nils-herrmann/clean_scirepeval huggingface dataset:
OmniArt: Multi-task Deep Learning for Artistic Data Analysis

Vast amounts of artistic data is scattered on-line from both museums and art applications. Collecting, processing and studying it with respect to all accompanying attributes is an expensive process. With a motivation to speed up and improve the quality of categorical analysis in the artistic domain, in this paper we propose an efficient and accurate method for multi-task learning with a shared representation applied in the artistic domain. We continue to show how different multi-task configurations of our method behave on artistic data and outperform handcrafted feature approaches as well as convolutional neural networks. In addition to the method and analysis, we propose a challenge like nature to the new aggregated data set with almost half a million samples and structuredmeta-data to encourage further research and societal engagement.  Gjorgji Strezoski and Marcel Worring. 2017.  Multi-task Deep Learning for Artistic Data Analysis.
Refined experts: improving classification in large taxonomies

While large-scale taxonomies--especially for web pages--have been in existence for some time, approaches to automatically classify documents into these taxonomies have met with limited success compared to the more general progress made in text classification. We argue that this stems from three causes: increasing sparsity of training data at deeper nodes in the taxonomy, error propagation where a mistake made high in the hierarchy cannot be recovered, and increasingly complex decision surfaces in higher nodes in the hierarchy. While prior research has focused on the first problem, we introduce methods that target the latter two problems--first by biasing the training distribution to reduce error propagation and second by propagating up "first-guess" expert information in a bottom-up manner before making a refined top down choice. Finally, we present an empirical study demonstrating that the suggested changes lead to 10--30% improvements in F1 scores versus an accepted competitive baseline, hierarchical SVMs.
Development of the Human Infant Intestinal Microbiota

Almost immediately after a human being is born, so too is a new microbial ecosystem, one that resides in that person's gastrointestinal tract. Although it is a universal and integral part of human biology, the temporal progression of this process, the sources of the microbes that make up the ecosystem, how and why it varies from one infant to another, and how the composition of this ecosystem influences human physiology, development, and disease are still poorly understood. As a step toward systematically investigating these questions, we designed a microarray to detect and quantitate the small subunit ribosomal RNA (SSU rRNA) gene sequences of most currently recognized species and taxonomic groups of bacteria. We used this microarray, along with sequencing of cloned libraries of PCR-amplified SSU rDNA, to profile the microbial communities in an average of 26 stool samples each from 14 healthy, full-term human infants, including a pair of dizygotic twins, beginning with the first stool after birth and continuing at defined intervals throughout the first year of life. To investigate possible origins of the infant microbiota, we also profiled vaginal and milk samples from most of the mothers, and stool samples from all of the mothers, most of the fathers, and two siblings. The composition and temporal patterns of the microbial communities varied widely from baby to baby. Despite considerable temporal variation, the distinct features of each baby's microbial community were recognizable for intervals of weeks to months. The strikingly parallel temporal patterns of the twins suggested that incidental environmental exposures play a major role in determining the distinctive characteristics of the microbial community in each baby. By the end of the first year of life, the idiosyncratic microbial ecosystems in each baby, although still distinct, had converged toward a profile characteristic of the adult gastrointestinal tract.
Sequence discriminative distributed training of long short-term memory recurrent neural networks

We recently showed that Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform state-of-the-art deep neural networks (DNNs) for large scale acoustic modeling where the models were trained with the cross-entropy (CE) criterion. It has also been shown that sequence discriminative training of DNNs initially trained with the CE criterion gives significant improvements. In this paper, we investigate sequence discriminative training of LSTM RNNs in a large scale acoustic modeling task. We train the models in a distributed manner using asynchronous stochastic gradient descent optimization technique. We compare two sequence discriminative criteria – maximum mutual information and state-level minimum Bayes risk, and we investigate a number of variations of the basic training strategy to better understand issues raised by both the sequential model, and the objective function. We obtain significant gains over the CE trained LSTM RNN model using sequence discriminative training techniques.
Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine

Semantic Web Services (SWSs) represent the most recent and revolutionary technology developed for machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. In this paper, we propose a mapping algorithm that facilitates the redefinition of the conventional web services annotations (i.e., WSDL) using semantic annotations (i.e., OWL-S). This algorithm will be a part of a new discovery mechanism that relies on the semantic annotations of the web services to perform its task. The “local ontology repository” and “ontology search and standardization engine” are the backbone of this algorithm. Both of them target to define any data type in the system using a standard ontology-based concept. The originality of the proposed mapping algorithm is its applicability and consideration of the standardization problem. The proposed algorithm is implemented and its components are validated using some test collections and real examples. An experimental test of the proposed techniques is reported, showing the impact of the proposed algorithm in decreasing the time and the effort of the mapping process. Moreover, the experimental results promises that the proposed algorithm will have a positive impact on the discovery process as a whole.
Video Coding for Streaming Media Delivery on the Internet

We provide an overview of an architecture of today's Internet streaming media delivery networks and describe various problems that such systems pose with regard to video coding. We demonstrate that based on the distribution model (live or on-demand), the type of network delivery mechanism (unicast vs. multicast), and the optimization criteria associated with particular segments of the network (e.g. minimization of distortion for a given connection rate, minimization of traffic in the dedicated delivery network, etc.), it is possible to identify several models of communication that require different treatment from both source and channel coding perspectives. We explain how some of these problems can be addressed using a conventional framework of temporal motion-compensated, transform-based video compression algorithm, supported by appropriate channel-adaptation mechanisms in client and server components of a streaming media system. Most of these techniques have already been implemented in RealNetworks RealSystem 8 and its RealVideo 8 codec, which we are using to illustrate our results. We also provide a comparative study of the efficiency of our RealVideo 8 algorithm, and report improvements on the order of 0.5-2.0 dB relative to ITU-T H.263+ algorithm, and around 0.5-1.0 dB compared to ISO MPEG-4 codec
Efficient deep models for monocular road segmentation

This paper addresses the problem of road scene segmentation in conventional RGB images by exploiting recent advances in semantic segmentation via convolutional neural networks (CNNs). Segmentation networks are very large and do not currently run at interactive frame rates. To make this technique applicable to robotics we propose several architecture refinements that provide the best trade-off between segmentation quality and runtime. This is achieved by a new mapping between classes and filters at the expansion side of the network. The network is trained end-to-end and yields precise road/lane predictions at the original input resolution in roughly 50ms. Compared to the state of the art, the network achieves top accuracies on the KITTI dataset for road and lane segmentation while providing a 20× speed-up. We demonstrate that the improved efficiency is not due to the road segmentation task. Also on segmentation datasets with larger scene complexity, the accuracy does not suffer from the large speed-up.
Modified cuckoo search algorithm with self adaptive parameter method

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been applied to solve a wide range of real-world optimization problem. In this paper, the proposed method uses two new mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, the new rules are combined through a linear decreasing probability rule. Then, self adaptive parameter setting is introduced as a uniform random value to enhance the diversity of the population based on the relative success number of the proposed two new parameters in the previous period. To verify the performance of SACS, 16 benchmark functions chosen from literature are employed. Experimental results indicate that the proposed method performs better than, or at least comparable to state-of-the-art methods from literature when considering the quality of the solutions obtained. In the last part, experiments have been conducted on Lorenz system and Chen system to estimate the parameters of these two chaotic systems. Simulation results further demonstrate the proposed method is very effective.
Semi-Supervised Spam Detection in Twitter Stream

Most existing techniques for spam detection on Twitter aim to identify and block users who post spam tweets. In this paper, we propose a semi-supervised spam detection (S3D) framework for spam detection at tweet-level. The proposed framework consists of two main modules: spam detection module operating in real-time mode and model update module operating in batch mode. The spam detection module consists of four lightweight detectors: 1) blacklisted domain detector to label tweets containing blacklisted URLs; 2) near-duplicate detector to label tweets that are near-duplicates of confidently prelabeled tweets; 3) reliable ham detector to label tweets that are posted by trusted users and that do not contain spammy words; and 4) multiclassifier-based detector labels the remaining tweets. The information required by the detection module is updated in batch mode based on the tweets that are labeled in the previous time window. Experiments on a large-scale data set show that the framework adaptively learns patterns of new spam activities and maintain good accuracy for spam detection in a tweet stream.
Learning Longer Memory in Recurrent Neural Networks

Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using simple optimizers, such as stochastic gradient descent, due to the so-called vanishing gradient problem. In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfectly possible using gradient descent. This is achieved by using a slight structural modification of the simple recurrent neural network architecture. We encourage some of the hidden units to change their state slowly by making part of the recurrent weight matrix close to identity, thus forming a kind of longer term memory. We evaluate our model on language modeling tasks on benchmark datasets, where we obtain similar performance to the much more complex Long Short Term Memory (LSTM) networks (Hochreiter & Schmidhuber, 1997).
Scalable training of L1-regularized log-linear models

The L-BFGS limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L2 regularization, but it cannot be used for an L1-regularized loss due to its non-differentiability whenever some parameter is zero. Efficient algorithms have been proposed for this task, but they are impractical when the number of parameters is very large. We present an algorithm Orthant-Wise Limited-memory Quasi-Newton (OWL-QN), based on L-BFGS, that can efficiently optimize the L1-regularized log-likelihood of log-linear models with millions of parameters. In our experiments on a parse reranking task, our algorithm was several orders of magnitude faster than an alternative algorithm, and substantially faster than L-BFGS on the analogous L2-regularized problem. We also present a proof that OWL-QN is guaranteed to converge to a globally optimal parameter vector.
98.1%-Efficiency Hysteretic-Current-Mode Noninverting Buck–Boost DC-DC Converter With Smooth Mode Transition

A noninverting buck-boost dc-dc converter can work in buck, boost, or buck-boost mode. Hence, it provides a good solution when the input voltage may be higher or lower than the output voltage. However, a buck-boost converter requires four power transistors, rather than two. Therefore, its efficiency decreases, due to the conduction and switching losses of the two extra power transistors. Another issue of a buck-boost converter is how to smoothly switch its operational mode, when its input voltage approaches its output voltage. A hysteretic-current-mode noninverting buck-boost converter with high efficiency and smooth mode transition is proposed, and it was designed and fabricated using TSMC 0.35-μm CMOS 2P4 M 3.3 V/5V mixed-signal polycide process. The input voltage may range from 2.5 to 5 V, the output voltage is 3.3 V, and the maximal load current is 400 mA. According to the measured results, the maximal efficiency reaches 98.1%, and the efficiencies measured in the entire input voltage and loading ranges are all above 80%.
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research

The results of a multi-year research program to identify the factors associated with variations in subjective workload within and between different types of tasks are reviewed. Subjective evaluations of 10 workload-related factors were obtained from 16 different experiments. The experimental tasks included simple cognitive and manual control tasks, complex laboratory and supervisory control tasks, and aircraft simulation. Task-, behavior-, and subject-related correlates of subjective workload experiences varied as a function of difficulty manipulations within experiments, different sources of workload between experiments, and individual differences in workload definition. A multi-dimensional rating scale is proposed in which information about the magnitude and sources of six workload-related factors are combined to derive a sensitive and reliable estimate of workload. .
A Realtime Pattern Generator for Biped Walking

For realtime walking control of a biped robot, we analyze the dynamics of a three-dimensional inverted pendulum whose motions are constrained onto an arbitrarily defined plane. This analysis leads us a simple linear dynamics, the Three-Dimensional Linear Inverted Pendulum Mode (3D-LIPM). Geometric nature of trajectories under the 3D-LIPM is discussed, and an algorithm for walking pattern generation is presented. Experimental results of realtime walking control of a 12 d.o.f. biped robot HRP-2L using an input device such as a game pad are also shown.
Mechanical designs of active upper-limb exoskeleton robots: State-of-the-art and design difficulties

Active upper-limb exoskeleton robots have been developing from 1960s. In recent years, the mechanical designs and control algorithms of active upper-limb exoskeleton robots were developed significantly. This paper reviews the state-of-the-art of active upper-limb exoskeleton robots that are applied in the areas of rehabilitation and assistive robotics. In addition, the main requirements of the active upper-limb exoskeleton robot are identified and the mechanical designs of existing active upper-limb exoskeleton robot are classified. The design difficulties of an active upper-limb exoskeleton robot are discussed.
Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy.
Ontology learning from text: A look back and into the future

Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.
A High-Isolation, Wideband and Dual-Linear Polarization Patch Antenna

The design of a dual-polarization stacked patch antenna to be used in GSM-UMTS base station arrays is presented. The patch shows a high matching level in a broadband and isolation between elements that make it a suitable radiating element for base station arrays. Moreover, the most relevant achievement of this element is the isolation between the two polarization ports of the same element in the antenna operating bandwidth. A prototype has been manufactured and measured. The measurements, that match the design objectives, are also presented.
Air+touch: interweaving touch & in-air gestures

We present Air+Touch, a new class of interactions that interweave touch events with in-air gestures, offering a unified input modality with expressiveness greater than each input modality alone. We demonstrate how air and touch are highly complementary: touch is used to designate targets and segment in-air gestures, while in-air gestures add expressivity to touch events. For example, a user can draw a circle in the air and tap to trigger a context menu, do a finger 'high jump' between two touches to select a region of text, or drag and in-air 'pigtail' to copy text to the clipboard. Through an observational study, we devised a basic taxonomy of Air+Touch interactions, based on whether the in-air component occurs before, between or after touches. To illustrate the potential of our approach, we built four applications that showcase seven exemplar Air+Touch interactions we created.
The Toyota Way in Services : The Case of Lean Product Development

Executive Overview Toyota’s Production System (TPS) is based on “lean” principles including a focus on the customer, continual improvement and quality through waste reduction, and tightly integrated upstream and downstream processes as part of a lean value chain. Most manufacturing companies have adopted some type of “lean initiative,” and the lean movement recently has gone beyond the shop floor to white-collar offices and is even spreading to service industries. Unfortunately, most of these efforts represent limited, piecemeal approaches—quick fixes to reduce lead time and costs and to increase quality—that almost never create a true learning culture. We outline and illustrate the management principles of TPS that can be applied beyond manufacturing to any technical or service process. It is a true systems approach that effectively integrates people, processes, and technology—one that must be adopted as a continual, comprehensive, and coordinated effort for change and learning across the organization.
Efficient Far-Field Radio Frequency Energy Harvesting for Passively Powered Sensor Networks

An RF-DC power conversion system is designed to efficiently convert far-field RF energy to DC voltages at very low received power and voltages. Passive rectifier circuits are designed in a 0.25 mum CMOS technology using floating gate transistors as rectifying diodes. The 36-stage rectifier can rectify input voltages as low as 50 mV with a voltage gain of 6.4 and operates with received power as low as 5.5 muW(22.6 dBm). Optimized for far field, the circuit operates at a distance of 44 m from a 4 W EIRP source. The high voltage range achieved at low load current make it ideal for use in passively powered sensor networks.
Measuring Similarity between Ontologies

Ontologies now play an important role for many knowledge-intensive applications for which they provide a source of precisely defined terms. However, with their wide-spread usage there come problems concerning their proliferation. Ontology engineers or users frequently have a core ontology that they use, e.g., for browsing or querying data, but they need to extend it with, adapt it to, or compare it with the large set of other ontologies. For the task of detecting and retrieving relevant ontologies, one needs means for measuring the similarity between ontologies. We present a set of ontology similarity measures and a multiple-phase empirical evaluation.
The Application of the Cascaded Multilevel Converters in Grid Connected Photovoltaic Systems

Unique features of multi-level converters have recently nominated them as significant alternatives for solid-state power converting units, even in the low and medium power range. The fact that multilevel converters need several DC sources in the DC side, makes them attractive for photovoltaic (PV) applications. This paper presents a new control strategy to control cascaded multilevel converters in a multi-string configuration for single phase grid connected systems. Eventually, simulation results are provided to validate the control system under various insolation conditions.