nils-herrmann/clean_scirepeval huggingface dataset:
Multilabel classification via calibrated label ranking

Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operate on an underlying (utility) scale which is not calibrated in the sense that it lacks a natural zero point. We propose a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of these approaches. In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique. The key idea of the approach is to introduce an artificial calibration label that, in each example, separates the relevant from the irrelevant labels. We show that this technique can be viewed as a combination of pairwise preference learning and the conventional relevance classification technique, where a separate classifier is trained to predict whether a label is relevant or not. Empirical results in the area of text categorization, image classification and gene analysis underscore the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.
AN IMPROVED NON-LOCAL DENOISING ALGORITHM

Recently, the NLMeans filter has been proposed by Buades et al. for the suppression of white Gaussian noise. This filter exploits the repetitive character of structures in an image, unlike conventional denoising algorithms, which typically operate in a local neighbourhood. Even though the method is quite intuitive and potentially very powerful, the PSNR and visual results are somewhat inferior to other recent state-of-the-art non-local algorithms, like KSVD and BM-3D. In this paper, we show that the NLMeans algorithm is basically the first iteration of the Jacobi optimization algorithm for robustly estimating the noise-free image. Based on this insight, we present additional improvements to the NLMeans algorithm and also an extension to noise reduction of coloured (correlated) noise. For white noise, PSNR results show that the proposed method is very competitive with the BM-3D method, while the visual quality of our method is better due to the lower presence of artifacts. For correlated noise on the other hand, we obtain a significant improvement in denoising performance compared to recent wavelet-based techniques.
Non-distributional Word Vector Representations

Data-driven representation learning for words is a technique of central importance in NLP. While indisputably useful as a source of features in downstream tasks, such vectors tend to consist of uninterpretable components whose relationship to the categories of traditional lexical semantic theories is tenuous at best. We present a method for constructing interpretable word vectors from hand-crafted linguistic resources like WordNet, FrameNet etc. These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We analyze their performance on state-of-the-art evaluation methods for distributional models of word vectors and find they are competitive to standard distributional approaches.
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring

1 Department of Decision Sciences and Information Management, K.U.Leuven Naamsestraat 69, B-3000 Leuven, Belgium {David.Martens;Johan.Huysmans; Bart.Baesens;Jan.Vanthienen}@econ.kuleuven.be 2 School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore rudys@comp.nus.edu.sg 3 University of Southampton, School of Management, Highfield Southampton, SO17 1BJ, UK Bart@soton.ac.uk
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions.

Multilayer connectionist models of memory based on the encoder model using the backpropagation learning rule are evaluated. The models are applied to standard recognition memory procedures in which items are studied sequentially and then tested for retention. Sequential learning in these models leads to 2 major problems. First, well-learned information is forgotten rapidly as new information is learned. Second, discrimination between studied items and new items either decreases or is nonmonotonic as a function of learning. To address these problems, manipulations of the network within the multilayer model and several variants of the multilayer model were examined, including a model with prelearned memory and a context model, but none solved the problems. The problems discussed provide limitations on connectionist models applied to human memory and in tasks where information to be learned is not all available during learning.
A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that PSO can achieve: a) as much as 3 times cost savings as compared to BRS, and b) good distribution of workload onto resources.
Texture segmentation for seam carving

This paper analyzes the relationships between texture characteristics of an image and seam-carving (SC) algorithm for image retargeting (resizing). SC is a smart image content aware algorithm. The algorithm, changes the aspect ratio of an image while keeping the "important" objects untouched. Based on a subjective test, we combine between an image's characteristics and a saliency map which provides best results. Furthermore, in order to determine the significance of these results, we use principal component analysis (PCA) statistical method, which identifies those relationships in the subjective test's results. Finally, we present a novel saliency map for SC algorithm. This saliency map is based on a new classification model, which uses statistical connections between neighboring pixels and significantly reduces the level of distortion along the process, providing optimal preservation of important objects within the image.
The Problem of Curcumin and Its Bioavailability: Could Its Gastrointestinal Influence Contribute to Its Overall Health-Enhancing Effects?

Curcumin, from the spice turmeric, exhibits anti-inflammatory, antioxidant, anticancer, antiviral, and neurotrophic activity and therefore holds promise as a therapeutic agent to prevent and treat several disorders. However, a major barrier to curcumin's clinical efficacy is its poor bioavailability. Efforts have therefore been dedicated to developing curcumin formulations with greater bioavailability and systemic tissue distribution. However, it is proposed in this review that curcumin's potential as a therapeutic agent may not solely rely on its bioavailability, but rather its medicinal benefits may also arise from its positive influence on gastrointestinal health and function. In this review, in vitro, animal, and human studies investigating the effects of curcumin on intestinal microbiota, intestinal permeability, gut inflammation and oxidative stress, anaphylactic response, and bacterial, parasitic, and fungal infections are summarized. It is argued that positive changes in these areas can have wide-ranging influences on both intestinal and extraintestinal diseases, and therefore presents as a possible mechanism behind curcumin's therapeutic efficacy.
Learning What and Where to Draw

Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, birds, and flowers. While existing models can synthesize images based on global constraints such as a class label or caption, they do not provide control over pose or object location. We propose a new model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes images given instructions describing what content to draw in which location. We show high-quality 128× 128 image synthesis on the Caltech-UCSD Birds dataset, conditioned on both informal text descriptions and also object location. Our system exposes control over both the bounding box around the bird and its constituent parts. By modeling the conditional distributions over part locations, our system also enables conditioning on arbitrary subsets of parts (e.g. only the beak and tail), yielding an efficient interface for picking part locations.
Convergence Analysis of Gradient Descent Algorithms with Proportional Updates

The rise of deep learning in recent years has brought with it increasingly clever optimization methods to deal with complex, non-linear loss functions [13]. These methods are often designed with convex optimization in mind, but have been shown to work well in practice even for the highly non-convex optimization associated with neural networks. However, one significant drawback of these methods when they are applied to deep learning is that the magnitude of the update step is sometimes disproportionate to the magnitude of the weights (much smaller or larger), leading to training instabilities such as vanishing and exploding gradients . An idea to combat this issue is gradient descent with proportional updates.
How conscious experience and working memory interact

Active components of classical working memory are conscious, but traditional theory does not account for this fact. Global Workspace theory suggests that consciousness is needed to recruit unconscious specialized networks that carry out detailed working memory functions. The IDA model provides a fine-grained analysis of this process, specifically of two classical working-memory tasks, verbal rehearsal and the utilization of a visual image. In the process, new light is shed on the interactions between conscious and unconscious aspects of working memory.
Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets

Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.
Group-Lasso on Splines for Spectrum Cartography

The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finite-dimensional (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that “better” explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Computer simulations and tests on real data corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced.
TWOS: A Dataset of Malicious Insider Threat Behavior Based on a Gamified Competition

In this paper we present the design and outcome of a gamified competition that was devised in order to obtain a dataset containing realistic instances of insider threats. The competition simulated user interactions in/among competing companies, where two types of behaviors (normal and malicious) were incentivized. For the case of malicious behavior, we designed sessions for two types of insider threats (masqueraders and traitors). The game involved the participation of 6 teams consisting of 4 students who competed with each other for a period of 5 days, while their activities were monitored considering several heterogeneous sources (mouse, keyboard, process and file-system monitor, network traffic, emails and login/logout). In sum, we obtained 320 hours of active participation that included 18 hours of masquerader data and at least two instances of traitor data. Additionally to malicious behaviors, the students explored various defensive and offensive strategies, such as denial of service attacks and obfuscation techniques, in an effort to get ahead in the competition. The TWOS dataset is publicly accessible for further research purposes.
On the security of public key protocols

Recently the use of public key encryption to provide secure network communication has received considerable attention. Such public key systems are usually effective against passive eavesdroppers, who merely tap the lines and try to decipher the message. It has been pointed out, however, that an improperly designed protocol could be vulnerable to an active saboteur, one who may impersonate another user or alter the message being transmitted. Several models are formulated in which the security of protocols can be discussed precisely. Algorithms and characterizations that can be used to determine protocol security in these models are given.
Education 4.0 for tall thin engineer in a data driven society

The rise of digital technology creates disruptive innovation, new opportunities in communication, it transforms industrial production and it changes the way we work, learn and live. That leads to the fourth Industrial Revolution, which changes the working world enormously. New skills, future-ready curricula, new learning and training concepts as well as the flexibility in education are necessary. To deliver that, it needs a fourth revolution in the educational system with a strong active presence and most important Artificial Intelligence (AI) driven features for better performance. Education 4.0 needs also a strong partnership between industry and academicals environment in the formation of human resources for Industry 4.0. We present in this paper our answers of education regarding the challenges of fourth revolution in Industry and the seven AI driven didactic methods identified for the University of Future.
Object-Aware Identification of Microservices

Microservices is an architectural style inspired by service-oriented computing that structures an application as a collection of cohesive and loosely coupled components, which implement business capabilities. One of today’s problems in designing microservice architectures is to decompose a system into cohesive, loosely coupled, and fine-grained microservices. Identification of microservices is usually performed intuitively, based on the experience of the system designers, however, if the functionalities of a system are highly interconnected, it is a challenging task to decompose the system into appropriate microservices. To tackle this challenge, we present a microservice identification method that decomposes a system using clustering technique. To this end, we model a system as a set of business processes and take two aspects of structural dependency and data object dependency of functionalities into account. Furthermore, we conduct a study to evaluate the effect of process characteristics on the accuracy of identification approaches.
Incorporating Structural Alignment Biases into an Attentional Neural Translation Model

Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. However their modelling formulation is overly simplistic, and omits several key inductive biases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agreement over translation directions. We show improvements over a baseline attentional model and standard phrase-based model over several language pairs, evaluating on difficult languages in a low resource setting.
Hierarchical Multi-class Segmentation of Glioma Images Using Networks with Multi-level Activation Function

For many segmentation tasks, especially for the biomedical image, the topological prior is vital information which is useful to exploit. The containment/nesting is a typical inter-class geometric relationship. In the MICCAI Brain tumor segmentation challenge, with its three hierarchically nested classes ’whole tumor’, ’tumor core’, ’active tumor’, the nested classes relationship is introduced into the 3D-residual-Unet architecture. The network comprises a context aggregation pathway and a localization pathway, which encodes increasingly abstract representation of the input as going deeper into the network, and then recombines these representations with shallower features to precisely localize the interest domain via a localization path. The nested-class-prior is combined by proposing the multi-class activation function and its corresponding loss function. The model is trained on the training dataset of Brats2018, and 20% of the dataset is regarded as the validation dataset to determine parameters. When the parameters are fixed, we retrain the model on the whole training dataset. The performance achieved on the validation leaderboard is 86%, 77% and 72% Dice scores for the whole tumor, enhancing tumor and tumor core classes without relying on ensembles or complicated post-processing steps. Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.
Toward sociable robots

This paper explores the topic of social robots—the class of robots that people anthropomorphize in order to interact with them. From the diverse and growing number of applications for such robots, a few distinct modes of interaction are beginning to emerge. We distinguish four such classes: socially evocative, social interface, socially receptive, and sociable. For the remainder of the paper, we explore a few key features of sociable robots that distinguish them from the others. We use the vocal turn-taking behavior of our robot, Kismet, as a case study to highlight these points.
Fundamental Limitations on Search Algorithms: Evolutionary Computing in Perspective

The past twenty years has seen a rapid growth of interest in stochastic search algorithms, particularly those inspired by natural processes in physics and biology. Impressive results have been demonstrated on complex practical optimisation problems and related search applications taken from a variety of fields, but the theoretical understanding of these algorithms remains weak. This results partly from the insufficient attention that has been paid to results showing certain fundamental limitations on universal search algorithms, including the so-called “No Free Lunch” Theorem. This paper extends these results and draws out some of their implications for the design of search algorithms, and for the construction of useful representations. The resulting insights focus attention on tailoring algorithms and representations to particular problem classes by exploiting domain knowledge. This highlights the fundamental importance of gaining a better theoretical grasp of the ways in which such knowledge may be systematically exploited as a major research agenda for the future.
Double Pole Four Throw switch design with CMOS inverter

To avoid the uses of multiple RF chain associated with the multiple antennas, RF switch is most essential component. Multiple antennas systems are used to replace traditional single antennas circuitry in the radio transceiver system in order to improve the transmission capability and reliability. The desired switching system must have low cost and simple structure, yet still can capture all the advantage of Multiple Inputs Multiple Output (MIMO) systems. In this paper, we have proposed a Double Pole Four Throw (DP4T) RF switch using Complementary Metal Oxide Semiconductor (CMOS) technology with inverter property. This switch is low in cost, and capable of selecting data streams to or from the two antennas for transmitting or receiving processes, respectively.