nils-herrmann/clean_scirepeval huggingface dataset:
Learning SURF Cascade for Fast and Accurate Object Detection

This paper presents a novel learning framework for training boosting cascade based object detector from large scale dataset. The framework is derived from the well-known Viola-Jones (VJ) framework but distinguished by three key differences. First, the proposed framework adopts multi-dimensional SURF features instead of single dimensional Haar features to describe local patches. In this way, the number of used local patches can be reduced from hundreds of thousands to several hundreds. Second, it adopts logistic regression as weak classifier for each local patch instead of decision trees in the VJ framework. Third, we adopt AUC as a single criterion for the convergence test during cascade training rather than the two trade-off criteria (false-positive-rate and hit-rate) in the VJ framework. The benefit is that the false-positive-rate can be adaptive among different cascade stages, and thus yields much faster convergence speed of SURF cascade. Combining these points together, the proposed approach has three good properties. First, the boosting cascade can be trained very efficiently. Experiments show that the proposed approach can train object detectors from billions of negative samples within one hour even on personal computers. Second, the built detector is comparable to the state-of-the-art algorithm not only on the accuracy but also on the processing speed. Third, the built detector is small in model-size due to short cascade stages.
Commitments and conventions: The foundation of coordination in multi-agent systems

Distributed Artificial Intelligence systems, in which multiple agents interact to improve their individual performance and to enhance the system’s overall utility, are becoming an increasingly pervasive means of conceptualising a diverse range of applications. As the discipline matures, researchers are beginning to strive for the underlying theories and principles which guide the central processes of coordination and cooperation. Here agent communities are modelled using a distributed goal search formalism and it is argued that commitments (pledges to undertake a specified course of action) and conventions (means of monitoring commitments in changing circumstances) are the foundation of coordination in multi-agent systems. An analysis of existing coordination models which use concepts akin to commitments and conventions is undertaken before a new unifying framework is presented. Finally a number of prominent coordination techniques which do not explicitly involve commitments or conventions are reformulated in these terms to demonstrate their compliance with the central hypothesis of this paper.
A Buyer's Guide to Conic Fitting

In this paper we evaluate several methods of fitting data to conic sections. Conic fitting is a commonly required task in machine vision, but many algorithms perform badly on incomplete or noisy data. We evaluate several algorithms under various noise and degeneracy conditions, identify the key parameters which affect sensitivity, and present the results of comparative experiments which emphasize the algorithms' behaviours under common examples of degenerate data. In addition, complexity analyses in terms of flop counts are provided in order to further inform the choice of algorithm for a specific application.
USING JTAG BASED ON HOL / SCALA / LMS / JIKESRVM / JVM INFORMATICS FRAMEWORK – AN INSIGHT INTO FORENSIC IMAGING OF EMBEDDED SYSTEMS IN THE CONTEXT OF SMART DEVICES

In this technical note the author is interested in exploiting the advantages of Higher Order Logic(HOL),Scala,JVM,JikesRVM & LMS in the IoT scenario.The title is highly self explanatory hence detailed explanation is skipped. index words: IoT/JVM/Scala/HOL/LMS(Lightweight Modular Staging)/Forensic Imaging/Firmware/JTAG.  [i] “JTAG Explained (finally!): Why "IoT", Software Security Engineers, and Manufacturers Should Care “ Source : http://blog.senr.io/blog/jtag-explained [ii] Forensic imaging of embedded systems using JTAG (boundary-scan) https://doi.org/10.1016/j.diin.2006.01.003 [iii] Scala Based FPGA Design Flow ( https://doi.org/10.1145/3020078.3021762 Informatics Framework  Figure I – Approximate Informatics Framework – HOL/Scala/JTAG/JikesRVM/LMS/IoT ** Readers please note “the actual implementation might vary to some extent”. Analysis of the Informatics Framework: Informatics framework was explored based on HOL/Scala/JTAG/JVM/IoT to perform forensic imaging of embedded systems in the context of Smart Devices.[Refs from 1 to 9 are used ].Figure I illustrates the main idea to help those who are interested in this interesting domain of embedded systems. s with future perspectives : A simple HOL/Scala/LMS/JVM/IoT based “Informatics Framework” was designed and presented in this technical note.It is sincerely hoped that many new comers in this promising area will explore this challenging domain of “Forensic Imaging of Embedded Systems” using JTAG.  https://isabelle.in.tum.de/ HOL [b] https://www.isa-afp.org/entries/AWN.html HOL based Wireless Library  Lightweight Modular Staging (LMS) is a runtime code generation approach.  Spiral for DSP based in Scala.
AOD-Net: All-in-One Dehazing Network

This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level tasks on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN, we witness a large improvement of the object detection performance on hazy images.
SUMO ( Simulation of Urban MObility ) An open-source traffic simulation

As no exact model of traffic flow exists due to its high complexity and chaotic organisation, researchers mainly try to predict traffic using simulations. Within this field, many simulation packages exist and differ in their software architecture paradigm as well as in the models that describe traffic itself. We will introduce yet another system which, in contrast to most of the other simulation software packages, is available as an open-source program and may therefore be extended in order to fit a researcher’s own needs and also be used as a reference testbed for new traffic models.
AlphaGAN: Generative adversarial networks for natural image matting

We present the first generative adversarial network (GAN) for natural image matting. Our novel generator network is trained to predict visually appealing alphas with the addition of the adversarial loss from the discriminator that is trained to classify wellcomposited images. Further, we improve existing encoder-decoder architectures to better deal with the spatial localization issues inherited in convolutional neural networks (CNN) by using dilated convolutions to capture global context information without downscaling feature maps and losing spatial information. We present state-of-the-art results on the alphamatting online benchmark for the gradient error and give comparable results in others. Our method is particularly well suited for fine structures like hair, which is of great importance in practical matting applications, e.g. in film/TV production.
Deep Reinforcement Learning for Autonomous Driving

Reinforcement learning has steadily improved and outperform human in lots of traditional games since the resurgence of deep neural network. However, these success is not easy to be copied to autonomous driving because the state spaces in real world are extreme complex and action spaces are continuous and fine control is required. Moreover, the autonomous driving vehicles must also keep functional safety under the complex environments. To deal with these challenges, we first adopt the deep deterministic policy gradient (DDPG) algorithm, which has the capacity to handle complex state and action spaces in continuous domain. We then choose The Open Racing Car Simulator (TORCS) as our environment to avoid physical damage. Meanwhile, we select a set of appropriate sensor information from TORCS and design our own rewarder. In order to fit DDPG algorithm to TORCS, we design our network architecture for both actor and critic inside DDPG paradigm. To demonstrate the effectiveness of our model, We evaluate on different modes in TORCS and show both quantitative and qualitative results.
Improving the Extraction of Text in PDFs by Simulating the Human Reading Order

Text preprocessing and segmentation are critical tasks in search and text mining applications. Due to the huge amount of documents that are exclusively presented in PDF format, most of the Data Mining (DM) and Information Retrieval (IR) systems must extract content from the PDF files. In some occasions this is a difficult task: the result of the extraction process from a PDF file is plain text, and it should be returned in the same order as a human would read the original PDF file. However, current tools for PDF text extraction fail in this objective when working with complex documents with multiple columns. For instance, this is the case of official government bulletins with legal information. In this task, it is mandatory to get correct and ordered text as a result of the application of the PDF extractor. It is very usual that a legal article in a document refers to a previous article and they should be offered in the right sequential order. To overcome these difficulties we have designed a new method for extraction of text in PDFs that simulates the human reading order. We evaluated our method and compared it against other PDF extraction tools and algorithms. Evaluation of our approach shows that it significantly outperforms the results of the existing tools and algorithms.
Factoring large integers using parallel Quadratic Sieve

Integer factorization is a well studied topic. Parts of the cryptography we use each day rely on the fact that this problem is di cult. One method one can use for factorizing a large composite number is the Quadratic Sieve algorithm. This method is among the best known today. We present a parallel implementation of the Quadratic Sieve using the Message Passing Interface (MPI). We also discuss the performance of this implementation which shows that this approach is a good one.
A Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes

In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power. Nevertheless, it is well-known that flat reinforcement learning algorithms are often have trouble learning and are even data-efficient with respect to tasks having hierarchical structures, e.g., those consisting of multiple subtasks. Hierarchical reinforcement learning is a principled approach that can tackle such challenging tasks. On the other hand, many real-world tasks usually have only partial observability in which state measurements are often imperfect and partially observable. The problems of RL in such settings can be formulated as a partially observable Markov decision process (POMDP). In this paper, we study hierarchical RL in a POMDP in which the tasks have only partial observability and possess hierarchical properties. We propose a hierarchical deep reinforcement learning approach for learning in hierarchical POMDP. The deep hierarchical RL algorithm is proposed for domains to both MDP and POMDP learning. We evaluate the proposed algorithm using various challenging hierarchical POMDPs.
Feature selection using Deep Neural Networks

Feature descriptors involved in video processing are generally high dimensional in nature. Even though the extracted features are high dimensional, many a times the task at hand depends only on a small subset of these features. For example, if two actions like running and walking have to be identified, extracting features related to the leg movement of the person is enough. Since, this subset is not known apriori, we tend to use all the features, irrespective of the complexity of the task at hand. Selecting task-aware features may not only improve the efficiency but also the accuracy of the system. In this work, we propose a supervised approach for task-aware selection of features using Deep Neural Networks (DNN) in the context of action recognition. The activation potentials contributed by each of the individual input dimensions at the first hidden layer are used for selecting the most appropriate features. The selected features are found to give better classification performance than the original high-dimensional features. It is also shown that the classification performance of the proposed feature selection technique is superior to the low-dimensional representation obtained by principal component analysis (PCA).
A Model for Text Summarization

Text summarization is a process for creating a concise version of document(s) preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At the first stage, to discover all topics the sentences set is clustered by using k-means method. At the second stage, optimum selection of sentences is proposed. From each cluster the salient sentences are selected according to their contribution to the topic (cluster) and their proximity to other sentences in cluster to avoid redundancy in summaries until the appointed summary length is reached. Sentence selection is modeled as an optimization problem. In this study, to solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed. With a test on benchmark DUC2001 and DUC2002 data sets, the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the traditional methods of sentence selection and the top three performing systems for DUC2001 and DUC2002.  Diversity, k-Means, Optimization Model, Sentence Clustering, Text Summarization
Deep Query Ranking for Question Answering over Knowledge Bases

We study question answering systems over knowledge graphs which map an input natural language question into candidate formal queries. Often, a ranking mechanism is used to discern the queries with higher similarity to the given question. Considering the intrinsic complexity of the natural language, finding the most accurate formal counter-part is a challenging task. In our recent paper , we leveraged Tree-LSTM to exploit the syntactical structure of input question as well as the candidate formal queries to compute the similarities. An empirical study shows that taking the structural information of the input question and candidate query into account enhances the performance, when compared to the baseline system.
Chinese Open Relation Extraction for Knowledge Acquisition

This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules. We employ the proposed CORE techniques to extract more than 13 million entity-relations for an open domain question answering application. To our best knowledge, CORE is the first Chinese Open IE system for knowledge acquisition.
OBDA Beyond Relational DBs: A Study for MongoDB

The database landscape has been significantly diversified during the last decade, resulting in the emergence of a variety of non-relational (also called NoSQL) databases, e.g., XML and JSON-document databases, key-value stores, and graph databases. To facilitate access to such databases and to enable data integration of non-relational data sources, we generalize the well-known ontologybased data access (OBDA) framework so as to allow for querying arbitrary databases through a mediating ontology. We instantiate this framework to MongoDB, a popular JSON-document database, and implement an prototype extension of the virtual OBDA system Ontop for answering SPARQL queries over MongoDB.
Effects of long-term resistance training and simultaneous electro-stimulation on muscle strength and functional mobility in multiple sclerosis.

Resistance training studies in multiple sclerosis (MS) often use short intervention periods. Furthermore, training efficiency could be optimized by unilateral training and/or electrical stimulation.



To examine the effect(s) of unilateral long-term (20 weeks) standardized resistance training with and without simultaneous electro-stimulation on leg muscle strength and overall functional mobility.



A randomized controlled trial involving 36 persons with MS. At baseline (PRE) and after 10 (MID) and 20 (POST) weeks of standardized (ACSM) light to moderately intense unilateral leg resistance training (RES(O), n = 11) only or resistance training with simultaneous electro-stimulation (RES(E), n = 11, 100 Hz, biphasic symmetrical wave, 400 µs), maximal isometric strength of the knee extensors and flexors (45°, 90° knee angle) and dynamic (60-180°/s) knee-extensor strength was measured and compared with a control group (CON, n = 14). Functional mobility was evaluated using the Timed Get Up and Go, Timed 25 Foot Walk, Two-Minute Walk Test, Functional Reach and Rivermead Mobility Index.



Maximal isometric knee extensor (90°, MID: +10 ± 3%, POST: +10  ±  4%) in RES(O) and knee flexor (45°, POST: +7 ± 4%; 90°, POST: +9  ±  5%) in RES(E) strength increased (p < 0.05) compared with CON but RES(O) and RES(E) did not differ. Also, impaired legs responded positively to resistance training (unilateral leg strength analysis) and functional reaching increased significantly in RES(O) (+18%) compared with CON. Dynamic muscle strength and the remaining functional mobility tests did not change.



Long-term light to moderately intense resistance training improves muscle strength in persons with MS but simultaneous electro-stimulation does not further improve training outcome.
Massively Parallel Methods for Deep Reinforcement Learning

We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; parallel learners that are trained from stored experience; a distributed neural network to represent the value function or behaviour policy; and a distributed store of experience. We used our architecture to implement the Deep Q-Network algorithm (DQN) (Mnih et al., 2013). Our distributed algorithm was applied to 49 games from Atari 2600 games from the Arcade Learning Environment, using identical hyperparameters. Our performance surpassed non-distributed DQN in 41 of the 49 games and also reduced the wall-time required to achieve these results by an order of magnitude on most games.
Characterisation and Deposition Studies of Recrystallised Lactose from Binary Mixtures of Ethanol/Butanol for Improved Drug Delivery from Dry Powder Inhalers

Dry powder inhaler formulations comprising commercial lactose–drug blends can show restricted detachment of drug from lactose during aerosolisation, which can lead to poor fine particle fractions (FPFs) which are suboptimal. The aim of the present study was to investigate whether the crystallisation of lactose from different ethanol/butanol co-solvent mixtures could be employed as a method of altering the FPF of salbutamol sulphate from powder blends. Lactose particles were prepared by an anti-solvent recrystallisation process using various ratios of the two solvents. Crystallised lactose or commercial lactose was mixed with salbutamol sulphate and in vitro deposition studies were performed using a multistage liquid impinger. Solid-state characterisation results showed that commercial lactose was primarily composed of the α-anomer whilst the crystallised lactose samples comprised a α/β mixture containing a lower number of moles of water per mole of lactose compared to the commercial lactose. The crystallised lactose particles were also less elongated and more irregular in shape with rougher surfaces. Formulation blends containing crystallised lactose showed better aerosolisation performance and dose uniformity when compared to commercial lactose. The highest FPF of salbutamol sulphate (38.0 ± 2.5%) was obtained for the lactose samples that were crystallised from a mixture of ethanol/butanol (20:60) compared to a FPF of 19.7 ± 1.9% obtained for commercial lactose. Engineered lactose carriers with modified anomer content and physicochemical properties, when compared to the commercial grade, produced formulations which generated a high FPF.
Deep Learning for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMTC) refers to the problem of assigning to each document its most relevant subset of class labels from an extremely large label collection, where the number of labels could reach hundreds of thousands or millions. The huge label space raises research challenges such as data sparsity and scalability. Significant progress has been made in recent years by the development of new machine learning methods, such as tree induction with large-margin partitions of the instance spaces and label-vector embedding in the target space. However, deep learning has not been explored for XMTC, despite its big successes in other related areas. This paper presents the first attempt at applying deep learning to XMTC, with a family of new Convolutional Neural Network (CNN) models which are tailored for multi-label classification in particular. With a comparative evaluation of 7 state-of-the-art methods on 6 benchmark datasets where the number of labels is up to 670,000, we show that the proposed CNN approach successfully scaled to the largest datasets, and consistently produced the best or the second best results on all the datasets. On the Wikipedia dataset with over 2 million documents and 500,000 labels in particular, it outperformed the second best method by 11.7%~15.3% in precision@K and by 11.5%~11.7% in NDCG@K for K = 1,3,5.
CRF-net: Single Image Radiometric Calibration using CNNs

In this paper we present CRF-net, a CNN-based solution for estimating the camera response function from a single photograph. We follow the recent trend of using synthetic training data, and generate a large set of training pairs based on a small set of radio-metrically linear images and the DoRF database of camera response functions. The resulting CRF-net estimates the parameters of the EMoR camera response model directly from a single photograph. Experimentally, we show that CRF-net is able to accurately recover the camera response function from a single photograph under a wide range of conditions.
Principles of Data Mining

Data mining is the discovery of interesting, unexpected or valuable structures in large datasets. As such, it has two rather different aspects. One of these concerns large-scale, 'global' structures, and the aim is to model the shapes, or features of the shapes, of distributions. The other concerns small-scale, 'local' structures, and the aim is to detect these anomalies and decide if they are real or chance occurrences. In the context of signal detection in the pharmaceutical sector, most interest lies in the second of the above two aspects; however, signal detection occurs relative to an assumed background model, therefore, some discussion of the first aspect is also necessary. This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.