nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing MapReduce for GPUs with effective shared memory usage

Accelerators and heterogeneous architectures in general, and GPUs in particular, have recently emerged as major players in high performance computing. For many classes of applications, MapReduce has emerged as the framework for easing parallel programming and improving programmer productivity. There have already been several efforts on implementing MapReduce on GPUs.
 In this paper, we propose a new implementation of MapReduce for GPUs, which is very effective in utilizing shared memory, a small programmable cache on modern GPUs. The main idea is to use a reduction-based method to execute a MapReduce application. The reduction-based method allows us to carry out reductions in shared memory. To support a general and efficient implementation, we support the following features: a memory hierarchy for maintaining the reduction object, a multi-group scheme in shared memory to trade-off space requirements and locking overheads, a general and efficient data structure for the reduction object, and an efficient swapping mechanism.
 We have evaluated our framework with seven commonly used MapReduce applications and compared it with the sequential implementations, MapCG, a recent MapReduce implementation on GPUs, and Ji et al.'s work, a recent MapReduce implementation that utilizes shared memory in a different way. The main observations from our experimental results are as follows. For four of the seven applications that can be considered as reduction-intensive applications, our framework has a speedup of between 5 and 200 over MapCG (for large datasets). Similarly, we achieved a speedup of between 2 and 60 over Ji et al.'s work.
An Overview of NLIDB Approaches and Implementation for Airline Reservation System

Relational databases are queried using database query languages such as SQL. Natural language interfaces to databases (NLIDB) are systems that translate a natural language sentence into a database query. In this modern techno-crazy world, as more and more laymen access various systems and applications through their smart phones and tablets, the need for Natural Language Interfaces (NLIs) has increased manifold. The challenges in Natural language Query processing are interpreting the sentence correctly, removal of various ambiguity and mapping to the appropriate context. Natural language access problem is actually composed of two stages Linguistic processing and Database processing. NLIDB techniques encompass a wide variety of approaches. The approaches include traditional methods such as Pattern Matching, Syntactic Parsing and Semantic Grammar to modern systems such as Intermediate Query Generation, Machine Learning and Ontologies. In this report, various approaches to build NLIDB systems have been analyzed and compared along with their advantages, disadvantages and application areas. Also, a natural language interface to a flight reservation system has been implemented comprising of flight and booking inquiry systems.
LOG-PERIODIC TREE-DIPOLE ANTENNA ( PLPTDA )

A miniaturized printed log-periodic fractal dipole antenna is proposed. Tree fractal structure is introduced in an antenna design and evolves the traditional Euclidean log-periodic dipole array into the log-periodic second-iteration tree-dipole array (LPT2DA) for the first time. Main parameters and characteristics of the proposed antenna are discussed. A fabricated proof-of-concept prototype of the proposed antenna is etched on a FR4 substrate with a relative permittivity of 4.4 and volume of 490 mm × 245 mm × 1.5 mm. The impedance bandwidth (measured VSWR < 2) of the fabricated antenna with approximate 40% reduction of traditional log-periodic dipole antenna is from 0.37 to 3.55GHz with a ratio of about 9.59 : 1. Both numerical and experimental results show that the proposed antenna has stable directional radiation patterns and apparently miniaturized effect, which are suitable for various ultra-wideband applications.
Temporal Tessellation: A Unified Approach for Video Analysis

We present a general approach to video understanding, inspired by semantic transfer techniques that have been successfully used for 2D image analysis. Our method considers a video to be a 1D sequence of clips, each one associated with its own semantics. The nature of these semantics – natural language captions or other labels – depends on the task at hand. A test video is processed by forming correspondences between its clips and the clips of reference videos with known semantics, following which, reference semantics can be transferred to the test video. We describe two matching methods, both designed to ensure that (a) reference clips appear similar to test clips and (b), taken together, the semantics of the selected reference clips is consistent and maintains temporal coherence. We use our method for video captioning on the LSMDC’16 benchmark, video summarization on the SumMe and TV-Sum benchmarks, Temporal Action Detection on the Thumos2014 benchmark, and sound prediction on the Greatest Hits benchmark. Our method not only surpasses the state of the art, in four out of five benchmarks, but importantly, it is the only single method we know of that was successfully applied to such a diverse range of tasks.
Airfare prices prediction using machine learning techniques

This paper deals with the problem of airfare prices prediction. For this purpose a set of features characterizing a typical flight is decided, supposing that these features affect the price of an air ticket. The features are applied to eight state of the art machine learning (ML) models, used to predict the air tickets prices, and the performance of the models is compared to each other. Along with the prediction accuracy of each model, this paper studies the dependency of the accuracy on the feature set used to represent an airfare. For the experiments a novel dataset consisting of 1814 data flights of the Aegean Airlines for a specific international destination (from Thessaloniki to Stuttgart) is constructed and used to train each ML model. The derived experimental results reveal that the ML models are able to handle this regression problem with almost 88% accuracy, for a certain type of flight features.
A novel zero weight/activation-aware hardware architecture of convolutional neural network

It is imperative to accelerate convolutional neural networks (CNNs) due to their ever-widening application areas from server, mobile to IoT devices. Based on the fact that CNNs can be characterized by a significant amount of zero values in both kernel weights and activations, we propose a novel hardware accelerator for CNNs exploiting zero weights and activations. We also report a zero-induced load imbalance problem, which exists in zero-aware parallel CNN hardware architectures, and present a zero-aware kernel allocation as a solution. According to our experiments with a cycle-accurate simulation model, RTL, and layout design of the proposed architecture running two real deep CNNs, pruned AlexNet  and VGG-16 , our architecture offers 4x/1.8x (AlexNet) and 5.2x/2.1x (VGG-16) speedup compared with state-of-the-art zero-agnostic/zero-activation-aware architectures.
Self-Driven Synchronous Rectification System With Input Voltage Tracking for Converters With a Symmetrically Driven Transformer

Synchronous rectification (SR) is mandatory to achieve good efficiencies with low output voltages. If a transformer is driven asymmetrically without dead times, the self-driven SR (SDSR) is a very interesting solution. However, if the transformer is driven symmetrically, the synchronous rectifiers are off during the dead times, and as a consequence, the efficiency is lowered. This paper deals with the optimization of an SDSR system that keeps the rectifiers on even during the dead times. The input voltage is tracked, and the information is used to adapt the gate to the source voltage of the synchronous rectifiers and improve the efficiency. The system has been implemented in a prototype, and the results have been compared with the ones obtained in the same prototype without SDSR.
Accelerated Mirror Descent in Continuous and Discrete Time

We study accelerated mirror descent dynamics in continuous and discrete time. Combining the original continuous-time motivation of mirror descent with a recent ODE interpretation of Nesterov’s accelerated method, we propose a family of continuous-time descent dynamics for convex functions with Lipschitz gradients, such that the solution trajectories converge to the optimum at a O(1/t2) rate. We then show that a large family of first-order accelerated methods can be obtained as a discretization of the ODE, and these methods converge at a O(1/k2) rate. This connection between accelerated mirror descent and the ODE provides an intuitive approach to the design and analysis of accelerated first-order algorithms.
Social media as a catalyst for online deliberation? Exploring the affordances of Facebook and YouTube for political expression

This manuscript aims to assess the potential of social media as a channel to foster democratic deliberation. It does this by examining whether the types of discussions that citizens maintain in two of the most used social media channels managed by the White House – Facebook and YouTube – meet the necessary conditions for deliberative democracy. For this purpose 7230 messages were analyzed and assessed in terms of indicators developed to evaluate online discourse derived from the work of Habermas. By contrasting social media channels that differ in the affordances of identifiability and networked information access (two traditional predictors of online deliberation), we seek to contribute a deeper understanding of social media and its impact on deliberation. Drawing on the social identification/deindividuation (SIDE) model of computer mediated communication and network theories, we predict that political discussions in Facebook will present a more egalitarian distribution of comments between discussants and higher level of politeness in their messages. Consistent with our theoretical framework, results confirm that Facebook expands the flow of information to other networks and enables more symmetrical conversations among users, whereas politeness is lower in the more anonymous and deindividuated YouTube.
Human Identity and Gender Recognition From Gait Sequences With Arbitrary Walking Directions

We investigate the problem of human identity and gender recognition from gait sequences with arbitrary walking directions. Most current approaches make the unrealistic assumption that persons walk along a fixed direction or a pre-defined path. Given a gait sequence collected from arbitrary walking directions, we first obtain human silhouettes by background subtraction and cluster them into several clusters. For each cluster, we compute the cluster-based averaged gait image as features. Then, we propose a sparse reconstruction based metric learning method to learn a distance metric to minimize the intra-class sparse reconstruction errors and maximize the inter-class sparse reconstruction errors simultaneously, so that discriminative information can be exploited for recognition. The experimental results show the efficacy of our approach.
Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices.

Attention can be focused volitionally by "top-down" signals derived from task demands and automatically by "bottom-up" signals from salient stimuli. The frontal and parietal cortices are involved, but their neural activity has not been directly compared. Therefore, we recorded from them simultaneously in monkeys. Prefrontal neurons reflected the target location first during top-down attention, whereas parietal neurons signaled it earlier during bottom-up attention. Synchrony between frontal and parietal areas was stronger in lower frequencies during top-down attention and in higher frequencies during bottom-up attention. This result indicates that top-down and bottom-up signals arise from the frontal and sensory cortex, respectively, and different modes of attention may emphasize synchrony at different frequencies.
Design and implementation of a 300% strain soft artificial muscle

We present the inverse pneumatic artificial muscle (IPAM), a new soft actuator that is powered by pneumatics in a manner inverse to traditional pneumatic muscles: low pressure, rather than high, contracts the muscle. The IPAM improves on the 50-year-old standard in soft pneumatic actuators, the McKibben muscle, but retains many of the advantages that have drawn roboticists to this artificial muscle over the years. The McKibben muscle produces up to 40% strain, and has nonlinear control with friction and hysteresis, whereas the IPAM attains strains of over 300% and has a nearly linear mapping between input pressure and force/length output and no sliding friction. Crucially, the IPAM retains the soft structure, low weight, compliance, and robustness that the McKibben muscle boasts. We present a simple model to describe the behavior of the muscle, as well as force, displacement, pressure, and speed tests validating the model and characterizing the IPAM's performance. Further, we present two practical implementations using the IPAM: an active brace and a robotic finger.
Through-Wall Imaging of Moving Targets Using UWB Random Noise Radar

For a through-wall ultrawideband (UWB) random noise radar using array antennas, subtraction of successive frames of the cross-correlation signals between each received element signal and the transmitted signal is able to isolate moving targets in heavy clutter. Images of moving targets are subsequently obtained using the back projection (BP) algorithm. This technique is not constrained to noise radar, but can also be applied to other kinds of radar systems. Different models based on the finite-difference time-domain (FDTD) algorithm are set up to simulate different through-wall scenarios of moving targets. Simulation results show that the heavy clutter is suppressed, and the signal-to-clutter ratio (SCR) is greatly enhanced using this approach. Multiple moving targets can be detected, localized, and tracked for any random movement.
A Hybrid Detecting Fraudulent Financial Statements Model Using Rough Set Theory and Support Vector Machines

The detection of fraudulent financial statements (FFS) is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. Although nonfinancial ratios are generally acknowledged as the key factor contributing to the FFS of a corporation, they are usually excluded from early detection models. The objective of this study is to increase the accuracy of FFS detection by integrating the rough set theory (RST) and support vector machines (SVM) approaches, while adopting both financial and nonfinancial ratios as predictive variables. The results showed that the proposed hybrid approach (RSTþSVM) has the best classification rate as well as the lowest occurrence of Types I and II errors, and that nonfinancial ratios are indeed valuable information in FFS detection.
Exploring web scale language models for search query processing

It has been widely observed that search queries are composed in a very different style from that of the body or the title of a document. Many techniques explicitly accounting for this language style discrepancy have shown promising results for information retrieval, yet a large scale analysis on the extent of the language differences has been lacking. In this paper, we present an extensive study on this issue by examining the language model properties of search queries and the three text streams associated with each web document: the body, the title, and the anchor text. Our information theoretical analysis shows that queries seem to be composed in a way most similar to how authors summarize documents in anchor texts or titles, offering a quantitative explanation to the observations in past work.
 We apply these web scale n-gram language models to three search query processing (SQP) tasks: query spelling correction, query bracketing and long query segmentation. By controlling the size and the order of different language models, we find that the perplexity metric to be a good accuracy indicator for these query processing tasks. We show that using smoothed language models yields significant accuracy gains for query bracketing for instance, compared to using web counts as in the literature. We also demonstrate that applying web-scale language models can have marked accuracy advantage over smaller ones.
Building brand identity in competitive markets : a conceptual model

The purpose of this conceptual paper is to identify important elements of brand building based on a literature review and case studies of successful brands in India.  This paper is based on a review of the literature and takes a case study approach. The paper suggests the framework for building brand identity in sequential order, namely, positioning the brand, communicating the brand message, delivering the brand performance, and leveraging the brand equity.  Brand-building effort has to be aligned with organizational processes that help deliver the promises to customers through all company departments, intermediaries, suppliers, etc., as all these play an important role in the experience customers have with the brand.  The paper uses case studies of leading Indian brands to illustrate the importance of action elements in building brands in competitive markets.
Exploration for Multi-task Reinforcement Learning with Deep Generative Models

Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as E, Rmax, Thompson sampling assume a single stationary MDP and are not suitable for system identification in the multi-task setting. We present a novel method to facilitate exploration in multi-task reinforcement learning using deep generative models. We supplement our method with a low dimensional energy model to learn the underlying MDP distribution and provide a resilient and adaptive exploration signal to the agent. We evaluate our method on a new set of environments and provide intuitive interpretation of our results.
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach

Feature selection has been proven to be effective and efficient in preparing high-dimensional data for data mining and machine learning problems. Since real-world data is usually unlabeled, unsupervised feature selection has received increasing attention in recent years. Without label information, unsupervised feature selection needs alternative criteria to define feature relevance. Recently, data reconstruction error emerged as a new criterion for unsupervised feature selection, which defines feature relevance as the capability of features to approximate original data via a reconstruction function. Most existing algorithms in this family assume predefined, linear reconstruction functions. However, the reconstruction function should be data dependent and may not always be linear especially when the original data is high-dimensional. In this paper, we investigate how to learn the reconstruction function from the data automatically for unsupervised feature selection, and propose a novel reconstruction-based unsupervised feature selection framework REFS, which embeds the reconstruction function learning process into feature selection. Experiments on various types of realworld datasets demonstrate the effectiveness of the proposed framework REFS.
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms

Continued advances in mobile networks and positioning technologies have created a strong market push for location-based applications. Examples include location-aware emergency response, location-based advertisement, and location-based entertainment. An important challenge in the wide deployment of location-based services (LBSs) is the privacy-aware management of location information, providing safeguards for location privacy of mobile clients against vulnerabilities for abuse. This paper describes a scalable architecture for protecting the location privacy from various privacy threats resulting from uncontrolled usage of LBSs. This architecture includes the development of a personalized location anonymization model and a suite of location perturbation algorithms. A unique characteristic of our location privacy architecture is the use of a flexible privacy personalization framework to support location k-anonymity for a wide range of mobile clients with context-sensitive privacy requirements. This framework enables each mobile client to specify the minimum level of anonymity that it desires and the maximum temporal and spatial tolerances that it is willing to accept when requesting k-anonymity-preserving LBSs. We devise an efficient message perturbation engine to implement the proposed location privacy framework. The prototype that we develop is designed to be run by the anonymity server on a trusted platform and performs location anonymization on LBS request messages of mobile clients such as identity removal and spatio-temporal cloaking of the location information. We study the effectiveness of our location cloaking algorithms under various conditions by using realistic location data that is synthetically generated from real road maps and traffic volume data. Our experiments show that the personalized location k-anonymity model, together with our location perturbation engine, can achieve high resilience to location privacy threats without introducing any significant performance penalty.
Forward Models for Physiological Motor Control

Based on theoretical and computational studies it has been suggested that the central nervous system (CNS) internally simulates the behaviour of the motor system in planning, control and learning. Such an internal "forward" model is a representation of the motor system that uses the current state of the motor system and motor command to predict the next state. We will outline the uses of such internal models for solving several fundamental computational problems in motor control and then review the evidence for their existence and use by the CNS. Finally we speculate how the location of an internal model within the CNS may be identified.
Deep Learning 3D Shape Surfaces Using Geometry Images

1. Established relevance of authalic spherical parametrization for creating geometry images used subsequently in CNN. 2. Robust authalic parametrization of arbitrary shapes using area restoring diffeomorphic flow and barycentric mapping. 3. Creation of geometry images (a) with appropriate shape feature for rigid/non-rigid shape analysis, (b) which are robust to cut and amenable to learn using CNNs.  Cuts & Data Augmentation