nils-herrmann/clean_scirepeval huggingface dataset:
Knowing Where You Stand: Physical Isolation, Perceived Respect, and Organizational Identification Among Virtual Employees

This research investigates the relationship between virtual employees’ degree of physical isolation and their perceived respect in the organization. Respect is an identity-based status perception that reflects the extent to which one is included and valued as a member of the organization. We hypothesize that the degree of physical isolation is negatively associated with virtual employees’ perceived respect and that this relationship explains the lower organizational identification among more physically isolated virtual employees. In two field studies using survey methods, we find that perceived respect is negatively associated with the degree of physical isolation, and respect mediates the relationship between physical isolation and organizational identification. These effects hold for shorterand longer-tenured employees alike. Our research contributes to the virtual work literature by drawing attention to physical isolation and the important but neglected role of status perceptions in shaping virtual employees’ organizational identification. We also contribute to the literature on perceived respect by demonstrating how respect is affected by the physical context of work.
Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure

Analysis and processing of very large data sets, or big data, poses a significant challenge. Massive data sets are collected and studied in numerous domains, from engineering sciences to social networks, biomolecular research, commerce, and security. Extracting valuable information from big data requires innovative approaches that efficiently process large amounts of data as well as handle and, moreover, utilize their structure. This article discusses a paradigm for large-scale data analysis based on the discrete signal processing (DSP) on graphs (DSPG). DSPG extends signal processing concepts and methodologies from the classical signal processing theory to data indexed by general graphs. Big data analysis presents several challenges to DSPG, in particular, in filtering and frequency analysis of very large data sets. We review fundamental concepts of DSPG, including graph signals and graph filters, graph Fourier transform, graph frequency, and spectrum ordering, and compare them with their counterparts from the classical signal processing theory. We then consider product graphs as a graph model that helps extend the application of DSPG methods to large data sets through efficient implementation based on parallelization and vectorization. We relate the presented framework to existing methods for large-scale data processing and illustrate it with an application to data compression.
Fast rule mining in ontological knowledge bases with AMIE $$+$$ +

Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive logic programming (ILP) can be used to mine logical rules from these KBs, such as “If two persons are married, then they (usually) live in the same city.” While ILP is a mature field, mining logical rules from KBs is difficult, because KBs make an open-world assumption. This means that absent information cannot be taken as counterexamples. Our approach AMIE (Galárraga et al. in WWW, 2013) has shown how rules can be mined effectively from KBs even in the absence of counterexamples. In this paper, we show how this approach can be optimized to mine even larger KBs with more than 12M statements. Extensive experiments show how our new approach, AMIE $$+$$ + , extends to areas of mining that were previously beyond reach.
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees

Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available. Unfortunately, much of that potential is not being realized because it would require sharing data in a way that compromises privacy. In this paper, we investigate a method for ensuring (differential) privacy of the generator of the Generative Adversarial Nets (GAN) framework. The resulting model can be used for generating synthetic data on which algorithms can be trained and validated, and on which competitions can be conducted, without compromising the privacy of the original dataset. Our method modifies the Private Aggregation of Teacher Ensembles (PATE) framework and applies it to GANs. Our modified framework (which we call PATE-GAN) allows us to tightly bound the influence of any individual sample on the model, resulting in tight differential privacy guarantees and thus an improved performance over models with the same guarantees. We also look at measuring the quality of synthetic data from a new angle; we assert that for the synthetic data to be useful for machine learning researchers, the relative performance of two algorithms (trained and tested) on the synthetic dataset should be the same as their relative performance (when trained and tested) on the original dataset. Our experiments, on various datasets, demonstrate that PATE-GAN consistently outperforms the stateof-the-art method with respect to this and other notions of synthetic data quality.
A new data mining methodology for generating new service ideas

To survive in today’s market, decision makers including investors and their managerial teams should continuously attempt to realize the customers’ unspoken needs and requirements by discovering their behavioral patterns. Discovering customers’ patterns puts these decision makers in a better position in which higher qualified services can be designed and provided. Association rule mining is a well-known approach to discover these patterns. Although extracted rules could express customers’ behaviors in an easy-to-understand way, the number of rules in real applications could be problematic. Moreover, the customers’ comments are not usually considered for constructing/evaluating the rules. To tackle these issues, a system framework is proposed in this paper in which all association rules are clustered using a new similarity measure. For each cluster, a new type of graph is developed known as sub-graph in this paper. Each sub-graph has unique messages that can partially contribute to designing new services. Furthermore, for the first time, the real satisfaction levels are embedded into the association rules to enrich them in an innovative way. The main interesting point is that the satisfaction levels are only assessed for the overall system, not for current services. We also illustrate how our proposed methodology works through artificial and real datasets and also demonstrate the superiority of our proposed clustering algorithm compared to other popular methods.
Botnet Detection Through Fine Flow Classification

The prevalence of botnets, which is defined as a group of infec ted machines, have become the predominant factor among all the internet malicious attacks such as DDoS, Spam, and Cl ick fraud. The number of botnets is steadily increasing, and the characteristic C &C channels have evolved from IRC to HTTP, FTP, and DNS, etc., a nd from the centralized structure to P2P and Fast Flux Network Services. In counter to the escalat ions of the botnet developments, the internet security comm unity have designed many botnet detection and disruption systems which can be summarized into two categories: Honeynet-base d nd Passive Traffic Monitoring, while the Passive Traffic Monito ring could be further divided into Behavior-based, DNS-bas ed, and Mining-based techniques. Among all the Intrusion Detectio n System designs, the mining-based method, operated on the fl ow level internet traffic, has shown some promising resilience again st the botnets evolutions. A preliminary experiment has bee n conducted in this paper observing the discriminating capabilities of the Hierarchical and K mean clustering algorithms and explo ring a RTT adjustment procedure to mix the botnet trace with the backgr ound internet traffic.
Power Line Communication with Tree Based Interleaver in Iterative IDMA Systems

The power distribution networks are primary employed, in Power line communication (PLC) system, for transferring data during communication process. For achieving even higher data rate in multiple user environments, researchers have proposed various multiple access schemes for power line communication systems including CDMA and OFDM mechanisms. This paper includes the performance analysis of power line channel with recently proposed multiple access scheme popularly known as Interleave-Division Multiple-Access (IDMA) scheme utilizing unequal power allocation with random and tree based interleaving mechanisms. During the analysis, the simulations have been performed in MATLAB environment. The simulation results demonstrate suitability of IDMA mechanism for powerline communication in comparison to other available mechanisms. Also, tree based interleaver is observed with similar BER performance to that of random interleaver with reduced complexity and bandwidths requirements.
jModelTest 2: more models, new heuristics and parallel computing

jModelTest 2: more models, new heuristics and parallel computing Diego Darriba, Guillermo L. Taboada, Ramón Doallo and David Posada Supplementary Table 1. New features in jModelTest 2 Supplementary Table 2. Model selection accuracy Supplementary Table 3. Mean square errors for model averaged estimates Supplementary Note 1. Hill-climbing hierarchical clustering algorithm Supplementary Note 2. Heuristic filtering Supplementary Note 3. Simulations from prior distributions Supplementary Note 4. Speed-up benchmark on real and simulated datasets
Computer-Aided Diagnosis of Mammographic Masses Using Scalable Image Retrieval

Computer-aided diagnosis of masses in mammograms is important to the prevention of breast cancer. Many approaches tackle this problem through content-based image retrieval techniques. However, most of them fall short of scalability in the retrieval stage, and their diagnostic accuracy is, therefore, restricted. To overcome this drawback, we propose a scalable method for retrieval and diagnosis of mammographic masses. Specifically, for a query mammographic region of interest (ROI), scale-invariant feature transform (SIFT) features are extracted and searched in a vocabulary tree, which stores all the quantized features of previously diagnosed mammographic ROIs. In addition, to fully exert the discriminative power of SIFT features, contextual information in the vocabulary tree is employed to refine the weights of tree nodes. The retrieved ROIs are then used to determine whether the query ROI contains a mass. The presented method has excellent scalability due to the low spatial-temporal cost of vocabulary tree. Extensive experiments are conducted on a large dataset of 11 553 ROIs extracted from the digital database for screening mammography, which demonstrate the accuracy and scalability of our approach.
Pomegranate: fast and flexible probabilistic modeling in python

We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with—or outperform—other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code. The code is available at https://github.com/jmschrei/pomegranate
Fusion for Free - Efficient Algebraic Effect Handlers

Algebraic effect handlers are a recently popular approach for modelling side-effects that separates the syntax and semantics of effectful operations. The shape of syntax is captured by functors, and free monads over these functors denote syntax trees. The semantics is captured by algebras, and effect handlers pass these over the syntax trees to interpret them into a semantic domain. This approach is inherently modular: different functors can be composed to make trees with richer structure. Such trees are interpreted by applying several handlers in sequence, each removing the syntactic constructs it recognizes. Unfortunately, the construction and traversal of intermediate trees is painfully inefficient and has hindered the adoption of the handler approach. This paper explains how a sequence of handlers can be fused into one, so that multiple tree traversals can be reduced to a single one and no intermediate trees need to be allocated. At the heart of this optimization is keeping the notion of a free monad abstract, thus enabling a change of representation that opens up the possibility of fusion. We demonstrate how the ensuing code can be inlined at compile time to produce efficient handlers.
Executive control in language processing

During communication, speakers and listeners need the mechanisms of executive control to organize thoughts and actions along internal goals. Speakers may use executive functions to select the right word over competing alternatives to refer to the concept in mind. Listeners may use executive functions to coordinate the outputs of multiple linguistic processes to reach a coherent interpretation of what others say. Bilinguals may use executive functions to control which language is to use or to switch from one language to another. The control mechanisms recruited in language processing may be similar to those recruited in perception and attention, supported by a network of frontal, parietal and sub-cortical brain structures. Here we review existing evidences regarding the involvement of domain-general executive control in language processing. We will explain how executive functions are employed to control interference in comprehension and production, within and across languages.
An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5%.
Strategic Business / IT Alignment using Goal Models

Since few years, enterprise information technologies (IT) are no more seen as a simple technological support for business strategies in the enterprise. Moreover, standalone IT departments are created in order to support evolution and growth of IT in the enterprise. Often, IT department defines a specific strategy describing vision, goals and objectives of IT development in the organization. However, to remain competitive, IT strategy and IT investment should be coherent with global enterprise strategies. The continuous process of preserving coherence between Business/IT strategies is widely known as strategic Business/IT alignment. Our work aims at discussing the relation between interview based Business IT alignement discovery and the Strategic Alignement Model proposed by Venkatraman. The paper proposes also modeling tools and engineering methodologies to support this alignment process.
Microbial co-operation in the rhizosphere.

Soil microbial populations are immersed in a framework of interactions known to affect plant fitness and soil quality. They are involved in fundamental activities that ensure the stability and productivity of both agricultural systems and natural ecosystems. Strategic and applied research has demonstrated that certain co-operative microbial activities can be exploited, as a low-input biotechnology, to help sustainable, environmentally-friendly, agro-technological practices. Much research is addressed at improving understanding of the diversity, dynamics, and significance of rhizosphere microbial populations and their co-operative activities. An analysis of the co-operative microbial activities known to affect plant development is the general aim of this review. In particular, this article summarizes and discusses significant aspects of this general topic, including (i) the analysis of the key activities carried out by the diverse trophic and functional groups of micro-organisms involved in co-operative rhizosphere interactions; (ii) a critical discussion of the direct microbe-microbe interactions which results in processes benefiting sustainable agro-ecosystem development; and (iii) beneficial microbial interactions involving arbuscular mycorrhiza, the omnipresent fungus-plant beneficial symbiosis. The trends of this thematic area will be outlined, from molecular biology and ecophysiological issues to the biotechnological developments for integrated management, to indicate where research is needed in the future.
Toward Abnormal Trajectory and Event Detection in Video Surveillance

In this paper, we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes. Existing approaches for abnormal behavior detection do either use trajectory-based or pixel-based methods. Unlike these approaches, we propose an integrated pipeline that incorporates the output of object trajectory analysis and pixel-based analysis for abnormal behavior inference. This enables to detect abnormal behaviors related to speed and direction of object trajectories, as well as complex behaviors related to finer motion of each object. By applying our approach on three different data sets, we show that our approach is able to detect several types of abnormal group behaviors with less number of false alarms compared with existing approaches.
Colonic transit studies: normal values for adults and children with comparison of radiological and scintigraphic methods

The sitz or plastic marker study for colonic transit has been around for many years. It is applicable where an X-ray machine exists, is widely used and is accepted as the gold standard for diagnosing constipation. Recently, radiopharmaceutical methods have been developed. The theme of this review is their possible roles in the assessment of paediatric bowel motility disorders in patients presenting to paediatric surgeons. This review presents data on total and segmental transit in normal adults and children and comparing the two techniques in adults. Reliability and reproducibility are presented. Normative data for colonic transit in adults and children are discussed and parameters for assessing abnormal transit are reviewed. Normal colonic transit takes 20–56 h. Plastic marker studies are more readily accessible, but the assessment may be misleading with current methods. Plastic markers show faster transit than scintigraphy. It is difficult to compare the two techniques because methods of reporting are different. Using scintigraphy, repeatability is good. Separation of normal from slow transit in the ascending colon is apparent at 24 and 48 h, but the determination of transit through the distal colon/rectum in adults may require studies of more than 7 days. In conclusion, plastic marker studies and scintigraphy show similar transit rates in young adults and children. However, scintigraphy has advantages of allowing transit through the stomach and small intestine to be measured and has proved useful in the diagnostic workup of children with intractable constipation.
Tracing Data Lineage Using Schema Transformation Pathways

With the increasing amount and diversity of information available on the Internet, there has been a huge growth in information systems that need to integrate data from distributed, heterogeneous data sources. Tracing the lineage of the integrated data is one of the current problems being addressed in data warehouse research. In this paper, we propose a new approach for tracing data linage based on schema transformation pathways. We show how the individual transformation steps in a transformation pathway can be used to trace the derivation of the integrated data in a step-wise fashion, thus simplifying the lineage tracing process.
The Fox News Effect: Media Bias and Voting∗

Does media bias affect voting? We address this question by looking at the entry of Fox News in cable markets and its impact on voting. Between October 1996 and November 2000, the conservative Fox News Channel was introduced in the cable programming of 20 percent of US towns. Fox News availability in 2000 appears to be largely idiosyncratic. Using a data set of voting data for 9,256 towns, we investigate if Republicans gained vote share in towns where Fox News entered the cable market by the year 2000. We find a significant effect of the introduction of Fox News on the vote share in Presidential elections between 1996 and 2000. Republicans gain 0.4 to 0.6 percentage points in the towns which broadcast Fox News. The results are robust to town-level controls, district and county fixed effects, and alternative specifications. We also find a significant effect of Fox News on Senate vote share and, to a lesser extent, on voter turnout. Our estimates imply that Fox News convinced 3 to 8 percent of its viewers to vote Republican. The evidence is most consistent with voters suffering from persuasion bias. We also discuss a model of rational voter updating that fits most facts. ∗George Akerlof, Stephen Ansolabehere, Larry M. Bartels, Matthew Gentzkow, Alan Gerber, Jay Hamilton, Alan Krueger, Marco Manacorda, Enrico Moretti, Torsten Persson, Sam Popkin, Riccardo Puglisi, Matthew Rabin, Jesse Shapiro, David Stromberg, and audiences at Bonn University, EUI (Florence), Fuqua, Harvard University, IIES (Stockholm), Princeton University, UC Berkeley, University of Chicago GSB, and the NBER 2005 Political Economy and Labor Studies Meetings provided useful comments. We would like to specially thank Jim Collins and Matthew Gentzkow for providing the Scarborough data. Shawn Bananzadeh, Jessica Chan, Marguerite Converse, Neil Dandavati, Tatyana Deryugina, Monica Deza, Dylan Fox, Melissa Galicia, Calvin Ho, Sudhamas Khanchanawong, Richard Kim, Martin Kohan, Vipul Kumar, Jonathan Leung, Clarice Li, Tze Yang Lim, Ming Mai, Sameer Parekh, Sharmini Radakrishnan, Rohan Relan, Chanda Singh, Matthew Stone, Nan Zhang, Sibo Zhao, and Liya Zhu helped collect the voting and the cable data. Dan Acland, Saurabh Bhargava, Avi Ebenstein, and Devin Pope provided excellent research assistance.