nils-herrmann/clean_scirepeval huggingface dataset:
Motion-based recognition a survey

Motion perception and interpretation plays an important role in the human visual system. It helps us recognize diierent objects and their motion in a scene, infer their relative depth, their rigidity, etc. In psychology, this process has been studied extensively by Johansson using moving light displays (MLDs). MLDs consist of bright spots attached to the joints of an actor dressed in black, and moving in front of a dark background. The collection of spots carry only 2D information and no structural information, since they are not connected. A set of static spots remained meaningless to observers, while their relative movement created a vivid impression of a person walking, running, dancing, etc. The gender of a person, and even the gait of a friend can be recognized based solely on the motion of those spots. There are two theories about the interpretation of MLD type stimuli, from a psychology point of view. In the rst, people use motion information in the MLD to recover the 3D structure and subsequently use the structure for recognition (structure from motion problem). The second theory of motion analysis deals with the direct use of motion information for recognition. In motion-based recognition approach, the emphasis is not on the static structure, and motion information is not extracted one frame at a time. Instead, a sequence containing a large number of frames is used to extract motion information in its continuum. The advantage here is that a longer sequence leads to recognition of higher level movements, like walking or running. This paper provides a review of recent developments in the computer vision aspect of motion-based recognition. We will identify two main steps in motion-based recognition. The rst step is the extraction of motion information and its organization into motion models. The second step consists of the matching of some unknown input with a model. Several methods for the recognition of objects and motions will then be reported. They include methods such as cyclic motion detection and recognition, lipreading, hand gestures interpretation, motion verb recognition and temporal textures classiication. Tracking and recognition of human motion, like walking, skipping, and running, will also be discussed. Finally, we will conclude the paper with some thoughts about future directions for motion-based recognition.
Toward Long-Distance Subdiffraction Imaging Using Coherent Camera Arrays

In this work, we propose using camera arrays coupled with coherent illumination as an effective method of improving spatial resolution in long distance images by a factor often and beyond. Recent advances in ptychography have demonstrated that one can image beyond the diffraction limit of the objective lens in a microscope. We demonstrate a similar imaging system to image beyond the diffraction limit in long range imaging. We emulate a camera array with a single camera attached to an XY translation stage. We show that an appropriate phase retrieval based reconstruction algorithm can be used to effectively recover the lost high resolution details from the multiple low resolution acquired images. We analyze the effects of noise, required degree of image overlap, and the effect of increasing synthetic aperture size on the reconstructed image quality. We show that coherent camera arrays have the potential to greatly improve imaging performance. Our simulations show resolution gains of 10× and more are achievable. Furthermore, experimental results from our proof-of-concept systems show resolution gains of 4 × -7× for real scenes. All experimental data and code is made publicly available on the project webpage. Finally, we introduce and analyze in simulation a new strategy to capture macroscopic Fourier Ptychography images in a single snapshot, albeit using a camera array.
Grayscale Template-Matching Invariant to Rotation, Scale, Translation, Brightness and Contrast

In this paper, we consider the grayscale template-matching problem, invariant to rotation, scale, translation, brightness and contrast, without previous operations that discard grayscale information, like detection of edges, detection of interest points or segmentation/binarization of the images. The obvious “brute force” solution performs a series of conventional template matchings between the image to analyze and the template query shape rotated by every angle, translated to every position and scaled by every factor (within some specified range of scale factors). Clearly, this takes too long and thus is not practical. We propose a technique that substantially accelerates this searching, while obtaining the same result as the original brute force algorithm. In some experiments, our algorithm was 400 times faster than the brute force algorithm. Our algorithm consists of three cascaded filters. These filters successively exclude pixels that have no chance of matching the template from further processing.
Automatic Nepali Number Plate Recognition with Support Vector Machines

Automatic number plate recognition is the task of extracting vehicle registration plates and labeling it for its underlying identity number. It uses optical character recognition on images to read symbols present on the number plates. Generally, numberplate recognition system includes plate localization, segmentation, character extraction and labeling. This research paper describes machine learning based automated Nepali number plate recognition model. Various image processing algorithms are implemented to detect number plate and to extract individual characters from it. Recognition system then uses Support Vector Machine (SVM) based learning and prediction on calculated Histograms of Oriented Gradients (HOG) features from each character. The system is evaluated on self-created Nepali number plate dataset. Evaluation accuracy of number plate character dataset is obtained as; 6.79% of average system error rate, 87.59% of average precision, 98.66% of average recall and 92.79% of average f-score. The accuracy of the complete number plate labeling experiment is obtained as 75.0%. Accuracy of the automatic number plate recognition is greatly influenced by the segmentation accuracy of the individual characters along with the size, resolution, pose, and illumination of the given image. Nepali License Plate Recognition, Number Plate Detection, Feature Extraction, Histograms of Oriented Gradients, Optical Character Recognition, Support Vector Machines, Computer Vision, Machine Learning
Miniaturized Dual-Band Wilkinson Power Divider With Self-Compensation Structure

A miniaturized dual-band Wilkinson power divider with a parallel LC circuit at the midpoints of two coupled-line sections is proposed in this paper. General design equations for parallel inductor L and capacitor C are derived from even- and odd-mode analysis. Generally speaking, characteristic impedances between even and odd modes are different in two coupled-line sections, and their electrical lengths are also different in inhomogeneous medium. This paper proved that a parallel LC circuit compensates for the characteristic impedance differences and the electrical length differences for dual-band operation. In other words, the proposed model provides self-compensation structure, and no extra compensation circuits are needed. Moreover, the upper limit of the frequency ratio range can be adjusted by two coupling strengths, where loose coupling for the first coupled-line section and tight coupling for the second coupled-line section are preferred for a wider frequency ratio range. Finally, an experimental circuit shows good agreement with the theoretical simulation.
A Neural Attention Model for Sentence Summarization

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.
STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation

Successive point-of-interest (POI) recommendation in location-based social networks (LBSNs) becomes a significant task since it helps users to navigate a number of candidate POIs and provides the best POI recommendations based on users’ most recent check-in knowledge. However, all existing methods for successive POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences, but ignore an important fact that successive POI recommendation is a time-subtle recommendation task. In fact, even with the same previous check-in information, users would prefer different successive POIs at different time. To capture the impact of time on successive POI recommendation, in this paper, we propose a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time. In particular, the proposed STELLAR model is built upon a ranking-based pairwise tensor factorization framework with a fine-grained modeling of user-POI, POI-time, and POI-POI interactions for successive POI recommendation. Moreover, we propose a new interval-aware weight utility function to differentiate successive check-ins’ correlations, which breaks the time interval constraint in prior work. Evaluations on two real-world datasets demonstrate that the STELLAR model outperforms state-of-the-art successive POI recommendation model about 20% in Precision@5 and Recall@5.
Integration of triple-band GSM antenna switch module using SOI CMOS

An integrated triple-band GSM antenna switch module, fabricated in RF CMOS on a sapphire substrate, is presented in this paper. The low cost and compact size requirements in wireless and mobile communication systems motivate the continuing integration of the analog portions of the design. The antenna switch die incorporates a FET switch, transmit path filters, and all bias and control circuitry on the same substrate using a 0.5 /spl mu/m CMOS process. A revised version of the die is also proposed, which makes use of an additional copper interconnect layer to reduce die area.
Links between systems thinking and complex decision making

There is a widely held belief that systems thinking is an answer to the increasing complexity of the world as well as the workplace. Despite strong interest and assertions, however, the relationship between systems thinking and complex decision making has received scant attention in the literature. Using Richmond’s (1997) classification scheme as the theoretical base, this paper investigates the link between systems thinking and complex decision making using Verbal Protocol Analysis (VPA) methodology. The findings of the study indicate that while the degree of systems thinking does matter, certain types of systemic thinking would be more relevant to performance. Further, evidence shows that the subject’s approach to the problem is also a highly pertinent factor in task performance, in that better performers displayed a distinctive pattern of thought that differed from that of the poor performers. Better performing subjects attempted to gain an understanding of the system structure before they proceeded to develop strategies and take action. The findings revealed a cyclical thought pattern that was consistently followed by better performing participants. This pattern, termed the CPA cycle, consists of three distinct phases of conception, planning, and action. This research contributes to the fields of systems thinking and complex decision making by integrating knowledge and methodology from several disciplines including psychology, management and IT. Specific contributions include a novel research methodology and, in particular, operationalization of the systems thinking paradigm, as well as identification of disaggregated factors affecting complex decision making. The managerial and organizational implications of the research are compelling and invite further research in this nascent field.  However, there is little empirical evidence to support the notion that systems thinking is indeed effectual in dealing with complexity. Despite some notable research work (see, for example, Booth Sweeney and Sterman 2000; Doyle 1997; Buchner 1995; Pennington et al. 1995; Brehmer 1992; Funke 1991; Sterman 1989a, b), there is a curious gap in the literature on the relationship between systems thinking and complex decision making. According to Doyle (1997): Many claims have been made concerning the ability of systems thinking interventions to change the nature and quality of thought about complex systems, . . . [yet] important questions about the relationship between systems thinking and basic cognitive processes such as learning, memory, problem solving, decision making, and updating mental models remain unanswered. System  (www.interscience.wiley.com). DOI: 10.1002/sdr.281  21 ∗ Correspondence to: K. E. Maani, Department of Management Science and Information Systems, School of Business and Economics, The University of Auckland, Private Bag 92019 Auckland, New Zealand.  k.maani@auckland.ac.nz This paper is dedicated to the memory of Barry Richmond whose work has inspired this research Kambiz Maani is an
Word Sense Induction Disambiguation Using Hierarchical Random Graphs

Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extraction and others. Most of the work in these areas formulate their problem in a graph-based setting and apply unsupervised graph clustering to obtain a set of clusters. Recent studies suggest that graphs often exhibit a hierarchical structure that goes beyond simple flat clustering. This paper presents an unsupervised method for inferring the hierarchical grouping of the senses of a polysemous word. The inferred hierarchical structures are applied to the problem of word sense disambiguation, where we show that our method performs significantly better than traditional graph-based methods and agglomerative clustering yielding improvements over state-of-the-art WSD systems based on sense induction.
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes

We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic segmentation is built on a deep autoencoder stack trained exclusively on synthetic depth data generated from our novel 3D scene library, SynthCam3D. Importantly, our network is able to segment real world scenes without any noise modelling. We present encouraging preliminary results.
Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts' law research in HCI

This paper makes seven recommendations to HCI researchers wishing to construct Fitts’ law models for either movement time prediction, or for the comparison of conditions in an experiment. These seven recommendations support (and in some cases supplement) the methods described in the recent ISO 9241-9 standard on the evaluation of pointing devices. In addition to improving the robustness of Fitts’ law models, these recommendations (if widely employed) will improve the comparability and consistency of forthcoming publications. Arguments to support these recommendations are presented, as are concise reviews of 24 published Fitts’ law models of the mouse, and 9 studies that used the new ISO standard. r 2004
Ontologies: How can They be Built?

Ontologies are an important component in many areas, such as knowledge management and organization, electronic commerce and information retrieval and extraction. Several methodologies for ontology building have been proposed. In this article, we provide an overview of ontology building. We start by characterizing the ontology building process and its life cycle. We present the most representative methodologies for building ontologies from scratch, and the proposed techniques, guidelines and methods to help in the construction task. We analyze and compare these methodologies. We describe current research issues in ontology reuse. Finally, we discuss the current trends in ontology building and its future challenges, namely, the new issues for building ontologies for the Semantic Web.
Sexual strategies theory: an evolutionary perspective on human mating.

This article proposes a contextual-evolutionary theory of human mating strategies. Both men and women are hypothesized to have evolved distinct psychological mechanisms that underlie short-term and long-term strategies. Men and women confront different adaptive problems in short-term as opposed to long-term mating contexts. Consequently, different mate preferences become activated from their strategic repertoires. Nine key hypotheses and 22 predictions from Sexual Strategies Theory are outlined and tested empirically. Adaptive problems sensitive to context include sexual accessibility, fertility assessment, commitment seeking and avoidance, immediate and enduring resource procurement, paternity certainty, assessment of mate value, and parental investment. Discussion summarizes 6 additional sources of behavioral data, outlines adaptive problems common to both sexes, and suggests additional contexts likely to cause shifts in mating strategy.
An efficient implementation of deep convolutional neural networks on a mobile coprocessor

In this paper we present a hardware accelerated real-time implementation of deep convolutional neural networks (DCNNs). DCNNs are becoming popular because of advances in the processing capabilities of general purpose processors. However, DCNNs produce hundreds of intermediate results whose constant memory accesses result in inefficient use of general purpose processor hardware. By using an efficient routing strategy, we are able to maximize utilization of available hardware resources but also obtain high performance in real world applications. Our system, consisting of an ARM Cortex-A9 processor and a coprocessor, is capable of a peak performance of 40 G-ops/s while consuming less than 4W of power. The entire platform is in a small form factor which, combined with its high performance at low power consumption makes it feasible to use this hardware in applications like micro-UAVs, surveillance systems and autonomous robots.
Interactive memory systems in the human brain

Learning and memory in humans rely upon several memory systems, which appear to have dissociable brain substrates. A fundamental question concerns whether, and how, these memory systems interact. Here we show using functional magnetic resonance imaging (FMRI) that these memory systems may compete with each other during classification learning in humans. The medial temporal lobe and basal ganglia were differently engaged across subjects during classification learning depending upon whether the task emphasized declarative or nondeclarative memory, even when the to-be-learned material and the level of performance did not differ. Consistent with competition between memory systems suggested by animal studies and neuroimaging, activity in these regions was negatively correlated across individuals. Further examination of classification learning using event-related FMRI showed rapid modulation of activity in these regions at the beginning of learning, suggesting that subjects relied upon the medial temporal lobe early in learning. However, this dependence rapidly declined with training, as predicted by previous computational models of associative learning.
Eun-Ju Lee in Computer-Mediated Communication Virtual Gender Identity : The Linguistic Assimilation to Gendered Avatars

This research examined how individuals’ gendered avatar might alter their use of gender-based language (i.e., references to emotion, apologies, and tentative language) in text-based computer-mediated communication. Specifically, the experiment tested if men and women would linguistically assimilate a virtual gender identity intimated by randomly assigned gendered avatars (either matched or mismatched to their true gender). Results supported the notion that gender-matched avatars increase the likelihood of gender-typical language use, whereas gender-mismatched avatars promoted countertypical language, especially among women. The gender of a partner’s avatar, however, did not influence participants’ language. Results generally comport with self-categorization theory’s gender salience explanation of gender-based language use.
Understanding Probabilistic Classifiers

Probabilistic classifiers are developed by assuming generative models which are product distributions over the original attribute space (as in naive Bayes) or more involved spaces (as in general Bayesian networks). While this paradigm has been shown experimentally successful on real world applications, despite vastly simplified probabilistic assumptions, the question of why these approaches work is still open. This paper resolves this question. We show that almost all joint distributions with a given set of marginals (i.e., all distributions that could have given rise to the classifier learned) or, equivalently, almost all data sets that yield this set of marginals, are very close (in terms of distributional distance) to the product distribution on the marginals; the number of these distributions goes down exponentially with their distance from the product distribution. Consequently, as we show, for almost all joint distributions with this set of marginals, the penalty incurred in using the marginal distribution rather than the true one is small. In addition to resolving the puzzle surrounding the success of probabilistic classifiers our results contribute to understanding the tradeoffs in developing probabilistic classifiers and will help in developing better classifiers.
CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces

Micro-expressions are facial expressions which are fleeting and reveal genuine emotions that people try to conceal. These are important clues for detecting lies and dangerous behaviors and therefore have potential applications in various fields such as the clinical field and national security. However, recognition through the naked eye is very difficult. Therefore, researchers in the field of computer vision have tried to develop micro-expression detection and recognition algorithms but lack spontaneous micro-expression databases. In this study, we attempted to create a database of spontaneous micro-expressions which were elicited from neutralized faces. Based on previous psychological studies, we designed an effective procedure in lab situations to elicit spontaneous micro-expressions and analyzed the video data with care to offer valid and reliable codings. From 1500 elicited facial movements filmed under 60fps, 195 micro-expressions were selected. These samples were coded so that the first, peak and last frames were tagged. Action units (AUs) were marked to give an objective and accurate description of the facial movements. Emotions were labeled based on psychological studies and participants' self-report to enhance the validity.