nils-herrmann/clean_scirepeval huggingface dataset:
A comparison of autoencoder and statistical features for cattle behaviour classification

A study is presented comparing the effectiveness of unsupervised feature representations with handcrafted features for cattle behaviour classification. Precision management of cattle requires the interaction of individual animals to be continuously monitored on the farm. Consequently, classifiers are trained to infer the behaviour of the animals using the observations from the sensors that are fitted upon them. Historically, domain knowledge drives the generation of features for cattle behaviour classifiers. When new behaviours are introduced into the system, however, it is often necessary to modify the feature set; this requires additional design and more data. Autoencoders, on the other hand, can skip this design step by learning a common, unsupervised feature representation for training. Whilst stacked autoencoders successfully represent structured data including speech, language and images, deep networks have not been used to model cattle motion. Hence, we investigate using a stacked autoencoder to learn a feature representation for cattle behaviour classification. Experimental results demonstrate that the autoencoder features perform reasonably well in comparison to the statistical features that are selected using prior knowledge of behaviour motion.
A Learning-based Neural Network Model for the Detection and Classification of SQL Injection Attacks

Structured﻿Query﻿Language﻿ injection﻿ (SQLi)﻿ attack﻿ is﻿ a﻿ code﻿ injection﻿ technique﻿where﻿hackers﻿ inject﻿SQL﻿commands﻿into﻿a﻿database﻿via﻿a﻿vulnerable﻿web﻿application.﻿Injected﻿SQL﻿commands﻿can﻿ modify﻿the﻿back-end﻿SQL﻿database﻿and﻿thus﻿compromise﻿the﻿security﻿of﻿a﻿web﻿application.﻿In﻿the﻿ previous﻿publications,﻿the﻿author﻿has﻿proposed﻿a﻿Neural﻿Network﻿(NN)-based﻿model﻿for﻿detections﻿ and﻿classifications﻿of﻿ the﻿SQLi﻿attacks.﻿The﻿proposed﻿model﻿was﻿built﻿from﻿three﻿elements:﻿1)﻿a﻿ Uniform﻿Resource﻿Locator﻿(URL)﻿generator,﻿2)﻿a﻿URL﻿classifier,﻿and﻿3)﻿a﻿NN﻿model.﻿The﻿proposed﻿ model﻿was﻿ successful﻿ to:﻿1)﻿detect﻿ each﻿generated﻿URL﻿as﻿either﻿a﻿benign﻿URL﻿or﻿a﻿malicious,﻿ and﻿2)﻿identify﻿the﻿type﻿of﻿SQLi﻿attack﻿for﻿each﻿malicious﻿URL.﻿The﻿published﻿results﻿proved﻿the﻿ effectiveness﻿of﻿the﻿proposal.﻿In﻿this﻿paper,﻿the﻿author﻿re-evaluates﻿the﻿performance﻿of﻿the﻿proposal﻿ through﻿two﻿scenarios﻿using﻿controversial﻿data﻿sets.﻿The﻿results﻿of﻿the﻿experiments﻿are﻿presented﻿in﻿ order﻿to﻿demonstrate﻿the﻿effectiveness﻿of﻿the﻿proposed﻿model﻿in﻿terms﻿of﻿accuracy,﻿true-positive﻿rate﻿ as﻿well﻿as﻿false-positive﻿rate.  Artificial Intelligence, Databases, Intrusion Detection, Machine Learning, Neural Networks, SQL Injection Attacks,
Self-control as limited resource: regulatory depletion patterns.

If self-regulation conforms to an energy or strength model, then self-control should be impaired by prior exertion. In Study 1, trying to regulate one's emotional response to an upsetting movie was followed by a decrease in physical stamina. In Study 2, suppressing forbidden thoughts led to a subsequent tendency to give up quickly on unsolvable anagrams. In Study 3, suppressing thoughts impaired subsequent efforts to control the expression of amusement and enjoyment. In Study 4, autobiographical accounts of successful versus failed emotional control linked prior regulatory demands and fatigue to self-regulatory failure. A strength model of self-regulation fits the data better than activation, priming, skill, or constant capacity models of self-regulation.
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

We investigate the problem of lung nodule malignancy suspiciousness (the likelihood of nodule malignancy) classification using thoracic Computed Tomography (CT) images. Unlike traditional studies primarily relying on cautious nodule segmentation and time-consuming feature extraction, we tackle a more challenging task on directly modeling raw nodule patches and building an end-to-end machinelearning architecture for classifying lung nodule malignancy suspiciousness. We present a Multi-crop Convolutional Neural Network (MC-CNN) to automatically extract nodule salient information by employing a novel multi-crop pooling strategy which crops different regions from convolutional feature maps and then applies max-pooling different times. Extensive experimental results show that the proposed method not only achieves state-of-the-art nodule suspiciousness classification performance, but also effectively characterizes nodule semantic attributes (subtlety and margin) and nodule diameter which are potentially helpful in modeling nodule malignancy. & 2016
Scripting smart contracts for distributed ledger technology

We give an overview of the scripting languages used in existing cryptocurrencies, and in particular we review in some detail the scripting languages of Bitcoin, Nxt and Ethereum, in the context of a high-level overview of Distributed Ledger Technology and cryptocurrencies. We survey different approaches, and give an overview of critiques of existing languages. We also cover technologies that might be used to underpin extensions and innovations in scripting and contracts, including technologies for verification, such as zero knowledge proofs, proof-carrying code and static analysis, as well as approaches to making systems more efficient, e.g. Merkelized Abstract Syntax Trees.
A Survey of Table Recognition : Models , Observations , Transformations , and Inferences

Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables, and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies the decisions that are made by a table recognizer, and the assumptions and inferencing techniques that underlie these decisions.
Learning place-dependant features for long-term vision-based localisation

In order for autonomous vehicles to achieve life-long operation in outdoor environments, navigation systems must be able to cope with visual change—whether it’s short term, such as variable lighting or weather conditions, or long term, such as different seasons. As a Global Positioning System (GPS) is not always reliable, autonomous vehicles must be self sufficient with onboard sensors. This thesis examines the problem of localisation against a known map across extreme lighting and weather conditions using only a stereo camera as the primary sensor. The method presented departs from traditional techniques that blindly apply out-of-the-box interest-point detectors to all images of all places. This naive approach fails to take into account any prior knowledge that exists about the environment in which the robot is operating. Furthermore, the point-feature approach often fails when there are dramatic appearance changes, as associating low-level features such as corners or edges is extremely difficult and sometimes not possible. By leveraging knowledge of prior appearance, this thesis presents an unsupervised method for learning a set of distinctive and stable (i.e., stable under appearance changes) feature detectors that are unique to a specific place in the environment. In other words, we learn place-dependent feature detectors that enable vastly superior performance in terms of robustness in exchange for a reduced, but tolerable metric precision. By folding in a method for masking distracting objects in dynamic environments and examining a simple model for external illuminates, such as the sun, this thesis presents a robust localisation system that is able to achieve metric estimates from night-today or summer-to-winter conditions. Results are presented from various locations in the UK, including the Begbroke Science Park, Woodstock, Oxford, and central London. Statement of Authorship This thesis is submitted to the Department of Engineering Science, University of Oxford, in fulfilment of the requirements for the degree of Doctor of Philosophy. This thesis is entirely my own work, and except where otherwise stated, describes my own research. Colin McManus, Lady Margaret Hall Funding The work described in this thesis was funded by Nissan Motors.
An LCD column driver using a switch capacitor DAC

LCD column drivers have traditionally used nonlinear R-string style digital-to-analog converters (DAC). This paper describes an architecture that uses 840 linear charge redistribution 10/12-bit DACs to implement a 420-output column driver. Each DAC performs its conversion in less than 15 /spl mu/s and draws less than 5 /spl mu/A. This architecture allows 10-bit independent color control in a 17 mm/sup 2/ die for the LCD television market.
Procedural Arrangement of Furniture for Real-Time Walkthroughs

This paper presents a procedural approach to generate furniture arrangements for large virtual indoor scenes. The interiors of buildings in 3D city scenes are often omitted. Our solution creates rich furniture arrangements for all rooms of complex buildings and even for entire cities. The key idea is to only furnish the rooms in the vicinity of the viewer while the user explores a building in real time. In order to compute the object layout we introduce an agent-based solution and demonstrate the flexibility and effectiveness of the agent approach. Furthermore, we describe advanced features of the system, like procedural furniture geometry, persistent room layouts, and styles for high-level control.
Managing the interstitials, a System of Systems framework suited for the Ballistic Missile Defense System

Recent engineering experiences with the Missile Defense Agency (MDA) Ballistic Missile Defense System (BMDS) highlight the need to analyze the BMDS System of Systems (SoS) including the numerous potential interactions between independently developed elements of the system. The term “interstitials” is used to define the domain of interfaces, interoperability, and integration between constituent systems in an SoS. The authors feel that this domain, at an SoS level, has received insufficient attention within systems engineering literature. The BMDS represents a challenging SoS case study as many of its initial elements were assembled from existing programs of record. The elements tend to perform as designed but their performance measures may not be consistent with the higher level SoS requirements. One of the BMDS challenges is interoperability, to focus the independent elements to interact in a number of ways, either subtle or overt, for a predictable and sustainable national capability. New capabilities desired by national leadership may involve modifications to kill chains, Command and Control (C2) constructs, improved coordination, and performance. These capabilities must be realized through modifications to programs of record and integration across elements of the system that have their own independent programmatic momentum. A challenge of SoS Engineering is to objectively evaluate competing solutions and assess the technical viability of tradeoff options. This paper will present a multifaceted technical approach for integrating a complex, adaptive SoS to achieve a functional capability. Architectural frameworks will be explored, a mathematical technique utilizing graph theory will be introduced, adjuncts to more traditional modeling and simulation techniques such as agent based modeling will be explored, and, finally, newly developed technical and managerial metrics to describe design maturity will be introduced. A theater BMDS construct will be used as a representative set of elements together with the *Author to whom all correspondence should be addressed (e-mail: DLGR_NSWC_G25@navy.mil; DLGR_NSWC_K@Navy.mil; DLGR_NSWC_W@navy.mil; DLGR_NSWC_W@Navy.mil). †Commanding Officer, 6149 Welsh Road, Suite 203, Dahlgren, VA 22448-5130
UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis

This paper describes our system participating in the aspect-based sentiment analysis task of Semeval 2014. The goal was to identify the aspects of given target entities and the sentiment expressed towards each aspect. We firstly introduce a system based on supervised machine learning, which is strictly constrained and uses the training data as the only source of information. This system is then extended by unsupervised methods for latent semantics discovery (LDA and semantic spaces) as well as the approach based on sentiment vocabularies. The evaluation was done on two domains, restaurants and laptops. We show that our approach leads to very promising results.
Markov Chain Sampling Methods for Dirichlet Process Mixture Models

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The 3GPP NB-IoT system architecture for the Internet of Things

The first version of the 3GPP NarrowBand-IoT (NB-IoT) standards has been finalized in June 2016 as part of Release 13. NB-IoT is a promising new radio access technology which can coexist with existing GSM, UMTS and LTE deployments. In fact, NB-IoT specifications have been integrated into LTE standards. NB-IoT goes a step further than the MTC  (Machine Type Communication) specification, focusing on extremely low cost devices, massive deployments and reduced data rates with a carrier bandwidth of just 200 kHz (hence its name). In this paper we will take a close look at 3GPP specifications to discover which are the modifications required in traditional LTE deployments to provide connectivity to the upcoming Cat-NB1 User Equipments (UEs).
The GRASP Taxonomy of Human Grasp Types

In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand. The goal is to extract the largest set of different grasps that were referenced in the literature and arrange them in a systematic way. The taxonomy provides a common terminology to define human hand configurations and is important in many domains such as human-computer interaction and tangible user interfaces where an understanding of the human is basis for a proper interface. Overall, 33 different grasp types are found and arranged into the GRASP taxonomy. Within the taxonomy, grasps are arranged according to 1) opposition type, 2) the virtual finger assignments, 3) type in terms of power, precision, or intermediate grasp, and 4) the position of the thumb. The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition. We also show that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more general grasps if only the hand configuration is considered without the object shape/size.
A Better Balun? Done The Design of a 4:1 Wideband Balun Using a Parallel-Connected Transmission-Line Balun

This article presents a 4:1 wide-band balun that won the student design competition for wide-band baluns held during the 2016 IEEE Microwave Theory and Techniques Society (MTT-S) International Microwave Symposium (IMS2016) in San Francisco, California. For this contest, sponsored by Technical Committee MTT-17, participants were required to implement and evaluate their own baluns, with the winning entry achieving the widest bandwidth while satisfying the conditions of the competition rules during measurements at IMS2016. Some of the conditions were revised for this year's competition compared with previous competitions as follows.
Empirical studies assessing the quality of health information for consumers on the world wide web: a systematic review.

The quality of consumer health information on the World Wide Web is an important issue for medicine, but to date no systematic and comprehensive synthesis of the methods and evidence has been performed.



To establish a methodological framework on how quality on the Web is evaluated in practice, to determine the heterogeneity of the results and conclusions, and to compare the methodological rigor of these studies, to determine to what extent the conclusions depend on the methodology used, and to suggest future directions for research.



We searched MEDLINE and PREMEDLINE (1966 through September 2001), Science Citation Index (1997 through September 2001), Social Sciences Citation Index (1997 through September 2001), Arts and Humanities Citation Index (1997 through September 2001), LISA (1969 through July 2001), CINAHL (1982 through July 2001), PsychINFO (1988 through September 2001), EMBASE (1988 through June 2001), and SIGLE (1980 through June 2001). We also conducted hand searches, general Internet searches, and a personal bibliographic database search.



We included published and unpublished empirical studies in any language in which investigators searched the Web systematically for specific health information, evaluated the quality of Web sites or pages, and reported quantitative results. We screened 7830 citations and retrieved 170 potentially eligible full articles. A total of 79 distinct studies met the inclusion criteria, evaluating 5941 health Web sites and 1329 Web pages, and reporting 408 evaluation results for 86 different quality criteria.



Two reviewers independently extracted study characteristics, medical domains, search strategies used, methods and criteria of quality assessment, results (percentage of sites or pages rated as inadequate pertaining to a quality criterion), and quality and rigor of study methods and reporting.



Most frequently used quality criteria used include accuracy, completeness, readability, design, disclosures, and references provided. Fifty-five studies (70%) concluded that quality is a problem on the Web, 17 (22%) remained neutral, and 7 studies (9%) came to a positive conclusion. Positive studies scored significantly lower in search (P =.02) and evaluation (P =.04) methods.



Due to differences in study methods and rigor, quality criteria, study population, and topic chosen, study results and conclusions on health-related Web sites vary widely. Operational definitions of quality criteria are needed.
On the usefulness of query features for learning to rank

Learning to rank studies have mostly focused on query-dependent and query-independent document features, which enable the learning of ranking models of increased effectiveness. Modern learning to rank techniques based on regression trees can support query features, which are document-independent, and hence have the same values for all documents being ranked for a query. In doing so, such techniques are able to learn sub-trees that are specific to certain types of query. However, it is unclear which classes of features are useful for learning to rank, as previous studies leveraged anonymised features. In this work, we examine the usefulness of four classes of query features, based on topic classification, the history of the query in a query log, the predicted performance of the query, and the presence of concepts such as persons and organisations in the query. Through experiments on the ClueWeb09 collection, our results using a state-of-the-art learning to rank technique based on regression trees show that all four classes of query features can significantly improve upon an effective learned model that does not use any query feature.
An L1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition

Identifying sparse salient structures from dense pixels is a longstanding problem in visual computing. Solutions to this problem can benefit both image manipulation and understanding. In this paper, we introduce an image transform based on the L1 norm for piecewise image flattening. This transform can effectively preserve and sharpen salient edges and contours while eliminating insignificant details, producing a nearly piecewise constant image with sparse structures. A variant of this image transform can perform edge-preserving smoothing more effectively than existing state-of-the-art algorithms. We further present a new method for complex scene-level intrinsic image decomposition. Our method relies on the above image transform to suppress surface shading variations, and perform probabilistic reflectance clustering on the flattened image instead of the original input image to achieve higher accuracy. Extensive testing on the Intrinsic-Images-in-the-Wild database indicates our method can perform significantly better than existing techniques both visually and numerically. The obtained intrinsic images have been successfully used in two applications, surface retexturing and 3D object compositing in photographs.
A Context-Driven IoT Middleware Architecture

The Internet of Things (IoT) refers to an environment of ubiquitous sensing and actuation from devices connected to the web backend. IoT applications leverage contextual information about entities in the system for reasoning and actuation. These context-aware applications are difficult to scale to the large amount of heterogeneous data in the IoT, as the current state-of-the-art is black-box, monolithic, application-specific implementations. We propose a middleware framework for context-aware applications that generates intermediate, reusable context extracted from input by breaking down applications into a set of functional units, or context engines. Leveraging existing IoT ontologies, we can replace application-specific implementations with a composition of context engines that use statistical learning to generate output, improving context reuse and reducing computational redundancy and complexity. We implement an IoT application using our framework, extracting residential user activity from plug loads, and demonstrate a reduction in computational complexity by 23% and execution overhead by 69%.
Learn To Pay Attention

We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map. Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parameterised by the score matrices, must alone be used for classification. Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values. Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter. Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets. When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset. We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.