nils-herrmann/clean_scirepeval huggingface dataset:
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies

We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated by using a discriminative loss, and we describe a weighted sampling strategy which significantly reduces computation while improving stability, sample efficiency, and rate of convergence. One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme. We also establish close connections between BlackOut, importance sampling, and noise contrastive estimation (NCE). Our experiments, on the recently released one billion word language modeling benchmark, demonstrate scalability and accuracy of BlackOut; we outperform the state-of-the art, and achieve the lowest perplexity scores on this dataset. Moreover, unlike other established methods which typically require GPUs or CPU clusters, we show that a carefully implemented version of BlackOut requires only 1-10 days on a single machine to train a RNNLM with a million word vocabulary and billions of parameters on one billion of words.
Word embeddings for speech recognition

Speech recognition systems have used the concept of states as a way to decompose words into sub-word units for decades. As the number of such states now reaches the number of words used to train acoustic models, it is interesting to consider approaches that relax the assumption that words are made of states. We present here an alternative construction, where words are projected into a continuous embedding space where words that sound alike are nearby in the Euclidean sense. We show how embeddings can still allow to score words that were not in the training dictionary. Initial experiments using a lattice rescoring approach and model combination on a large realistic dataset show improvements in word error rate.
Real-time view recognition and event detection for sports video

In this paper, we present a general framework and new effective algorithms to detect the syntactic structures that are at a level higher than shots. In sports video, such high-level structures are often characterized by the specific views (e.g., pitching or serve) and the subsequent temporal transition patterns within each temporal structural segment. We have developed robust statistical models for detecting the domain-specific views with real-time performance and high accuracy. The models combine domain-independent global color filtering method and domain-specific constraints on the spatio-temporal properties of the segmented regions (e.g., locations, shapes, and motion of the objects). The real-time performance was accomplished by using efficient compressed-domain processing at the front end and computational expensive object-level processing on filtered candidates only. High-level events (e.g., strokes, net plays, baseline plays) are also detected after the view recognition. Results of such structure and event detection allow for efficient browsing and summarization of long sports video programs.
Rethinking virtual network embedding: substrate support for path splitting and migration

Network virtualization is a powerful way to run multiple architectures or experiments simultaneously on a shared infrastructure. However, making efficient use of the underlying resources requires effective techniques for virtual network embedding--mapping each virtual network to specific nodes and links in the substrate network. Since the general embedding problem is computationally intractable, past research restricted the problem space to allow efficient solutions, or focused on designing heuristic algorithms. In this paper, we advocate a different approach: rethinking the design of the substrate network to enable simpler embedding algorithms and more efficient use of resources, without restricting the problem space. In particular, we simplify virtual link embedding by: i) allowing the substrate network to split a virtual link over multiple substrate paths and ii) employing path migration to periodically re-optimize the utilization of the substrate network. We also explore node-mapping algorithms that are customized to common classes of virtual-network topologies. Our simulation experiments show that path splitting, path migration,and customized embedding algorithms enable a substrate network to satisfy a much larger mix of virtual networks
Argumentation Mining on the Web from Information Seeking Perspective

In this paper, we argue that an annotation scheme for argumentation mining is a function of the task requirements and the corpus properties. There is no one-sizefits-all argumentation theory to be applied to realistic data on the Web. In two annotation studies, we experiment with 80 German newspaper editorials from the Web and about one thousand English documents from forums, comments, and blogs. Our example topics are taken from the educational domain. To formalize the problem of annotating arguments, in the first case, we apply a Claim-Premise scheme, and in the second case, we modify Toulmin’s scheme. We find that the choice of the argument components to be annotated strongly depends on the register, the length of the document, and inherently on the literary devices and structures used for expressing argumentation. We hope that these findings will facilitate the creation of reliably annotated argumentation corpora for a wide range of tasks and corpus types and will help to bridge the gap between argumentation theories and actual application needs.
A capabilities-based approach to obtaining a deeper understanding of information technology governance effectiveness: Evidence from IT steering committees

Given the substantial investment in information technology (IT), and the significant impact it has on organizational success, organisations consume considerable resources to manage acquisition and use of IT in organizations. While, various arguments proposed suggest which IT governance arrangements may work best, our understanding of the effectiveness of such initiatives is limited. We examine the relationship between the effectiveness of IT steering committee-driven IT governance initiatives and firm’s IT management and IT infrastructure related capabilities. We further propose that firm’s IT-related capabilities, generated through IT governance initiatives should improve its business processes and firm-level performance. We test these relationships empirically by a field survey of 216 firms. Results of this study suggest that a firms’ effectiveness of IT steering committee-driven IT governance initiatives positively relate to the level of their IT-related capabilities. We also found positive relationships between IT-related capabilities and internal process-level performance. Our results also support the conjecture that improvement in internal process-level performance will be positively related to improvement in customer service and firm-level performance. For researchers, we demonstrate that the resource-based theory provides a more robust explanation of the determinants of firms IT governance initiatives. This would be ideal in evaluating other IT governance initiatives effectiveness in relation to how they contribute to building performance-differentiating IT-related capabilities. For decision makers, we hope our study has reiterated the notion that IT governance is truly a coordinated effort, embracing all levels of human resources.
Balancing trade-offs in test-suite reduction

Regression testing is an important activity but can get expensive for large test suites. Test-suite reduction speeds up regression testing by identifying and removing redundant tests based on a given set of requirements. Traditional research on test-suite reduction is rather diverse but most commonly shares three properties: (1) requirements are defined by a coverage criterion such as statement coverage; (2) the reduced test suite has to satisfy all the requirements as the original test suite; and (3) the quality of the reduced test suites is measured on the software version on which the reduction is performed. These properties make it hard for test engineers to decide how to use reduced test suites. We address all three properties of traditional test-suite reduction: (1) we evaluate test-suite reduction with requirements defined by killed mutants; (2) we evaluate inadequate reduction that does not require reduced test suites to satisfy all the requirements; and (3) we propose evolution-aware metrics that evaluate the quality of the reduced test suites across multiple software versions. Our evaluations allow a more thorough exploration of trade-offs in test-suite reduction, and our evolution-aware metrics show how the quality of reduced test suites can change after the version where the reduction is performed. We compare the trade-offs among various reductions on 18 projects with a total of 261,235 tests over 3,590 commits and a cumulative history spanning 35 years of development. Our results help test engineers make a more informed decision about balancing size, coverage, and fault-detection loss of reduced test suites.
Upper-limb robot-assisted therapy in rehabilitation of acute stroke patients: focused review and results of new randomized controlled trial.

The successful motor rehabilitation of stroke patients requires early intensive and task-specific therapy. A recent Cochrane Review, although based on a limited number of randomized controlled trials (RCTs), showed that early robotic training of the upper limb (i.e., during acute or subacute phase) can enhance motor learning and improve functional abilities more than chronic-phase training. In this article, a new subacute-phase RCT with the Neuro-Rehabilitation-roBot (NeReBot) is presented. While in our first study we used the NeReBot in addition to conventional therapy, in this new trial we used the same device in substitution of standard proximal upper-limb rehabilitation. With this protocol, robot patients achieved similar reductions in motor impairment and enhancements in paretic upper-limb function to those gained by patients in a control group. By analyzing these results and those of previous studies, we hypothesize a new robotic protocol for acute and subacute stroke patients based on both treatment modalities (in addition and in substitution).
Exploring patterns in dictionary definitions for synonym extraction

Automatic determination of synonyms and/or semantically related words has various applications in Natural Language Processing. Two mainstream paradigms to date, lexicon-based and distributional approaches, both exhibit pros and cons with regard to coverage, complexity, and quality. In this paper, we propose three novel methods—two rule-based methods and one machine learning approach—to identify synonyms from definition texts in a machinereadable dictionary. Extracted synonyms are evaluated in two extrinsic experiments and one intrinsic experiment. Evaluation results show that our pattern-based approach achieves best performance in one of the experiments and satisfactory results in the other, comparable to corpus-based state-of-the-art results.
Context-based Speech Recognition Error Detection and Correction

In this paper we present preliminary results of a novel unsupervised approach for highprecision detection and correction of errors in the output of automatic speech recognition systems. We model the likely contexts of all words in an ASR system vocabulary by performing a lexical co-occurrence analysis using a large corpus of output from the speech system. We then identify regions in the data that contain likely contexts for a given query word. Finally, we detect words or sequences of words in the contextual regions that are unlikely to appear in the context and that are phonetically similar to the query word. Initial experiments indicate that this technique can produce high-precision targeted detection and correction of misrecognized query words.
Obstacle Avoidance for Low-Speed Autonomous Vehicles With Barrier Function

This paper presents an obstacle avoidance algorithm for low speed autonomous vehicles (AV), with guaranteed safety. A supervisory control algorithm is constructed based on a barrier function method, which works in a plug-and-play fashion with any lower level navigation algorithm. When the risk of collision is low, the barrier function is not active; when the risk is high, based on the distance to an “avoidable set,” the barrier function controller will intervene, using a mixed integer program to ensure safety with minimal control effort. This method is applied to solve the navigation and pedestrian avoidance problem of a low speed AV. Its performance is compared with two benchmark algorithms: a potential field method and the Hamilton–Jacobi method.
DVAFS: Trading computational accuracy for energy through dynamic-voltage-accuracy-frequency-scaling

Several applications in machine learning and machine-to-human interactions tolerate small deviations in their computations. Digital systems can exploit this fault-tolerance to increase their energy-efficiency, which is crucial in embedded applications. Hence, this paper introduces a new means of Approximate Computing: Dynamic-Voltage-Accuracy-Frequency-Scaling (DVAFS), a circuit-level technique enabling a dynamic trade-off of energy versus computational accuracy that outperforms other Approximate Computing techniques. The usage and applicability of DVAFS is illustrated in the context of Deep Neural Networks, the current state-of-the-art in advanced recognition. These networks are typically executed on CPU's or GPU's due to their high computational complexity, making their deployment on battery-constrained platforms only possible through wireless connections with the cloud. This work shows how deep learning can be brought to IoT devices by running every layer of the network at its optimal computational accuracy. Finally, we demonstrate a DVAFS processor for Convolutional Neural Networks, achieving efficiencies of multiple TOPS/W.
The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013

ChEBI (http://www.ebi.ac.uk/chebi) is a database and ontology of chemical entities of biological interest. Over the past few years, ChEBI has continued to grow steadily in content, and has added several new features. In addition to incorporating all user-requested compounds, our annotation efforts have emphasized immunology, natural products and metabolites in many species. All database entries are now 'is_a' classified within the ontology, meaning that all of the chemicals are available to semantic reasoning tools that harness the classification hierarchy. We have completely aligned the ontology with the Open Biomedical Ontologies (OBO) Foundry-recommended upper level Basic Formal Ontology. Furthermore, we have aligned our chemical classification with the classification of chemical-involving processes in the Gene Ontology (GO), and as a result of this effort, the majority of chemical-involving processes in GO are now defined in terms of the ChEBI entities that participate in them. This effort necessitated incorporating many additional biologically relevant compounds. We have incorporated additional data types including reference citations, and the species and component for metabolites. Finally, our website and web services have had several enhancements, most notably the provision of a dynamic new interactive graph-based ontology visualization.
Self-automated parking lots for autonomous vehicles based on vehicular ad hoc networking

Parking is a major problem of car transportation, with important implications in traffic congestion and urban landscape. Reducing the space needed to park cars has led to the development of fully automated and mechanical parking systems. These systems are, however, limitedly deployed because of their construction and maintenance costs. Leveraging on semi and fully-autonomous vehicular technology, as well as on the electric propulsion paradigm and in vehicular ad hoc networking, we propose a new parking concept where the mobility of parked vehicles is managed by a parking lot controller to create space for cars entering or exiting the parking lot, in a collaborative manner. We show that the space needed to park such vehicles can be reduced to half the space needed with conventional parking lot designs. We also show that the total travelled distance of vehicles in this new parking lot paradigm can be 30% less than in conventional parking lots. Our proposal can have important consequences in parking costs and in urban landscape.
The state of the art in end-user software engineering

Most programs today are written not by professional software developers, but by people with expertise in other domains working towards goals for which they need computational support. For example, a teacher might write a grading spreadsheet to save time grading, or an interaction designer might use an interface builder to test some user interface design ideas. Although these end-user programmers may not have the same goals as professional developers, they do face many of the same software engineering challenges, including understanding their requirements, as well as making decisions about design, reuse, integration, testing, and debugging. This article summarizes and classifies research on these activities, defining the area of End-User Software Engineering (EUSE) and related terminology. The article then discusses empirical research about end-user software engineering activities and the technologies designed to support them. The article also addresses several crosscutting issues in the design of EUSE tools, including the roles of risk, reward, and domain complexity, and self-efficacy in the design of EUSE tools and the potential of educating users about software engineering principles.
Structured Relation Discovery using Generative Models

We explore unsupervised approaches to relation extraction between two named entities; for instance, the semantic bornIn relation between a person and location entity. Concretely, we propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of observed triples of entity mention pairs and the surface syntactic dependency path between them. The output of each model is a clustering of observed relation tuples and their associated textual expressions to underlying semantic relation types. Our proposed models exploit entity type constraints within a relation as well as features on the dependency path between entity mentions. We examine effectiveness of our approach via multiple evaluations and demonstrate 12% error reduction in precision over a state-of-the-art weakly supervised baseline.
A 3D-printed compact dual-circularly polarized corrugated horn with integrated septum polarizer

This paper presents a Compact Dual-Circularly Polarized Corrugated Horn with Integrated Septum Polarizer in the X-band. Usually such a complicated structure would be fabricated in parts and assembled together. However, exploiting the versatility afforded by Metal 3D-printing, a complete prototype is fabricated as a single part, enabling a compact design. Any variation due to mating tolerance of separate parts is eliminated. The prototype is designed to work from 9.5GHz to 10GHz. It has an impedance match of better than |S11|<−15dB and a gain about 13.4dBic at 9.75GHz. The efficiency is better than 95% in the operating band.
A Kinect-based system for cognitive rehabilitation exercises monitoring

In this paper, a 3D computer vision system for cognitive assessment and rehabilitation based on the Kinect device is presented. It is intended for individuals with body scheme dysfunctions and left-right confusion. The system processes depth information to overcome the shortcomings of a previously presented 2D vision system for the same application. It achieves left and right-hand tracking, and face and facial feature detection (eye, nose, and ears) detection. The system is easily implemented with a consumer-grade computer and an affordable Kinect device and is robust to drastic background and illumination changes. The system was tested and achieved a successful monitoring percentage of 96.28%. The automation of the human body parts motion monitoring, its analysis in relation to the psychomotor exercise indicated to the patient, and the storage of the result of the realization of a set of exercises free the rehabilitation experts of doing such demanding tasks. The vision-based system is potentially applicable to other tasks with minor changes.
Development of inverted pendulum robot capable of climbing stairs using planetary wheel mechanism

Improved robot mobility is necessary in order to realize the expected uses of robots in the human life environment, which includes obstacles such as stairs and narrow passages. Because objects which humans require are often located on desks or tables, which are at some height above ground level, a long vertical dimension is also necessary in the robot design. To meet these requirements, this study focused on a type of mobile robot using an inverted pendulum, which enables a robot with a long vertical dimension to negotiate narrow passages. Since it is difficult for inverted pendulum robots to climb steps, this study proposes a planetary wheel mechanism which makes it possible for an inverted pendulum robot to ascend differences in level. This mechanism has an extremely simple structure, comprising a belt, pulley, and arm, and has the advantage of not requiring addition of a new actuator. This paper describes the composition of the proposed mechanism and its principle of operation, and also describes the control method. A prototype of the robot was produced, and it was shown experimentally that the robot was capable of climbing stairs with a height of 120 mm-130 mm at a rate of approximately 2.4 s per one step.
A review of computational intelligence in RTS games

Real-time strategy games offer a wide variety of fundamental AI research challenges. Most of these challenges have applications outside the game domain. This paper provides a review on computational intelligence in real-time strategy games (RTS). It starts with challenges in real-time strategy games, then it reviews different tasks to overcome this challenges. Later, it describes the techniques used to solve this challenges and it makes a relationship between techniques and tasks. Finally, it presents a set of different frameworks used as test-beds for the techniques employed. This paper is intended to be a starting point for future researchers on this topic.
Pre-Translation for Neural Machine Translation

Recently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT)-based systems, in some cases, the NMT system produces translations that have a completely different meaning. This is especially the case when rare words occur. When using statistical machine translation, it has already been shown that significant gains can be achieved by simplifying the input in a preprocessing step. A commonly used example is the pre-reordering approach. In this work, we used phrase-based machine translation to pre-translate the input into the target language. Then a neural machine translation system generates the final hypothesis using the pre-translation. Thereby, we use either only the output of the phrase-based machine translation (PBMT) system or a combination of the PBMT output and the source sentence. We evaluate the technique on the English to German translation task. Using this approach we are able to outperform the PBMT system as well as the baseline neural MT system by up to 2 BLEU points. We analyzed the influence of the quality of the initial system on the final result.