nils-herrmann/clean_scirepeval huggingface dataset:
Low cost silver ink RFID tag antennas

We discuss silver ink as a low cost option for manufacturing RFID tags at ultra high frequencies (UHF). An analysis of two different RFID tag antennas, made from silver ink and from copper, is presented at UHF. The influence of each material on tag performance is discussed along with simulation results and measurement data which are in good agreement. It is observed that RFID tag performance depends both on material and on the shape of the antenna. For some classes of antennas, silver ink with higher conductivity performs as well as copper, which makes it an attractive low cost alternative material to copper for RFID tag antennas.
Automated categorization in the international patent classification

A new reference collection of patent documents for training and testing automated categorization systems is established and described in detail. This collection is tailored for automating the attribution of international patent classification codes to patent applications and is made publicly available for future research work. We report the results of applying a variety of machine learning algorithms to the automated categorization of English-language patent documents. This procedure involves a complex hierarchical taxonomy, within which we classify documents into 114 classes and 451 subclasses. Several measures of categorization success are described and evaluated. We investigate how best to resolve the training problems related to the attribution of multiple classification codes to each patent document.
Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks

Automating the detection of anomalous events within long video sequences is challenging due to the ambiguity of how such events are defined. We approach the problem by learning generative models that can identify anomalies in videos using limited supervision. We propose end-to-end trainable composite Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to predict the evolution of a video sequence from a small number of input frames. Regularity scores are derived from the reconstruction errors of a set of predictions with abnormal video sequences yielding lower regularity scores as they diverge further from the actual sequence over time. The models utilize a composite structure and examine the effects of 'conditioning' in learning more meaningful representations. The best model is chosen based on the reconstruction and prediction accuracies. The Conv-LSTM models are evaluated both qualitatively and quantitatively, demonstrating competitive results on anomaly detection datasets. Conv-LSTM units are shown to be an effective tool for modeling and predicting video sequences.
G-Asks: An Intelligent Automatic Question Generation System for Academic Writing Support

Many electronic feedback systems have been proposed for writing support. However, most of these systems only aim at supporting writing to communicate instead of writing to learn, as in the case of literature review writing. Trigger questions are potentially forms of support for writing to learn, but current automatic question generation approaches focus on factual question generation for reading comprehension or vocabulary assessment. This article presents a novel Automatic Question Generation (AQG) system, called G-Asks, which generates specific trigger questions as a form of support for students' learning through writing. We conducted a large-scale case study, including 24 human supervisors and 33 research students, in an Engineering Research Method course and compared questions generated by G-Asks with human generated questions. The results indicate that G-Asks can generate questions as useful as human supervisors (‘useful’ is one of five question quality measures) while significantly outperforming Human Peer and Generic Questions in most quality measures after filtering out questions with grammatical and semantic errors. Furthermore, we identified the most frequent question types, derived from the human supervisors’ questions and discussed how the human supervisors generate such questions from the source text. General Terms: Automatic Question Generation, Natural Language Processing, Academic Writing Support
Gratitude and subjective well-being in early adolescence: examining gender differences.

Gratitude was examined among 154 students to identify benefits from its experience and expression. Students completed measures of subjective well-being, social support, prosocial behavior, and physical symptoms. Positive associations were found between gratitude and positive affect, global and domain specific life satisfaction, optimism, social support, and prosocial behavior; most relations remained even after controlling for positive affect. Gratitude demonstrated a negative relation with physical symptoms, but not with negative affect. Relational fulfillment mediated the relation between gratitude and physical symptoms. Gratitude demonstrated strong relations with the following positive affects: proud, hopeful, inspired, forgiving, and excited. The relation between gratitude and family support was moderated by gender, indicating that boys, compared with girls, appear to derive more social benefits from gratitude. Strengths, limitations, and implications are discussed.
GEMINI: A Natural Language System for Spoken-Language Understanding

The demands on a natural language understanding system used for spoken language differ somewhat from the demands of text processing. For processing spoken language, there is a tension between the system being as robust as necessary, and as constrained as possible. The robust system will a t tempt to find as sensible an interpretation as possible, even in the presence of performance errors by the speaker, or recognition errors by the speech recognizer. In contrast, in order to provide language constraints to a speech recognizer, a system should be able to detect that a recognized string is not a sentence of English, and disprefer that recognition hypothesis from the speech recognizer. If the coupling is to be tight, with parsing and recognition interleaved, then the parser should be able to enforce as many constraints as possible for partial utterances. The approach taken in Gemini is to tightly constrain language recognition to limit overgeneration, but to extend the language analysis to recognize certain characteristic patterns of spoken utterances (but not generally thought of as part of grammar) and to recognize specific types of performance errors by the speaker.
Neuro-Symbolic Program Synthesis

Recent years have seen the proposal of a number of neural architectures for the problem of Program Induction. Given a set of input-output examples, these architectures are able to learn mappings that generalize to new test inputs. While achieving impressive results, these approaches have a number of important limitations: (a) they are computationally expensive and hard to train, (b) a model has to be trained for each task (program) separately, and (c) it is hard to interpret or verify the correctness of the learnt mapping (as it is defined by a neural network). In this paper, we propose a novel technique, Neuro-Symbolic Program Synthesis, to overcome the above-mentioned problems. Once trained, our approach can automatically construct computer programs in a domain-specific language that are consistent with a set of input-output examples provided at test time. Our method is based on two novel neural modules. The first module, called the cross correlation I/O network, given a set of input-output examples, produces a continuous representation of the set of I/O examples. The second module, the RecursiveReverse-Recursive Neural Network (R3NN), given the continuous representation of the examples, synthesizes a program by incrementally expanding partial programs. We demonstrate the effectiveness of our approach by applying it to the rich and complex domain of regular expression based string transformations. Experiments show that the R3NN model is not only able to construct programs from new input-output examples, but it is also able to construct new programs for tasks that it had never observed before during training.
Privacy: Theory meets Practice on the Map

In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics community. The target application for this work is a mapping program that shows the commuting patterns of the population of the United States. The source data for this application were collected by the U.S. Census Bureau, but due to privacy constraints, they cannot be used directly by the mapping program. Instead, we generate synthetic data that statistically mimic the original data while providing privacy guarantees. We use these synthetic data as a surrogate for the original data. We find that while some existing definitions of privacy are inapplicable to our target application, others are too conservative and render the synthetic data useless since they guard against privacy breaches that are very unlikely. Moreover, the data in our target application is sparse, and none of the existing solutions are tailored to anonymize sparse data. In this paper, we propose solutions to address the above issues.
Introducing the Arabic WordNet project

Arabic is the official language of hundreds of millions of people in twenty Middle East and northern African countries, and is the religious language of all Muslims of various ethnicities around the world. Surprisingly little has been done in the field of computerised language and lexical resources. It is therefore motivating to develop an Arabic (WordNet) lexical resource that discovers the richness of Arabic as described in Elkateb (2005). This paper describes our approach towards building a lexical resource in Standard Arabic. Arabic WordNet (AWN) will be based on the design and contents of the universally accepted Princeton WordNet (PWN) and will be mappable straightforwardly onto PWN 2.0 and EuroWordNet (EWN), enabling translation on the lexical level to English and dozens of other languages. Several tools specific to this task will be developed. AWN will be a linguistic resource with a deep formal semantic foundation. Besides the standard wordnet representation of senses, word meanings are defined with a machine understandable semantics in first order logic. The basis for this semantics is the Suggested Upper Merged Ontology (SUMO) and its associated domain ontologies. We will greatly extend the ontology and its set of mappings to provide formal terms and definitions equivalent to each synset.
The Web and its Journalisms: Considering the Consequences of Different Types of Newsmedia Online

The internet – specifically its graphic interface, the world wide web – has had a major impact on all levels of (information) societies throughout the world. Specifically for journalism as it is practiced online, we can now identify the effect that this has had on the profession and its culture(s). This article defines four particular types of online journalism and discusses them in terms of key characteristics of online publishing – hypertextuality, interactivity, multimediality – and considers the current and potential impacts that these online journalisms can have on the ways in which one can define journalism as it functions in elective democracies worldwide. It is argued that the application of particular online characteristics not only has consequences for the type of journalism produced on the web, but that these characteristics and online journalisms indeed connect to broader and more profound changes and redefinitions of professional journalism and its (news) culture as a whole.
Frustratingly Easy Neural Domain Adaptation

Popular techniques for domain adaptation such as the feature augmentation method of Daumé III (2009) have mostly been considered for sparse binary-valued features, but not for dense realvalued features such as those used in neural networks. In this paper, we describe simple neural extensions of these techniques. First, we propose a natural generalization of the feature augmentation method that uses K + 1 LSTMs where one model captures global patterns across all K domains and the remaining K models capture domain-specific information. Second, we propose a novel application of the framework for learning shared structures by Ando and Zhang (2005) to domain adaptation, and also provide a neural extension of their approach. In experiments on slot tagging over 17 domains, our methods give clear performance improvement over Daumé III (2009) applied on feature-rich CRFs.
Intellectual Capital Disclosure and Market Capitalization

The objectives of this paper are to determine the level of the intellectual capital (IC) disclosure among Malaysian Listed Companies and to investigate the effect of IC information on market capitalization (MCAP). A sample of 185 companies listed in Bursa Malaysia was selected consisting of five industries which are Information Technology, Consumer Product, Industrial Product, Trading/Services and Finance. The descriptive statistics, content analysis were performed to analyze the data. The result found that a high percentage, about 69 percent of the companies selected disclosed intellectual capital in their annual reports. The study also found there is positive significant effect of IC information on market capitalization.
Online shopping drivers and barriers for older adults: Age and gender differences

The use of the Internet by older adults is growing at a substantial rate. They are becoming an increasingly important potential market for electronic commerce. However, previous researchers and practitioners have focused mainly on the youth market and paid less attention to issues related to the online behaviors of older consumers. To bridge the gap, the purpose of this study is to increase a better understanding of the drivers and barriers affecting older consumers’ intention to shop online. To this end, this study is developed by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) and innovation resistance theory. By comparing younger consumers with their older counterparts, in terms of gender the findings indicate that the major factors driving older adults toward online shopping are performance expectation and social influence which is the same with younger. On the other hand, the major barriers include value, risk, and tradition which is different from younger. Consequently, it is notable that older adults show no gender differences in regards to the drivers and barriers.
Terrorism and the Internet : New Media — New Threat ?

The Internet is a powerful political instrument, wh ich is increasingly employed by terrorists to forward their goals. The fiv most prominent contemporary terrorist uses of the Net are information provision , fi ancing, networking, recruitment, and information gathering. This article describes a nd explains each of these uses and follows up with examples. The final section of the paper describes the responses of government, law enforcement, intelligence agencies, and others to the terrorism-Internet nexus. There is a particular emphasis within the te xt on the UK experience, although examples from other jurisdictions are also employed . ___________________________________________________________________ “Terrorists use the Internet just like everybody el se” Richard Clarke (2004) 1 ___________________________________________________________________
Attractiveness of facial averageness and symmetry in non-western cultures: in search of biologically based standards of beauty.

Averageness and symmetry are attractive in Western faces and are good candidates for biologically based standards of beauty. A hallmark of such standards is that they are shared across cultures. We examined whether facial averageness and symmetry are attractive in non-Western cultures. Increasing the averageness of individual faces, by warping those faces towards an averaged composite of the same race and sex, increased the attractiveness of both Chinese (experiment 1) and Japanese (experiment 2) faces, for Chinese and Japanese participants, respectively. Decreasing averageness by moving the faces away from an average shape decreased attractiveness. We also manipulated the symmetry of Japanese faces by blending each original face with its mirror image to create perfectly symmetric versions. Japanese raters preferred the perfectly symmetric versions to the original faces (experiment 2). These findings show that preferences for facial averageness and symmetry are not restricted to Western cultures, consistent with the view that they are biologically based. Interestingly, it made little difference whether averageness was manipulated by using own-race or other-race averaged composites and there was no preference for own-race averaged composites over other-race or mixed-race composites (experiment 1). We discuss the implications of these results for understanding what makes average faces attractive. We also discuss some limitations of our studies, and consider other lines of converging evidence that may help determine whether preferences for average and symmetric faces are biologically based.
SASE: Complex Event Processing over Streams (Demo)

RFID technology is gaining adoption on an increasin g scale for tracking and monitoring purposes. Wide deployments of RFID devices will soon generate an unprecedented volume of data. Emerging applications require the RFID data to be f ilt red and correlated for complex pattern detection and transf ormed to events that provide meaningful, actionable informat ion to end applications. In this work, we design and develop S ASE, a complex event processing system that performs such dat ainformation transformation over real-time streams. We design a complex event language for specifying application l gic for such transformation, devise new query processing techniq ues to efficiently implement the language, and develop a comp rehensive system that collects, cleans, and processes RFID da ta for delivery of relevant, timely information as well as stor ing necessary data for future querying. We demonstrate an initial prototype of SASE through a real-world retail management scenari o.
An Empirical Investigation of Statistical Significance in NLP

We investigate two aspects of the empirical behavior of paired significance tests for NLP systems. First, when one system appears to outperform another, how does significance level relate in practice to the magnitude of the gain, to the size of the test set, to the similarity of the systems, and so on? Is it true that for each task there is a gain which roughly implies significance? We explore these issues across a range of NLP tasks using both large collections of past systems’ outputs and variants of single systems. Next, once significance levels are computed, how well does the standard i.i.d. notion of significance hold up in practical settings where future distributions are neither independent nor identically distributed, such as across domains? We explore this question using a range of test set variations for constituency parsing.
An always-on 3.8μJ/86% CIFAR-10 mixed-signal binary CNN processor with all memory on chip in 28nm CMOS

The trend of pushing deep learning from cloud to edge due to concerns of latency, bandwidth, and privacy has created demand for low-energy deep convolutional neural networks (CNNs). The single-layer classifier in  achieves sub-nJ operation, but is limited to moderate accuracy on low-complexity tasks (90% on MNIST). Larger CNN chips provide dataflow computing for high-complexity tasks (AlexNet) at mJ energy [2], but edge deployment remains a challenge due to off-chip DRAM access energy. This paper describes a mixed-signal binary CNN processor that performs image classification of moderate complexity (86% on CIFAR-10) and employs near-memory computing to achieve a classification energy of 3.8μJ, a 40x improvement over TrueNorth . We accomplish this using (1) the BinaryNet algorithm for CNNs with weights and activations constrained to +1/−1 [4], which drastically simplifies multiplications (XNOR) and allows integrating all memory on-chip; (2) an energy-efficient switched-capacitor (SC) neuron that addresses BinaryNet's challenge of wide vector summation; (3) architectural parallelism, parameter reuse, and locality.
Factors affecting the success of Open Source Software

With the rapid rise in the use of Open Source Software (OSS) in all types of applications, it is important to know which factors can lead to OSS success. OSS projects evolve and transform over time; therefore success must be examined longitudinally over a period of time. In this research, we examine two measures of project success: project popularity and developer activity, of 283 OSS projects over a span of 3 years, in order to observe changes over time. A comprehensive research model of OSS success is developed which includes both extrinsic and intrinsic attributes. Results show that while many of the hypothesized relationships are supported, there were marked differences in some of the relationships at different points in time lending support to the notion that different factors need to be emphasized as the OSS project unfolds over time.
Enhancing Road Maps by Parsing Aerial Images Around the World

In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and localization tasks. In this paper we propose to exploit aerial images in order to enhance freely available world maps. Towards this goal, we make use of OpenStreetMap and formulate the problem as the one of inference in a Markov random field parameterized in terms of the location of the road-segment centerlines as well as their width. This parameterization enables very efficient inference and returns only topologically correct roads. In particular, we can segment all OSM roads in the whole world in a single day using a small cluster of 10 computers. Importantly, our approach generalizes very well, it can be trained using only 1.5 km2 aerial imagery and produce very accurate results in any location across the globe. We demonstrate the effectiveness of our approach outperforming the state-of-the-art in two new benchmarks that we collect. We then show how our enhanced maps are beneficial for semantic segmentation of ground images.
Exosomes Mediate Stromal Mobilization of Autocrine Wnt-PCP Signaling in Breast Cancer Cell Migration

Stroma in the tumor microenvironment plays a critical role in cancer progression, but how it promotes metastasis is poorly understood. Exosomes are small vesicles secreted by many cell types and enable a potent mode of intercellular communication. Here, we report that fibroblast-secreted exosomes promote breast cancer cell (BCC) protrusive activity and motility via Wnt-planar cell polarity (PCP) signaling. We show that exosome-stimulated BCC protrusions display mutually exclusive localization of the core PCP complexes, Fzd-Dvl and Vangl-Pk. In orthotopic mouse models of breast cancer, coinjection of BCCs with fibroblasts dramatically enhances metastasis that is dependent on PCP signaling in BCCs and the exosome component, Cd81 in fibroblasts. Moreover, we demonstrate that trafficking in BCCs promotes tethering of autocrine Wnt11 to fibroblast-derived exosomes. This work reveals an intercellular communication pathway whereby fibroblast exosomes mobilize autocrine Wnt-PCP signaling to drive BCC invasive behavior.