nils-herrmann/clean_scirepeval huggingface dataset:
INSTANCE-BASED LEARNING: Nearest Neighbour with Generalisation

Instance-based learning is a machine learning method that classifies new examples by comparing them to those already seen and in memory. There are two types of instance-based learning; nearest neighbour and case-based reasoning. Of these two methods, nearest neighbour fell into disfavour during the 1980s, but regained popularity recently due to its simplicity and ease of implementation. Nearest neighbour learning is not without problems. It is difficult to define a distance function that works well for both discrete and continuous attributes. Noise and irrelevant attributes also pose problems. Finally, the specificity bias adopted by instance-based learning, while often an advantage, can over-represent small rules at the expense of more general concepts, leading to a marked decrease in classification performance for some domains. Generalised exemplars offer a solution. Examples that share the same class are grouped together, and so represent large rules more fully. This reduces the role of the distance function to determining the class when no rule covers the new example, which reduces the number of classification errors that result from inaccuracies of the distance function, and increases the influence of large rules while still representing small ones. This thesis investigates non-nested generalised exemplars as a way of improving the performance of nearest neighbour. The method is tested using benchmark domains and the results compared with documented results for ungeneralised exemplars, nested generalised exemplars, rule induction methods and a composite rule induction and nearest neighbour learner. The benefits of generalisation are isolated and the performance improvement measured. The results show that non-nested generalisation of exemplars improves the classification performance of nearest neighbour systems and reduces classification time. who have provided help over the past year. Grateful thanks must go to my supervisor, Ian Witten, for giving me the freedom to pursue my studies independently while always being there when needed, and to my partner, Suky, for her unending tolerance and support. Thank you also to the other machine learning students at the University of Waikato, for your valuable assistance and friendship.
A novel 0.35µm 800V BCD technology platform for offline applications

Here is presented the development of the new BCD800 platform which conjugates 3.3V CMOS logic, 5/25/30V power devices and a 800V nLDMOS in a 0.35μm technology node. LV components can be placed into a floating pocket which can be referred up to 650V, furthermore this process features an innovative lateral junction isolation module obtained by a boron-doped poly-filled deep trench with great advantages in terms of performances and area saving. The process industrialization has been demonstrated and a fully functional test vehicle is available in the form of a single-chip High Voltage Smart Gate Driver for half bridges.
The Development and Implementation of Scaffolding-Based Self-Regulated Learning System for e/m-Learning

This paper proposes a self-regulated learning (SRL) system with scaffolding support in order to develop independent learning skills among students. The SRL system uses self-regulated learning and scaffolding theories to appeal to both instructors and learners. On the part of the instructors, a Content Accessibility Subsystem is provided to easily organize learning materials and to dynamically provide different levels of support for their learners. As for the learners, many subsystems are proposed that provide a conducive mobile learning environment for them. With the application of the scaffolding theory, the system can easily adjust to provide help to the learners, facilitating SRL processes anytime and anywhere, and establishing the learners’ SRL patterns gradually. The learners in the experiment deemed that that the proposed system could provide them selfregulatory attributes. The experiment results show that the average SRL score of learners increases, though the improvement is not significant. However, the result also showed that the SR skills of students in the group of Low SR significantly improved.
Goal-oriented Dialogue Policy Learning from Failures

Reinforcement learning methods have been used for learning dialogue policies. However, learning an effective dialogue policy frequently requires prohibitively many conversations. This is partly because of the sparse rewards in dialogues, and the very few successful dialogues in early learning phase. Hindsight experience replay (HER) enables learning from failures, but the vanilla HER is inapplicable to dialogue learning due to the implicit goals. In this work, we develop two complex HER methods providing different tradeoffs between complexity and performance, and, for the first time, enabled HER-based dialogue policy learning. Experiments using a realistic user simulator show that our HER methods perform better than existing experience replay methods (as applied to deep Q-networks) in learning rate.
Security aspects of the in-vehicle network in the connected car

In this paper, we briefly survey the research with respect to the security of the connected car, and in particular its in-vehicle network. The aim is to highlight the current state of the research; which are the problems found, and what solutions have been suggested. We have structured our investigation by categorizing the research into the following five categories: problems in the in-vehicle network, architectural security features, intrusion detection systems, honeypots, and threats and attacks. We conclude that even though quite some effort has already been expended in the area, most of it has been directed towards problem definition and not so much towards security solutions. We also highlight a few areas that we believe are of immediate concern.
Applications, challenges, and prospective in emerging body area networking technologies

Advances in wireless technology and supporting infrastructure provide unprecedented opportunity for ubiquitous real-time healthcare and fitness monitoring without constraining the activities of the user. Wirelessly connected miniaturized sensors and actuators placed in, on, and around the body form a body area network for continuous, automated, and unobtrusive monitoring of physiological signs to support medical, lifestyle and entertainment applications. BAN technology is in the early stage of development, and several research challenges have to be overcome for it to be widely accepted. In this article we study the core set of application, functional, and technical requirements of the BAN. We also discuss fundamental research challenges such as scalability (in terms of data rate, power consumption, and duty cycle), antenna design, interference mitigation, coexistence, QoS, reliability, security, privacy, and energy efficiency. Several candidate technologies poised to address the emerging BAN market are evaluated, and their merits and demerits are highlighted. A brief overview of standardization activities relevant to BANs is also presented.
Automatic abstraction for verification of cyber-physical systems

Models of cyber-physical systems are inherently complex since they must represent hardware, software, and the physical environment. Formal verification of these models is often precluded by state explosion. Fortunately, many important properties may only depend upon a relatively small portion of the system being accurately modeled. This paper presents an automatic abstraction methodology that simplifies the model accordingly. Preliminary results on a fault-tolerant temperature sensor are encouraging.
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis

Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints; 2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is lightweight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our WarpingGAN that significantly outperforms all existing methods on two large datasets.
1 Augmented Reality

This paper is an overview of technologies that fall under the term augmented reality. Augmented reality refers to a system in which the physical surroundings of a person are mixed with real-time computer generated information creating an enhanced perception of surrounding environment. Being partly virtual and real, augmented reality applications have quite extreme requirements to be practical to use. It also has very much potential in numerous different application areas. These issues make augmented reality both an interesting and challenging subject from scientific and business perspectives.
A Mixed Filtering Approach for Track Condition Monitoring Using Accelerometers on the Axle Box and Bogie

This paper describes a method of estimating irregularities in railway tracks using acceleration data measured from high-speed trains. Track irregularities are the main causes of the vibration of high-speed trains and thus should be carefully monitored to maintain the stability and ride quality of the trains. A mixed filtering approach is proposed for stable displacement estimation and waveband classification of the irregularities in the measured acceleration. Accelerometers are mounted on the axle box and the bogie of a high-speed train to measure the acceleration in the lateral and vertical directions. The estimated results are compared with those of a commercial track geometry measurement system. Finally, the performance of the proposed approach and the relationship between the mounted location of the accelerometers and the estimated track irregularities are discussed.
An improved boosting based on feature selection for corporate bankruptcy prediction

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction.
A Standard Mutual Authentication Protocol for Cloud Computing Based Health Care System

Telecare Medical Information System (TMIS) supports a standard platform to the patient for getting necessary medical treatment from the doctor(s) via Internet communication. Security protection is important for medical records (data) of the patients because of very sensitive information. Besides, patient anonymity is another most important property, which must be protected. Most recently, Chiou et al. suggested an authentication protocol for TMIS by utilizing the concept of cloud environment. They claimed that their protocol is patient anonymous and well security protected. We reviewed their protocol and found that it is completely insecure against patient anonymity. Further, the same protocol is not protected against mobile device stolen attack. In order to improve security level and complexity, we design a light weight authentication protocol for the same environment. Our security analysis ensures resilience of all possible security attacks. The performance of our protocol is relatively standard in comparison with the related previous research.
Role of affective self-regulatory efficacy in diverse spheres of psychosocial functioning.

This prospective study with 464 older adolescents (14 to 19 years at Time 1; 16 to 21 years at Time 2) tested the structural paths of influence through which perceived self-efficacy for affect regulation operates in concert with perceived behavioral efficacy in governing diverse spheres of psychosocial functioning. Self-efficacy to regulate positive and negative affect is accompanied by high efficacy to manage one's academic development, to resist social pressures for antisocial activities, and to engage oneself with empathy in others' emotional experiences. Perceived self-efficacy for affect regulation essentially operated mediationally through the latter behavioral forms of self-efficacy rather than directly on prosocial behavior, delinquent conduct, and depression. Perceived empathic self-efficacy functioned as a generalized contributor to psychosocial functioning. It was accompanied by prosocial behavior and low involvement in delinquency but increased vulnerability to depression in adolescent females.
Umbrella wheel — A stair-climbing and obstacle-handling wheel design concept

This paper proposes a new design for stair-climbing using a wheel that can split into segments and walk up stairs or surmount other obstacles often found where humans traverse, while still being able to retain a perfectly round shape for traveling on smooth ground. Using this change of configuration, staircases with a wide range of dimensions can be covered efficiently and safely. The design, named Umbrella Wheel, can consist of as many wheel segments as desired, and as few as two. A smaller or higher number of wheel segments has advantages and disadvantages depending on the specific situation. Modeling the trajectory of the wheel when as it ascends or descends stairs is given and the results are analyzed.
Multi-User Precoding and Channel Estimation for Hybrid Millimeter Wave Systems

In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed mmWave channel estimation algorithm first exploits multiple frequency tones to estimate the strongest angle-of-arrivals (AoAs) at both base station (BS) and user sides for the design of analog beamforming matrices. Then, all the users transmit orthogonal pilot symbols to the BS along the directions of the estimated strongest AoAs in order to estimate the channel. The estimated channel will be adopted to design the digital zero-forcing (ZF) precoder at the BS for the multi-user downlink transmission. The proposed channel estimation algorithm is applicable to both the non-sparse and sparse mmWave channel environments. Furthermore, we derive a tight achievable rate upper bound of the digital ZF precoding with the proposed channel estimation algorithm scheme. Our analytical and simulation results show that the proposed scheme obtains a considerable achievable rate of fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Besides, considering the effect of various types of errors, i.e., random phase errors, transceiver analog beamforming errors, and equivalent channel estimation errors, we derive a closed-form approximation for the achievable rate of the considered scheme. We illustrate the robustness of the proposed channel estimation and multi-user downlink precoding scheme against the system imperfection.
When saliency meets sentiment: Understanding how image content invokes emotion and sentiment

Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers. On the other hand, psychological studies have confirmed that salient objects in an image often invoke emotions. In this work, we investigate more fine-grained and more comprehensive interaction between visual saliency and visual sentiment. In particular, we partition images in several meta-level scene-type dimensions that are relevant to most images, including: open-closed, natural-manmade, indoor-outdoor, and face-noface. Facilitated by state of the art saliency detection algorithm and sentiment classification algorithm, we examine how the sentiment of the salient region(s) in an image relates to the overall sentiment of the image. The experiments on a representative image emotion dataset have shown interesting correlation between saliency and sentiment in different scene types and shed light on the mechanism of visual sentiment evocation.
A Survey of Caching Mechanisms in Information-Centric Networking

Information-Centric Networking (ICN) is a novel networking paradigm that attracts increasing research interests in recent years. In-network caching has been viewed as an attractive feature of ICN because it can reduce network traffic, alleviate server bottleneck, and reduce the user access latencies. Because of this, the network community has proposed many in-network caching mechanisms that aim at optimizing various performance metrics such as cache hit ratio and cache hit distance. In this survey, we present a comprehensive overview of the recently proposed in-network caching mechanisms for ICN. For each caching mechanism, we describe it in detail, present examples to illustrate how it works, and analyze its possible benefits and drawbacks. We also compare some typical in-network caching mechanisms through extensive simulations and discuss the remaining research challenges.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
Annotation for free: video tagging by mining user search behavior

The problem of tagging is mostly considered from the perspectives of machine learning and data-driven philosophy. A fundamental issue that underlies the success of these approaches is the visual similarity, ranging from the nearest neighbor search to manifold learning, to identify similar instances of an example for tag completion. The need to searching for millions of visual examples in high-dimensional feature space, however, makes the task computationally expensive. Moreover, the results can suffer from robustness problem, when the underlying data, such as online videos, are rich of semantics and the similarity is difficult to be learnt from low-level features. This paper studies the exploration of user searching behavior through click-through data, which is largely available and freely accessible by search engines, for learning video relationship and applying the relationship for economic way of annotating online videos. We demonstrated that, by a simple approach using co-click statistics, promising results were obtained in contrast to feature-based similarity measurement. Furthermore, considering the long tail effect that few videos dominate most clicks, a new method based on~polynomial~semantic indexing is proposed to learn a latent space~for alleviating the sparsity problem of click-through data. The proposed approaches are then applied for three major tasks in tagging: tag assignment, ranking, and enrichment. On~a bipartite graph constructed from click-through data with~over 15 million queries and 20 million video URL clicks,~we showed that annotation can be performed for free with competitive performance and minimum computing resource, representing a new and promising paradigm for video tagging in addition to machine learning and data-driven methodologies.