nils-herrmann/clean_scirepeval huggingface dataset:
Trojan data layouts: right shoes for a running elephant

MapReduce is becoming ubiquitous in large-scale data analysis. Several recent works have shown that the performance of Hadoop MapReduce could be improved, for instance, by creating indexes in a non-invasive manner. However, they ignore the impact of the data layout used inside data blocks of Hadoop Distributed File System (HDFS). In this paper, we analyze different data layouts in detail in the context of MapReduce and argue that Row, Column, and PAX layouts can lead to poor system performance. We propose a new data layout, coined Trojan Layout, that internally organizes data blocks into attribute groups according to the workload in order to improve data access times. A salient feature of Trojan Layout is that it fully preserves the fault-tolerance properties of MapReduce. We implement our Trojan Layout idea in HDFS 0.20.3 and call the resulting system Trojan HDFS. We exploit the fact that HDFS stores multiple replicas of each data block on different computing nodes. Trojan HDFS automatically creates a different Trojan Layout per replica to better fit the workload. As a result, we are able to schedule incoming MapReduce jobs to data block replicas with the most suitable Trojan Layout. We evaluate our approach using three real-world workloads. We compare Trojan Layouts against Hadoop using Row and PAX layouts. The results demonstrate that Trojan Layout allows MapReduce jobs to read their input data up to 4.8 times faster than Row layout and up to 3.5 times faster than PAX layout.
DYNAMIC CAPABILITIES AND THE EMERGENCE OF INTRA-INDUSTRY DIFFERENTIAL FIRM PERFORMANCE: INSIGHTS FROM A SIMULATION STUDY∗ By

This paper explores how the dynamic capabilities of firms may account for the emergence of differential firm performance within an industry. Synthesizing insights from both strategic and organizational theory, four performance-relevant attributes of dynamic capabilities are proposed: timing of dynamic capability deployment, imitation as part of the search for alternative resource configurations, cost of dynamic capability deployment, and learning to deploy dynamic capabilities. Theoretical propositions are developed suggesting how these attributes contribute to the emergence of differential firm performance. A formal model is presented in which dynamic capability is modeled as a set of routines guiding a firm’s evolutionary processes of change. Simulation of the model yields insights into the process of change through dynamic capability deployment, and permits refinement of the theoretical propositions. One of the interesting findings of this study is that even if dynamic capabilities are equifinal across firms, robust performance differences may arise across firms if the costs and timing of dynamic capability deployment differ across firms.
Composite Connectors for Composing Software Components

In a component-based system, connectors are used to compose components. Connectors should have a semantics that makes them simple to construct and use. At the same time, their semantics should be rich enough to endow them with desirable properties such as genericity, compositionality and reusability. For connector construction, compositionality would be particularly useful, since it would facilitate systematic construction. In this paper we describe a hierarchical approach to connector definition and construction that allows connectors to be defined and constructed from sub-connectors. These composite connectors are indeed generic, compositional and reusable. They behave like design patterns, and provide powerful composition connectors.
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences

We provide two fundamental results on the population (infinite-sample) likelihood function of Gaussian mixture models with M ≥ 3 components. Our first main result shows that the population likelihood function has bad local maxima even in the special case of equally-weighted mixtures of well-separated and spherical Gaussians. We prove that the log-likelihood value of these bad local maxima can be arbitrarily worse than that of any global optimum, thereby resolving an open question of Srebro [21]. Our second main result shows that the EM algorithm (or a first-order variant of it) with random initialization will converge to bad critical points with probability at least 1 − e. We further establish that a first-order variant of EM will not converge to strict saddle points almost surely, indicating that the poor performance of the first-order method can be attributed to the existence of bad local maxima rather than bad saddle points. Overall, our results highlight the necessity of careful initialization when using the EM algorithm in practice, even when applied in highly favorable settings.
The “ What ” and “ Why ” of Goal Pursuits : Human Needs and the Self-Determination of Behavior

Self-determination theory (SDT) maintains that an understanding of human motivation requires a consideration of innate psychological needs for competence, autonomy, and relatedness. We discuss the SDT concept of needs as it relates to previous need theories, emphasizing that needs specify the necessary conditions for psychological growth, integrity, and well-being. This concept of needs leads to the hypotheses that different regulatory processes underlying goal pursuits are differentially associated with effective functioning and well-being and also that different goal contents have different relations to the quality of behavior and mental health, specifically because different regulatory processes and different goal contents are associated with differing degrees of need satisfaction. Social contexts and individual differences that support satisfaction of the basic needs facilitate natural growth processes including intrinsically motivated behavior and integration of extrinsic motivations, whereas those that forestall autonomy, competence, or relatedness are associated with poorer motivation, performance, and well-being. We also discuss the relation of the psychological needs to cultural values, evolutionary processes, and other contemporary motivation theories.
Avoiding the Streetlight Effect: I/O Workload Analysis with SSDs in Mind

Storage systems are designed and optimized relying on wisdom derived from analysis studies of file-system and block-level workloads. However, while SSDs are becoming a dominant building block in many storage systems, their design continues to build on knowledge derived from analysis targeted at hard disk optimization. Though still valuable, it does not cover important aspects relevant for SSD performance. In a sense, we are “searching under the streetlight”, possibly missing important opportunities for optimizing storage system design. We present the first I/O workload analysis designed with SSDs in mind. We characterize traces from four repositories and examine their ‘temperature’ ranges, sensitivity to page size, and ‘logical locality’. Our initial r esults reveal nontrivial aspects that can significantly influ ence the design and performance of SSD-based systems.
Generating Summaries of Multiple News Articles

We present a natural language system which summarizes a series of news articles on the same event. It uses summarization operators, identified through empirical analysis of a corpus of news summaries, to group together templates from the output of the systems developed for ARPA’s Message Understanding Conferences. Depending on the available resources (e.g., space), summaries of different length can be produced. Our research also provides a methodological framework for future work on the summarization task and on the evaluation of news summarization systems.
Brief Announcement: Dynamic Determinacy Race Detection for Task Parallelism with Futures

Existing dynamic determinacy race detectors for task-parallel programs are limited to programs with strict computation graphs, where a task can only wait for its descendant tasks to complete. In this paper, we present the first known determinacy race detector for non-strict computation graphs with futures. The space and time complexity of our algorithm are similar to those of the classical SP-bags algorithm, when using only structured parallel constructs such as spawn-sync and async-finish. In the presence of point-to-point synchronization using futures, the complexity of the algorithm increases by a factor determined by the number of future operations, which includes future task creation and future get operations. The experimental results show that the slowdown factor observed for our algorithm relative to the sequential version is in the range of 1.00x to 9.92x, which is very much in line with slowdowns experienced for fully strict computation graphs.
The Field Behind the Screen : Using Netnography For Marketing Research in Online Communities

The author develops “netnography” as an online marketing research technique for providing consumer insight. “Netnography” is ethnography adapted to the study of online communities. As a method, “netnography” is faster, simpler, and less expensive than traditional ethnography, and more naturalistic and unobtrusive than focus groups or interviews. It provides information on the symbolism, meanings, and consumption patterns of online consumer groups. The author provides guidelines that acknowledge the online environment, respect the inherent flexibility and openness of ethnography, and provide rigor and ethics in the conduct of marketing research. As an illustrative example, the author provides a netnography of an online coffee newsgroup and discusses its marketing implications.
An ESPAR Antenna for Beamspace-MIMO Systems Using PSK Modulation Schemes

In this paper the use of electronically steerable passive array radiator (ESPAR) antennas is introduced for achieving increased spectral efficiency characteristics in multiple-input, multiple-output (MIMO) systems using a single active element, and compact antennas. The proposed ESPAR antenna is capable of mapping phase-shift-keying (PSK) modulated symbols to be transmitted onto orthogonal basis functions on the wavevector domain of the multi-element antenna (MEA), instead of the traditional approach of sending different symbol streams in different locations on the antenna domain. In this way, different symbols are transmitted simultaneously towards different angles of departure at the transmitter side. We show that the proposed system, called beamspace-MIMO (BS-MIMO) can achieve performance characteristics comparable to traditional MIMO systems while using a single radio-frequency (RF) front-end and ESPAR antenna arrays of total length equal to lambda/8, for popular PSK modulation schemes such as binary-PSK (BPSK), quaternary-PSK (QPSK), as well as for their offset-PSK and differential-PSK variations.
A review of central retinal artery occlusion: clinical presentation and management

Central retinal artery occlusion (CRAO) is an ophthalmic emergency and the ocular analogue of cerebral stroke. Best evidence reflects that over three-quarters of patients suffer profound acute visual loss with a visual acuity of 20/400 or worse. This results in a reduced functional capacity and quality of life. There is also an increased risk of subsequent cerebral stroke and ischaemic heart disease. There are no current guideline-endorsed therapies, although the use of tissue plasminogen activator (tPA) has been investigated in two randomized controlled trials. This review will describe the pathophysiology, epidemiology, and clinical features of CRAO, and discuss current and future treatments, including the use of tPA in further clinical trials.
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares

In modern data analysis, random sampling is an efficient and widely-used strategy to overcome the computational difficulties brought by large sample size. In previous studies, researchers conducted random sampling which is according to the input data but independent on the response variable, however the response variable may also be informative for sampling. In this paper we propose an adaptive sampling called the gradient-based sampling which is dependent on both the input data and the output for fast solving of least-square (LS) problems. We draw the data points by random sampling from the full data according to their gradient values. This sampling is computationally saving, since the running time of computing the sampling probabilities is reduced to O(nd) where n is the full sample size and d is the dimension of the input. Theoretically, we establish an error bound analysis of the general importance sampling with respect to LS solution from full data. The result establishes an improved performance of the use of our gradientbased sampling. Synthetic and real data sets are used to empirically argue that the gradient-based sampling has an obvious advantage over existing sampling methods from two aspects of statistical efficiency and computational saving.
An ANN-Based Synthesis Model for the Single-Feed Circularly-Polarized Square Microstrip Antenna With Truncated Corners

An artificial neural network-based synthesis model is proposed for the design of single-feed circularly-polarized square microstrip antenna (CPSMA) with truncated corners. To obtain the training data sets, the resonant frequency and Q-factor of square microstrip antennas are calculated by empirical formulae. Then the size of the truncated corners and the operation frequency with the best axial ratio are obtained. Using the Levenberg-Marquardt (LM) algorithm, a three hidden layered network is trained to achieve an accurate synthesis model. At last, the model is validated by comparing its results with the electromagnetic simulation and measurement. It is extremely useful to antenna engineers for directly obtaining patch physical dimensions of the single-feed CPSMA with truncated corners.
Domain Adaptation: Learning Bounds and Algorithms

This paper addresses the general problem of domain adaptation which arises in a variety of applications where the distribution of the labeled sample available somewhat differs from that of the test data. Building on previous work by Ben-David et al. (2007), we introduce a novel distance between distributions,discrepancy distance , that is tailored to adaptation problems with arbitrary loss functions. We give Rademacher complexity bounds for estimating the discrepancy distance from finite samples for different loss functions. Using this distance, we derive novel generalization bounds for domain adaptation for a wide family of loss functions. We also present a series of novel adaptation bounds for large classes of regularization-based algorithms, including support vector machines and kernel ridge regression based on the empirical discrepancy. This motivates our analysis of the problem of minimizing the empirical discrepancy for various loss functions for which we also give novel algorithms. We report the results of preliminary experiments that demonstrate the benefits of our discrepancy minimization algorithms for domain adaptation.
Effect of Corporate Governance Structure on the Financial Performance of Johannesburg Stock Exchange ( JSE )-Listed Mining Firms

There have been many corporate collapses and financial crises in recent years linked to a lack of effective corporate governance. The South African King IV Code of Corporate Governance recommends that corporate governing bodies should be comprised of an appropriate balance of knowledge, diversity, and independence for discharging their duties objectively and more efficiently. This study examines the effect of corporate governance structures on firm financial performance. The secondary data of selected Johannesburg Stock Exchange (JSE), Socially Responsible Investment (SRI) Index-listed mining firms’ sustainability reports, and integrated annual financial statements are used. Using panel data analysis of the random effects model, we determined the relationship between board independence and board size and the return on equity (ROE) for the period 2010–2015. Results indicate a weak negative correlation between ROE and board size, and a weak, but positive, correlation between ROE and board independence. Additionally, there is a positive, but weak, correlation between ROE and sales growth, but a negative and weak relationship between ROE and firm size. The study suggests that effective corporate governance through a small effective board and monitoring by an independent board result in increased firm financial performance. We recommend that South African companies see compliance with the recommendations of the King IV Code on Corporate Governance not as a liability, but an ethical investment that may likely yield financial benefit in the long-term. Although complying with corporate governance principles does not necessarily translate into a significant economic benefit, firms should, however, continue to adopt corporate governance for ethical reasons to meet stakeholder’s social and environmental needs for sustainable development.
Compact Dual-Band Antenna With Electronic Beam-Steering and Beamforming Capability

A low-cost compact dual-band antenna that can achieve electronic beam steering in the horizontal plane across a range from 0° to 360° and adaptive beamforming is presented. Multiple radiation patterns can be generated by the antenna for interference canceling. To reduce the size and cost, the antenna employs the configuration of electronically steerable parasitic array radiators (ESPARs). The parasitic elements are 12 folded monopole antennas, which surround a short monocone antenna. The short monocone antenna serves as the driven element. Each folded monopole is loaded with a p-i-n diode, and by controlling the dc voltages applied to the p-i-n diodes, the antenna can achieve electronic beam steering and adaptive beamforming. To prove the concept, a prototype antenna was developed whose frequency bands are 1.8-2.2 and 2.85-3.15 GHz. The height of the antenna has been reduced to 0.12 λ at 1.8 GHz.
Loc-Auth: Location-enabled authentication through attribute-based encryption

Traditional user authentication involves entering a username and password into a system. Strong authentication security demands, among other requirements, long, frequently hard-to-remember passwords. Two-factor authentication aids in the security, even though, as a side effect, might worsen user experience. We depict a mobile sign-on scheme that benefits from the dynamic relationship between a user's attributes, the service the user wishes to utilize, and location (where the user is, and what services are available there) as an authentication factor. We demonstrate our scheme employing Bluetooth Low Energy beacons for location awareness and the expressiveness of Attribute-Based Encryption to capture and leverage the described relationship. Bluetooth Low Energy beacons broadcast encrypted messages with encoded access policies. Within range of the beacons, a user with appropriate attributes is able to decrypt the broadcast message and obtain parameters that allow the user to perform a short or simplified login.
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval

This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detect the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding method, the Paragraph Vector, is performed. The results show that the proposed method in this paper significantly outperforms Paragraph Vector method for web document retrieval task.
Feature based image retrieval system using Zernike moments and Daubechies Wavelet Transform

In image processing research field, image retrieval is extensively used in various application. Increasing need of the image retrieval, it is quiet most exciting research field. In image retrieval system, features are the most significant process used for indexing, retrieving and classifying the images. For computer systems, automatic indexing, storing and retrieving larger image collections effectively are a critical task. Nowadays several retrieval systems were implemented to overcome these issues but still there is a lack of speed and accuracy during image retrieval process. First, address the various issues on performance degradation of image retrieval then analyze and compare the methods and results in previous work. Second, discover the effective approach to be used to increase the accuracy of retrieval system significantly. This work provides a framework based on low level features extraction using Daubechies Wavelet Transform (DWT) and Zernike moments. Based on that features images are retrieved by using the distance measure. Zernike moments constitute a powerful shape descriptor due to its strength and narrative capability. Experimental results shows that our scheme provides significant improvement on retrieval accuracy compared to existing system based on the combination of both the color and edge features by using Discrete Wavelet Transform. In this paper, wang's image dataset is used for experiments.
Effects of cognitive-behavioral therapy on brain activation in specific phobia

Little is known about the effects of successful psychotherapy on brain function in subjects with anxiety disorders. The present study aimed to identify changes in brain activation following cognitive-behavioral therapy (CBT) in subjects suffering from specific phobia. Using functional magnetic resonance imaging (fMRI), brain activation to spider videos was measured in 28 spider phobic and 14 healthy control subjects. Phobics were randomly assigned to a therapy-group (TG) and a waiting-list control group (WG). Both groups of phobics were scanned twice. Between scanning sessions, CBT was given to the TG. Before therapy, brain activation did not differ between both groups of phobics. As compared to control subjects, phobics showed greater responses to spider vs. control videos in the insula and anterior cingulate cortex (ACC). CBT strongly reduced phobic symptoms in the TG while the WG remained behaviorally unchanged. In the second scanning session, a significant reduction of hyperactivity in the insula and ACC was found in the TG compared to the WG. These results propose that increased activation in the insula and ACC is associated with specific phobia, whereas an attenuation of these brain responses correlates with successful therapeutic intervention.