nils-herrmann/clean_scirepeval huggingface dataset:
Efficient and Robust Automated Machine Learning

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub AUTO-SKLEARN, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of AUTO-SKLEARN.
Wideband Microstrip Patch Antenna With U-Shaped Parasitic Elements

A wideband U-shaped parasitic patch antenna is proposed. Two parasitic elements are incorporated into the radiating edges of a rectangular patch whose length and width are lambdag/2 and lambdag/4, respectively, in order to achieve wide bandwidth with relatively small size. Coupling between the main patch and U-shaped parasitic patches is realized by either horizontal or vertical gaps. These gaps are found to be the main factors of the wideband impedance matching. The proposed antenna is designed and fabricated on a small size ground plane (25 mmtimes30 mm) for application of compact transceivers. The fabricated antenna on a FR4 substrate shows an impedance bandwidth of 27.3% (1.5 GHz) at 5.5 GHz center frequency. The measured radiation patterns are similar to those of a conventional patch antenna with slightly higher gains of 6.4 dB and 5.2 dB at each resonant frequency
Home Automation and Security System Using Android ADK

Today we are living in 21 st century where automation is playing important role in human life. Home automation allows us to control household appliances like light, door, fan, AC etc. It also provides home security and emergency system to be activated. Home automation not only refers to reduce human efforts but also energy efficiency and time saving. The main objective of home automation and security is to help handicapped and old aged people which will enable them to control home appliances and alert them in critical situations. This paper put forwards the design of home automation and security system using Android ADK. The design is based on a standalone embedded system board Android ADK(Accessory Development Kit) at home. Home appliances are connected to the ADK and communication is established between the ADK and Android mobile device or tablet. The home appliances are connected to the input/output ports of the embedded system board and their status is passed to the ADK. We would develop an authentication to the system for authorized person to access home appliances. The device with low cost and scalable to less modification to the core is much important. It presents the design and implementation of automation system that can monitor and control home appliances via android phone or tablet. Home Automation and Security; Arduino; Embedded Systems; Android ADK; Android phone; Tablet
Congestion-Aware Path Selection for Tor

Tor, an anonymity network formed by volunteer nodes, uses the estimated bandwidth of the nodes as a central feature of its path selection algorithm. The current load on nodes is not considered in this algorithm, however, and we observe that some nodes persist in being under-utilized or congested. This can degrade the network’s performance, discourage Tor adoption, and consequently reduce the size of Tor’s anonymity set. In an effort to reduce congestion and improve load balancing, we propose a congestion-aware path selection algorithm. Using latency as an indicator of congestion, clients use opportunistic and lightweight active measurements to evaluate the congestion state of nodes, and reject nodes that appear congested. Through experiments conducted on the live Tor network, we verify our hypothesis that clients can infer congestion using latency and show that congestion-aware path selection can improve performance.
Graph construction and b-matching for semi-supervised learning

Graph based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems. A crucial step in graph based SSL methods is the conversion of data into a weighted graph. However, most of the SSL literature focuses on developing label inference algorithms without extensively studying the graph building method and its effect on performance. This article provides an empirical study of leading semi-supervised methods under a wide range of graph construction algorithms. These SSL inference algorithms include the Local and Global Consistency (LGC) method, the Gaussian Random Field (GRF) method, the Graph Transduction via Alternating Minimization (GTAM) method as well as other techniques. Several approaches for graph construction, sparsification and weighting are explored including the popular k-nearest neighbors method (kNN) and the b-matching method. As opposed to the greedily constructed kNN graph, the b-matched graph ensures each node in the graph has the same number of edges and produces a balanced or regular graph. Experimental results on both artificial data and real benchmark datasets indicate that b-matching produces more robust graphs and therefore provides significantly better prediction accuracy without any significant change in computation time.
Design and Evaluation of an RPL-based Multi-Sink Routing Protocol for Low-Power and Lossy Networks

Due to the energy and resource constraints of the embedded Wireless Sensor Networking (WSN) devices that form Low-Power and Lossy Networks (LLNs), efficient support for mobile data sinks is essential. Further, such support should be fully compatible with the broad class of networking mechanisms now defined for LLN support, such as the routing protocol RPL. This paper describes the Hierarchical network of Observable devices with Itinerant Sinks Transporting data (HOIST) protocol. HOIST is designed to service LLNs using an RPL-friendly three-tier hybrid cluster-tree architecture to support sensor nodes deployed over geographically segregated areas. The mobile aspects of HOIST allow for collection from such remote fields without the need for long-distance wireless transfers, which would result in a higher level of energy drain. HOIST supports both real-time data capture and collected data retrieval. We have implemented and field-tested HOIST in a working physical system, as well as in simulation, with results indicating viability for both sparse and dense deployments of nodes under multiple, geographically constrained, uncontrolled mobility models.
FPGA acceleration of multilevel ORB feature extraction for computer vision

In this paper, we present the first multilevel implementation of the Harris-Stephens corner detector and the ORB feature extractor running on FPGA hardware, for computer vision and robotics applications. ORB is a fundamental component of many robotics applications, and requires significant computation. The design has been validated both in behavioural simulation and in implementation on an Arria V FPGA connected to a desktop PC via PCI-Express. A Linux kernel-mode driver and userspace library allow integration of the acceleration hardware into C++ programs. The device has significantly higher throughput than a CPU implementation (150 MPixel/s vs 27 MPixel/s) and a GPU implementation (40 MPixel/s), with much lower power draw (5.3 W vs 145 W). This throughput is equivalent to 72 fps at 1920 × 1080 or 488 fps at 640 × 480.
A Systematic Study of Online Class Imbalance Learning With Concept Drift

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. As the first systematic study of handling concept drift in class-imbalanced data streams, this paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges. Then, an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance.
Human balance and posture control during standing and walking

The fact that we as humans are bipeds and locomote over the ground with one foot in contact (walking), no feet in contact (running), or both feet in contact (standing) creates a major challenge to our balance control system. Because two-thirds of our body mass is located two-thirds of body height above the ground we are an inherently unstable system unless a control system is continuously acting. The purpose of this review is to introduce the reader to the basic anatomy, mechanics, and neuromuscular control that is seen in the current literature related to balance control. The degeneration of the balance control system in the elderly and in many pathologies has forced researchers and clinicians to understand more about how the system works and how to quantify its status at any point in time. With the increase in our ageing population and with increased life expectancy of our elderly the importance of maintaining mobility is becoming ever more critical. Injuries and loss of life due to falls in the elderly is a very major factor facing them. Table 1 compares the deaths due to falls with the deaths due to motor vehicle accidents (MVA) as a function of age as reported by Statistics Canada in 1991. The startling fact is that the deaths due to falls in the SO+ population is almost as high as the MVA deaths in the accident-prone l5-29-year group. However, when we look at the deaths per 100 000 for these two causes the figures are even more startling. The young 15-29-year-old group had 21.5 MVA deaths per 100 000 while the elderly deaths
Cryptography And Network Security Principles And Practices

The field of network and Internet security consists of measures to deter, prevent, detect, and correct security violations that involve the transmission of information. That is a broad statement that covers a host of possibilities. To give you a feel for the areas covered in this paper, consider the following examples of security violations: 1. User A transmits a file to user B. The file contains sensitive information (e.g., payroll records) that is to be protected from disclosure. User C, who is not authorized to read the file, is able to monitor the transmission and capture a copy of the file during its transmission.This book focuses on two broad areas: cryptographic algorithms and protocols, which have a broad range of applications; and network and Internet security, which rely heavily on cryptographic techniques. Cryptographic algorithms and protocols can be grouped into four main areas: •  Used to conceal the contents of blocks or streams of data of any size, including messages, files, encryption keys, and passwords.  Used to conceal small blocks of data, such as encryption keys and hash function values, which are used in digital signatures. •  Used to protect blocks of data, such as messages, from alteration. • Authentication protocols: These are schemes based on the use of cryptographicalgorithms designed to authenticate the identity of entities. Computer and network security can be described as the protection afforded to an automated information system in order to attain the applicable objectives of preserving the integrity, availability, and confidentiality of information system resources (includes hardware, software, firmware, information/ data, and telecommunications).
Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.
Increased social fear and decreased fear of objects in monkeys with neonatal amygdala lesions

The amygdala has been implicated in the mediation of emotional and species-specific social behavior (Kling et al., 1970; Kling and Brothers, 1992; Kluver and Bucy, 1939; Rosvold et al., 1954). Humans with bilateral amygdala damage are impaired in judging negative emotion in facial expressions and making accurate judgements of trustworthiness (Adolphs et al., 1998, 1994). Amygdala dysfunction has also been implicated in human disorders ranging from social anxiety (Birbaumer et al., 1998) to depression (Drevets, 2000) to autism (Bachevalier, 1994; Baron-Cohen et al., 2000; Bauman and Kemper, 1993). We produced selective amygdala lesions in 2-week-old macaque monkeys who were returned to their mothers for rearing. At 6-8 months of age, the lesioned animals demonstrated less fear of novel objects such as rubber snakes than age-matched controls. However, they displayed substantially more fear behavior than controls during dyadic social interactions. These results suggest that neonatal amygdala lesions dissociate a system that mediates social fear from one that mediates fear of inanimate objects. Furthermore, much of the age-appropriate repertoire of social behavior was present in amygdala-lesioned infants indicating that these lesions do not produce autistic-like behavior in monkeys. Finally, amygdala lesions early in development have different effects on social behavior than lesions produced in adulthood.
Improved Neural Machine Translation with SMT Features

Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in fluent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method significantly improves the translation quality of the state-of-the-art NMT system on Chinese-toEnglish translation tasks. Our method produces a gain of up to 2.33 BLEU score on NIST open test sets.
Cross-Linguistic Sentiment Analysis: From English to Spanish

We explore the adaptation of English resources and techniques for text sentiment analysis to a new language, Spanish. Our main focus is the modification of an existing English semantic orientation calculator and the building of dictionaries; however we also compare alternate approaches, including machine translation and Support Vector Machine classification. The results indicate that, although languageindependent methods provide a decent baseline performance, there is also a significant cost to automation, and thus the best path to long-term improvement is through the inclusion of language-specific knowledge and resources.
Ontology-driven Requirements Engineering: Building the OntoREM Meta Model

The present paper gives an insight into the process of developing the metamodel of OntoREM, an ontology-driven requirements engineering methodology, which is currently being developed at the University of the West of England in close cooperation with Airbus. The approach suggested by Noy and McGuinness (2000) has been followed to build a first version of the metamodel of OntoREM as an ontology specified in OWL using Protege-OWL. The outcomes and shortcomings of applying this approach in the context of OntoREM have been assessed showing a major shift in the way that requirements engineering is practiced from process-driven to also knowledge- driven requirements engineering. This, however, necessitates further empirical evaluation of the implementation of OntoREM.
The evolution, challenges, and future of knowledge representation in product design systems

Product design is a highly involved, often ill-defined, complex and iterative process, and the needs and specifications of the required artifact get more refined only as the design process moves toward its goal. An effective computer support tool that helps the designer make better-informed decisions requires efficient knowledge representation schemes. In today’s world, there is a virtual explosion in the amount of raw data available to the designer, and knowledge representation is critical in order to sift through this data and make sense of it. In addition, the need to stay competitive has shrunk product development time through the use of simultaneous and collaborative design processes, which depend on effective transfer of knowledge between teams. Finally, the awareness that decisions made early in the design process have a higher impact in terms of energy, cost, and sustainability, has resulted in the need to project knowledge typically required in the later stages of design to the earlier stages. Research in design rationale systems, product families, systems engineering, and ontology engineering has sought to capture knowledge from earlier product design decisions, from the breakdown of product functions and associated physical features, and from customer requirements and feedback reports. VR (Virtual reality) systems and multidisciplinary modeling have enabled the simulation of scenarios in the manufacture, assembly, and use of the product. This has helped capture vital knowledge from these stages of the product life and use it in design validation and testing. While there have been considerable and significant developments in knowledge capture and representation in product design, it is useful to sometimes review our position in the area, study the evolution of research in product design, and from past and current trends, try and foresee future developments. The goal of this paper is thus to review both our understanding of the field and the support tools that exist for the purpose, and identify the trends and possible directions research can evolve in the future.
Modulation and Multiple Access for 5G Networks

Fifth generation (5G) wireless networks face various challenges in order to support large-scale heterogeneous traffic and users, therefore new modulation and multiple access (MA) schemes are being developed to meet the changing demands. As this research space is ever increasing, it becomes more important to analyze the various approaches, therefore, in this paper we present a comprehensive overview of the most promising modulation and MA schemes for 5G networks. Unlike other surreys of 5G networks, this paper focuses on multiplexing techniques, including modulation techniques in orthogonal MA (OMA) and various types of non-OMA (NOMA) techniques. Specifically, we first introduce different types of modulation schemes, potential for OMA, and compare their performance in terms of spectral efficiency, out-of-band leakage, and bit-error rate. We then pay close attention to various types of NOMA candidates, including power-domain NOMA, code-domain NOMA, and NOMA multiplexing in multiple domains. From this exploration, we can identify the opportunities and challenges that will have the most significant impacts on modulation and MA designs for 5G networks.
Mobile marketing: the role of permission and acceptance

The escalation and convergence of distributed networks and wireless telecommunications has created a tremendous potential platform for providing business services. In consumer markets, mobile marketing is expected to be a key growth area. The immediacy, interactivity and mobility of wireless devices provide a novel platform for marketing. The personal and ubiquitous nature of devices means that interactivity can be provided anytime and anywhere. However, as experience has shown, it is important to keep the consumer in mind. Mobile marketing permission and acceptance are core issues that marketers have yet to fully explain or resolve. This paper provides direction in this area. After briefly discussing some background on mobile marketing, the paper conceptualises key characteristics for mobile marketing permission and acceptance. The paper concludes with predictions on the future of mobile marketing and some core areas of further research.
Smartphone Applications Usability Evaluation: A Hybrid Model and Its Implementation

Evaluating the usability of smartphone applications is crucial for their success, so developers can learn how to adapt them considering the dynamicity of mobile scenarios. The HCI community recommends considering different requirements when evaluating those applications, such as quantitative data (metrics), subjective evaluation (users’ impressions) and context data (e.g. environment and devices conditions). We observed a lack in the literature of approaches that support those three requirements combined into a single experiment; generally one or a pair of them is used. Besides, performing usability evaluation on real mobile scenarios is hard to achieve and most proposals are based on laboratory-controlled experiments. In this paper, we present our proposal for a hybrid usability evaluation of smartphone applications, which is composed by a model and an infrastructure that implements it. The model describes how to automatically monitor and collect context data and usability metrics, how those data can be processed for analysis support and how users’ impressions can be collected. An infrastructure is provided to implement the model allowing it to be plugged into any smartphone Android-based application. To evaluate our proposal, we performed a field experiment, with 21 users using three mobile applications during a 6-month period, in their day-to-day scenarios.