nils-herrmann/clean_scirepeval huggingface dataset:
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation based on image-level labels aims for alleviating the data scarcity problem by training with coarse labels. State-of-the-art methods rely on image-level labels to generate proxy segmentation masks, then train the segmentation network on these masks with various constraints. These methods consider each image independently and lack the exploration of cross-image relationships. We argue the cross-image relationship is vital to weakly supervised learning. We propose an end-to-end affinity module for explicitly modeling the relationship among a group of images. By means of this, one image can benefit from the complementary information from other images, and the supervision guidance can be shared in the group. The proposed method improves over the baseline with a large margin. Our method achieves 64.1% mIOU score on Pascal VOC 2012 validation set, and 64.7% mIOU score on test set, which is a new state-of-theart by only using image-level labels, demonstrating the effectiveness of the method.
PCA-Initialized Deep Neural Networks Applied to Document Image Analysis

In this paper, we present a novel approach for initializing deep neural networks, i.e., by using Principal Component Analysis (PCA) to initialize neural layers. Usually, the initialization of the weights of a deep neural network is done in one of the three following ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or as auto-encoder, and 3) re-use of layers from another network (transfer learning). Therefore, typically, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn a PCA into an auto-encoder, by generating an encoder layer of the PCA parameters and furthermore adding a decoding layer. We analyze the initialization technique on real documents. First, we show that a PCA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis we investigate the effectiveness of PCAbased initialization and show that it outperforms state-of-the-art random weight initialization methods.
Low-resolution Convolutional Neural Networks for video face recognition

Security and safety applications such as surveillance or forensics demand face recognition in low-resolution video data. We propose a face recognition method based on a Convolutional Neural Network (CNN) with a manifold-based track comparison strategy for low-resolution video face recognition. The low-resolution domain is addressed by adjusting the network architecture to prevent bottlenecks or significant upscaling of face images. The CNN is trained with a combination of a large-scale self-collected video face dataset and large-scale public image face datasets resulting in about 1.4M training images. To handle large amounts of video data and for effective comparison, the CNN face descriptors are compared efficiently on track level by local patch means. Our setup achieves 80.3 percent accuracy on a 32×32 pixels low-resolution version of the YouTube Faces Database and outperforms local image descriptors as well as the state-of-the-art VGG-Face network [20] in this domain. The superior performance of the proposed method is confirmed on a self-collected in-the-wild surveillance dataset.
Exploiting deep residual networks for human action recognition from skeletal data

The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTURGB+D dataset.
Learning Fine-Scaled Depth Maps from Single RGB Images

Inferring depth from a single RGB image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate depth maps, but when the maps are projected into 3D, they lack local detail and are often highly distorted. We propose a fastto-train multi-scale convolutional neural network to predict depth and add two novel contributions which add accuracy and detail to the resulting depth maps. First, we define a set loss over multiple images. By regularizing the estimation between a common set of images, we prevent the network from over-fitting and can achieve better accuracy than competing methods. We further enhance our depth prediction with local gradient estimates that preserve sharp edges and detailing. Experiments on the NYU Depth v2 dataset show that our depth predictions outperform state-of-the-art and lead to 3D reconstructions that are realistic and rich with detail.
Intelligent Detection System for e-banking Phishing websites using Fuzzy Data Mining

Detecting and identifying e-banking Phishing websites is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining Techniques can be an effective tool in assessing and identifying e-banking phishing websites since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the „fuzziness‟ in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria‟s with a layer structure. A Case study was applied to illustrate and simulate the phishing process. Our experimental results showed the significance and importance of the e-banking phishing website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final e-banking phishing website rate. KeywordsPhishing, Fuzzy Logic, Data Mining, Classification, association, apriori, e-banking risk assessment
A Two-Wired Ultra-High Input Impedance Active Electrode

This paper presents a novel two-wired active electrode that achieves ultrahigh input impedance using power supply bootstrapping. The proposed circuit reduces the input capacitance of a buffer amplifier while enabling measurements using leads with only two wires, providing a low-complexity and low-cost solution for interference rejection and artifact reduction in dc-coupled dry-contact biopotential measurements. An implemented prototype shows that, even using standard operational amplifiers, an input capacitance as low as 71 fF can be obtained, maintaining a high impedance in a 0–1 kHz bandwidth, sufficient for ECG, EEG, and EMG measurements. The circuit has a simple and easily replicable implementation that requires no individual adjustment. A common mode rejection ratio (CMRR) above 103 dB at 50 Hz was achieved and the increased rejection to interference due to the potential divider effect was experimentally tested maintaining a 92-dB CMRR at 50 Hz with a 1.2-M $\Omega$ source impedance unbalance. ECG measurements were conducted to validate the active electrode against a traditional alternative, and a test with dry-contact EEG electrodes was successfully conducted. Although the proposed circuit is intended to acquire superficial electrophysiological signals using dry electrodes, it can be used for measurement from other high-impedance sources, such as micropipette electrodes.
Nodes' Evolution Diversity and Link Prediction in Social Networks

Recently, social networks have witnessed a massive surge in popularity. A key issue in social network research is network evolution analysis, which assumes that all the autonomous nodes in a social network follow uniform evolution mechanisms. However, different nodes in a social network should have different evolution mechanisms to generate different edges. This is proposed as the underlying idea to ensure the nodes’ evolution diversity in this paper. Our approach involves identifying the micro-level node evolution that generates different edges by introducing the existing link prediction methods from the perspectives of nodes. We also propose the edge generation coefficient to evaluate the extent to which an edge's generation can be explained by a link prediction method. To quantify the nodes’ evolution diversity, we define the diverse evolution distance. Furthermore, a diverse node adaption algorithm is proposed to indirectly analyze the evolution of the entire network based on the nodes’ evolution diversity. Extensive experiments on disparate real-world networks demonstrate that the introduction of the nodes’ evolution diversity is important and beneficial for analyzing the network evolution. The diverse node adaption algorithm outperforms other state-of-the-art link prediction algorithms in terms of both accuracy and universality. The greater the nodes’ evolution diversity, the more obvious its advantages.
A Model of Data Warehousing Process Maturity

Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, “single-version of truth” data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.
A randomized trial of a low-carbohydrate diet for obesity.

Despite the popularity of the low-carbohydrate, high-protein, high-fat (Atkins) diet, no randomized, controlled trials have evaluated its efficacy.



We conducted a one-year, multicenter, controlled trial involving 63 obese men and women who were randomly assigned to either a low-carbohydrate, high-protein, high-fat diet or a low-calorie, high-carbohydrate, low-fat (conventional) diet. Professional contact was minimal to replicate the approach used by most dieters.



Subjects on the low-carbohydrate diet had lost more weight than subjects on the conventional diet at 3 months (mean [+/-SD], -6.8+/-5.0 vs. -2.7+/-3.7 percent of body weight; P=0.001) and 6 months (-7.0+/-6.5 vs. -3.2+/-5.6 percent of body weight, P=0.02), but the difference at 12 months was not significant (-4.4+/-6.7 vs. -2.5+/-6.3 percent of body weight, P=0.26). After three months, no significant differences were found between the groups in total or low-density lipoprotein cholesterol concentrations. The increase in high-density lipoprotein cholesterol concentrations and the decrease in triglyceride concentrations were greater among subjects on the low-carbohydrate diet than among those on the conventional diet throughout most of the study. Both diets significantly decreased diastolic blood pressure and the insulin response to an oral glucose load.



The low-carbohydrate diet produced a greater weight loss (absolute difference, approximately 4 percent) than did the conventional diet for the first six months, but the differences were not significant at one year. The low-carbohydrate diet was associated with a greater improvement in some risk factors for coronary heart disease. Adherence was poor and attrition was high in both groups. Longer and larger studies are required to determine the long-term safety and efficacy of low-carbohydrate, high-protein, high-fat diets.
Improving recommendation lists through topic diversification

In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.
INFTY: an integrated OCR system for mathematical documents

An integrated OCR system for mathematical documents, called INFTY, is presented. INFTY consists of four procedures, i.e., layout analysis, character recognition, structure analysis of mathematical expressions, and manual error correction. In those procedures, several novel techniques are utilized for better recognition performance. Experimental results on about 500 pages of mathematical documents showed high character recognition rates on both mathematical expressions and ordinary texts, and sufficient performance on the structure analysis of the mathematical expressions.
Computational intelligence in sports: Challenges and opportunities within a new research domain

Computational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain.
Selection of Significant Features Using Monte Carlo Feature Selection

Feature selection methods identify subsets of features in large datasets. Such methods have become popular in data-intensive areas, and performing feature selection prior tomodel constructionmay reduce the computational cost and improve the model quality. Monte Carlo Feature Selection (MCFS) is a feature selection method aimed at finding features to use for classification. Here we suggest a strategy using a z-test to compute the significance of a feature using MCFS. We have used simulated data with both informative and random features, and compared the z-test with a permutation test and a test implemented into the MCFS software. The z-test had a higher agreement with the permutation test compared with the built-in test. Furthermore, it avoided a bias related to the distribution of feature values that may have affected the built-in test. In conclusion, the suggested method has the potential to improve feature selection using MCFS.
Semantic mapping for mobile robotics tasks: A survey

The evolution of contemporary mobile robotics has given thrust to a series of additional conjunct technologies. Of such is the semantic mapping, which provides an abstraction of space and a means for human-robot communication. The recent introduction and evolution of semantic mapping motivated this survey, in which an explicit analysis of the existing methods is sought. The several algorithms are categorized according to their primary characteristics, namely scalability, inference model, temporal coherence and topological map usage. The applications involving semantic maps are also outlined in the work at hand, emphasizing on human interaction, knowledge representation and planning. The existence of public available validation datasets and benchmarking, suitable for the evaluation of semantic mapping techniques is also discussed in detail. Last, an attempt to address open issues and questions is also made.
Medicinal strategies in the treatment of obesity

When prevention fails, medicinal treatment of obesity may become a necessity. Any strategic medicinal development must recognize that obesity is a chronic, stigmatized and costly disease that is increasing in prevalence. Because obesity can rarely be cured, treatment strategies are effective only as long as they are used, and combined therapy may be more effective than monotherapy. For a drug to have significant impact on body weight it must ultimately reduce energy intake, increase energy expenditure, or both. Currently approved drugs for long-term treatment of obesity include sibutramine, which inhibits food intake, and orlistat, which blocks fat digestion.
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data

The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular and genetic data, in an attempt to make a progress in medicine, health, cognitive science, engineering, education, neuro-economics, Brain-Computer Interfaces (BCI), and games. Yet, there is no unifying computational framework to deal with all these types of data in order to better understand this data and the processes that generated it. Standard machine learning techniques only partially succeeded and they were not designed in the first instance to deal with such complex data. Therefore, there is a need for a new paradigm to deal with STBD. This paper reviews some methods of spiking neural networks (SNN) and argues that SNN are suitable for the creation of a unifying computational framework for learning and understanding of various STBD, such as EEG, fMRI, genetic, DTI, MEG, and NIRS, in their integration and interaction. One of the reasons is that SNN use the same computational principle that generates STBD, namely spiking information processing. This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD. Gene information is included optionally in the form of gene regulatory networks (GRN) if this is relevant to the problem and the data. A NeuCube model learns from STBD and creates connections between clusters of neurons that manifest chains (trajectories) of neuronal activity. Once learning is applied, a NeuCube model can reproduce these trajectories, even if only part of the input STBD or the stimuli data is presented, thus acting as an associative memory. The NeuCube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships 'hidden' in the data. NeuCube will allow the integration in one model of various brain data, information and knowledge, related to a single subject (personalized modeling) or to a population of subjects. The use of NeuCube for classification of STBD is illustrated in a case study problem of EEG data. NeuCube models result in a better accuracy of STBD classification than standard machine learning techniques. They are robust to noise (so typical in brain data) and facilitate a better interpretation of the results and understanding of the STBD and the brain conditions under which data was collected. Future directions for the use of SNN for STBD are discussed.
FlowVisor : A Network Virtualization Layer

Network virtualization has long been a goal of of the network research community. With it, multiple isolated logical networks each with potentially different addressing and forwarding mechanisms can share the same physical infrastructure. Typically this is achieved by taking advantage of the flexibility of software (e.g. [20, 23]) or by duplicating components in (often specialized) hardware[19]. In this paper we present a new approach to switch virtualization in which the same hardware forwarding plane can be shared among multiple logical networks, each with distinct forwarding logic. We use this switch-level virtualization to build a research platform which allows multiple network experiments to run side-by-side with production traffic while still providing isolation and hardware forwarding speeds. We also show that this approach is compatible with commodity switching chipsets and does not require the use of programmable hardware such as FPGAs or network processors. We build and deploy this virtualization platform on our own production network and demonstrate its use in practice by running five experiments simultaneously within a campus network. Further, we quantify the overhead of our approach and evaluate the completeness of the isolation between virtual slices.
Automatic Labeling of Topics

An algorithm for the automatic labeling of topics accordingly to a hierarchy is presented. Its main ingredients are a set of similarity measures and a set of topic labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy. The hierarchy is obtained from the Google Directory service, extracted via an ad-hoc developed software procedure and expanded through the use of the OpenOffice English Thesaurus. The performance of the proposed algorithm is investigated by using a document corpus consisting of 33,801 documents and a dictionary consisting of 111,795 words. The results are encouraging, while particularly interesting and significant labeling cases emerged