nils-herrmann/clean_scirepeval huggingface dataset:
Topic segmentation: algorithms and applications

ALGORITHMS AND APPLICATIONS Je rey C. Reynar Supervisor: Mitchell P. Marcus Most documents are about more than one subject, but the majority of natural language processing algorithms and information retrieval techniques implicitly assume that every document has just one topic. The work described herein is about clues which mark shifts to new topics, algorithms for identifying topic boundaries and the uses of such boundaries once identi ed. A number of topic shift indicators have been proposed in the literature. We review these features, suggest several new ones and test most of them in implemented topic segmentation algorithms. Hints about topic boundaries include repetitions of character sequences, patterns of word and word n-gram repetition, word frequency, the presence of cue words and phrases and the use of synonyms. The algorithms we present use cues singly or in combination to identify topic shifts in several kinds of documents. One algorithm tracks compression performance, which is an indicator of topic shift because self-similarity within topic segments should be greater than between-segment similarity. Another technique relies on word repetition and places boundaries by minimizing word repetitions across segment boundaries. A third method compares the performance of a language model with and without knowledge of the contents of preceding sentences to determine whether a topic shift has occurred. We use the output vi of this algorithm in a statistical model which incorporates synonymy, bigram repetition and other features for topic segmentation. We benchmark our algorithms and compare them to algorithms from the literature using concatenations of documents, and then perform further evaluation of our techniques using a collection of news broadcasts transcribed both by annotators and using a speech recognition system. We also test the e ectiveness of our algorithms for identifying both chapter boundaries in works of literature and story boundaries in Spanish news broadcasts. We suggest ways to improve information retrieval, language modeling and various natural language processing algorithms by exploiting the topic segmentation.
Aspect Specific Sentiment Analysis using Hierarchical Deep Learning

This paper focuses on the problem of aspect-specific sentiment analysis. The goal here is to not only extract aspects of a product or service, but also to identify specific sentiments being expressed about them. Most existing algorithms address this problem by treating aspect extraction and sentiment analysis as separate phases or by enforcing explicit modeling assumptions on how these two phases should overlap and interact. In this paper, we propose a novel approach based on a hierarchical deep learning framework which overcomes the aforementioned drawbacks. We experiment with various models of semantic compositionality within this framework. Experimental results on real world datasets show that the proposed framework outperforms other state-of-the-art techniques. In addition, we also demonstrate how domain adaptation using word vectors can benefit the task of aspect specific sentiment analyis.
Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics.

Timbre spaces have been used in music perception to study the perceptual relationships between instruments based on dissimilarity ratings. However, these spaces do not generalize to novel examples and do not provide an invertible mapping, preventing audio synthesis. In parallel, generative models have aimed to provide methods for synthesizing novel timbres. However, these systems do not provide an understanding of their inner workings and are usually not related to any perceptually relevant information. Here, we show that Variational Auto-Encoders (VAE) can alleviate all of these limitations by constructing generative timbre spaces. To do so, we adapt VAEs to learn an audio latent space, while using perceptual ratings from timbre studies to regularize the organization of this space. The resulting space allows us to analyze novel instruments, while being able to synthesize audio from any point of this space. We introduce a specific regularization allowing to enforce any given similarity distances onto these spaces. We show that the resulting space provide almost similar distance relationships as timbre spaces. We evaluate several spectral transforms and show that the Non-Stationary Gabor Transform (NSGT) provides the highest correlation to timbre spaces and the best quality of synthesis. Furthermore, we show that these spaces can generalize to novel instruments and can generate any path between instruments to understand their timbre relationships. As these spaces are continuous, we study how audio descriptors behave along the latent dimensions. We show that even though descriptors have an overall non-linear topology, they follow a locally smooth evolution. Based on this, we introduce a method for descriptor-based synthesis and show that we can control the descriptors of an instrument while keeping its timbre structure.
Continuous Blood Pressure Measurement From Invasive to Unobtrusive: Celebration of 200th Birth Anniversary of Carl Ludwig

The year 2016 marks the 200th birth anniversary of Carl Friedrich Wilhelm Ludwig (1816-1895). As one of the most remarkable scientists, Ludwig invented the kymograph, which for the first time enabled the recording of continuous blood pressure (BP), opening the door to the modern study of physiology. Almost a century later, intraarterial BP monitoring through an arterial line has been used clinically. Subsequently, arterial tonometry and volume clamp method were developed and applied in continuous BP measurement in a noninvasive way. In the last two decades, additional efforts have been made to transform the method of unobtrusive continuous BP monitoring without the use of a cuff. This review summarizes the key milestones in continuous BP measurement; that is, kymograph, intraarterial BP monitoring, arterial tonometry, volume clamp method, and cuffless BP technologies. Our emphasis is on recent studies of unobtrusive BP measurements as well as on challenges and future directions.
Design of a UWB unidirectional radiation compound spiral antenna

The design of a modified unidirectional radiation miniaturized compound spiral antenna fed by an exponentially tapered balun is shown in this paper. The compound spiral antenna is designed by combining a typical equiangular spiral antenna with Archimedean spiral line. By taking advantage of a modified reflector to make the antenna into a microstrip form, the structure realizes a typical unidirectional radiation pattern for planar spiral antennas, which naturally increases radiation gain. In the range from 5 to 20 GHz, simulated radiation patterns, axial ratio plots and VSWR plots show good performance. The comparison with the compound spiral antenna and traditional UWB antennas shows the improvement of the axial ratio.
Sit-to-Stand Trainer: An Apparatus for Training “Normal-Like” Sit to Stand Movement

Sit-to-stand (STS) transfer training is probably the most demanding task in rehabilitation. We have developed an innovative STS trainer that offers variable levels of mechanical support and speeds of STS transfer. In a group of neurologically intact individuals we compared kinematics, kinetics and electromyography (EMG) patterns of STS transfer assessed in three experimental conditions with increasing degree of mechanical support (MIN STS-T, MED STS-T, and MAX STS-T) to natural, unassisted STS movement (NO STS-T). The resulting ankle, knee, hip joint and trunk angles in experimental conditions MED STS-T and MIN STS-T were very similar to experimental condition NO STS-T. Vertical ground reaction forces and EMG patterns in the tibialis anterior, quadriceps and hamstrings show a clear trend toward “normal” patterns as the level of mechanical support from the device is progressively reduced. We have further tested the feasibility of the STS trainer in five stroke subjects at two levels of support showing that increased voluntary effort is needed when the support is reduced. Based on these results we conclude that negligible constraints are imposed by the device on a user's STS transfer kinematics, which is an important prerequisite for considering clinical use of the device for training in neurologically impaired.
Using NLP techniques for file fragment classification

The classification of file fragments is an important problem in digital forensics. The literature does not include comprehensive work on applying machine learning techniques to this problem. In this work, we explore the use of techniques from natural language processing to classify file fragments. We take a supervised learning approach, based on the use of support vector machines combined with the bag-of-words model, where text documents are represented as unordered bags of words. This technique has been repeatedly shown to be effective and robust in classifying text documents (e.g., in distinguishing positive movie reviews from negative ones). In our approach, we represent file fragments as “bags of bytes” with feature vectors consisting of unigram and bigram counts, as well as other statistical measurements (including entropy and others). We made use of the publicly available Garfinkel data corpus to generate file fragments for training and testing. We ran a series of experiments, and found that this approach is effective in this domain as well. a 2012 O. Zhulyn, S. Fitzgerald & G. Mathews.
Conceptual and Database Modelling of Graph Databases

Comparing graph databases with traditional, e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is not explicitly defined, also a conceptual modelling is not used at all. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model and show its relationship to a graph database model. We focus also on integrity constraints modelling functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases and extend them to conditional functional dependencies.
Fly4SmartCity: A cloud robotics service for smart city applications

In this paper a possible implementation of a cloud-robotics service in a smart city scenario is proposed, in which small Unmanned Aerial Vehicles (UAVs) are employed for emergency management operations. The emerging cloud robotics paradigm considers robots as simple agents connected to a remote network infrastructure; this networked approach allows them to benefit from off-board powerful computational and storage resources on one hand, and to leverage open data about the urban fabric and common knowledge on the other hand. This work describes how the integration of a capable, reliable and stable cloud platform, small unmanned vehicles, high-bandwidth/low-latency mobile networks and open data publicly available on the internet makes the design of a real-world UAV-based emergency management service possible. Simulated and real experiments show the feasibility of the approach. Legal and operational issues which at present still prevent a practical application of these techniques are also addressed.
Boosting for transfer learning

Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model.
Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems

This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.
A Reorder Buffer Design for High Performance Processors

Modern reorder buffers (ROBs) were conceived to improve processor performance by allowing instruction execution out of the original program order and run ahead of sequential instruction code exploiting existing instruction level parallelism (ILP). The ROB is a functional structure of a processor execution engine that supports speculative execution, physical register recycling, and precise exception recovering. Traditionally, the ROB is considered as a monolithic circular buffer with incoming instructions at the tail pointer after the decoding stage and completing instructions at the head pointer after the commitment stage. The latter stage verifies instructions that have been dispatched, issued, executed, and are not completed speculatively. This paper presents a design of distributed reorder buffer microarchitecture by using small structures near building blocks which work together, using the same tail and head pointer values on all structures for synchronization. The reduction of area, and therefore, the reduction of power and delay make this design suitable for both embedded and high performance microprocessors.
High gain antipodal tapered slot antenna With sine-shaped corrugation and fermi profile substrate slotted cut-out for MMW 5G

A high gain, high-efficiency antipodal tapered slot antenna with sine-shaped corrugation and a Fermi profile substrate cut-out has been developed for 5G millimeter wave (MMW) communications. A parametric study of a new substrate cutout with Fermi profile is demonstrated to reduce the sidelobe level at the E-plane and H-plane as well as to increase antenna efficiency by an average of 88% over a band of 20-40 GHz. A low-cost printed circuit board (PCB) is processed simply with a CNC Milling machine to fabricate the proposed antenna with Fermi profile substrate cut-out. The measured reflection coefficient is found to be less than -14 dB over a frequency range of 20-40 GHz. Furthermore, the measured gain of the proposed antenna is 17 dB at 30 GHz and the measured radiation pattern and gain is almost constant within the wide bandwidth from 30-40 GHz. Therefore, this antenna is proposed for use in an H-plane array structure such as for point-to-point communication systems, a switched-beam system.
Using Machine Learning to Refine Black-Box Test Specifications and Test Suites

In the context of open source development or software evolution, developers often face test suites which have been developed with no apparent rationale and which may need to be augmented or refined to ensure sufficient dependability, or even reduced to meet tight deadlines. We refer to this process as the re-engineering of test suites. It is important to provide both methodological and tool support to help people understand the limitations of test suites and their possible redundancies, so as to be able to refine them in a cost effective manner. To address this problem in the case of black-box testing, we propose a methodology based on machine learning that has shown promising results on a case study.
Recent progress in coded structured light as a technique to solve the correspondence problem: a survey

We present a survey of the most significant techniques, used in the last few years, concerning the coded structured light methods employed to get 3D information. In fact, depth perception is one of the most important subjects in computer vision. Stereovision is an attractive and widely used method, but, it is rather limited to make 3D surface maps, due to the correspondence problem. The correspondence problem can be improved using a method based on structured light concept, projecting a given pattern on the measuring surfaces. However, some relations between the projected pattern and the reflected one must be solved. This relationship can be directly found codifying the projected light, so that, each imaged region of the projected pattern carries the needed information to solve the correspondence problem. ( 1998 Pattern Recognition Society.  Pattern projection Correspondence problem Active stereo Depth perception Range data Computer vision.
The tempotron: a neuron that learns spike timing–based decisions

The timing of action potentials in sensory neurons contains substantial information about the eliciting stimuli. Although the computational advantages of spike timing–based neuronal codes have long been recognized, it is unclear whether, and if so how, neurons can learn to read out such representations. We propose a new, biologically plausible supervised synaptic learning rule that enables neurons to efficiently learn a broad range of decision rules, even when information is embedded in the spatiotemporal structure of spike patterns rather than in mean firing rates. The number of categorizations of random spatiotemporal patterns that a neuron can implement is several times larger than the number of its synapses. The underlying nonlinear temporal computation allows neurons to access information beyond single-neuron statistics and to discriminate between inputs on the basis of multineuronal spike statistics. Our work demonstrates the high capacity of neural systems to learn to decode information embedded in distributed patterns of spike synchrony.
Cloud Computing - The Business Perspective

If cloud computing (CC) is to achieve its potential, there needs to be a clear understanding of the various issues involved, both from the perspectives of the providers and the consumers of the technology. There is an equally urgent need for understanding the business-related issues surrounding CC. We interviewed several industry executives who are either involved as developers or are evaluating CC as an enterprise user. We identify the strengths, weaknesses, opportunities and threats for the industry. We also identify the various issues that will affect the different stakeholders of CC. We issue a set of recommendations for the practitioners who will provide and manage this technology. For IS researchers, we outline the different areas of research that need attention so that we are in a position to advise the industry in the years to come. Finally, we outline some of the key issues facing governmental agencies who will be involved in the regulation of cloud computing.
GENETIC ALGORITHM APPLIED TO OPTIMIZATION OF THE SHIP HULL FORM WITH RESPECT TO SEAKEEPING PERFORMANCE

Hull form optimization from a hydrodynamic performance point of view is an important aspect in preliminary ship design. This study presents a computational method to estimate the ship seakeeping in regular head waves. In the optimization process, the genetic algorithm (GA) is linked to the computational method to obtain an optimum hull form by taking into account the displacement as a design constraint. New hull forms are obtained from the wellknown S60 hull and the classical Wigley hull taken as initial hulls in the optimization process at two Froude numbers (Fn=0.2 and Fn=0.3). The optimization variables are a combination of ship hull offsets and main dimensions. The objective function of the optimization procedure includes the peak values for vertical absolute motion at the centre of gravity (CG) and the bow point (0.15Lwl) behind the forward perpendicular (FP).
The fully informed particle swarm: simpler, maybe better

The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.
Coupling-Feed Circularly Polarized RFID Tag Antenna Mountable on Metallic Surface

A square patch passive RFID tag antenna designed for UHF band is presented in this article. To achieve compact size and circular polarization (CP) radiation, the square patch is embedded with a cross slot, while an L-shaped open-end microstrip line linked to a tag-chip and terminated by a shorting pin is capacitively coupled to the patch. By selecting an appropriate length for the microstrip line and its coupling distance with the radiating element, easy control on the input impedance of the proposed tag antenna which leads to excellent impedance matching is achieved. The measured 10-dB return-loss bandwidth of the tag antenna is 50 MHz (904-954 MHz), while its 3-dB axial-ratio bandwidth is 6 MHz (922-928 MHz). Further experiment shows that the tag antenna can provide better reading range when mounted on a metallic surface.
Ultra-Low-Voltage Operation of CMOS Analog Circuits: Amplifiers, Oscillators, and Rectifiers

This letter presents MOS analog circuits that can operate with very low supply voltages. Operation of the MOS transistor in the triode region is emphasized owing to the limited voltages available. Special attention has been given to the properties of the zero-VT transistor due to its high drive capability at low voltages. Simulation and measurement results for a Colpitts oscillator prototype set up around a zero-VT MOSFET demonstrated that the oscillator operates with supply voltages lower than the thermal voltage kT/q. Finally, ultra-low-voltage rectifiers using diodes or diode-connected MOSFETs operating in weak inversion are analyzed.