nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning

This paper presents the techniques employed in our team's submissions to the 2015 Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression Recognition in the Wild. The objective of this sub-challenge is to classify the emotions expressed by the primary human subject in static images extracted from movies. We follow a transfer learning approach for deep Convolutional Neural Network (CNN) architectures. Starting from a network pre-trained on the generic ImageNet dataset, we perform supervised fine-tuning on the network in a two-stage process, first on datasets relevant to facial expressions, followed by the contest's dataset. Experimental results show that this cascading fine-tuning approach achieves better results, compared to a single stage fine-tuning with the combined datasets. Our best submission exhibited an overall accuracy of 48.5% in the validation set and 55.6% in the test set, which compares favorably to the respective 35.96% and 39.13% of the challenge baseline.
Improved Bat Algorithm (IBA) on Continuous Optimization Problems

—Heuristic optimization techniques have became very popular techniques and have widespread usage areas. Since they do not include mathematical terms, heuristic methods have been carried out on many fields by researchers. Main purpose of these techniques is to achieve good performance on problem of interest. One of these techniques is Bat Algorithm (BA). BA is an optimization algorithm based on echolocation characteristic of bats and developed by mimics of bats' foraging behavior. In this study, exploration and exploitation mechanisms of BA are improved by three modifications. Performance of proposed and standard version of algorithm is compared on ten basic benchmark test problems. Results indicate that proposed version is better than standard version in terms of solution quality.
Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach

Existing graph layout algorithms are usually not able to optimize all the aesthetic properties desired in a graph layout. To evaluate how well the desired visual features are reflected in a graph layout, many readability metrics have been proposed in the past decades. However, the calculation of these readability metrics often requires access to the node and edge coordinates and is usually computationally inefficient, especially for dense graphs. Importantly, when the node and edge coordinates are not accessible, it becomes impossible to evaluate the graph layouts quantitatively. In this paper, we present a novel deep learning-based approach to evaluate the readability of graph layouts by directly using graph images. A convolutional neural network architecture is proposed and trained on a benchmark dataset of graph images, which is composed of synthetically-generated graphs and graphs created by sampling from real large networks. Multiple representative readability metrics (including edge crossing, node spread, and group overlap) are considered in the proposed approach. We quantitatively compare our approach to traditional methods and qualitatively evaluate our approach by showing usage scenarios and visualizing convolutional layers. This work is a first step towards using deep learning based methods to quantitatively evaluate images from the visualization field.
Detection of collimation field in digital radiography using Frobenius norm of Hessian

Collimators are used to focus the radiation beam to a certain anatomical region in order to reduce the patient dose. Redundant shielded region should be removed before diagnostic image processing, as it can impair image enhancement algorithms. Manual selection of irradiated image region is impractical and should be automated. In this paper we propose a Frobenius norm of Hessian based algorithm for automatic collimation field detection. Algorithm validation was performed through, among others, maximum absolute difference between manually annotated and automatically determined collimation region. Our algorithm was tested on a database that contains 680 images and the percentage of correctly annotated regions is 96.03 %.
Reliable detection and skew correction method of license plate for PTZ camera-based license plate recognition system

The skewness of license plates affects negatively all processing steps of license plate recognition including character segmentation and character classification. The skewness of the license plate in the captured image may appear severely under PTZ camera environments since license plate appears to have various postures in the captured image due to panning and tilting positions of PTZ cameras. Thus far, most of previous works on this subject have mainly resolved skewness problem due to rotation in plane, but not rotation in depth. In this paper, by utilizing planar projective transformation (planar homography), we propose a reliable skew correction method of license plates which handles also the skewness due to rotation in depth. For more reliable deskewing process, the license plate needs to be detected more reliably so that this paper also proposes a reliable license plate detection method. The effectiveness of the proposed skew correction method as well as the proposed license plate detection method is verified though experiments.
VOS-GAN: Adversarial Learning of Visual-Temporal Dynamics for Unsupervised Dense Prediction in Videos

Recent GAN-based video generation approaches model videos as the combination of a time-independent scene component and a time-varying motion component, thus factorizing the generation problem into generating background and foreground separately. One of the main limitations of current approaches is that both factors are learned by mapping one source latent space to videos, which complicates the generation task as a single data point must be informative of both background and foreground content. In this paper we propose a GAN framework for video generation that, instead, employs two latent spaces in order to structure the generative process in a more natural way: 1) a latent space to generate the static visual content of a scene (background), which remains the same for the whole video, and 2) a latent space where motion is encoded as a trajectory between sampled points and whose dynamics are modeled through an RNN encoder (jointly trained with the generator and the discriminator) and then mapped by the generator to visual objects’ motion. Additionally, we extend current video discrimination approaches by incorporating in the learning procedure motion estimation and, leveraging the peculiarity of the generation process, unsupervised pixel-wise dense predictions. Extensive performance evaluation showed that our approach is able to a) synthesize more realistic videos than state-of-the-art methods, b) learn effectively both local and global video dynamics, as demonstrated by the results achieved on a video action recognition task over the UCF-101 dataset, and c) accurately perform unsupervised video object segmentation on standard video benchmarks, such as DAVIS, SegTrack and F4K-
A minutiae-based matching algorithms in fingerprint recognition systems

This study presents advantages of the most importan t methods of minutiae-based matching algorithm in f ingerprint recognition systems. Minutia matching is the most p opular approach to fingerprint identification and v erification. Fingerprint matching usually consist of two procedures: minutia extraction and minutia matching. The performance m ostly depends on the accuracy of the minutia extraction procedure. Minut iae matching designate the time complexity of appli ed solution.
Equal opportunity tactic: Redesigning and applying competition games in classrooms

Competition, despite its potential drawbacks, is an easily adopted and frequently used motivator in classrooms. Individual abilities, in the years of schooling, are inevitably different, and performance in competition is heavily ability dependent, resulting that more-able students always win while less-able students always lose. Students easily perceive how well they perform through the result of competition, which is termed as perceived performance in this paper. Consistently demonstrating lower perceived performance than their peers, the less-able students feel discouraged and frustrated, hardly having the same opportunity for owning the sense of achievement as the more-able students. In this study, the authors designed a computerized mechanism, equal opportunity tactic, to lessen the difference in perceived performance between more-able and less-able students. Equal opportunity tactic is incorporated into a version of a competitive learning game called AnswerMatching, in which every student is assigned an opponent with similar ability. An experiment was also conducted to preliminarily investigate the effectiveness and effects of the tactic. Results showed that equal opportunity tactic could reduce the effect of individual ability difference on the perceived performance as well as the belief about how well students could achieve. In other words, less-able students could have similar opportunity of success and build confidence similar to more-able students in a competition.
Philosophy in the Flesh : The Embodied Mind and its Challenge to Western Thought

Part I supports these sentences with findings from cognitive science "that human reason is a form of animal reason, a reason inextricably tied to our bodies and the peculiarities of our brains" and "that our bodies, brains, and interactions with our environment provide the mostly unconscious basis for our everyday metaphysics, that is, our sense of what is real." The third sentence is a summary of their earlier work on metaphor (Lakoff and Johnson 1980), which in Part II of this book they extend to a more detailed analysis of the metaphors underlying basic philosophical issues, such as time, events and causes, mind, self, and morality. Part III applies that analysis to the metaphors tacitly assumed by philosophers ranging from the Presocratics to Noam Chomsky. Part IV presents arguments for "empirically responsible philosophy" and its potential for understanding "who we are, how we experience our world, and how we ought to live." Finally, the appendix summarizes research inspired by this philosophy that has produced computational simulations of certain aspects of embodied minds. Given such a broad scope, the authors have not been able to cover all the topics with equal success. Instead of challenging all of western philosophy, they should have concentrated on their major opponent, Noam Chomsky and his philosophy of language. Lakoff began his career in linguistics as a student and later a teacher of Chomsky's version of transformational grammar. But in the late 1960s, he joined with other former students to promote generative semantics as an alternative to Chomsky's generative syntax. The result was a series of "linguistic wars," whose history has been retold by various participants over the past twenty years. Chapter 22 of this book presents a strong case against Chomsky's "autonomous syntax" and for an approach that bases syntax on semantics and semantics on the bodily mechanisms of perception and action. The philosophical and linguistic arguments of Chapter 22 are reinforced in the appendix by a summary of the Neural Theory of Language (NTL), which is being developed by Jerome Feldman, George Lakoff, Lokendra Shastri, and their students. To date, the NTL group has undertaken three major tasks in language understanding
Seven-Level PWM Inverter Employing Series-Connected Capacitors Paralleled to a Single DC Voltage Source

This paper presents an effective circuit configuration of a multilevel inverter that can increase the number of output voltage levels with a reduced number of circuit components. The proposed seven-level pulsewidth-modulation inverter consists of a single dc voltage source with a series of capacitors, diodes, active switches for synthesizing output voltage levels, and an H-bridge cell. After theoretical analysis, we carry out computer-aided simulations and experiments to verify the validity of the proposed approach. Here, we also introduce a modified switching strategy to solve the capacitor voltage unbalancing that occurs in series-connected capacitors.
ECDH based security model for IoT using ESP8266

Now a days, Internet of Things (IoT) is the emerging trend in technology that aims at making our life simpler using sensors and smart devices. All of them are connected to the Internet. Because of this it is possible to access and control them from any place and at any time. The most important aspect in this networking is communication between the smart devices. The correctness in the behavior of the devices mostly depends on its efficiency to send the data properly. Thus security is very important in IoT implementations. In this paper Elliptic Curve Diffie-Hellman(ECDH) key exchange on NIST P-192 curve for secured communication between ESP8266 modules has been discussed and implemented using NodeMCU.
Evaluating Information Extraction

The issue of how to experimentally evaluate information extraction (IE) systems has received hardly any satisfactory solution in the literature. In this paper we propose a novel evaluation model for IE and argue that, among others, it allows (i) a correct appreciation of the degree of overlap between predicted and true segments, and (ii) a fair evaluation of the ability of a system to correctly identify segment boundaries. We describe the properties of this models, also by presenting the result of a re-evaluation of the results of the CoNLL’03 and CoNLL’02 Shared Tasks on Named Entity Extraction.
Learning to rank for question routing in community question answering

This paper focuses on the problem of Question Routing (QR) in Community Question Answering (CQA), which aims to route newly posted questions to the potential answerers who are most likely to answer them. Traditional methods to solve this problem only consider the text similarity features between the newly posted question and the user profile, while ignoring the important statistical features, including the question-specific statistical feature and the user-specific statistical features. Moreover, traditional methods are based on unsupervised learning, which is not easy to introduce the rich features into them. This paper proposes a general framework based on the learning to rank concepts for QR. Training sets consist of triples (q, asker, answerers) are first collected. Then, by introducing the intrinsic relationships between the asker and the answerers in each CQA session to capture the intrinsic labels/orders of the users about their expertise degree of the question q, two different methods, including the SVM-based and RankingSVM-based methods, are presented to learn the models with different example creation processes from the training set. Finally, the potential answerers are ranked using the trained models. Extensive experiments conducted on a real world CQA dataset from Stack Overflow show that our proposed two methods can both outperform the traditional query likelihood language model (QLLM) as well as the state-of-the-art Latent Dirichlet Allocation based model (LDA). Specifically, the RankingSVM-based method achieves statistical significant improvements over the SVM-based method and has gained the best performance.
Identification of phthalate esters in the serum of young Puerto Rican girls with premature breast development.

Premature breast development (thelarche) is the growth of mammary tissue in girls younger than 8 years of age without other manifestations of puberty. Puerto Rico has the highest known incidence of premature thelarche ever reported. In the last two decades since this serious public health anomaly has been observed, no explanation for this phenomenon has been found. Some organic pollutants, including pesticides and some plasticizers, can disrupt normal sexual development in wildlife, and many of these have been widely used in Puerto Rico. This investigation was designed to identify pollutants in the serum of Puerto Rican girls with premature thelarche. A method for blood serum analysis was optimized and validated using pesticides and phthalate esters as model compounds of endocrine-disrupting chemicals. Recovery was > 80% for all compounds. We performed final detection by gas chromatography/mass spectrometry. We analyzed 41 serum samples from thelarche patients and 35 control samples. No pesticides or their metabolite residues were detected in the serum of the study or control subjects. Significantly high levels of phthalates [dimethyl, diethyl, dibutyl, and di-(2-ethylhexyl)] and its major metabolite mono-(2-ethylhexyl) phthalate were identified in 28 (68%) samples from thelarche patients. Of the control samples analyzed, only one showed significant levels of di-isooctyl phthalate. The phthalates that we identified have been classified as endocrine disruptors. This study suggests a possible association between plasticizers with known estrogenic and antiandrogenic activity and the cause of premature breast development in a human female population.
The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines

The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing, and machine learning. This paper introduces the 5th CHiME Challenge, which considers the task of distant multimicrophone conversational ASR in real home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech and recorded by 6 Kinect microphone arrays and 4 binaural microphone pairs. The challenge features a single-array track and a multiple-array track and, for each track, distinct rankings will be produced for systems focusing on robustness with respect to distant-microphone capture vs. systems attempting to address all aspects of the task including conversational language modeling. We discuss the rationale for the challenge and provide a detailed description of the data collection procedure, the task, and the baseline systems for array synchronization, speech enhancement, and conventional and end-to-end ASR.
Leaky wave antenna integrated into gap waveguide technology

A novel leaky wave antenna, based on the gap waveguide technology, is here proposed. A groove gap-waveguide is used as feeding and it also acts as antenna at the same time. The proposed antenna provides an excellent performance while maintaining a simple design. To demonstrate the potential of this radiation mechanism, an antenna was designed to operate between 9 GHz and 11.5 GHz. The antenna has a gain around 12 dB and provides a radiation pattern that steers its radiation direction with the frequency.
Measurement of a complete HV IGBT I-V-characteristic up to the breakdown point

This paper describes how to measure the complete output characteristic of a high-voltage IGBT non-destructively up to the breakdown point and beyond. Hereby, a deep knowledge of the IGBT behaviour at high voltages and saturation currents is gained. To construct the complete characteristic, short-circuit and curve-tracer measurements are combined. The results are compared and recapitulated with semiconductor simulations of IGBT models fitted to experimental characteristics.
The gut microbiota in IBD

IBD—ulcerative colitis and Crohn's disease—is emerging as a worldwide epidemic. An association between the increased incidence of IBD and environmental factors linked to socioeconomic development has been persistently detected in different parts of the world. The lifestyle in developed countries might impair the natural patterns of microbial colonization of the human gut. The interaction of microbes with mucosal immune compartments in the gut seems to have a major role in priming and regulating immunity. In IBD, mucosal lesions are generated by an excessive or dysregulated immune response against commensal microbes in the gut. In individuals with a genetic susceptibility to IBD, abnormal microbial colonization of the gastrointestinal tract might be the origin of such dysregulation. Developments in gene-sequencing technologies, as well as increased availability of powerful bioinformatic tools, have enabled novel insights into the microbial composition of the human gut microbiota and the effect of microbial communities on human physiology and disease. Studies that used these technologies indicate that dysbiosis (that is, abnormal microbiota composition) and decreased complexity of the gut microbial ecosystem are common features in patients with Crohn's disease or ulcerative colitis. Whether such changes are a cause or a consequence of the disease remains to be elucidated.
A Multistage Training Framework for Acoustic-to-Word Model

Acoustic-to-word (A2W) prediction model based on Connectionist Temporal Classification (CTC) criterion has gained increasing interest in recent studies. Although previous studies have shown that A2W system could achieve competitive Word Error Rate (WER), there is still performance gap compared with the conventional speech recognition system when the amount of training data is not exceptionally large. In this study, we empirically investigate advanced model initializations and training strategies to achieve competitive speech recognition performance on 300 hour subset of the Switchboard task (SWB-300Hr). We first investigate the use of hierarchical CTC pretraining for improved model initialization. We also explore curriculum training strategy to gradually increase the target vocabulary size from 10k to 20k. Finally, joint CTC and Cross Entropy (CE) training techniques are studied to further improve the performance of A2W system. The combination of hierarchical-CTC model initialization, curriculum training and joint CTC-CE training translates to a relative of 12.1% reduction in WER. Our final A2W system evaluated on Hub5-2000 test sets achieves a WER of 11.4/20.8 for Switchboard and CallHome parts without using language model and complex decoder.
Twitter in disaster mode: opportunistic communication and distribution of sensor data in emergencies

Recent events (earthquakes, floods, etc.) have shown that users heavily rely on online social networks (OSN) to communicate and organize during disasters and in their aftermath. In this paper, we discuss what features could be added to OSN apps for smart phones -- for the example of Twitter -- to make them even more useful for disaster situations. In particular, we consider cases where the fixed communication infrastructure is partially or totally wiped out and propose to equip regular Twitter apps with a disaster mode. The disaster mode relies on opportunistic communication and epidemic spreading of Tweets from phone to phone. Such "disaster-ready" applications would allow to resume (although limited) communication instantaneously and help distressed people to self-organize until regular communication networks are functioning again, or, temporary emergency communication infrastructure is installed.
 We argue why we believe that Twitter with its simplicity and versatile features (e.g., retweet and hashtag) is a good platform to support a variety of different situations and present Twimight, our disaster ready Twitter application. In addition, we propose Twimight as a platform for disseminating sensor data providing information such as locations of drinkable water sources. Eventually, we propose to rely on interest matching to scale Twitter hashtag-based searches in an opportunistic environment. The combination of these features make our opportunistic Twitter the ideal emergency kit in situations of disasters. We discuss and define the main implementation and research challenges (both technical and non-technical).