nils-herrmann/clean_scirepeval huggingface dataset:
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

Despite the steady progress in video analysis led by the adoption of convolutional neural networks (CNNs), the relative improvement has been less drastic as that in 2D static image classification. Three main challenges exist including spatial (image) feature representation, temporal information representation, and model/computation complexity. It was recently shown by Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained on ImageNet, could be a promising way for spatial and temporal representation learning. However, as for model/computation complexity, 3D CNNs are much more expensive than 2D CNNs and prone to overfit. We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices. In particular, we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions. Rather surprisingly, best result (in both speed and accuracy) is achieved when replacing the 3D convolutions at the bottom of the network, suggesting that temporal representation learning on high-level “semantic” features is more useful. Our conclusion generalizes to datasets with very different properties. When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating, our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks (Kinetics, Something-something, UCF101 and HMDB), as well as two action detection (localization) benchmarks (JHMDB and UCF101-24).
Design of Power Supply for Driving High Power Piezoelectric Actuators

Subject of this contribution is a novel PWM controlled inverter of the kW power range, feeding a multi- mass ultrasonic motor (MM-USM) via a LLCC-type filter. The system is designed in a way to reduce the total harmonic distortion of the motor voltage and to locally compensate for the reactive power of the motor. Operating a three- level inverter with PWM pattern of eliminated low-order harmonics allows the use of a small and lightweight output filter showing besides improved dynamic behavior.
Deep features to classify skin lesions

Diagnosing an unknown skin lesion is the first step to determine appropriate treatment. We demonstrate that a linear classifier, trained on features extracted from a convolutional neural network pretrained on natural images, distinguishes among up to ten skin lesions with a higher accuracy than previously published state-of-the-art results on the same dataset. Further, in contrast to competing works, our approach requires no lesion segmentations nor complex preprocessing. We gain consistent additional improvements to accuracy using a per image normalization, a fully convolutional network to extract multi-scale features, and by pooling over an augmented feature space. Compared to state-of-the-art, our proposed approach achieves a favourable accuracy of 85.8% over 5-classes (compared to 75.1%) with noticeable improvements in accuracy for underrepresented classes (e.g., 60% compared to 15.6%). Over the entire 10-class dataset of 1300 images captured from a standard (non-dermoscopic) camera, our method achieves an accuracy of 81.8% outperforming the 67% accuracy previously reported.
Diagnosis of diabetic retinopathy using morphological process and SVM classifier

Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabetes for 10 years or more, which can also lead to vision loss. Detection of diabetic retinopathy in advance, protects patients from vision loss. The major symptom of diabetic retinopathy is the exudates. Exudate is a fluid that filters from the circulatory system into lesions or area of inflammation. Detecting retinal fundus diseases in an early stage, helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it. This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions. Decision making of the severity level of the disease was performed by SVM classifier.
ONLINE SIGNATURE VERIFICATION: A ROBUST APPROACH FOR PERSIAN SIGNATURES

In this paper, the specific trait of Persian signatures is applied to signature verification. Efficient features, which can discriminate among Persian signatures, are investigated in this approach. Persian signatures, in comparison with other languages signatures, have more curvature and end in a specific style. An experiment has been designed to determine the function indicating the most robust features of Persian signatures. To improve the performance of verification, a combination of shape based and dynamic extracted features is applied to Persian signature verification. To classify these signatures, Support Vector Machine (SVM) is applied. The proposed method is examined on two common Persian datasets, the new proposed Persian dataset in this paper (Noshirvani Dynamic Signature Dataset) and an international dataset (SVC2004). For three Persian datasets EER value are equal to 3, 3.93, 4.79, while for SVC2004 the EER value is 4.43. These experiments led to identification of new features combinations that are more robust. The results show the overperformance of these features among all of the previous works on the Persian signature databases; however, it does not reach the best reported results in an international database. This can be deduced that language specific approaches may show better results.
On Probabilistic Analysis of Disagreement in Synchronous Consensus Protocols

This paper presents a probabilistic analysis of disagreement for a family of simple synchronous consensus algorithms aimed at solving the 1-of-n selection problem in presence of unrestricted communication failures. In this problem, a set of n nodes are to select one common value among n proposed values. There are two possible outcomes of each node's selection process: decide to select a value or abort. We have disagreement if some nodes select the same value while other nodes decide to abort. Previous research has shown that it is impossible to guarantee agreement among the nodes subjected to an unbounded number of message losses. Our aim is to find decision algorithms for which the probability of disagreement is as low as possible. In this paper, we investigate two different decision criteria, one optimistic and one pessimistic. We assume two communication failure models, symmetric and asymmetric. For symmetric communication failures, we present the closed-form expressions for the probability of disagreement. For asymmetric failures, we analyse the algorithm using a probabilistic model checking tool. Our results show that the choice of decision criterion significantly influences the probability of disagreement for the 1-of-n selection algorithm. The optimistic decision criterion shows a lower probability of disagreement compare to the pessimistic one when the probability of message loss is less than 30% to 70%. On the other hand, the optimistic decision criterion has in general a higher maximum probability of disagreement compared to the pessimistic criterion.
Object Detection in Videos by Short and Long Range Object Linking

We address the problem of detecting objects in videos with the interest in exploring temporal contexts. Our core idea is to link objects in the short and long ranges for improving the classification quality. Our approach first proposes a set of candidate spatio-temporal cuboids, each of which serves as a container associating the object across short range frames, for a short video segment. It then regresses the precise box locations in each frame over each cuboid proposal, yielding a tubelet with a single classification score which is aggregated from the scores of the boxes in the tubelet. Third, we extend the non-maximum suppression algorithm to remove spatially-overlapping tubelets in the short segment, avoiding tubelets broken by the framewise NMS. Finally, we link the tubelets across temporallyoverlapping short segments over the whole video, in order to boost the classification scores for positive detections by aggregating the scores in the linked tubelets. Experiments on the ImageNet VID dataset shows that our approach achieves the state-of-the-art performance.
Survey on NoSQL database

With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL.
Prototypical Networks for Few-shot Learning

We propose Prototypical Networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical Networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. We further extend Prototypical Networks to zero-shot learning and achieve state-ofthe-art results on the CU-Birds dataset.
Visible-frequency hyperbolic metasurface

Metamaterials are artificial optical media composed of sub-wavelength metallic and dielectric building blocks that feature optical phenomena not present in naturally occurring materials. Although they can serve as the basis for unique optical devices that mould the flow of light in unconventional ways, three-dimensional metamaterials suffer from extreme propagation losses. Two-dimensional metamaterials (metasurfaces) such as hyperbolic metasurfaces for propagating surface plasmon polaritons have the potential to alleviate this problem. Because the surface plasmon polaritons are guided at a metal–dielectric interface (rather than passing through metallic components), these hyperbolic metasurfaces have been predicted to suffer much lower propagation loss while still exhibiting optical phenomena akin to those in three-dimensional metamaterials. Moreover, because of their planar nature, these devices enable the construction of integrated metamaterial circuits as well as easy coupling with other optoelectronic elements. Here we report the experimental realization of a visible-frequency hyperbolic metasurface using single-crystal silver nanostructures defined by lithographic and etching techniques. The resulting devices display the characteristic properties of metamaterials, such as negative refraction and diffraction-free propagation, with device performance greatly exceeding those of previous demonstrations. Moreover, hyperbolic metasurfaces exhibit strong, dispersion-dependent spin–orbit coupling, enabling polarization- and wavelength-dependent routeing of surface plasmon polaritons and two-dimensional chiral optical components. These results open the door to realizing integrated optical meta-circuits, with wide-ranging applications in areas from imaging and sensing to quantum optics and quantum information science.
Creative Help: A Story Writing Assistant

We present Creative Help, an application that helps writers by generating suggestions for the next sentence in a story as it being written. Users can modify or delete suggestions according to their own vision of the unfolding narrative. The application tracks users’ changes to suggestions in order to measure their perceived helpfulness to the story, with fewer edits indicating more helpful suggestions. We demonstrate how the edit distance between a suggestion and its resulting modification can be used to comparatively evaluate different models for generating suggestions. We describe a generation model that uses case-based reasoning to find relevant suggestions from a large corpus of stories. The application shows that this model generates suggestions that are more helpful than randomly selected suggestions at a level of marginal statistical significance. By giving users control over the generated content, Creative Help provides a new opportunity in open-domain interactive storytelling.
How to Construct a Minimal Theory of Mind

What could someone represent that would enable her to track, at least within limits, others’ perceptions, knowledge states and beliefs including false beliefs? An obvious possibility is that she might represent these very aitudes as such. It is sometimes tacitly or explicitly assumed that this is the only possible answer. However we argue that several recent discoveries in developmental, cognitive, and comparative psychology indicate the need for other, less obvious possibilities. Our aim is to meet this need by describing the construction of a minimal theory of mind. Minimal theory of mind is rich enough to explain systematic success on tasks held to be acid tests for theory of mind cognition including many false belief tasks. Yet minimal theory of mind does not require representing propositional aitudes, or any other kind of representation, as such. Minimal theory of mind may be what enables those with limited cognitive resources or lile conceptual sophistication, such as infants, chimpanzees, scrub-jays and human adults under load, to track others’ perceptions, knowledge states
Dynamic mixed membership blockmodel for evolving networks

In a dynamic social or biological environment, interactions between the underlying actors can undergo large and systematic changes. Each actor can assume multiple roles and their degrees of affiliation to these roles can also exhibit rich temporal phenomena. We propose a state space mixed membership stochastic blockmodel which can track across time the evolving roles of the actors. We also derive an efficient variational inference procedure for our model, and apply it to the Enron email networks, and rewiring gene regulatory networks of yeast. In both cases, our model reveals interesting dynamical roles of the actors.
CM-Builder: An Automated NL-Based CASE Tool

This paper describes a natural language-based CASE tool called CM-Builder which aims at supporting the Analysis stage of software development in an Object-Oriented framework. CM-Builder uses robust Natural Language Processing techniques to analyse software requirements texts written in English and build an integrated discourse model of the processed text, represented in a Semantic Network. This Semantic Network is then used to automatically construct an initial UML Class Model representing the object classes mentioned in the text and the relationships among them. The initial model can be directly input to a graphical CASE tool for further refinement by a human analyst. CM-Builder has been quantitatively evaluated in blind trials against a collection of unseen software requirements texts and we present the results of this evaluation, together with the evaluation methodology. The results are very encouraging and demonstrate that tools such as CM-Builder have the potential to play an important role in the software development process.
A non-segmented PSpice model of SiC MOSFETs

To solve the simulation convergence problem of Silicon Carbide metal-oxide semiconductor field effect transistor (SiC MOSFET) models, this paper proposes a non-segmented model for SiC MOSFETs, which uses non-segmented, smooth continuous equations to describe the static and dynamic characteristics of SiC MOSFET. Further, the static characteristic of SiC MOSFET obtained by the non-segmented model is verified by comparing the simulation curves with the static curves provided in datasheet, and the dynamic characteristic is verified by comparing the simulation rise time and fall time of voltage with the datasheet based on the double pulse simulation circuit. The accuracy and good convergence of non-segmented model provide a good way to research the power converters with SiC MOSFETs by simulation way.
Transferring sentiment knowledge between words and tweets

Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.
A Theory of Intra-Agent Replanning

When autonomous agents execute in the real world, the world state as well as the objectives may change from the agent’s original conception of those things. In such cases, the agent’s planning process must modify the existing plan to make it amenable to the new conditions, and to resume execution. The need for inter-agent replanning, in terms of commitments to other agents, is understood in the multi-agent systems community. Such inter-agent replanning also motivates an intraagent replanning problem for each individual agent. However, the single-agent planning community has mostly limited its view of replanning to reducing the computational effort involved, by minimally perturbing the current plan structure to replan. This is not very appropriate as a general model for intra-agent replanning, which may consist of various techniques that are employed according to the scenario at hand. In this paper, we present a general replanning problem that is built on various types of replanning constraints. We show that these constraints can model different types of replanning, including the similarity-based approaches used in the past and sensitivity to commitments made to other agents. Finally, we show that partial satisfaction planning provides a good substrate for modeling this general replanning problem.
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework

This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines -dominance, a measure of convergence speed named -progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm's feasible parameteri- zation space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.
Bert: a scheduler for best-effort and realtime paths

We describe a new algorithm, called BERT, that can be used to schedule both best effort and realtime tasks on a multimedia workstation. BERT exploits two innovations. First, it is based on the virtual clock algorithm originally developed to schedule bandwidth on packet switches. Because this algorithm maintains a relationship between virtual time and real time, our algorithm allows us to simultaneously factor realtime deadlines into the scheduling decision, and to allocate some fraction of the processor to best effort tasks. Second, BERT includes a mechanism that allows one task to steal cycles from another. This mechanism is valuable for two reasons: it allows us to distinguish among tasks based on their importance, and it results in a robust algorithm that is not overly sensitive to the task having made a perfect reservation.
Semantic Web Services-A Survey

The technology where the meaning of the information and the service of the web is defined by making the web to understand and satisfies the request of the people is called Semantic Web Services. That is the idea of having data on the web defined and linked in a way that it can be used by machines not just for display purpose, but for automation, integration and reuse of data across various application .The idea of the semantic is raised to overcome the limitation of the Web services such as Average WWW searches examines only about 25% of potentially relevant sites and return a lot of unwanted information, Information on web is not suitable for software agent and Doubling of size. It is built on top of the Web Services extended with rich semantic representations along with capabilities for automatic reasoning developed in the field of artificial intelligence. This survey attempts to give an overview of the underlying concepts and technologies along with the categorization, selection and discovery of services based on semantic.
Transition-Based Disfluency Detection using LSTMs

We model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a set of transition actions without syntax information. Compared with sequence labeling methods, it can capture non-local chunk-level features; compared with joint parsing and disfluency detection methods, it is free for noise in syntax. Experiments show that our model achieves state-of-theart F-score on both the commonly used English Switchboard test set and a set of in-house annotated Chinese data.