use of weighted vests for children with autism spectrum disorders and developmental disabilities is a common practice as part of sensory integration therapy programs. The purpose of the current investigation was to extend the research on the use of weighted vests for children with autism and developmental delays in a methodologically rigorous study. The study was conducted using an alternating treatment design. This allowed the comparison of three different conditions: weighted vest, vest with no weight (which served as a placebo), and no vest (which served as a baseline). The results showed no differentiation in engagement between conditions for any of the participants. Implications for practice and future research are provided.
AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets

The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering technique with numerous applications in many fields. However, its O(n2) time complexity still remains a severe weakness. In this paper, we propose a novel anytime approach to cope with this problem by reducing both the range query and the label propagation time of DBSCAN. Our algorithm, called AnyDBC, compresses the data into smaller density-connected subsets called primitive clusters and labels objects based on connected components of these primitive clusters for reducing the label propagation time. Moreover, instead of passively performing the range query for all objects like existing techniques, AnyDBC iteratively and actively learns the current cluster structure of the data and selects a few most promising objects for refining clusters at each iteration. Thus, in the end, it performs substantially fewer range queries compared to DBSCAN while still guaranteeing the exact final result of DBSCAN. Experiments show speedup factors of orders of magnitude compared to DBSCAN and its fastest variants on very large real and synthetic complex datasets.
Practical design considerations for a LLC multi-resonant DC-DC converter in battery charging applications

In this paper, resonant tank design procedure and practical design considerations are presented for a high performance LLC multi-resonant dc-dc converter in a two-stage smart battery charger for neighborhood electric vehicle applications. The multi-resonant converter has been analyzed and its performance characteristics are presented. It eliminates both low and high frequency current ripple on the battery, thus maximizing battery life without penalizing the volume of the charger. Simulation and experimental results are presented for a prototype unit converting 390 V from the input dc link to an output voltage range of 48 V to 72 V dc at 650 W. The prototype achieves a peak efficiency of 96%.
Institutionally Distributed Deep Learning Networks

Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In such cases, sharing a deep learning model is a more attractive alternative. The best method of performing such a task is unclear, however. In this study, we simulate the dissemination of learning deep learning network models across four institutions using various heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in three independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance (testing accuracy = 77.3%) that was closest to that of centrally hosted patient data (testing accuracy = 78.7%). We also found that there is an improvement in the performance of cyclical weight transfer heuristic with high frequency of weight transfer.
Depixelizing pixel art

We describe a novel algorithm for extracting a resolution-independent vector representation from pixel art images, which enables magnifying the results by an arbitrary amount without image degradation. Our algorithm resolves pixel-scale features in the input and converts them into regions with smoothly varying shading that are crisply separated by piecewise-smooth contour curves. In the original image, pixels are represented on a square pixel lattice, where diagonal neighbors are only connected through a single point. This causes thin features to become visually disconnected under magnification by conventional means, and creates ambiguities in the connectedness and separation of diagonal neighbors. The key to our algorithm is in resolving these ambiguities. This enables us to reshape the pixel cells so that neighboring pixels belonging to the same feature are connected through edges, thereby preserving the feature connectivity under magnification. We reduce pixel aliasing artifacts and improve smoothness by fitting spline curves to contours in the image and optimizing their control points.
Cogging Torque Definitions for Magnetic Gears and Magnetically Geared Electrical Machines

Although cogging torque in magnetic gears (MGs) has been studied, the additional degree of freedom inherent to MGs makes it unclear exactly what is defined as the cogging torque of a MG. Although MGs generally have relatively smooth torque transfer characteristics, this paper reveals that when the gear is used in an up-speed configuration, cogging effects may be amplified under startup conditions. This paper distinguishes between two kinds of cogging torque in MGs, termed the synchronous cogging torque and the true cogging torque. Calculation of the true cogging torque is more difficult than the synchronous cogging torque, but an approximation to the true cogging torque can be obtained using the synchronous cogging torque. The theory is verified experimentally and supported further by results from dynamic simulations. The impact on magnetically geared machines is also considered.
Automics: souvenir generating photoware for theme parks

Automics is a photo-souvenir service which utilises mobile devices to support the capture, sharing and annotation of digital images amongst groups of visitors to theme parks. The prototype service mixes individual and group photo-capture with existing in-park, on-ride photo services, to allow users to create printed photo-stories. Herein we discuss initial fieldwork in theme parks that grounded the design of Automics, our development of the service prototype, and its real-world evaluation with theme park visitors. We relate our findings on user experience of the service to a literature on mobile photoware, finding implications for the design of souvenir services.
A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition

Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facilitate real-time applications. However, the majority of the related works have proposed a position-dependent HAR, i.e., the target subject has to fix the smartphone in a pre-defined position. Few studies have tackled the problem of position-independent HAR. They have tackled the problem either using handcrafted features that are less influenced by the position of the smartphone or by building a position-aware HAR. The performance of these studies still needs more improvement to produce a reliable smartphone-based HAR. Thus, in this paper, we propose a deep convolution neural network model that provides a robust position-independent HAR system. We build and evaluate the performance of the proposed model using the RealWorld HAR public dataset. We find that our deep learning proposed model increases the overall performance compared to the state-of-the-art traditional machine learning method from 84% to 88% for position-independent HAR. In addition, the position detection performance of our model improves superiorly from 89% to 98%. Finally, the recognition time of the proposed model is evaluated in order to validate the applicability of the model for real-time applications.
Hypercolumns for object segmentation and fine-grained localization

Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as a feature representation. However, the information in this layer may be too coarse spatially to allow precise localization. On the contrary, earlier layers may be precise in localization but will not capture semantics. To get the best of both worlds, we define the hypercolumn at a pixel as the vector of activations of all CNN units above that pixel. Using hypercolumns as pixel descriptors, we show results on three fine-grained localization tasks: simultaneous detection and segmentation [22], where we improve state-of-the-art from 49.7 mean APr [22] to 60.0, keypoint localization, where we get a 3.3 point boost over [20], and part labeling, where we show a 6.6 point gain over a strong baseline.