nils-herrmann/clean_scirepeval huggingface dataset:
Ethical theory, codes of ethics and IS practice

Ethical issues, with respect to computer-based information systems, are important to the individual IS practitioner. These same issues also have an important impact on the moral well-being of organizations and societies. Considerable discussion has taken place in the Information Systems (IS) literature on specific ethical issues, but there is little published work which relates these issues to mainstream ethical theory. This paper describes a range of ethical theories drawn from philosophy literature and uses these theories to critique aspects of the newly revised ACM Code of Ethics. Some in the IS field on problematic ethical issues which are not resolved by the Code are then identified and discussed. The paper draws some implications and conclusions on the value of ethical theory and codes of practice, and on further work to develop existing ethical themes and to promote new initiatives in the ethical domain.
Geotagging Named Entities in News and Online Documents

News sources generate constant streams of text with many references to real world entities; understanding the content from such sources often requires effectively detecting the geographic foci of the entities. We study the problem of associating geography to named entities in online documents. More specifically, given a named entity and a page (or a set of pages) where the entity is mentioned, the problem being studied is how the geographic focus of the name can be resolved at a location granularity (e.g. city or country), assuming that the name has a geographic focus. We further study dispersion, and show that the dispersion of a name can be estimated with a good accuracy, allowing a geo-centre to be detected at an exact dispersion level. Two key features of our approach are: (i) minimal assumption is made on the structure of the mentions hence the approach can be applied to a diverse and heterogeneous set of web pages, and (ii) the approach is unsupervised, leveraging shallow English linguistic features and the large volume of location data in public domain.
 We evaluate our methods under different task settings and with different categories of named entities. Our evaluation reveals that the geo-centre of a name can be estimated with a good accuracy based on some simple statistics of the mentions, and that the accuracy of the estimation varies with the categories of the names.
Interpreting Semantic Relations in Noun Compounds via Verb Semantics

We propose a novel method for automatically interpreting compound nouns based on a predefined set of semantic relations. First we map verb tokens in sentential contexts to a fixed set of seed verbs using WordNet::Similarity and Moby’s Thesaurus. We then match the sentences with semantic relations based on the semantics of the seed verbs and grammatical roles of the head noun and modifier. Based on the semantics of the matched sentences, we then build a classifier using TiMBL. The performance of our final system at interpreting NCs is 52.6%.
Effectiveness and Adoption of a Drawing-to-Learn Study Tool for Recall and Problem Solving: Minute Sketches with Folded Lists

Drawing by learners can be an effective way to develop memory and generate visual models for higher-order skills in biology, but students are often reluctant to adopt drawing as a study method. We designed a nonclassroom intervention that instructed introductory biology college students in a drawing method, minute sketches in folded lists (MSFL), and allowed them to self-assess their recall and problem solving, first in a simple recall task involving non-European alphabets and later using unfamiliar biology content. In two preliminary ex situ experiments, students had greater recall on the simple learning task, non-European alphabets with associated phonetic sounds, using MSFL in comparison with a preferred method, visual review (VR). In the intervention, students studying using MSFL and VR had ∼50-80% greater recall of content studied with MSFL and, in a subset of trials, better performance on problem-solving tasks on biology content. Eight months after beginning the intervention, participants had shifted self-reported use of drawing from 2% to 20% of study time. For a small subset of participants, MSFL had become a preferred study method, and 70% of participants reported continued use of MSFL. This brief, low-cost intervention resulted in enduring changes in study behavior.
Electronic Intermediaries: Trust Building and Market Differentiation

As electronic commerce becomes increasingly popular, new intermediaries are emerging and transforming marketing and distribution channels. Intermediaries in electronic marketplaces provide the IT and business infrastructure to facilitate the completion of commercial transaction over interorganizational computer networks. If electronic intermediary services are introduced to wholesale markets where qualities vary, the provision of IT alone cannot create reliable electronic marketplaces for traders who have no pre-established relationships. To build trust among market participants, electronic intermediaries should establish policies and processes that regulate responsibilities and duties of market participants and legitimate transactions. Institutional policies and processes reduce risks and help establish trust among market participants. This paper provides empirical evidence that trust building processes by electronic intermediaries can lead to concentration of electronic transactions on high quality products, thus differentiating electronic and traditional markets.
Hiding Information into OOXML Documents: New Steganographic Perspectives

The simplest container of digital information is “the file” and among the vast array of files currently available, MS-Office files are probably the most widely used. The “Microsoft Compound Document File Format” (MCDFF) has often been used to host secret information. The new format created by Microsoft, first used with MS-Office 2007, makes use of a new standard, the ”Office Open XML Formats” (OOXML). The benefits include that the new format introduces the OOXML format, which lowers the risk of information leakage, as well as the use of MS-Office files as containers for steganography. In this work the authors, starting from the classification of information hiding adapted from Bauer, analyze four new methods for embedding data into the OOXML file format. These methods can be extremely useful when using MS-Office documents for steganographic purposes. The authors, analyzing a scenario composed of about 50.000 MS-Office files, highlight how the proposed methods are really helpful in real applications. An evaluation of the limits of the proposed methods is carried out by comparing them against the tool introduced by Microsoft to sanitize MS-Office files. The methods presented can be combined in order to extend the amount of data to be hidden in a single cover file.
CrossFAB: Bridging the Gap between Personal Fabrication Research in HCI, Computer Graphics, Robotics, Art, Architecture, and Material Science

Since 2012, personal fabrication has emerged as a new topic in the HCI community with an increasing number of publications every year. While within HCI, fabrication researchers have already built a strong community; almost no collaboration with other related disciplines, such as computer graphics, robotics, art, architecture, and material science, exists. We believe that especially in the context of HCI, collaboration with other fields can provide valuable input and create new perspectives on HCI challenges. This one-day workshop aims at bridging the gap between related disciplines in the area of personal fabrication by bringing together researchers from different fields. The focus of this workshop is to form connections across disciplines that can give rise to ongoing collaborations and to inspire HCI researchers to reach out into other topic areas for future research.
Constrained Hough Transforms for Curve Detection

This paper describes techniques to perform fast and accurate curve detection using constrained Hough transforms, in which localization error can be propagated efficiently into the parameter space. We first review a formal definition of Hough transform and modify it to allow the formal treatment localization error. We then analyze current Hough transform techniques with respect to this definition. It is shown that the Hough transform can be subdivided into many small subproblems without a decrease in performance, where each subproblem is constrained to consider only those curves that pass through some subset of the edge pixels up to the localization error. This property allows us to accurately and efficiently propagate localization error into the parameter space such that curves are detected robustly without finding false positives. The use of randomization techniques yields an algorithm with a worstcase complexity of O(n), where n is the number of edge pixels in the image, if we are only required to find curves that are significant with respect to the complexity of the image. Experiments are discussed that indicate that this method is superior to previous techniques for performing curve detection and results are given showing the detection of lines and circles in real images.
Intel® IBIST, the full vision realized

Third generation Intel® IBIST (IBIST) is the first full featured edition of what was originally envisioned in 1999. The objective was to create a standard infrastructure for validating, debugging, and testing high speed IOs (Input/Output) which could be supported by a common software toolset. This vision was realized in 2009 on Intel products. The IBIST methodology has become a standard at Intel. Today, IBIST is utilized from the very beginning of the product verification including initial power-on silicon debug. It a staple throughout the back-end product validation process and is also utilized in end-customer validation and high volume testing. Intel's platform Reliability, Availability, Serviceability(collectively referred to as RAS) architecture exploits the technology on a number of fronts as well. The content of this paper includes an overview of the problems which mandated this paradigm shift away from the historical IO testing methodologies, an IBIST architectural overview, and the key application spaces addressed by this technology.
Modeling visual problem solving as analogical reasoning.

We present a computational model of visual problem solving, designed to solve problems from the Raven's Progressive Matrices intelligence test. The model builds on the claim that analogical reasoning lies at the heart of visual problem solving, and intelligence more broadly. Images are compared via structure mapping, aligning the common relational structure in 2 images to identify commonalities and differences. These commonalities or differences can themselves be reified and used as the input for future comparisons. When images fail to align, the model dynamically rerepresents them to facilitate the comparison. In our analysis, we find that the model matches adult human performance on the Standard Progressive Matrices test, and that problems which are difficult for the model are also difficult for people. Furthermore, we show that model operations involving abstraction and rerepresentation are particularly difficult for people, suggesting that these operations may be critical for performing visual problem solving, and reasoning more generally, at the highest level.
Deep learning for Amazon satellite image analysis

Machine learning can be the key to saving the world from losing football field-sized forest areas each second. As deforestation in the Amazon basin causes devastating effects both on the ecosystem and the environment, there is urgent need to better understand and manage its changing landscape. A competition was recently conducted to develop algorithms to analyze satellite images of the Amazon. Successful algorithms will be able to detect subtle features in different image scenes, giving us the crucial data needed to be able to manage deforestation and its consequences more effectively. This paper presents our entry to the competition, the results obtained, and possible improvements to the algorithm.
Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram

The radar micro-Doppler signature of a target is determined by parts of the target moving or rotating in addition to the main body motion. The relative motion of parts is characteristic for different classes of targets, e.g. the flapping motion of a bird's wings vs. the spinning of propeller blades. In the present study, the micro-Doppler signature is exploited to discriminate birds and small unmanned aerial vehicles (UAVs). Emphasis is on micro-Doppler features that can be extracted from spectrograms and cepstrograms, enabling the human eye or indeed automatic classification algorithms to make a quick distinction between man-made objects and bio-life. In addition, in case of man-made objects, it is desired to further characterize the type of mini-UAV to aid the threat assessment. Also this characterization is done on the basis of micro-Doppler features.
Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions

The author compares the multi-attribute and subjective expected utility (SEU) models popular in research on consumer behavior to the approach offered by the theory of planed behavior (TPB). Unlike the multi-attribute and SEU models, the TPB relies not on revealed preferences to infer the underlying decision process but instead on direct assessment of its theoretical constructs. According to the theory, the consumer’s behavior is a function of intention to perform the behavior in question; the intention is based on attitude, subjective norm, and perceived behavioral control with respect to the behavior; and these factors are determined, respectively, by behavioral, normative, and control beliefs. The theory allows us to predict intentions and behavior with respect to the purchase or use of a single brand or product as well as in relation to choice among different brands or products.
Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery

An adaptive Markov random field (MRF) approach is proposed for classification of hyperspectral imagery in this letter. The main feature of the proposed method is the introduction of a relative homogeneity index for each pixel and the use of this index to determine an appropriate weighting coefficient for the spatial contribution in the MRF classification. In this way, overcorrection of spatially high variation areas can be avoided. Support vector machines are implemented for improved class modeling and better estimate of spectral contribution to this approach. Experimental results of a synthetic hyperspectral data set and a real hyperspectral image demonstrate that the proposed method works better on both homogeneous regions and class boundaries with improved classification accuracy.
Practical Identity-Based Encryption Without Random Oracles

We present an Identity Based Encryption (IBE) system that is fully secure in the standard model and has several advantages over previous such systems – namely, computational efficiency, shorter public parameters, and a “tight” security reduction, albeit to a stronger assumption that depends on the number of private key generation queries made by the adversary. Our assumption is a variant of Boneh et al.’s decisional Bilinear Diffie-Hellman Exponent assumption, which has been used to construct efficient hierarchical IBE and broadcast encryption systems. The construction is remarkably simple. It also provides recipient anonymity automatically, providing a second (and more efficient) solution to the problem of achieving anonymous IBE without random oracles. Finally, our proof of CCA2 security, which has more in common with the security proof for the Cramer-Shoup encryption scheme than with security proofs for other IBE systems, may be of independent interest.
Modulation Technique for Single-Phase Transformerless Photovoltaic Inverters With Reactive Power Capability

This paper underpins the principles for generating reactive power in single-phase transformerless photovoltaic (PV) inverters. Two mainstream and widely adopted PV inverters are explored, i.e., H5 and HERIC. With conventional modulation techniques, reactive power cannot be realized in H5 and HERIC due to the absence of freewheeling path in negative power region. Based on the study, modulation techniques are proposed to provide bidirectional current path during freewheeling period. With proposed modulation technique, reactive power control is achieved in H5 and HERIC inverters, without any modification on the converter structures. The performances of the proposed modulation techniques are studied via MATLAB simulation and further validated with experimental results.
Meshfree Approximation Methods with Matlab

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The effect of music therapy compared with general recreational activities in reducing agitation in people with dementia: a randomised controlled trial.

This study aimed to compare the effects of music therapy with general recreational day activities in reducing agitation in people with dementia, residing in nursing home facilities.



In a randomised controlled design, residents with dementia (n = 94) were allocated to either music therapy or recreational activities. Both music therapy and general activities were offered twice weekly for 4 months. Changes in agitation were measured with a modified Cohen-Mansfield Agitation Inventory (CMAI) at four intervals on each intervention day. A mixed model analysis was used to evaluate the effectiveness of music therapy, compared with general activities, on CMAI scores at 4 h after the intervention, controlled for CMAI scores at 1 h before the session and session number.



Data were analysed for 77 residents (43 randomised to music therapy and 34 to general activities). In both groups, the intervention resulted in a decrease in agitated behaviours from 1 h before to 4 h after each session. This decrease was somewhat greater in the music therapy group than in the general activities group, but this difference was statistically not significant (F = 2.885, p = 0.090) and disappeared completely after adjustment for Global Deterioration Scale stage (F = 1.500; p = 0.222).



Both music therapy and recreational activities lead to a short-term decrease in agitation, but there was no additional beneficial effect of music therapy over general activities. More research is required to provide insight in the effects of music therapy in reducing agitation in demented older people.
A survey of named entity recognition and classification

The term “Named Entity” , now widely used in Natural Language Processing, was coined for the Sixth Message Understanding Conference (MUC-6) (R. Grishman & Sundheim 1996). At that time, MUC was focusing on Information Extraction (IE) tasks where structured information of company activities and defense related activities is extracted from unstructured text, such as newspaper articles. In defining the task, people noticed that it is essential to recognize information units like names, including person, organization and location names, and numeric expressions including time, date, money and percent expressions. Identifying references to these entities in text was recognized as one of the important sub-tasks of IE and was called “Named Entity Recognition and Classification (NERC)” . We present here a survey of fifteen years of research in the NERC field, from 1991 to 2006. While early systems were making use of handcrafted rule-based algorithms, modern systems most often resort to machine learning techniques. We survey these techniques as well as other critical aspects of NERC such as features and evaluation methods. It was indeed concluded in a recent conference that the choice of features is at least as important as the choice of technique for obtaining a good NERC system (E. Tjong Kim Sang & De Meulder 2003). Moreover, the way NERC systems are evaluated and compared is essential to progress in the field. To the best of our knowledge, NERC features, techniques, and evaluation methods have not been surveyed extensively yet. The first section of this survey presents some observations on published work from the point of view of activity per year, supported languages, preferred textual genre and domain, and supported entity types. It was collected from the review of a hundred English language papers sampled from the major conferences and journals. We do not claim this review to be exhaustive or representative of all the research in all languages, but we believe it gives a good feel for the breadth and depth of previous work. Section 2 covers the algorithmic techniques that were proposed for addressing the NERC task. Most techniques are borrowed from the Machine Learning (ML) field. Instead of elaborating on techniques themselves, the third section lists and classifies the proposed features, i.e., descriptions and characteristic of words for algorithmic consumption. Section 4 presents some of the evaluation paradigms that were proposed throughout the major forums. Finally, we present our conclusions.
CAD-based pose estimation for random bin-picking of multiple objects using a RGB-D camera

In this paper, we propose a CAD-based 6-DOF pose estimation design for random bin-picking of multiple different objects using a Kinect RGB-D sensor. 3D CAD models of objects are constructed via a virtual camera, which generates a point cloud database for object recognition and pose estimation. A voxel grid filter is suggested to reduce the number of 3D point cloud of objects for reducing computing time of pose estimation. A voting-scheme method was adopted for the 6-DOF pose estimation a swell as object recognition of different type objects in the bin. Furthermore, an outlier filter is designed to filter out bad matching poses and occluded ones, so that the robot arm always picks up the upper object in the bin to increase pick up success rate. A series of experiments on a Kuka 6-axis robot revels that the proposed system works satisfactorily to pick up all random objects in the bin. The average recognition rate of three different type objects is 93.9% and the pickup success rate is 89.7%.
Probabilistic models for discovering e-communities

The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA). Previous work in SNA has emphasized the social network (SN) topology measured by communication frequencies while ignoring the semantic information in SNs. In this paper, we propose two generative Bayesian models for semantic community discovery in SNs, combining probabilistic modeling with community detection in SNs. To simulate the generative models, an EnF-Gibbs sampling algorithm is proposed to address the efficiency and performance problems of traditional methods. Experimental studies on Enron email corpus show that our approach successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities.