nils-herrmann/clean_scirepeval huggingface dataset:
A Review of Models and Frameworks for Designing Mobile Learning Experiences and Environments.

Mobile learning has become increasingly popular in the past decade due to the unprecedented technological affordances achieved through the advancement of mobile computing, which makes ubiquitous and situated learning possible. At the same time, there have been research and implementation projects whose efforts centered on developing mobile learning experiences for various learners’ profiles, accompanied by the development of models and frameworks for designing mobile learning experiences. This paper focuses on categorizing and synthesizing models and frameworks targeted specifically on mobile learning. A total of 17 papers were reviewed, and the models or frameworks were divided into five categories and discussed: 1) pedagogies and learning environment design; 2) platform/system design; 3) technology acceptance; 4) evaluation; and 5) psychological construct. This paper provides a review and synthesis of the models/frameworks. The categorization and analysis can also help inform evaluation, design, and development of curriculum and environments for meaningful mobile learning experiences for learners of various demographics.
Security Ontology: Simulating Threats to Corporate Assets

Threat analysis and mitigation, both essential for corporate security, are time consuming, complex and demand expert knowledge. We present an approach for simulating threats to corporate assets, taking the entire infrastructure into account. Using this approach effective countermeasures and their costs can be calculated quickly without expert knowledge and a subsequent security decisions will be based on objective criteria. The ontology used for the simulation is based on Landwehr’s [ALRL04] taxonomy of computer security and dependability.
A Survey on Query Processing and Optimization in Relational Database Management System

The query processer and optimizer is an important component in today’s relational database management system. This component is responsible for translating a user query, usually written in a non-procedural language like SQL – into an efficient query evaluation program that can be executed against database. In this paper, we identify many of the common issues, themes, and approaches that extend this work and the settings in which each piece of work is most appropriate. Our goal with this paper is to be a “value-add” over the existing papers on the material, providing not only a brief overview of each technique, but also a basic framework for understating the field of query processing and optimization in general.
TimeMachine: Timeline Generation for Knowledge-Base Entities

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.
Mining to Analyze News for Stock Market Prediction

This is a known fact that news and stock prices are closely related and news usually has a great influence on stock market investment. There have been many researches aimed at identifying that relationship or predicting stock market movements using news analysis. Recently, massive news tests, called unstructured big-data, have been used to predict stock price. In this paper, we introduce a method of mining text opinions to analyze Korean language news in order to predict rises and falls on the KOSPI (Korea Composite Stock Price Index). Our method consists of carrying out the NLP (Natural Language Processing) of news, describing its features, categorizing and extracting the sentiments and opinions expressed by the writers. The method then identifies the correlation between news and stock market fluctuations. In our experiment, we show that our method can be used to understand unstructured big-data, and we also reveal that news’ sentiment can be used in predicting stock price fluctuations, whether up or down. The algorithm extracted experiments can be used to make predictions about stock market movements.
The Influence of Base Rate and Case Information on Health-Risk Perceptions : A Unified Model of Self-Positivity and Self-Negativity Author ( s ) :

The Influence of Base Rate and Case Information on Health- A Unified Model of Self-Positivity and Self-Negativity Author(s): Dengfeng Yan and Jaideep Sengupta Reviewed work(s): Source: Journal of Consumer Research, Vol. 39, No. 5 (February 2013), pp. 931-946  The University of Chicago Press Stable URL: http://www.jstor.org/stable/10.1086/666596 . Accessed: 01/02/2013 23:40
Aggressive Text Detection for Cyberbullying

Aggressive text detection in social networks allows to identify offenses and misbehavior, and leverages tasks such as cyberbullying detection. We propose to automatically map a document with an aggressiveness score (thus treating aggressive text detection as a regression problem) and explore different approaches for this purpose. These include lexiconbased, supervised, fuzzy, and statistical approaches. We test the different methods over a dataset extracted from Twitter and compare them against human evaluation. Our results favor approaches that consider several features (particularly the presence of swear or profane words).
A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia

We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB). Given the KB, our goal is to comprehend a natural language query and provide corresponding accurate answers. Focusing on solving the non-aggregation questions, in this paper, we construct a subgraph of the knowledge base from the detected entities and propose a graph traversal method to solve both the semantic item mapping problem and the disambiguation problem in a joint way. Compared with existing work, we simplify the process of query intention understanding and pay more attention to the answer path ranking. We evaluate our method on a non-aggregation question dataset and further on a complete dataset. Experimental results show that our method achieves best performance compared with several state-of-the-art systems.
Approximating Polyhedra with Spheres for Time-Critical Collision Detection

This article presentsa method for approximatingpolyhedralobjects to support a time-critical collision-detectionalgorithm. The approximationsare hierarchies of spheres, and they allow the time-critical algorithm to progressivelyrefine the accuracy of its detection, stopping as needed to maintain the real-time performanceessential for interactive applications.The key to this approachis a preprocessthat automaticallybuilds tightly fitting hierarchies for rigid and articulatedobjects.The preprocessuses medial-axis surfaces, which are skeletal representations of objects. These skeletonsguide an optimizationtechniquethat gives the hierarchies accuracy properties appropriate for collision detection. In a sample application, hierarchies built this way allow the time-criticalcollision-detectionalgorithmto have acceptableaccuracy, improving significantly on that possible with hierarchies built by previous techniques. The performanceof the time-critical algorithmin this application is consistently 10 to 100 times better than a previous collision-detection algorithm, maintaining low latency and a nearIy constant frame rate of 10 frames per second on a conventional graphics workstation. The time-critical algorithm maintains its real-time performanceas objects become more complicated, even as they exceed previouslyreported complexitylevels by a factor of more than 10.
Substrate Integrated Waveguide (SIW) Rotman Lens and Its Ka-Band Multibeam Array Antenna Applications

A new type of substrate integrated waveguide (SIW) multibeam antenna is proposed for mobile satellite communications using beam switching and diversity techniques. It employs an SIW Rotman lens as the beamforming network. The prototype of a single multibeam antenna is implemented at 28.5 GHz with seven input ports and an antenna array constructed by nine SIW linear slot arrays, which can generate a corresponding number of beams along one dimension. Several such antennas are grouped in two different ways to cover a 2-D solid angle with multiple beams. Experiment results show that the 2-D solid angle around (-40deg, 40deg) X (-35deg, 35deg) or (-25deg, 25deg) x (-35deg, 35deg) are covered with 20 or 25 beams with 5 -dB beamwidth, respectively. It is demonstrated that this type of printed multibeam antenna is a good choice for communication applications where mobility and high gain are simultaneously required.
Sneak-path constraints in memristor crossbar arrays

In a memristor crossbar array, a memristor is positioned on each row-column intersection, and its resistance, low or high, represents two logical states. The state of every memristor can be sensed by the current flowing through the memristor. In this work, we study the sneak path problem in crossbars arrays, in which current can sneak through other cells, resulting in reading a wrong state of the memristor. Our main contributions are a new characterization of arrays free of sneak paths, and efficient methods to read the array cells while avoiding sneak paths. To each read method we match a constraint on the array content that guarantees sneak-path free readout, and calculate the resulting capacity.
Learning hierarchical representations for face verification with convolutional deep belief networks

Most modern face recognition systems rely on a feature representation given by a hand-crafted image descriptor, such as Local Binary Patterns (LBP), and achieve improved performance by combining several such representations. In this paper, we propose deep learning as a natural source for obtaining additional, complementary representations. To learn features in high-resolution images, we make use of convolutional deep belief networks. Moreover, to take advantage of global structure in an object class, we develop local convolutional restricted Boltzmann machines, a novel convolutional learning model that exploits the global structure by not assuming stationarity of features across the image, while maintaining scalability and robustness to small misalignments. We also present a novel application of deep learning to descriptors other than pixel intensity values, such as LBP. In addition, we compare performance of networks trained using unsupervised learning against networks with random filters, and empirically show that learning weights not only is necessary for obtaining good multilayer representations, but also provides robustness to the choice of the network architecture parameters. Finally, we show that a recognition system using only representations obtained from deep learning can achieve comparable accuracy with a system using a combination of hand-crafted image descriptors. Moreover, by combining these representations, we achieve state-of-the-art results on a real-world face verification database.
Lifetime study for a poly fuse in a 0.35 /spl mu/m polycide CMOS process

Poly fuses are used as the base element for one time programmable cells in a standard CMOS process. Using a defined programming current, the resistance of the poly fuse increases irreversibly over several orders of magnitude. The goal of this study is to show that a poly fuse has a sufficient life time stability to be used as a storage element even in high reliability circuits. This paper shows the drift of the resistance of a poly fuse over the whole range of programming currents for a standard polycide 0.35 /spl mu/m CMOS process. The poly fuse for the selected process is build using two different layers, which gives a special performance in terms of programming current.
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android

The increasing popularity of Android apps makes them the target of malware authors. To defend against this severe increase of Android malwares and help users make a better evaluation of apps at install time, several approaches have been proposed. However, most of these solutions suffer from some shortcomings; computationally expensive, not general or not robust enough. In this paper, we aim to mitigate Android malware installation through providing robust and lightweight classifiers. We have conducted a thorough analysis to extract relevant features to malware behavior captured at API level, and evaluated different classifiers using the generated feature set. Our results show that we are able to achieve an accuracy as high as 99% and a false positive rate as low as 2.2% using KNN classifier.
Employing U.S. Military Families to Provide Business Process Outsourcing Services: A Case study of Impact Sourcing and Reshoring

This paper describes how a startup business process outsourcing (BPO) provider named Liberty Source helped a large U.S.-based client reshore business services from an established Indian BPO provider. Founded in 2014, Liberty Source is a for-profit firm that provides a competitive alternative to offshoring while fulfilling its social mission to launch and sustain the careers of U.S. military spouses and veterans who face various employment disadvantages. Thus, the case describes reshoring in the context of impact sourcing. It addresses key impact sourcing issues pertaining to workforce development, scalability, and impact on employees. The impact was positive: the workers found the employment and stable salary were beneficial, “the military” culture fit well with the workers, and workers received considerable flexibility and greater career options. Liberty Source was able to reduce a client’s costs after reshoring the client’s processes because Liberty Source’s U.S. site had about 20 percent fewer full time equivalents (FTEs) FTEs than the original India location and because Liberty Source received subsidies. We found evidence that the offshore BPO provider and Liberty source experienced difficulties with finding enough skilled staff for the wages offered and both firms experienced attrition problems, although attrition was greater in India.
A New Approach to Linear Filtering and Prediction Problems

AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener’s basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon , they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation . These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin , Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don’t), has been pioneered by Davis  and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov . In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about how to obtain (both theoretically and practically) these nonlinear filters. Contributed by the Instruments and Regulators Division and presented at the Instruments and Regulators Conference, March 29– Apri1 2, 1959, of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. NOTE: Statements and opinions advanced in papers are to be understood as individual expressions of their authors and not those of the Society. Manuscript received at ASME Headquarters, February 24, 1959.
Speech Synthesis of Code-Mixed Text

Most Text to Speech (TTS) systems today assume that the input text is in a single language and is written in the same language that the text needs to be synthesized in. However, in bilingual and multilingual communities, code mixing or code switching occurs in speech, in which speakers switch between languages in the same utterance. Due to the popularity of social media, we now see code-mixing even in text in these multilingual communities. TTS systems capable of synthesizing such text need to be able to handle text that is written in multiple languages and scripts. Code-mixed text poses many challenges to TTS systems, such as language identification, spelling normalization and pronunciation modeling. In this work, we describe a preliminary framework for synthesizing code-mixed text. We carry out experiments on synthesizing code-mixed Hindi and English text. We find that there is a significant user preference for TTS systems that can correctly identify and pronounce words in different languages.
Reusing Scientific Data: How Earthquake Engineering Researchers Assess the Reusability of Colleagues’ Data

Investments in cyberinfrastructure and e-Science initiatives are motivated by the desire to accelerate scientific discovery. Always viewed as a foundation of science, data sharing is appropriately seen as critical to the success of such initiatives, but new technologies supporting increasingly data-intensive and collaborative science raise significant challenges and opportunities. Overcoming the technical and social challenges to broader data sharing is a common and important research objective, but increasing the supply and accessibility of scientific data is no guarantee data will be applied by scientists. Before reusing data created by others, scientists need to assess the data’s relevance, they seek confidence the data can be understood, and they must trust the data. Using interview data from earthquake engineering researchers affiliated with the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES), we examine how these scientists assess the reusability of colleagues’ experimental data for model validation.
Estimating locations and coverage areas of mobile network cells based on crowdsourced data

Mobile network operators usually keep the data about their mobile network topology very secret. However, having this data, many sophisticated and innovative applications could be realized. Today's smartphones provide the means to measure cell data with which the mobile network topology of different operators can be approximately derived by applying various position estimation algorithms. These approaches deliver different results in terms of distance to the real positions of base stations and positions calculated by Google. In this paper, we analyze and evaluate state-of-the-art approaches for estimating base station positions out of crowdsourced data and integrate the most suitable methode(s) into the OpenMobileNetwork, which is an open-source initiative for publishing mobile network data using the semantic principles of Linked Data. The results show that the grid-based approach provides the best estimates for base station positions when compared with their real locations.
Design consideration of recent advanced low-voltage CMOS boost converter for energy harvesting

With the emergence of nanoscale material-based energy harvesters such as thermoelectric generator and microbial fuel cells, energy-harvesting-assisted self-powered electronics systems are gaining popularity. The state-of-the-art low-voltage CMOS boost converter, a critical voltage converter circuit for low power energy harvesting sources will be reviewed in this paper. Fundamentals of the boost converter circuit startup problem are discussed and recent circuit solutions to solve this problem are compared and analyzed. Necessary design considerations and trade-offs regarding circuit topology, component and CMOS process are also addressed.
Word and Phrase Translation with word2vec

Word and phrase tables are key inputs to machine translations, but costly to produce. New unsupervised learning methods represent words and phrases in a high-dimensional vector space, and these monolingual embeddings have been shown to encode syntactic and semantic relationships between language elements. The information captured by these embeddings can be exploited for bilingual translation by learning a transformation matrix that allows matching relative positions across two monolingual vector spaces. This method aims to identify high-quality candidates for word and phrase translation more costeffectively from unlabeled data. This paper expands the scope of previous attempts of bilingual translation to four languages (English, German, Spanish, and French). It shows how to process the source data, train a neural network to learn the high-dimensional embeddings for individual languages and expands the framework for testing their quality beyond the English language. Furthermore, it shows how to learn bilingual transformation matrices and obtain candidates for word and phrase translation, and assess their quality.
Predicting Stock Market Trends by Recurrent Deep Neural Networks

Investors make decisions based on various factors, including consumer price index, price-earnings ratio, and also miscellaneous events reported by newspapers. In order to assist their decisions in a timely manner, many studies have been conducted to automatically analyze those information sources in the last decades. However, the majority of the efforts was made for utilizing numerical information, partly due to the difficulty to process natural language texts and to make sense of their temporal properties. This study sheds light on this problem by using deep learning, which has been attracting much attention in various areas of research including pattern mining and machine learning for its ability to automatically construct useful features from a large amount of data. Specifically, this study proposes an approach to market trend prediction based on a recurrent deep neural network to model temporal effects of past events. The validity of the proposed approach is demonstrated on the real-world data for ten Nikkei companies.