nils-herrmann/clean_scirepeval huggingface dataset:
Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion

In this article, we propose to investigate a new problem consisting in turning a distributional thesaurus into dense word vectors. We propose more precisely a method for performing such task by associating graph embedding and distributed representation adaptation. We have applied and evaluated it for English nouns at a large scale about its ability to retrieve synonyms. In this context, we have also illustrated the interest of the developed method for three different tasks: the improvement of already existing word embeddings, the fusion of heterogeneous representations and the expansion of synsets.
Truth and consequences: using the bogus pipeline to examine sex differences in self-reported sexuality.

Men report more permissive sexual attitudes and behavior than do women. This experiment tested whether these differences might result from false accommodation to gender norms (distorted reporting consistent with gender stereotypes). Participants completed questionnaires under three conditions. Sex differences in self-reported sexual behavior were negligible in a bogus pipeline condition in which participants believed lying could be detected, moderate in an anonymous condition, and greatest in an exposure threat condition in which the experimenter could potentially view participants responses. This pattern was clearest for behaviors considered less acceptable for women than men (e.g., masturbation, exposure to hardcore & softcore erotica). Results suggest that some sex differences in self-reported sexual behavior reflect responses influenced by normative expectations for men and women.
Keeping the Resident in the Loop: Adapting the Smart Home to the User

Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.
Road markings extraction based on threshold segmentation

A method for road markings extraction on urban streets acquired by a camera mounted on a moving vehicle is described. This method works in three stages. First, the search for the road markings is reduced to a suitable bird's eye view of road surface by using inverse perspective mapping. Secondly, an integrated approach for image segmentation is presented that combines local adaptive threshold and canny edge detection, which output a binary image by separating desirable foreground objects from the background. Thirdly, a geometrical analysis of the contours extracted from binary image is carried out, which extract candidates for further road markings recognition. This method can be helpful for road markings detection under uneven illumination conditions.
Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition

In this paper, we introduce the Minutia Cylinder-Code (MCC): a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles. The cylinders can be created starting from a subset of the mandatory features (minutiae position and direction) defined by standards like ISO/IEC 19794-2 (2005). Thanks to the cylinder invariance, fixed-length, and bit-oriented coding, some simple but very effective metrics can be defined to compute local similarities and to consolidate them into a global score. Extensive experiments over FVC2006 databases prove the superiority of MCC with respect to three well-known techniques and demonstrate the feasibility of obtaining a very effective (and interoperable) fingerprint recognition implementation for light architectures.
A Proposal for a Part-of-Speech Tagset for the Albanian Language

Part-of-speech tagging is a basic step in Natural Language Processing that is often essential. Labeling the word forms of a text with fine-grained word-class information adds new value to it and can be a prerequisite for downstream processes like a dependency parser. Corpus linguists and lexicographers also benefit greatly from the improved search options that are available with tagged data. The Albanian language has some properties that pose difficulties for the creation of a part-of-speech tagset. In this paper, we discuss those difficulties and present a proposal for a part-of-speech tagset that can adequately represent the underlying linguistic phenomena.
Reflections on 25 Years of Ethnography in CSCW

In this article we focus attention on ethnography’s place in CSCW by reflecting on how ethnography in the context of CSCW has contributed to our understanding of the sociality and materiality of work and by exploring how the notion of the ‘field site’ as a construct in ethnography provides new ways of conceptualizing ‘work’ that extends beyond the workplace. We argue that the well known challenges of drawing design implications from ethnographic research have led to useful strategies for tightly coupling ethnography and design. We also offer some thoughts on recent controversies over what constitutes useful and proper ethnographic research in the context of CSCW. Finally, we argue that as the temporal and spatial horizons of inquiry have expanded, along with new domains of collaborative activity, ethnography continues to provide invaluable perspectives.
Beautification of Design Sketches Using Trainable Stroke Clustering and Curve Fitting

We propose a new sketch parsing and beautification method that converts digitally created design sketches into beautified line drawings. Our system uses a trainable, sequential bottom-up and top-down stroke clustering method that learns how to parse input pen strokes into groups of strokes each representing a single curve, followed by point-cloud ordering that facilitates curve fitting and smoothing. This approach enables greater conceptual freedom during visual ideation activities by allowing designers to develop their sketches using multiple, casually drawn strokes without requiring them to indicate the separation between different stroke groups. With the proposed method, raw sketches are seamlessly converted into vectorized geometric models, thus, facilitating downstream assessment and editing activities.
DFARPA: Differential fault attack resistant physical design automation

Differential Fault Analysis (DFA), aided by sophisticated mathematical analysis techniques for ciphers and precise fault injection methodologies, has become a potent threat to cryptographic implementations. In this paper, we propose, to the best of the our knowledge, the first “DFA-aware” physical design automation methodology, that effectively mitigates the threat posed by DFA. We first develop a novel floorplan heuristic, which resists the simultaneous corruption of cipher states necessary for successful fault attack, by exploiting the fact that most fault injections are localized in practice. Our technique results in the computational complexity of the fault attack to shoot up to exhaustive search levels, making them practically infeasible. In the second part of the work, we develop a routing mechanism, which tackles more precise and costly fault injection techniques, like laser and electromagnetic guns. We propose a routing technique by integrating a specially designed ring oscillator based sensor circuit around the potential fault attack targets without incurring any performance overhead. We demonstrate the effectiveness of our technique by applying it on state of the art ciphers.
A novel broadband dual-polarization antenna with high isolation

A novel broadband dual-polarization antenna composed of two orthogonal dipoles with high isolation is presented in this paper. Due to properly utilizing the strong regional mutual coupling between the two dipoles, a very good impedance matching and a sufficiently high isolation can be achieved simultaneously, the reasons for which will be explained at the conference. The measured results show that it has a 53.7% impedance bandwidth of 1.65GHz-2.86GHz with VSWR<;1.5 and isolation >32dB. As an important advantage, the input impedance of the antenna can be well controlled within certain limits by adjusting the configuration, thus the antenna can be fed directly by coaxial cables without using impedance transformers or other feeding circuits. Besides, it has a simple and compact configuration.
Machine Learning and Its Applications to Biology

The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechanization should be acknowledged when considering machine learning in broad terms. Firstly, it is intended that the classification and prediction tasks can be accomplished by a suitably programmed computing machine. That is, the product of machine learning is a classifier that can be feasibly used on available hardware. Secondly, it is intended that the creation of the classifier should itself be highly mechanized, and should not involve too much human input. This second facet is inevitably vague, but the basic objective is that the use of automatic algorithm construction methods can minimize the possibility that human biases could affect the selection and performance of the algorithm. Both the creation of the algorithm and its operation to classify objects or predict events are to be based on concrete, observable data. The history of relations between biology and the field of machine learning is long and complex. An early technique [1] for machine learning called the perceptron constituted an attempt to model actual neuronal behavior, and the field of artificial neural network (ANN) design emerged from this attempt. Early work on the analysis of translation initiation sequences  employed the perceptron to define criteria for start sites in Escherichia coli. Further artificial neural network architectures such as the adaptive resonance theory (ART)  and neocognitron [4] were inspired from the organization of the visual nervous system. In the intervening years, the flexibility of machine learning techniques has grown along with mathematical frameworks for measuring their reliability, and it is natural to hope that machine learning methods will improve the efficiency of discovery and understanding in the mounting volume and complexity of biological data. This tutorial is structured in four main components. Firstly, a brief section reviews definitions and mathematical prerequisites. Secondly, the field of supervised learning is described. Thirdly, methods of unsupervised learning are reviewed. Finally, a section reviews methods and examples as implemented in the open source data analysis and visualization language R (http://www.r-project.org).
An investigation of imitation learning algorithms for structured prediction

In the imitation learning paradigm algorithms learn from expert demonstrations in order to become able to accomplish a particular task. Daumé III et al. (2009) framed structured prediction in this paradigm and developed the search-based structured prediction algorithm (Searn) which has been applied successfully to various natural language processing tasks with state-of-the-art performance. Recently, Ross et al. (2011) proposed the dataset aggregation algorithm (DAgger) and compared it with Searn in sequential prediction tasks. In this paper, we compare these two algorithms in the context of a more complex structured prediction task, namely biomedical event extraction. We demonstrate that DAgger has more stable performance and faster learning than Searn, and that these advantages are more pronounced in the parameter-free versions of the algorithms.
Orthogonal Weight Normalization: Solution to Optimization Over Multiple Dependent Stiefel Manifolds in Deep Neural Networks

Orthogonal matrix has shown advantages in training Recurrent Neural Networks (RNNs), but such matrix is limited to be square for the hidden-to-hidden transformation in RNNs. In this paper, we generalize such square orthogonal matrix to orthogonal rectangular matrix and formulating this problem in feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent Stiefel Manifolds (OMDSM). We show that the orthogonal rectangular matrix can stabilize the distribution of network activations and regularize FNNs. We propose a novel orthogonal weight normalization method to solve OMDSM. Particularly, it constructs orthogonal transformation over proxy parameters to ensure the weight matrix is orthogonal. To guarantee stability, we minimize the distortions between proxy parameters and canonical weights over all tractable orthogonal transformations. In addition, we design orthogonal linear module (OLM) to learn orthogonal filter banks in practice, which can be used as an alternative to standard linear module. Extensive experiments demonstrate that by simply substituting OLM for standard linear module without revising any experimental protocols, our method improves the performance of the state-of-the-art networks, including Inception and residual networks on CIFAR and ImageNet datasets.
IEC 61850/61499 Control of Distributed Energy Resources: Concept, Guidelines, and Implementation

The increasing installation of distributed energy resources during the last years has lead to a fundamental change of the power system structure. As a consequence, utility operators are faced with new challenges in grid planning and operation. New and intelligent approaches - like smart grids - show promising results in increasing the hosting capacity for distributed and renewable resources. Standardized automation, control, and communication systems are important keys to realize such intelligent methods. In this paper, a standard-based control approach for distributed energy resources is introduced and implemented. It uses the IEC 61850 interoperability approach as well as the IEC 61499 reference model for distributed automation. Elementary implementation guidelines are provided to handle the hierarchical architecture of distributed control applications. In order to show the advantages of the proposed approach, a simulation example and a laboratory test are demonstrated using a prototypical open-source-based implementation.
Taylor expansion of the differential range for monostatic SAR

The polar format algorithm (PFA) for spotlight synthetic aperture radar (SAR) is based on a linear approximation for the differential range to a scatterer. We derive a second-order Taylor series approximation of the differential range. We provide a simple and concise derivation of both the far-field linear approximation of the differential range, which forms the basis of the PFA, and the corresponding approximation limits based on the second-order terms of the approximation.
Learning principles in program visualizations: A systematic literature review

Program visualizations help students understand the runtime behavior of other programs. They are educational tools to complement lectures or replace inefficient static drawings. A recent survey found 46 program visualizations developed from 1979 to 2012 reported that their effectiveness is unclear. They also evaluated learner engagement strategies implemented by visualization systems, but other learning principles were not considered. Learning principles are potential key factors in the success of program visualization as learning tools. In this paper, we identified 16 principles that may contribute to the effectiveness of a learning tool based on Vygotsky's learning theory. We hypothesize that some of these principles could be supported by incorporating visual concrete allegories and gamification. We conducted a literature review to know if these principles are supported by existing solutions. We found six new systems between 2012 and 2015. Very few systems consider a learning theory as theoretical framework. Only two out the 16 learning principles are supported by existing visualizations. All systems use unconnected visual metaphors, two use concrete visual metaphors, and one implemented a gamification principle. We expect that using concrete visual allegories and gamification in future program visualizations will significantly improve their effectiveness.
Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses

This paper presents an online detection-based two-stage multi-object tracking method in dense visual surveillances scenarios with a single camera. In the local stage, a particle filter with observer selection that could deal with partial object occlusion is used to generate a set of reliable tracklets. In the global stage, the detection responses are collected from a temporal sliding window to deal with ambiguity caused by full object occlusion to generate a set of potential tracklets. The reliable tracklets generated in the local stage and the potential tracklets generated within the temporal sliding window are associated by Hungarian algorithm on a modified pairwise tracklets association cost matrix to get the global optimal association. This method is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results prove the effectiveness of our method.
End-to-End Detection of Caller ID Spoofing Attacks

Caller ID (caller identification) is a service provided by telephone operators where the phone number and/or the name of the caller is transmitted to inform the callee who is calling. Today, most people trust the caller ID information and some banks even use Caller ID to authenticate customers. However, with the proliferation of smartphones and VoIP, it is easy to spoof caller ID information by installing a particular application on the smartphone or by using service providers that offer Caller ID spoofing. As the phone network is fragmented between countries and companies and upgrades of old hardware is costly, no mechanism is available today to let end-users easily detect Caller ID spoofing attacks. In this article, we propose a new approach of using end-to-end caller ID verification schemes that leverage features of the existing phone network infrastructure (CallerDec ). We design an SMS-based and a timing-based version of CallerDec that works with existing combinations of landlines, cellular and VoIP networks and can be deployed at the liberty of the users. We implemented both CallerDec schemes as an App for Android-based phones and validated their effectiveness in detecting spoofing attacks in various scenarios.
Automatic data-driven real-time segmentation and recognition of surgical workflow

With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection. The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision. On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases. Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.
Word Embeddings , Sense Embeddings and their Application to Word Sense Induction

This paper investigates the cutting-edge techniques for word embedding, sense embedding, and our evaluation results on large-scale datasets. Word embedding refers to a kind of methods that learn a distributed dense vector for each word in a vocabulary. Traditional word embedding methods first obtain the co-occurrence matrix then perform dimension reduction with PCA. Recent methods use neural language models that directly learn word vectors by predicting the context words of the target word. Moving one step forward, sense embedding learns a distributed vector for each sense of a word. They either define a sense as a cluster of contexts where the target word appears or define a sense based on a sense inventory. To evaluate the performance of the state-of-the-art sense embedding methods, I first compare them on the dominant word similarity datasets, then compare them on my experimental settings. In addition, I show that sense embedding is applicable to the task of word sense induction (WSI). Actually we are the first to show that sense embedding methods are competitive on WSI by building sense-embedding-based systems that demonstrate highly competitive performances on the SemEval 2010 WSI shared task. Finally, I propose several possible future research directions on word embedding and sense embedding. The University of Rochester Computer Science Department supported this work.
A prediction based approach for stock returns using autoregressive neural networks

This paper presents a prediction based neural networks approach for stock returns. An autoregressive neural network predictor is used to predict future stock returns. In this predictor, the differences between the values of the series of stock returns and a specified past value are the regression variables. Various error metrics have been used to evaluate the performance of the predictor. Experiments with real data from National stock exchange of India (NSE) were employed to examine the accuracy of this method.
Usability of CAPTCHAs or usability issues in CAPTCHA design

CAPTCHA is now almost a standard security technology, and has found widespread application in commercial websites. Usability and robustness are two fundamental issues with CAPTCHA, and they often interconnect with each other. This paper discusses usability issues that should be considered and addressed in the design of CAPTCHAs. Some of these issues are intuitive, but some others have subtle implications for robustness (or security). A simple but novel framework for examining CAPTCHA usability is also proposed.
Some hybrid models to improve Firefly algorithm performance

Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by the behavior of fireflies in nature. Though efficient, its parameters do not change during iterations, which is also true for particle swarm optimization. This paper propose a hybrid model to improve the FA algorithm by introducing learning automata to adjust firefly behavior, and using genetic algorithm to enhance global search and generate new solutions. We also propose an approach to stabilize firefly movement during iterations. Simulation results show better performance and accuracy than standard firefly algorithm.