nils-herrmann/clean_scirepeval huggingface dataset:
A 32nm logic technology featuring 2nd-generation high-k + metal-gate transistors, enhanced channel strain and 0.171μm2 SRAM cell size in a 291Mb array

A 32 nm generation logic technology is described incorporating 2nd-generation high-k + metal-gate technology, 193 nm immersion lithography for critical patterning layers, and enhanced channel strain techniques. The transistors feature 9 Aring EOT high-k gate dielectric, dual band-edge workfunction metal gates, and 4th-generation strained silicon, resulting in the highest drive currents yet reported for NMOS and PMOS. Process yield, performance and reliability are demonstrated on a 291 Mbit SRAM test vehicle, with 0.171 mum2 cell size, containing >1.9 billion transistors.
Style in the long tail: discovering unique interests with latent variable models in large scale social E-commerce

Purchasing decisions in many product categories are heavily influenced by the shopper's aesthetic preferences. It's insufficient to simply match a shopper with popular items from the category in question; a successful shopping experience also identifies products that match those aesthetics. The challenge of capturing shoppers' styles becomes more difficult as the size and diversity of the marketplace increases. At Etsy, an online marketplace for handmade and vintage goods with over 30 million diverse listings, the problem of capturing taste is particularly important -- users come to the site specifically to find items that match their eclectic styles.
 In this paper, we describe our methods and experiments for deploying two new style-based recommender systems on the Etsy site. We use Latent Dirichlet Allocation (LDA) to discover trending categories and styles on Etsy, which are then used to describe a user's "interest" profile. We also explore hashing methods to perform fast nearest neighbor search on a map-reduce framework, in order to efficiently obtain recommendations. These techniques have been implemented successfully at very large scale, substantially improving many key business metrics.
New IMA architecture approach based on IMA resources

Diehl Aerospace GmbH (DAs) is currently involved in national German Research & Technology (R&T) projects (e.g. SYSTAVIO, SESAM) and in European R&T projects like ASHLEY to extend and to improve the Integrated Modular Avionics (IMA) technology. Diehl Aerospace is investing to expand its current IMA technology to enable further integration of systems including hardware modules, associated software, tools and processes while increasing the level of standardization. An additional objective is to integrate more systems on a common computing platform which uses the same toolchain, processes and integration experiences. New IMA components enable integration of high integrity fast loop system applications such as control applications. Distributed architectures which provide new types of interfaces allow integration of secondary power distribution systems along with other IMA functions. Cross A/C type usage is also a future emphasis to increase standardization and decrease development and operating costs as well as improvements on time to market and affordability of systems.
Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.
Low-cost motion sensing of table tennis players for real time feedback

we present a motion capture device to measure in real-time table tennis strokes. A six degree-of-freedom sensing device, inserted into the racket handle, measures 3D acceleration and 3-axis angular velocity values at a high sampling rate. Data are wirelessly transmitted to a computer in real-time. This flexible system allows for recording and analyzing kinematics information on the motion of the racket, along with synchronized video and sound recordings. Recorded gesture data are analyzed using several algorithms we developed to segment and extract movement features, and to build a reference motion database.
Unsupervised Learning of Visual Representations using Videos

This is a review of unsupervised learning applied to videos with the aim of learning visual representations. We look at different realizations of the notion of temporal coherence across various models. We try to understand the challenges being faced, the strengths and weaknesses of different approaches and identify directions for future work. Unsupervised Learning of Visual Representations using Videos Nitish Srivastava Department of Computer Science, University of Toronto
A Universal Toolkit for Cryptographically Secure Privacy-Preserving Data Mining

The issue of potential data misuse rises whenever it is collected from several sources. In a common setting, a large database is either horizontally or vertically partitioned between multiple entities who want to find global trends from the data. Such tasks can be solved with secure multi-party computation (MPC) techniques. However, practitioners tend to consider such solutions inefficient. Furthermore, there are no established tools for applying secure multi-party computation in real-world applications. In this paper, we describe Sharemind—a toolkit, which allows data mining specialist with no cryptographic expertise to develop data mining algorithms with good security guarantees. We list the building blocks needed to deploy a privacy-preserving data mining application and explain the design decisions that make Sharemind applications efficient in practice. To validate the practical feasibility of our approach, we implemented and benchmarked four algorithms for frequent itemset mining.
Large‐scale Correlation of Accounts across Social Networks

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors or originators and do not necessarily reflect the views of the National Science Foundation.  Organizations are increasingly mining the personal data users generate as they carry out much of their day‐to‐day activities online. A range of new business models specifically exploit what users publish on their social network profiles, including services performing background checks and analytics providers who, e.g., associate demographics with consumer behavior. In this work we set out to understand the capabilities of machine learning techniques for linking independent accounts that users maintain on different social networks, based solely on the information people explicitly and publicly provide in their profiles. We perform a large scale study that assesses a range of correlation approaches for matching accounts between five popular social networks: Twitter, Facebook, Google+, Myspace, and Flickr. Our results show for instance that by exploiting usernames, real names, locations, and photos, we can robustly identify about 80% of the matching pairs of user accounts between any combination of two social networks among Twitter, Facebook and Google+. Our work is the first to demonstrate the feasibility of such conceptually simple privacy attacks at large scale, across several major networks, and with such efficiency.
An Information Foraging Theory Perspective on Tools for Debugging, Refactoring, and Reuse Tasks

Theories of human behavior are an important but largely untapped resource for software engineering research. They facilitate understanding of human developers’ needs and activities, and thus can serve as a valuable resource to researchers designing software engineering tools. Furthermore, theories abstract beyond specific methods and tools to fundamental principles that can be applied to new situations. Toward filling this gap, we investigate the applicability and utility of Information Foraging Theory (IFT) for understanding information-intensive software engineering tasks, drawing upon literature in three areas: debugging, refactoring, and reuse. In particular, we focus on software engineering tools that aim to support information-intensive activities, that is, activities in which developers spend time seeking information. Regarding applicability, we consider whether and how the mathematical equations within IFT can be used to explain why certain existing tools have proven empirically successful at helping software engineers. Regarding utility, we applied an IFT perspective to identify recurring design patterns in these successful tools, and consider what opportunities for future research are revealed by our IFT perspective.
The Maturing of a Business Intelligence Maturity Model

The ongoing high relevance of business intelligence (BI) for the management and competitiveness of organizations requires a continuous, transparent, and detailed assessment of existing BI solutions in the enterprise. This paper presents a BI maturity model (called biMM) that has been developed and refined over years. It is used for both, in surveys to determine the overall BI maturity in German speaking countries and for the individual assessment in organizations. A recently conducted survey shows that the current average BI maturity can be assigned to the third stage (out of five stages). Comparing future (planned) activities and current challenges allows the derivation of a BI research agenda. The need for action includes among others emphasizing BI specific organizational structures, such as the establishment of BI competence centers, a stronger focus on profitability, and improved effectiveness of the BI architecture.
Semi-Global Matching - Motivation, Developments and Applications

Since its original publication, the Semi-Global Matching (SGM) technique has been re-implemented by many researchers and companies. The method offers a very good trade off between runtime and accuracy, especially at object borders and fine structures. It is also robust against radiometric differences and not sensitive to the choice of parameters. Therefore, it is well suited for solving practical problems. The applications reach from remote sensing, like deriving digital surface models from aerial and satellite images, to robotics and driver assistance systems. This paper motivates and explains the method, shows current developments as well as examples from various applications.
Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain

Environmental topics have gained much consideration in corporate green operations. Globalization, stakeholder pressures, and stricter environmental regulations have made organizations develop environmental practices. Thus, green supply chain management (GSCM) is now a proactive approach for organizations to enhance their environmental performance and achieve competitive advantages. This study pioneers using the decision-making trial and evaluation laboratory (DEMATEL) method with intuitionistic fuzzy sets to handle the important and causal relationships between GSCM practices and performances. DEMATEL evaluates GSCM practices to find the main practices to improve both environmental and economic performances. This study uses intuitionistic fuzzy set theory to handle the linguistic imprecision and the ambiguity of human being’s judgment. A case study from the automotive industry is presented to evaluate the efficiency of the proposed method. The results reveal ‘‘internal management support’’, ‘‘green purchasing’’ and ‘‘ISO 14001 certification’’ are the most significant GSCM practices. The practical results of this study offer useful insights for managers to become more environmentally responsible, while improving their economic and environmental performance goals. Further, a sensitivity analysis of results, managerial implications, conclusions, limitations and future research opportunities are provided.
Generation and Analysis of a Large-Scale Urban Vehicular Mobility Dataset

The surge in vehicular network research has led, over the last few years, to the proposal of countless network solutions specifically designed for vehicular environments. A vast majority of such solutions has been evaluated by means of simulation, since experimental and analytical approaches are often impractical and intractable, respectively. The reliability of the simulative evaluation is thus paramount to the performance analysis of vehicular networks, and the first distinctive feature that has to be properly accounted for is the mobility of vehicles, i.e., network nodes. Notwithstanding the improvements that vehicular mobility modeling has undergone over the last decade, no vehicular mobility dataset is publicly available today that captures both the macroscopic and microscopic dynamics of road traffic over a large urban region. In this paper, we present a realistic synthetic dataset, covering 24 hours of car traffic in a 400-km2 region around the city of Köln, in Germany. We describe the generation process and outline how the dataset improves the traces currently employed for the simulative evaluation of vehicular networks. We also show the potential impact that such a comprehensive mobility dataset has on the network protocol performance analysis, demonstrating how incomplete representations of vehicular mobility may result in over-optimistic network connectivity and protocol performance.
Explainable Neural Networks based on Additive Index Models

Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much research in developing various approaches to understand the model behavior. In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features. Unlike fully connected neural networks, the features engineered by the xNN can be extracted from the network in a relatively straightforward manner and the results displayed. With appropriate regularization, the xNN provides a parsimonious explanation of the relationship between the features and the output. We illustrate this interpretable feature–engineering property on simulated examples.
Extending a Research-Paper Recommendation System with Bibliometric Measures

In recent years the number of academic publication increased strongly. As this information flood grows, it becomes more difficult for researchers to find relevant literature effectively. To overcome this difficulty, recommendation systems can be used which often utilize text similarity to find related documents. To improve those systems we add scientometrics as a ranking measure for popularity into these algorithms. In this paper we analyse whether and how scientometrics are useful in a recommender system.
Improved Learning Algorithms for Restricted Boltzmann Machines

A restricted Boltzmann machine (RBM) is often used as a building block for constructing deep neural networks and deep generative models which have gained popularity recently as one way to learn complex and large probabilistic models. In these deep models, it is generally known that the layer-wise pretraining of RBMs facilitates finding a more accurate model for the data. It is, hence, important to have an efficient learning method for RBM. The conventional learning is mostly performed using the stochastic gradients, often, with the approximate method such as contrastive divergence (CD) learning to overcome the computational difficulty. Unfortunately, training RBMs with this approach is known to be difficult, as learning easily diverges after initial convergence. This difficulty has been reported recently by many researchers. This thesis contributes important improvements that address the difficulty of training RBMs. Based on an advanced Markov-Chain Monte-Carlo sampling method called parallel tempering (PT), the thesis proposes a PT learning which can replace CD learning. In terms of both the learning performance and the computational overhead, PT learning is shown to be superior to CD learning through various experiments. The thesis also tackles the problem of choosing the right learning parameter by proposing a new algorithm, the adaptive learning rate, which is able to automatically choose the right learning rate during learning. A closer observation into the update rules suggested that learning by the traditional update rules is easily distracted depending on the representation of data sets. Based on this observation, the thesis proposes a new set of gradient update rules that are more robust to the representation of training data sets and the learning parameters. Extensive experiments on various data sets confirmed that the proposed rules indeed improve learning significantly. Additionally, a Gaussian-Bernoulli RBM (GBRBM) which is a variant of an RBM that can learn continuous real-valued data sets is reviewed, and the proposed improvements are tested upon it. The experiments showed that the improvements could also be made for GBRBMs. It is impossible for me to express my appreciation to Dr. Alexander Ilin and Dr. Tapani Raiko enough for their enormous help. If it were not them, this thesis would not have been made possible. I would like to thank my fellow MACADAMIA students and wish best for them all. Also, my three Korean friends–Byungjin Cho, Sungin Cho, and Eunah Cho– in Finland who happen to share the same last name with me have …
Artificial Neural Networks ’ Applications in Management

Finding more effective solution and tools for complicated managerial problems is one of the most important and dominant subjects in management studies. With the advancement of computer and communication technology, the tools that are using for management decisions have undergone a massive change. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component of business intelligence. In this article we describe the basic of neural networks as well as a review of selected works done in application of ANNs in management sciences.
User Interface Goals, AI Opportunities

Is AI antithetical to good user interface design? From the earliest times in the development of computers, activities in human-computer interaction (HCI) and AI have been intertwined. But as subfields of computer science, HCI and AI have always had a love-hate relationship. The goal of HCI is to make computers easier to use and more helpful to their users. The goal of artificial intelligence is to model human thinking and to embody those mechanisms in computers. How are these goals related? Some in HCI have seen these goals sometimes in opposition. They worry that the heuristic nature of many AI algorithms will lead to unreliability in the interface. They worry that AI’s emphasis on mimicking human decision-making functions might usurp the decision-making prerogative of the human user. These concerns are not completely without merit. There are certainly many examples of failed attempts to prematurely foist AI on the public. These attempts gave AI a bad name, at least at the time. But so too have there been failed attempts to popularize new HCI approaches. The first commercial versions of window systems, such as the Xerox Star and early versions of Microsoft Windows, weren’t well accepted at the time of their introduction. Later design iterations of window systems, such as the Macintosh and Windows 3.0, finally achieved success. Key was that these early failures did not lead their developers to conclude window systems were a bad idea. Researchers shouldn’t construe these (perceived) AI failures as a refutation of the idea of AI in interfaces. Modern PDA, smartphone, and tablet computers are now beginning to have quite usable handwriting recognition. Voice recognition is being increasingly employed on phones, and even in the noisy environment of cars. Animated agents, more polite, less intrusive, and better thought out, might also make a
60-GHz Millimeter-Wave Radio: Principle, Technology, and New Results

1 Center for Manufacturing Research, Tennessee Technological University (TTU), Cookeville, TN 38505, USA 2 Department of Electrical and Computer Engineering, Tennessee Technological University (TTU), Cookeville, TN 38505, USA 3 Panasonic Princeton Laboratory (PPRL), Panasonic R&D Company of America, 2 Research Way, Princeton, NJ 08540, USA 4 Network Development Center, Matsushita Electric Industrial Co., Ltd., 4-12-4 Higashi-shinagawa, Shinagawa-ku, Tokyo 140-8587, Japan
PHILOSOPHY AND THE MIRROR OF NATURE

Richard Rorty's Philosophy and the Mirror of Nature brings to light the deep sense of crisis within the profession of academic philosophy which is similar to the paralyzing pluralism in contemporary theology and the inveterate indeterminacy of literary criticism. Richard Rorty's provocative and profound meditations impel philosophers to examine the problematic status of their discipline— only to discover that modern European philosophy has come to an end. Rorty strikes a deathblow to modern European philosophy by telling a story about the emergence, development and decline of its primary props: the correspondence theory of truth, the notion of privileged representations and the idea of a self-reflective transcendental subject. Rorty's fascinating tale—his-story —is regulated by three fundamental shifts which he delineates in detail and promotes in principle: the move toward anti-realism or conventionalism in ontology, the move toward the demythologizing of the Myth of the Given or anti-foundationalism in epistemology, and the move toward detranscendentalizing the subject or dismissing the mind as a sphere of inquiry. The chief importance of Rorty's book is that it brings together in an original and intelligible narrative the major insights of the patriarchs of postmodern American philosophy—W. V. Quine, Wilfred Sellars, and Nelson Goodman— and persuasively presents the radical consequences of their views for contemporary philosophy. Rorty credits Wittgenstein, Heidegger and Dewey for having "brought us into a period of 'revolutionary' philosophy" by undermining the prevailing Cartesian and Kantian paradigms and advancing new conceptions of philosophy. And these monumental figures surely inspire Rorty. Yet, Rorty's philosophical debts—the actual sources of his particular anti-Cartesian and antiKantian arguments—are Quine's holism, Sellars' anti-foundationalism, and Goodman's pluralism. In short, despite his adamant attack on analytical philosophy—the last stage of modern European philosophy—Rorty feels most comfortable with the analytical form of philosophical argumentation (shunned by Wittgenstein and Heidegger). From the disparate figures of Wittgenstein, Heidegger, and Dewey, Rorty gets a historicist directive: to eschew the quest for certainty and the search for foundations.