nils-herrmann/clean_scirepeval huggingface dataset:
Multi-label Classification: Dealing with Imbalance by Combining Labels

Data imbalance is a common problem both in single-label classification (SLC) and multi-label classification (MLC). There is no doubt that the predicting result suffers from this problem. Although, a broad range of studies associate with imbalance problem, most of them focus on SLC and for MLC is relatively less. Actually, this problem arising in MLCis more frequent and complex than in SLC. In this paper, we proceed from dealing with imbalance problem for MLC and propose a new approach called DEML. DEML transforms the whole label set of multi-label dataset into some subsets and each subset is treated as a multi-class dataset with balanced class distribution, which not only addressing imbalance problem but also preserving dataset integrity and consistency. Extensive experiments show that DEML possesses highly competitive performance both in computation and effectiveness.
Differentially Private Regression with Gaussian Processes

A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the di erential privacy framework with the exibility of Gaussian processes (GPs). We propose a method using GPs to provide di erentially private (DP) regression. We then improve this method by crafting the DP noise covariance structure to efciently protect the training data, while minimising the scale of the added noise. We nd that this cloaking method achieves the greatest accuracy, while still providing privacy guarantees, and o ers practical DP for regression over multi-dimensional inputs. Together these methods provide a starter toolkit for combining differential privacy and GPs.
FPPB: A Fast and Privacy-Preserving Method Based on the Permissioned Blockchain for Fair Transactions in Sharing Economy

Blockchain is a distributed system with efficient transaction recording and has been widely adopted in sharing economy. Although many existing privacy-preserving methods on the blockchain have been proposed, finding a trade-off between keeping speed and preserving privacy of transactions remain challenging. To address this limitation, we propose a novel Fast and Privacy-preserving method based on the Permissioned Blockchain (FPPB) for fair transactions in sharing economy. Without breaking the verifying protocol and bringing additional off-blockchain interactive communication, FPPB protects the privacy and fairness of transactions. Additionally, experiments are implemented in EthereumJ (a Java implementation of the Ethereum protocol) to measure the performance of FPPB. Compared with normal transactions without cryptographic primitives, FPPB only slows down transactions slightly.
The influence of social media use on body image concerns

Internet-based media and especially social networking sites differ from traditional media in that they allow individuals to interact with their friends in their networks. Moreover, Internet-based media are easily available on devices such as smartphones or tablets. Previous research has demonstrated that mass media contribute powerfully to an individual’s body dissatisfaction. To date, research on the effects related to exposure to ‘newer’ forms of media, in particular social media on the Internet, is scarce. The purpose of the current study is to review the extant body of research dealing with the influence of social media on body image concerns, especially among adolescents. Adolescents, via the Internet, get access to different kinds of Internet-based media, such as social media (including social networking sites). Our results document the importance of idealized social media models—especially thin-ideal models for girls and muscular-ideal models for boys—in shaping the body perceptions of adolescents. However, the effects of pressure from social media on body image concerns in men need to be further investigated both in clinical and community samples.
Service quality , customer satisfaction , and customer value : A holistic perspective

The author proposes and tests an integrative model of service quality, customer value, and customer satisfaction. Using a sample from the luxury segment of the hotel industry, this study provides preliminary results supporting a holistic approach to hospitality customers’ postpurchase decision-making process. The model appears to possess practical validity as well as explanatory ability. Implications are discussed and suggestions are developed for both marketers and researchers.
SHREC ’ 18 : RGB-D Object-to-CAD Retrieval

Recent advances in consumer-grade depth sensors have enable the collection of massive real-world 3D objects. Together with the rise of deep learning, it brings great potential for large-scale 3D object retrieval. In this challenge, we aim to study and evaluate the performance of 3D object retrieval algorithms with RGB-D data. To support the study, we expanded the previous ObjectNN dataset [HTT∗17] to include RGB-D objects from both SceneNN [HPN∗16] and ScanNet [DCS∗17], with the CAD models from ShapeNetSem [CFG∗15]. Evaluation results show that while the RGB-D to CAD retrieval problem is indeed challenging due to incomplete RGB-D reconstructions, it can be addressed to a certain extent using deep learning techniques trained on multi-view 2D images or 3D point clouds. The best method in this track has a 82% retrieval accuracy.
Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.
SmoothGrad: removing noise by adding noise

Explaining the output of a deep network remains a challenge. In the case of an image classifier, one type of explanation is to identify pixels that strongly influence the final decision. A starting point for this strategy is the gradient of the class score function with respect to the input image. This gradient can be interpreted as a sensitivity map, and there are several techniques that elaborate on this basic idea. This paper makes two contributions: it introduces SMOOTHGRAD, a simple method that can help visually sharpen gradient-based sensitivity maps, and it discusses lessons in the visualization of these maps. We publish the code for our experiments and a website with our results.
Residual Connections Encourage Iterative Inference

Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent view argues that Resnets perform iterative refinement of features. We attempt to further expose properties of this aspect. To this end, we study Resnets both analytically and empirically. We formalize the notion of iterative refinement in Resnets by showing that residual connections naturally encourage features of residual blocks to move along the negative gradient of loss as we go from one block to the next. In addition, our empirical analysis suggests that Resnets are able to perform both representation learning and iterative refinement. In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features. Finally we observe that sharing residual layers naively leads to representation explosion and counterintuitively, overfitting, and we show that simple existing strategies can help alleviating this problem.
Towards a design theory for reducing aggression in psychiatric facilities

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BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era

Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies--BitCoin--have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, we connect two phenomena of the latest years--digital currencies, namely BitCoin, and search queries on Google Trends and Wikipedia--and study their relationship. We show that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.
Instructors ' Acceptance of Learning Management Systems : A Theoretical Framework

In the knowledge age, the use of the information technology (IT) tools including learning management systems (LMS) has became an imperative. The adoption rate of LMS in academic and training institutions is very promising worldwide. Learning Management system includes several tools that provide academic and training institutions efficient and effective means to support distance education and supplement their traditional way of teaching. LMS also provide academic insinuations mechanisms and tools to store, manage, and share its academic resources and knowledge. Instructors' acceptance is essential for the deployment of LMS. The success of LMS in any institution starts by instructors' acceptance, which in turns initiates and promotes learners' utilization of LMS. Consequently, the objective of this paper is to develop a theoretical framework for evaluating instructors' acceptance of LMS based on the Technology Acceptance Model. This framework provides a comprehensive look of the critical factors that influence the instructors' perceived ease of use and perceived usefulness of LMS and consequently the actual use. These critical factors are related to the instructor, organization, and technology. Instructor factors include self efficacy, attitude toward LMS, experience, teaching style and personal innovativeness. Organization factors include motivators, technology alignment, organization support, technical support and training. Technology factors include system quality, information quality and service quality.
A Deeper Look into Dependency-Based Word Embeddings

Context constructed without dependency labels • Simplified: Functionally similar dependency labels are collapsed • Basic: Standard dependency parse • Enhanced and Enhanced++: Dependency trees augmented (e.g., new edges between modifiers and conjuncts with parents’ labels) • Universal Dependencies (UD): Cross-lingual • Stanford Dependencies (SD): English-tailored • Prior work  has shown that embeddings trained using dependency contexts distinguish related words better than similar words. • What effects do decisions made with embeddings have on the characteristics of the word embeddings? • Do Universal Dependency (UD) embeddings capture different characteristics than English-tailored Stanford Dependency (SD) embeddings?
Cyber Warfare: A Review of Theories, Law, Policies, Actual Incidents - and the Dilemma of Anonymity

In this paper, the authors undertake a study of cyber warfare reviewing theories, law, policies, actual incidents and the dilemma of anonymity. Starting with the United Kingdom perspective on cyber warfare, the authors then consider United States' views including the perspective of its military on the law of war and its general inapplicability to cyber conflict. Consideration is then given to the work of the United Nations' group of cyber security specialists and diplomats who as of July 2010 have agreed upon a set of recommendations to the United Nations Secretary General for negotiations on an international computer security treaty. An examination of the use of a nation's cybercrime law to prosecute violations that occur over the Internet indicates the inherent limits caused by the jurisdictional limits of domestic law to address cross-border cybercrime scenarios. Actual incidents from Estonia (2007), Georgia (2008), Republic of Korea (2009), Japan (2010), ongoing attacks on the United States as well as other incidents and reports on ongoing attacks are considered as well. Despite the increasing sophistication of such cyber attacks, it is evident that these attacks were met with a limited use of law and policy to combat them that can be only be characterised as a response posture defined by restraint. Recommendations are then examined for overcoming the attribution problem. The paper then considers when do cyber attacks rise to the level of an act of war by reference to the work of scholars such as Schmitt and Wingfield. Further evaluation of the special impact that non-state actors may have and some theories on how to deal with the problem of asymmetric players are considered. Discussion and possible solutions are offered. A conclusion is offered drawing some guidance from the writings of the Chinese philosopher Sun Tzu. Finally, an appendix providing a technical overview of the problem of attribution and the dilemma of anonymity in cyberspace is provided. 1. The United Kingdom Perspective "If I went and bombed a power station in France, that would be an act of war. If I went on to the net and took out a power station, is that an act of war?
A novel interlaced chirp sequence radar concept with range-Doppler processing for automotive applications

In this paper, we introduce a novel concept for primary frequency-modulated continuous-wave (FMCW) radar. The approach is based on a phase locked loop-controlled interlaced chirp sequence (ICS) waveform and, in contrast to basic range-Doppler processing, it enables higher target velocities to be detected. It is thus very suitable for automotive applications. The interlaced ramps in the system are generated by two separate frequency synthesizers. These are combined by an RF switch to suppress transients caused by oscillator overshoot and to avoid incoherencies due to programming times of the phase locked loop (PLL) ICs. A prototype radar system was realized in K-Band. Promising test results bode well for other applications.
A Comparative Study of Real-Time Semantic Segmentation for Autonomous Driving

Semantic segmentation is a critical module in robotics related applications, especially autonomous driving. Most of the research on semantic segmentation is focused on improving the accuracy with less attention paid to computationally efficient solutions. Majority of the efficient semantic segmentation algorithms have customized optimizations without scalability and there is no systematic way to compare them. In this paper, we present a real-time segmentation benchmarking framework and study various segmentation algorithms for autonomous driving. We implemented a generic meta-architecture via a decoupled design where different types of encoders and decoders can be plugged in independently. We provide several example encoders including VGG16, Resnet18, MobileNet, and ShuffleNet and decoders including SkipNet, UNet and Dilation Frontend. The framework is scalable for addition of new encoders and decoders developed in the community for other vision tasks. We performed detailed experimental analysis on cityscapes dataset for various combinations of encoder and decoder. The modular framework enabled rapid prototyping of a custom efficient architecture which provides ~x143 GFLOPs reduction compared to SegNet and runs real-time at ~15 fps on NVIDIA Jetson TX2. The source code of the framework is publicly available.
LQR control for a quadrotor using unit quaternions: Modeling and simulation

Nowadays, quadrotors have become a popular UAV research platform because full control can be achieved through speed variations in each and every one of its four rotors. Here, a non-linear dynamic model based on quaternions for attitude is presented as well as its corresponding LQR Gain Scheduling Control. All considerations for the quadrotor movements are described through their state variables. Modeling is carried out through the Newton-Euler formalism. Finally, the control system is simulated and the results shown in a novel and direct unit quaternion. Thus, a successful trajectory and attitude control of a quadrotor is achieved.
Development of a Navigation Control System for an Autonomous Formula SAE-Electric Race Car

Autonomous Formula SAE-Electric Car This dissertation describes the development of a high level control system for an autonomous Formula SAE race car featuring fusion of a 6-DOF IMU, a consumer grade GPS and an automotive LIDAR.  SAE is a long-running annual competition organised by the Society of Automotive Engineers which has recently seen the introduction of the new class SAE-Electric. The car discussed in this dissertation features electric motors driving each of the two rear wheels via independent controllers and has full drive-by-wire control of the throttle, steering and (hydraulic) braking system. Whilst autonomous driving is outside the scope of the Formula-SAE competition, it has been the subject of significant research interest over the last several decades. It is intended that the Autonomous SAE car developed in this project will provide UWA with a platform for research into driverless performance cars. This project consists of the design and implementation of a navigation control system which uses a Linux PC to interface with a range of sensors as well as the drive-by-wire system, safety systems and a base station. The navigation control system is implemented as a multi-threaded C++ program featuring asynchronous communication with hardware outputs, sensor inputs and user interfaces. The Autonomous SAE Car can drive following a map consisting of “waypoints” and “fence posts” which are recorded by either driving the course manually or through a GoogleMaps based web interface. Mapped driving is augmented by the use of a LIDAR scanner for detection of obstacles including road edges for which a novel algorithm is presented. GPS is used as the primary navigation aid; however sensor fusion algorithms have been implemented in order to improve upon the measurement of the cars position and orientation through the use of a 6-DOF Inertial Measurement Unit.
FDTD Modeling of Metamaterials Theory and Applications

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Faithfully Explaining Rankings in a News Recommender System

ere is an increasing demand for algorithms to explain their outcomes. So far, there is no method that explains the rankings produced by a ranking algorithm. To address this gap we propose LISTEN, a LISTwise ExplaiNer, to explain rankings produced by a ranking algorithm. To eciently use LISTEN in production, we train a neural network to learn the underlying explanation space created by LISTEN; we call this model Q-LISTEN. We show that LISTEN produces faithful explanations and that Q-LISTEN is able to learn these explanations. Moreover, we show that LISTEN is safe to use in a real world environment: users of a news recommendation system do not behave signicantly dierently when they are exposed to explanations generated by LISTEN instead of manually generated explanations.
1 An Introduction to Conditional Random Fields for Relational Learning

1.1  Relational data has two characteristics: first, statistical dependencies exist between the entities we wish to model, and second, each entity often has a rich set of features that can aid classification. For example, when classifying Web documents, the page's text provides much information about the class label, but hyperlinks define a relationship between pages that can improve classification [Taskar et al., 2002]. Graphical models are a natural formalism for exploiting the dependence structure among entities. Traditionally, graphical models have been used to represent the joint probability distribution p(y, x), where the variables y represent the attributes of the entities that we wish to predict, and the input variables x represent our observed knowledge about the entities. But modeling the joint distribution can lead to difficulties when using the rich local features that can occur in relational data, because it requires modeling the distribution p(x), which can include complex dependencies. Modeling these dependencies among inputs can lead to intractable models, but ignoring them can lead to reduced performance. A solution to this problem is to directly model the conditional distribution p(y|x), which is sufficient for classification. This is the approach taken by conditional random fields [Lafferty et al., 2001]. A conditional random field is simply a conditional distribution p(y|x) with an associated graphical structure. Because the model is
Interpretable Visual Question Answering by Reasoning on Dependency Trees

Collaborative reasoning for understanding each image-question pair is very critical but underexplored for an interpretable visual question answering system. Although very recent works also attempted to use explicit compositional processes to assemble multiple subtasks embedded in the questions, their models heavily rely on annotations or handcrafted rules to obtain valid reasoning processes, leading to either heavy workloads or poor performance on composition reasoning. In this paper, to better align image and language domains in diverse and unrestricted cases, we propose a novel neural network model that performs global reasoning on a dependency tree parsed from the question, and we thus phrase our model as parse-tree-guided reasoning network (PTGRN). This network consists of three collaborative modules: i) an attention module to exploit the local visual evidence for each word parsed from the question, ii) a gated residual composition module to compose the previously mined evidence, and iii) a parse-tree-guided propagation module to pass the mined evidence along the parse tree. Our PTGRN is thus capable of building an interpretable VQA system that gradually derives the image cues following a question-driven parse-tree reasoning route. Experiments on relational datasets demonstrate the superiority of our PTGRN over current state-of-the-art VQA methods, and the visualization results highlight the explainable capability of our reasoning system.