nils-herrmann/clean_scirepeval huggingface dataset:
REALISTIC EVALUATION OF SEMI-SUPERVISED LEARNING ALGORITHMS

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. Approaches based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications. After creating a unified reimplementation of various widely-used SSL techniques, we test them in a suite of experiments designed to address these issues. We find that simple baselines which do not use unlabeled data can be competitive with the state-of-the-art, that SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and that performance can degrade substantially when the unlabeled dataset contains out-of-class examples. This workshop paper is outdated. Please visit the updated,
Simple Algorithms for Frequent Item Set Mining

In this paper I introduce SaM, a split and merge algorithm for frequent item set mining. Its core advantages are its extremely simple data structure and processing scheme, which not only make it quite easy to implement, but also very convenient to execute on external storage, thus rendering it a highly useful method if the transaction database to mine cannot be loaded into main memory. Furthermore, I review RElim (an algorithm I proposed in an earlier paper and improved in the meantime) and discuss different optimization options for both SaM and RElim. Finally, I present experiments comparing SaM and RElim with classical frequent item set mining algorithms (like Apriori, Eclat and FP-growth).
Beyond Instance-Level Image Retrieval: Leveraging Captions to Learn a Global Visual Representation for Semantic Retrieval

Querying with an example image is a simple and intuitive interface to retrieve information from a visual database. Most of the research in image retrieval has focused on the task of instance-level image retrieval, where the goal is to retrieve images that contain the same object instance as the query image. In this work we move beyond instance-level retrieval and consider the task of semantic image retrieval in complex scenes, where the goal is to retrieve images that share the same semantics as the query image. We show that, despite its subjective nature, the task of semantically ranking visual scenes is consistently implemented across a pool of human annotators. We also show that a similarity based on human-annotated region-level captions is highly correlated with the human ranking and constitutes a good computable surrogate. Following this observation, we learn a visual embedding of the images where the similarity in the visual space is correlated with their semantic similarity surrogate. We further extend our model to learn a joint embedding of visual and textual cues that allows one to query the database using a text modifier in addition to the query image, adapting the results to the modifier. Finally, our model can ground the ranking decisions by showing regions that contributed the most to the similarity between pairs of images, providing a visual explanation of the similarity.
Gender differences in mate selection preferences: a test of the parental investment model.

Evolutionary-related hypotheses about gender differences in mate selection preferences were derived from Triver's parental investment model, which contends that women are more likely than men to seek a mate who possesses nonphysical characteristics that maximize the survival or reproductive prospects of their offspring, and were examined in a meta-analysis of mate selection research (questionnaire studies, analyses of personal advertisements). As predicted, women accorded more weight than men to socioeconomic status, ambitiousness, character, and intelligence, and the largest gender differences were observed for cues to resource acquisition (status, ambitiousness). Also as predicted, gender differences were not found in preferences for characteristics unrelated to progeny survival (sense of humor, "personality"). Where valid comparisons could be made, the findings were generally invariant across generations, cultures, and research paradigms.
HICCUPS : Hidden Communication System for Corrupted Networks

This article presents HICCUPS (HIdden Communication system for CorrUPted networkS), a steganographic system dedicated to shared medium networks including wireless local area networks. The novelty of HICCUPS is: usage of secure telecommunications network armed with cryptographic mechanisms to provide steganographic system and proposal of new protocol with bandwidth allocation based on corrupted frames. All functional parts of the system and possibility of its implementation in existing public networks are discussed. An example of implementation framework for wireless local area networks IEEE 802.11 is also presented.
Face Aging with Contextual Generative Adversarial Nets

Face aging, which renders aging faces for an input face, has attracted extensive attention in the multimedia research. Recently, several conditional Generative Adversarial Nets (GANs) based methods have achieved great success. They can generate images fitting the real face distributions conditioned on each individual age group. However, these methods fail to capture the transition patterns, e.g., the gradual shape and texture changes between adjacent age groups. In this paper, we propose a novel Contextual Generative Adversarial Nets (C-GANs) to specifically take it into consideration. The C-GANs consists of a conditional transformation network and two discriminative networks. The conditional transformation network imitates the aging procedure with several specially designed residual blocks. The age discriminative network guides the synthesized face to fit the real conditional distribution. The transition pattern discriminative network is novel, aiming to distinguish the real transition patterns with the fake ones. It serves as an extra regularization term for the conditional transformation network, ensuring the generated image pairs to fit the corresponding real transition pattern distribution. Experimental results demonstrate the proposed framework produces appealing results by comparing with the state-of-the-art and ground truth. We also observe performance gain for cross-age face verification.
Generative Semantic Manipulation with Contrasting GAN

Generative Adversarial Networks (GANs) have recently achieved significant improvement on paired/unpaired image-to-image translation, such as photo→ sketch and artist painting style transfer. However, existing models can only be capable of transferring the low-level information (e.g. color or texture changes), but fail to edit high-level semantic meanings (e.g., geometric structure or content) of objects. On the other hand, while some researches can synthesize compelling real-world images given a class label or caption, they cannot condition on arbitrary shapes or structures, which largely limits their application scenarios and interpretive capability of model results. In this work, we focus on a more challenging semantic manipulation task, which aims to modify the semantic meaning of an object while preserving its own characteristics (e.g. viewpoints and shapes), such as cow→sheep, motor→ bicycle, cat→dog. To tackle such large semantic changes, we introduce a contrasting GAN (contrast-GAN) with a novel adversarial contrasting objective. Instead of directly making the synthesized samples close to target data as previous GANs did, our adversarial contrasting objective optimizes over the distance comparisons between samples, that is, enforcing the manipulated data be semantically closer to the real data with target category than the input data. Equipped with the new contrasting objective, a novel mask-conditional contrast-GAN architecture is proposed to enable disentangle image background with object semantic changes. Experiments on several semantic manipulation tasks on ImageNet and MSCOCO dataset show considerable performance gain by our contrast-GAN over other conditional GANs. Quantitative results further demonstrate the superiority of our model on generating manipulated results with high visual fidelity and reasonable object semantics.
ECG pattern recognition and classification using non-linear transformations and neural networks: A review

The most widely used signal in clinical practice is the ECG. ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important parameters measured from the temporal distribution of the ECG constituent waves. In this paper, we shall review some current trends on ECG pattern recognition. In particular, we shall review non-linear transformations of the ECG, the use of principal component analysis (linear and non-linear), ways to map the transformed data into n-dimensional spaces, and the use of neural networks (NN) based techniques for ECG pattern recognition and classification. The problems we shall deal with are the QRS/PVC recognition and classification, the recognition of ischemic beats and episodes, and the detection of atrial fibrillation. Finally, a generalised approach to the classification problems in n-dimensional spaces will be presented using among others NN, radial basis function networks (RBFN) and non-linear principal component analysis (NLPCA) techniques. The performance measures of the sensitivity and specificity of these algorithms will also be presented using as training and testing data sets from the MIT-BIH and the European ST-T databases.
High performance binary neural networks on the Xeon+FPGA™ platform

Convolutional neural networks (CNNs) are deployed in a wide range of image recognition, scene segmentation and object detection applications. Achieving state of the art accuracy in CNNs often results in large models and complex topologies that require significant compute resources to complete in a timely manner. Binarised neural networks (BNNs) have been proposed as an optimised variant of CNNs, which constrain the weights and activations to +1 or —1 and thus offer compact models and lower computational complexity per operation. This paper presents a high performance BNN accelerator on the Intel®Xeon+FPGA™ platform. The proposed accelerator is designed to take advantage of the Xeon+FPGA system in a way that a specialised FPGA architecture can be targeted for the most compute intensive parts of the BNN whilst other parts of the topology can be handled by the Xeon™ CPU. The implementation is evaluated by comparing the raw compute performance and energy efficiency for key layers in standard CNN topologies against an Nvidia Titan X Pascal GPU and other published FPGA BNN accelerators. The results show that our single-package integrated Arria™ 10 FPGA accelerator coupled with a high-end Xeon CPU can offer comparable performance and better energy efficiency than a high-end discrete Titan X GPU card. In addition, our solution delivers the best performance compared to previous BNN FPGA implementations.
Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum

Argumentative text has been analyzed both theoretically and computationally in terms of argumentative structure that consists of argument components (e.g., claims, premises) and their argumentative relations (e.g., support, attack). Less emphasis has been placed on analyzing the semantic types of argument components. We propose a two-tiered annotation scheme to label claims and premises and their semantic types in an online persuasive forum, Change My View, with the long-term goal of understanding what makes a message persuasive. Premises are annotated with the three types of persuasive modes: ethos, logos, pathos, while claims are labeled as interpretation, evaluation, agreement, or disagreement, the latter two designed to account for the dialogical nature of our corpus. We aim to answer three questions: 1) can humans reliably annotate the semantic types of argument components? 2) are types of premises/claims positioned in recurrent orders? and 3) are certain types of claims and/or premises more likely to appear in persuasive messages than in nonpersuasive messages?
Highway Networks

There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success. However, network training becomes more difficult with increasing depth and training of very deep networks remains an open problem.  We refer to networks with this architecture as highway networks, since they allow unimpeded information flow across several layers on information highways. The architecture is characterized by the use of gating units which learn to regulate the flow of information through a network. Highway networks with hundreds of layers can be trained directly using stochastic gradient descent and with a variety of activation functions, opening up the possibility of studying extremely deep and efficient architectures.  A full paper extending this study is available at , with additional references, experiments and analysis.
Land-use scene classification using multi-scale completed local binary patterns

In this paper, we introduce the completed local binary patterns (CLBP) operator for the first time on remote sensing land-use scene classification. To further improve the representation power of CLBP, we propose a multi-scale CLBP (MS-CLBP) descriptor to characterize the dominant texture features in multiple resolutions. Two different kinds of implementations of MS-CLBP equipped with the kernel-based extreme learning machine are investigated and compared in terms of classification accuracy and computational complexity. The proposed approach is extensively tested on the 21-class land-use dataset and the 19-class satellite scene dataset showing a consistent increase on performance when compared to the state of the arts.
General-Purpose Fuzzy Controller for DC – DC Converters

In this paper, a general-purpose fuzzy controller for dc–dc converters is investigated. Based on a qualitative description of the system to be controlled, fuzzy controllers are capable of good performances, even for those systems where linear control techniques fail, e.g., when a mathematical description is not available or is in the presence of wide parameter variations. The presented approach is general and can be applied to any dc–dc converter topologies. Controller implementation is relatively simple and can guarantee a small-signal response as fast and stable as other standard regulators and an improved large-signal response. Simulation results of Buck-Boost and Sepic converters show control potentialities.
Neuro-Fuzzy Systems: A Survey

– The techniques of artificial intelligence based in fuzzy logic and neural networks are frequently applied together. The reasons to combine these two paradigms come out of the difficulties and inherent limitations of each isolated paradigm. Generically, when they are used in a combined way, they are called Neuro-Fuzzy Systems. This term, however, is often used to assign a specific type of system that integrates both techniques. This type of system is characterised by a fuzzy system where fuzzy sets and fuzzy rules are adjusted using input output patterns. There are several different implementations of neuro-fuzzy systems, where each author defined its own model. This article summarizes a general vision of the area describing the most known hybrid neuro-fuzzy techniques, its advantages and disadvantages.
High-$Q$ Tunable Microwave Cavity Resonators and Filters Using SOI-Based RF MEMS Tuners

This paper presents the modeling, design, fabrication, and measurement of microelectromechanical systems-enabled continuously tunable evanescent-mode electromagnetic cavity resonators and filters with very high unloaded quality factors (Qu). Integrated electrostatically actuated thin diaphragms are used, for the first time, for tuning the frequency of the resonators/filters. An example tunable resonator with 2.6:1 (5.0-1.9 GHz) tuning ratio and Qu of 300-650 is presented. A continuously tunable two-pole filter from 3.04 to 4.71 GHz with 0.7% bandwidth and insertion loss of 3.55-2.38 dB is also shown as a technology demonstrator. Mechanical stability measurements show that the tunable resonators/filters exhibit very low frequency drift (less than 0.5% for 3 h) under constant bias voltage. This paper significantly expands upon previously reported tunable resonators.
Global physical activity levels: surveillance progress, pitfalls, and prospects

To implement effective non-communicable disease prevention programmes, policy makers need data for physical activity levels and trends. In this report, we describe physical activity levels worldwide with data for adults (15 years or older) from 122 countries and for adolescents (13-15-years-old) from 105 countries. Worldwide, 31·1% (95% CI 30·9-31·2) of adults are physically inactive, with proportions ranging from 17·0% (16·8-17·2) in southeast Asia to about 43% in the Americas and the eastern Mediterranean. Inactivity rises with age, is higher in women than in men, and is increased in high-income countries. The proportion of 13-15-year-olds doing fewer than 60 min of physical activity of moderate to vigorous intensity per day is 80·3% (80·1-80·5); boys are more active than are girls. Continued improvement in monitoring of physical activity would help to guide development of policies and programmes to increase activity levels and to reduce the burden of non-communicable diseases.
Distributed Power System Automation With IEC 61850, IEC 61499, and Intelligent Control

This paper presents a new approach to power system automation, based on distributed intelligence rather than traditional centralized control. The paper investigates the interplay between two international standards, IEC 61850 and IEC 61499, and proposes a way of combining of the application functions of IEC 61850-compliant devices with IEC 61499-compliant “glue logic,” using the communication services of IEC 61850-7-2. The resulting ability to customize control and automation logic will greatly enhance the flexibility and adaptability of automation systems, speeding progress toward the realization of the smart grid concept.
Distributed Smart-Home Decision-Making in a Hierarchical Interactive Smart Grid Architecture

In this paper, we develop a comprehensive real-time interactive framework for the utility and customers in a smart grid while ensuring grid-stability and quality-of-service (QoS). First, we propose a hierarchical architecture for the utility-customer interaction consisting of sub-components of customer load prediction, renewable generation integration, power-load balancing and demand response (DR). Within this hierarchical architecture, we focus on the problem of real-time scheduling in an abstract grid model consisting of one controller and multiple customer units. A scalable solution to the real-time scheduling problem is proposed by combining solutions to two sub-problems: (1) centralized sequential decision making at the controller to maximize an accumulated reward for the whole micro-grid and (2) distributed auctioning among all customers based on the optimal load profile obtained by solving the first problem to coordinate their interactions. We formulate the centralized sequential decision making at the controller as a hidden mode Markov decision process (HM-MDP). Next, a Vikrey auctioning game is designed to coordinate the actions of the individual smart-homes to actually achieve the optimal solution derived by the controller under realistic gird interaction assumptions. We show that though truthful bidding is a weakly dominant strategy for all smart-homes in the auctioning game, collusive equilibria do exist and can jeopardize the effectiveness and efficiency of the trading opportunity allocation. Analysis on the structure of the Bayesian Nash equilibrium solution set shows that the Vickrey auctioning game can be made more robust against collusion by customers (anticipating distributed smart-homes) by introducing a positive reserve price. The corresponding auctioning game is then shown to converge to the unique incentive compatible truthful bidding Bayesian Nash equilibrium, without jeopardizing the auctioneer's (microgrid controller's) profit. The paper also explicitly discusses how this two-step solution approach can be scaled to be suitable for more complicated smart grid architectures beyond the assumed abstract model.
Complete and robust no-fit polygon generation for the irregular stock cutting problem

The no-fit polygon is a construct that can be used between pairs of shapes for fast and efficient handling of geometry within irregular two-dimensional stock cutting problems. Previously, the no-fit polygon (NFP) has not been widely applied because of the perception that it is difficult to implement and because of the lack of generic approaches that can cope with all problem cases without specific case-by-case handling. This paper introduces a robust orbital method for the creation of no-fit polygons which does not suffer from the typical problem cases found in the other approaches from the literature. Furthermore, the algorithm only involves two simple geometric stages so it is easily understood and implemented. We demonstrate how the approach handles known degenerate cases such as holes, interlocking concavities and jigsaw type pieces and we give generation times for 32 irregular packing benchmark problems from the literature, including real world datasets, to allow further comparison with existing and future approaches.
Maternal filicide in Québec.

In an eight-year review (1991 to 1998) of all consecutive coroners' files in Québec, Canada, the authors identified a total of 34 cases of victims who were killed by their mothers. Most victims were less than six years of age, and there were several cases in which multiple siblings were murdered. There were 27 mothers in the sample, and 15 of those women committed suicide after the filicide. A psychiatric motive was determined for more than 85 percent of the mothers, and most of the mothers had received previous treatment for a depressive or psychotic disorder. Based on the characteristics of this sample, the authors developed a filicide classification system that is flexible and simple to use but must be standardized to become a useful tool for clinicians.
A taxonomy of business process modeling and information systems modeling techniques

Modelling has always been at the core of both organisational design and Information Systems (IS) development. Models enable decision-makers to filter out the irrelevant complexities of the real world, so that efforts can be directed towards the most important parts of the system under study. However, both business analysts and IS professionals may find it difficult to navigate through a maze of theoretical paradigms, methodological approaches, and representational formalisms that have been proposed for both Business Process Modelling (BPM) and Information Systems Modelling (ISM). This paper sets out to put an order to this chaos by proposing an evaluation framework and a novel taxonomy of BPM and ISM techniques. These findings, coupled with a detailed review of BPM/ISM techniques, can assist decision-makers in comparatively evaluating and selecting suitable modelling techniques depending on the characteristics and requirements of individual projects.