nils-herrmann/clean_scirepeval huggingface dataset:
Learning to lip read words by watching videos

Our aim is to recognise the words being spoken by a talking face, given only the video but not the audio. Existing works in this area have focussed on trying to recognise a small number of utterances in controlled environments (e.g. digits and alphabets), partially due to the shortage of suitable datasets. We make three novel contributions: first, we develop a pipeline for fully automated data collection from TV broadcasts. With this we have generated a dataset with over a million word instances, spoken by over a thousand different people; second, we develop a two-stream convolutional neural network that learns a joint embedding between the sound and the mouth motions from unlabelled data. We apply this network to the tasks of audio-to-video synchronisation and active speaker detection; third, we train convolutional and recurrent networks that are able to effectively learn and recognize hundreds of words from this large-scale dataset. In lip reading and in speaker detection, we demonstrate results that exceed the current state-of-the-art on public benchmark datasets.
Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities.
An Information Retrieval Approach to Short Text Conversation

Human computer conversation is regarded as one of the most difficult problems in artificial intelligence. In this paper, we address one of its key sub-problems, referred to as short text conversation, in which given a message from human, the computer returns a reasonable response to the message. We leverage the vast amount of short conversation data available on social media to study the issue. We propose formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval (IR) techniques to carry out the task. We investigate the significance as well as the limitation of the IR approach. Our experiments demonstrate that the retrieval-based model can make the system behave rather “intelligently”, when combined with a huge repository of conversation data from social media.
Articulation points guided redundancy elimination for betweenness centrality

Betweenness centrality (BC) is an important metrics in graph analysis which indicates critical vertices in large-scale networks based on shortest path enumeration. Typically, a BC algorithm constructs a shortest-path DAG for each vertex to calculate its BC score. However, for emerging real-world graphs, even the state-of-the-art BC algorithm will introduce a number of redundancies, as suggested by the existence of articulation points. Articulation points imply some common sub-DAGs in the DAGs for different vertices, but existing algorithms do not leverage such information and miss the optimization opportunity.
 We propose a redundancy elimination approach, which identifies the common sub-DAGs shared between the DAGs for different vertices. Our approach leverages the articulation points and reuses the results of the common sub-DAGs in calculating the BC scores, which eliminates redundant computations. We implemented the approach as an algorithm with two-level parallelism and evaluated it on a multicore platform. Compared to the state-of-the-art implementation using shared memory, our approach achieves an average speedup of 4.6x across a variety of real-world graphs, with the traversal rates up to 45 ~ 2400 MTEPS (Millions of Traversed Edges per Second).
OntoNotes: The 90% Solution

OntoNotes is a five year multi-site collaboration between BBN Technologies, Information Sciences Institute of University of Southern California, University of Colorado, University of Pennsylvania and Brandeis University. The goal of the OntoNotes project is to provide linguistic data annotated with a skeletal representation of the literal meaning of sentences including syntactic parse, predicate-argument structure, coreference, and word senses linked to an ontology, allowing a new generation of language understanding technologies to be developed with new functional capabilities. In its third year of existence, the OntoNotes project has generated a large amount of high quality data covering various layers of linguistic annotation. This is probably the first time that data of such quality has been available in large quantities covering multiple genres (newswire, broadcast news, broadcast conversation and weblogs) and languages (English, Chinese and Arabic). The guiding principle has been to find a “sweet spot” in the space of inter-tagger agreement, productivity, and depth of representation. The most effective use of this resource for research requires simultaneous access to multiple layers of annotation. This has been made possible by representing the corpus with a relational database to accommodate the dense connectedness of the data and ensure consistency across layers. In order to facilitate ease of understanding and manipulability, the database has also been supplemented with a object-oriented Python API. The tutorial consists of two parts. In the first part we will familiarize the user with this new resource, describe the various layers of annotations in some detail and discuss the linguistic principles and sometimes practical considerations behind the important design decisions that shapes the corpus. We will also describe the salient differences between the three languages at each layer of annotation and how linguistic peculiarities of different languages were handled in the data. In the second part, we will describe the data formats of each of the layers and talk about various design decisions that went into the creation of the architecture of the database and the individual tables comprising it, along with issues that came up during the representation process and compromises that were made without sacrificing some primary objectives one of which being the independent existence of each layer that is necessary to allow multi-site collaboration. We will explain how the database schema attempts to interconnect all the layers. Then we will go into the details of the Python API that allows easy access to each of the layers and show that by making the objects closely resemble database tables, the API allows for their flexible integration. This will be followed by a hands-on working session.
Parallel Computer Architecture: A Hardware/Software Approach

enter the circuit several times, possibly via different input processors. More elaborate combinatorial considerations than for standard VLSI circuits are needed to show how communication complexity can be used to get lower bounds on multilective VLSI computations. Near the end of the book, Hromkovic presents relations between communication complexity and some complexity measures of sequential computations. The core section introduces a uniform model of one-way communication protocols and shows that the corresponding uniform one-way communication complexity is strongly related to the size of deterministic finite automata. Then he relates communication complexity to the time and space complexity of Turing machines. Finally, he shows how communication complexity can be used to obtain lower bounds on the size and the depth of decisions nees and branching programs.
A Genetic NewGreedy Algorithm for Influence Maximization in Social Network

A user may be influenced by the other users of a social network by sharing information. Influence maximization is one of the critical research topics aimed at knowing the current circumstances of a social network, such as the general mood of the society. The goal of this problem is to find a seed set which has a maximum influence with respect to a propagation model. For the influence maximization problem is NP-Hard, it is obvious that an exhausted search algorithm is not able to find the solution in a reasonable time. It is also obvious that a greedy algorithm may not find a solution that satisfies all the requirements. Hence, a high-performance algorithm for solving the influence maximization problem, which leverages the strength of the greedy method and the genetic algorithm (GA) is presented in this paper. Experimental results show that the proposed algorithm can provide a better result than simple GA by about 10% in terms of the quality.
Detection of Slang Words in e-Data using semi-Supervised Learning

The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day, in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.
No-reference perceptual quality assessment of JPEG compressed images

Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signalto-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance. A Matlab implementation of the proposed method is available at http://anchovy.ece.ute xas.edu/ ̃zwang/research/nr jpeg quality/index.html .
Rule Based Part of Speech Tagging of Sindhi Language

Part of Speech (POS) tagging is a process of assigning correct syntactic categories to each word in the text. Tag set and word disambiguation rules are fundamental parts of any POS tagger. No work has hitherto been published of tag set in Sindhi language. The Sindhi lexicon for computational processing is also not available. In this study, the tag set for Sindhi POS, lexicon and word disambiguation rules are designed and developed. The Sindhi corpus is collected from a comprehensive Sindhi Dictionary. The corpus is based on the most recent available vocabulary used by local people. In this paper, preliminary achievements of rule based Sindhi Part of Speech (SPOS) tagger are presented. Tagging and tokenization algorithms are also designed for the implementation of SPOS. The outputs of SPOS are verified by Sindhi linguist. The development of SPOS tagger may have an important milestone towards computational Sindhi language processing.
Places: An Image Database for Deep Scene Understanding

The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach nearhuman semantic classification at tasks such as object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories and attributes, comprising a quasi-exhaustive list of the types of environments encountered in the world. Using state of the art Convolutional Neural Networks, we provide impressive baseline performances at scene classification. With its high-coverage and high-diversity of exemplars, the Places Database offers an ecosystem to guide future progress on currently intractable visual recognition problems.
A Minimax Theory for Adaptive Data Analysis

In adaptive data analysis, the user makes a sequence of queries on the data, where at each step the choice of query may depend on the results in previous steps. The releases are often randomized in order to reduce overfitting for such adaptively chosen queries. In this paper, we propose a minimax framework for adaptive data analysis. Assuming Gaussianity of queries, we establish the first sharp minimax lower bound on the squared error in the order of O( √ kσ n ), where k is the number of queries asked, and σ/n is the ordinary signal-to-noise ratio for a single query. Our lower bound is based on the construction of an approximately least favorable adversary who picks a sequence of queries that are most likely to be affected by overfitting. This approximately least favorable adversary uses only one level of adaptivity, suggesting that the minimax risk for 1-step adaptivity with k − 1 initial releases and that for k-step adaptivity are on the same order. The key technical component of the lower bound proof is a reduction to finding the convoluting distribution that optimally obfuscates the sign of a Gaussian signal. Our lower bound construction also reveals a transparent and elementary proof of the matching upper bound as an alternative approach to Russo & Zou (2015), who used information-theoretic tools to provide the same upper bound. We believe that the proposed framework opens up opportunities to obtain theoretical insights for many other settings of adaptive data analysis, which would extend the idea to more practical realms.
Design, implementation, and non-linear control of interior permanent magnet synchronous motor with flux concentration by improved PWM-rotor design

The design, implementation, and control of rotor topology for an interior permanent magnet synchronous motor (IPMSM) is presented in this paper. The method is an arrangement of buried magnets in a flux concentration structure, combined with Pulse Width Modulation (PWM), which act on the iron core of the rotor, known as the PWM-rotor. The unsatisfied harmonic contents of the flux density in the air-gap are reduced with this technique, and the total harmonics distortion (THD) of the back electromotive force (back-EMF) subsequently decreases. The design was chosen for a motor size of 350 W, 500 rpm, 36 slots stator lamination, and 12-poles. Moreover, a nonlinear control approach based on differential flatness properties is proposed for a vector control of the PMSM drive. Using the flatness property, we propose simple solutions to PMSM drive and stabilization problems. The design controller parameters are autonomous from the operating point; moreover, high dynamics in load torque disturbance rejection is achieved. To validate the proposed method, a hardware system was built, and digital estimation was accomplished with a dSPACE controller. Experimental and simulation results corroborate the excellence of the PMSM design and control scheme.
Real Time Text Mining on Twitter Data

Social media constitute a challenging new source of information for intelligence gathering and decision making. Twitter is one of the most popular social media sites and often becomes the primary source of information. Twitter messages are short and well suited for knowledge discovery. Twitter provides both researchers and practitioners a free Application Programming Interface (API) which allows them to gather and analyse large data sets of tweets. Twitter information aren't solely tweet texts, as Twitter’s API provides a lot of info to perform attention-grabbing analysis studies. The paper concisely describes method of knowledge gathering and therefore the main areas of knowledge mining, information discovery and information visual image from Twitter information. In this paper we can create a twitter app from which we can fetch the real time twitter tweets on a particular topic and stored it into R and then we can apply several text mining steps on the tweets to pre-process the tweets text and than we
Borderline personality disorder

Recent research findings have contributed to an improved understanding and treatment of borderline personality disorder. This disorder is characterised by severe functional impairments, a high risk of suicide, a negative effect on the course of depressive disorders, extensive use of treatment, and high costs to society. The course of this disorder is less stable than expected for personality disorders. The causes are not yet clear, but genetic factors and adverse life events seem to interact to lead to the disorder. Neurobiological research suggests that abnormalities in the frontolimbic networks are associated with many of the symptoms. Data for the effectiveness of pharmacotherapy vary and evidence is not yet robust. Specific forms of psychotherapy seem to be beneficial for at least some of the problems frequently reported in patients with borderline personality disorder. At present, there is no evidence to suggest that one specific form of psychotherapy is more effective than another. Further research is needed on the diagnosis, neurobiology, and treatment of borderline personality disorder.
Dual-band stepped-impedance transformer to full-height substrate-integrated waveguide

A dual-band transition from conventional to full-height substrate-integrated waveguide (SIW) is introduced. The transition makes use of multiple stepped-impedance sections to achieve dual-band behavior with a high return loss. An equivalent circuit model of the transition is developed, that enables fast optimization. A transition is designed that features two frequency bands at 20GHz and 30 GHz. For verification a back-to-back coplanar-waveguide to full-height SIW transition is fabricated and measured.
Bridgeless half-bridge AC-DC converter with series-connected two transformers

This paper proposes a bridgeless half-bridge ac-dc converter with series-connected two transformers. The proposed converter integrates the operation of the bridgeless power factor correction (PFC) boost rectifier and the asymmetrical pulse-width modulation half-bridge dc-dc converter. The proposed converter provides an isolated dc output voltage without any full-bridge diode rectifier. Conduction losses are lowered by eliminating the full-bridge diode rectifier. Switching losses are reduced by achieving zero-voltage switching of the power switches. To improve the power efficiency of the bridgeless half-bridge ac-dc converter, this paper presents the study results about the bridgeless half-bridge ac-dc converter with the series-connected two transformers. By using series-connected two transformers, the proposed convert has a simpler power circuit. The proposed converter gives a high efficiency, high power factor, and low cost. The proposed converter achieves a high efficiency of 93.5 % for 250 W (50 V / 5 A) output power at 90 Vrms line voltage.
Single actuator wave-like robot (SAW): design, modeling, and experiments.

In this paper, we present a single actuator wave-like robot, a novel bioinspired robot which can move forward or backward by producing a continuously advancing wave. The robot has a unique minimalistic mechanical design and produces an advancing sine wave, with a large amplitude, using only a single motor but with no internal straight spine. Over horizontal surfaces, the robot does not slide relative to the surface and its direction of locomotion is determined by the direction of rotation of the motor. We developed a kinematic model of the robot that accounts for the two-dimensional mechanics of motion and yields the speed of the links relative to the motor. Based on the optimization of the kinematic model, and accounting for the mechanical constraints, we have designed and built multiple versions of the robot with different sizes and experimentally tested them (see movie). The experimental results were within a few percentages of the expectations. The larger version attained a top speed of 57 cm s(-1) over a horizontal surface and is capable of climbing vertically when placed between two walls. By optimizing the parameters, we succeeded in making the robot travel by 13% faster than its own wave speed.
Character-based feature extraction with LSTM networks for POS-tagging task

In this paper we describe a work in progress on designing the continuous vector space word representations able to map unseen data adequately. We propose a LSTM-based feature extraction layer that reads in a sequence of characters corresponding to a word and outputs a single fixed-length real-valued vector. We then test our model on a POS tagging task on four typologically different languages. The results of the experiments suggest that the model can offer a solution to the out-of-vocabulary words problem, as in a comparable setting its OOV accuracy improves over that of a state of the art tagger.
Design and Control of Autonomous Underwater Robots: A Survey

During the 1990s, numerous worldwide research and development activities have occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R&D activities in the AUV community have increased. However, this is just the beginning for more advanced, yet practical and reliable AUVs. This paper surveys some key areas in current state-of-the-art underwater robotic technologies. It is by no means a complete survey but provides key references for future development. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs.
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia

We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. Compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-theart. We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top-ranking Wikipedia articles and create a corpus of over one million questionanswer pairs. We also provide a qualitative analysis for this large-scale generated corpus from Wikipedia.