nils-herrmann/clean_scirepeval huggingface dataset:
Nonparametric variational inference

Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation. The locations of these kernels and their bandwidth are treated as variational parameters and optimized to improve an approximate lower bound on the marginal likelihood of the data. Unlike most other variational approximations, using multiple kernels allows the approximation to capture multiple modes of the posterior. We demonstrate the efficacy of the nonparametric approximation with a hierarchical logistic regression model and a nonlinear matrix factorization model. We obtain predictive performance as good as or better than more specialized variational methods and MCMC approximations. The method is easy to apply to graphical models for which standard variational methods are difficult to derive.
KidCAD: digitally remixing toys through tangible tools

Children have great facility in the physical world, and can skillfully model in clay and draw expressive illustrations. Traditional digital modeling tools have focused on mouse, keyboard and stylus input. These tools may be complicated and difficult for young users to easily and quickly create exciting designs. We seek to bring physical interaction to digital modeling, to allow users to use existing physical objects as tangible building blocks for new designs. We introduce KidCAD a digital clay interface for children to remix toys. KidCAD allows children to imprint 2.5D shapes from physical objects into their digital models by deforming a malleable gel input device, deForm. Users can mashup existing objects, edit and sculpt or draw new designs on a 2.5D canvas using physical objects, hands and tools as well as 2D touch gestures. We report on a preliminary user study with 13 children, ages 7 to 10, which provides feedback for our design and helps guide future work in tangible modeling for children.
Anomaly Based Web Phishing Page Detection

Many anti-phishing schemes have recently been proposed in literature. Despite all those efforts, the threat of phishing attacks is not mitigated. One of the main reasons is that phishing attackers have the adaptability to change their tactics with little cost. In this paper, we propose a novel approach, which is independent of any specific phishing implementation. Our idea is to examine the anomalies in Web pages, in particular, the discrepancy between a Web site's identity and its structural features and HTTP transactions. It demands neither user expertise nor prior knowledge of the Web site. The evasion of our phishing detection entails high cost to the adversary. As shown by the experiments, our phishing detector functions with low miss rate and low false-positive rate
TEDAS: A Twitter-based Event Detection and Analysis System

Witnessing the emergence of Twitter, we propose a Twitter-based Event Detection and Analysis System (TEDAS), which helps to (1) detect new events, to (2) analyze the spatial and temporal pattern of an event, and to (3) identify importance of events. In this demonstration, we show the overall system architecture, explain in detail the implementation of the components that crawl, classify, and rank tweets and extract location from tweets, and present some interesting results of our system.
Online Nonnegative Matrix Factorization With Robust Stochastic Approximation

Nonnegative matrix factorization (NMF) has become a popular dimension-reduction method and has been widely applied to image processing and pattern recognition problems. However, conventional NMF learning methods require the entire dataset to reside in the memory and thus cannot be applied to large-scale or streaming datasets. In this paper, we propose an efficient online RSA-NMF algorithm (OR-NMF) that learns NMF in an incremental fashion and thus solves this problem. In particular, OR-NMF receives one sample or a chunk of samples per step and updates the bases via robust stochastic approximation. Benefitting from the smartly chosen learning rate and averaging technique, OR-NMF converges at the rate of in each update of the bases. Furthermore, we prove that OR-NMF almost surely converges to a local optimal solution by using the quasi-martingale. By using a buffering strategy, we keep both the time and space complexities of one step of the OR-NMF constant and make OR-NMF suitable for large-scale or streaming datasets. Preliminary experimental results on real-world datasets show that OR-NMF outperforms the existing online NMF (ONMF) algorithms in terms of efficiency. Experimental results of face recognition and image annotation on public datasets confirm the effectiveness of OR-NMF compared with the existing ONMF algorithms.
Generic and unified model of Switched Capacitor Converters

A generic modeling methodology that analyzes the losses in Switched Capacitors Converters (SCC) was developed and verified by simulation and experiments. The proposed analytical approach is unified, covering both hard and soft switched SCC topologies. The major advantage of the proposed model is that it expresses the losses as a function of the currents passing through each flying capacitor. Since these currents are linearly proportional to the output current, the model is also applicable to SCC with multiple capacitors. The proposed model provides an insight into the expected losses in SCC and the effects of their operational conditions such as duty cycle. As such, the model can help in the optimization of SCC systems and their control to achieve desired regulations.
Identity-Based Broadcast Encryption with Constant Size Ciphertexts and Private Keys

This paper describes the first identity-based broadcast encryption scheme (IBBE) with constant size ciphertexts and private keys. In our scheme, the public key is of size linear in the maximal size m of the set of receivers, which is smaller than the number of possible users (identities) in the system. Compared with a recent broadcast encryption system introduced by Boneh, Gentry and Waters (BGW), our system has comparable properties, but with a better efficiency: the public key is shorter than in BGW. Moreover, the total number of possible users in the system does not have to be fixed in the setup.
Problem solving with reinforcement learning

This thesis is concerned with practical issues surrounding the application of reinforcement learning techniques to tasks that take place in high dimensional continuous state-space environments. In particular, the extension of on-line updating methods is considered, where the term implies systems that learn as each experience arrives, rather than storing the experiences for use in a separate oo-line learning phase. Firstly, the use of alternative update rules in place of standard Q-learning (Watkins 1989) is examined to provide faster convergence rates. Secondly, the use of multi-layer perceptron (MLP) neural networks (Rumelhart, Hinton and Williams 1986) is investigated to provide suitable generalising function approximators. Finally, consideration is given to the combination of Adaptive Heuristic Critic (AHC) methods and Q-learning to produce systems combining the beneets of real-valued actions and discrete switching. The diierent update rules examined are based on Q-learning combined with the TD() algorithm (Sutton 1988). Several new algorithms, including Modiied Q-Learning and Summation Q-Learning, are examined, as well as alternatives such as Q() (Peng and Williams 1994). In addition, algorithms are presented for applying these Q-learning updates to train MLPs on-line during trials, as opposed to the backward-replay method used by Lin (1993b) that requires waiting until the end of each trial before updating can occur. The performance of the update rules is compared on the Race Track problem of Barto, Bradtke and Singh (1993) using a lookup table representation for the Q-function. Some of the methods are found to perform almost as well as Real-Time Dynamic Programming, despite the fact that the latter has the advantage of a full world model. The performance of the connectionist algorithms is compared on a larger and more complex robot navigation problem. Here a simulated mobile robot is trained to guide itself to a goal position in the presence of obstacles. The robot must rely on limited sensory feedback from its surroundings and make decisions that can be generalised to arbitrary layouts of obstacles. These simulations show that the performance of on-line learning algorithms is less sensitive to the choice of training parameters than backward-replay, and that the alternative Q-learning rules of Modiied Q-Learning and Q() are more robust than standard Q-learning updates. Finally, a combination of real-valued AHC and Q-learning, called Q-AHC learning, is presented, and various architectures are compared in performance on the robot problem. The resulting reinforcement learning system has the properties of providing on-line training, parallel computation, generalising function …
Integrating multi-purpose natural language understanding, robot's memory, and symbolic planning for task execution in humanoid robots

We propose an approach for instructing a robot using natural language to solve complex tasks in a dynamic environment. In this study, we elaborate on a framework that allows a humanoid robot to understand natural language, derive symbolic representations of its sensorimotor experience, generate complex plans according to the current world state, and monitor plan execution. The presented development supports replacing missing objects and suggesting possible object locations. It is a realization of the concept of structural bootstrapping developed in the context of the European project Xperience. The framework is implemented within the robot development environment ArmarX. We evaluate the framework on the humanoid robot ARMAR-III in the context of two experiments: a demonstration of the real execution of a complex task in the kitchen environment on ARMAR-III and an experiment with untrained users in a simulation environment.
That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets

We propose a novel data augmentation approach to enhance computational behavioral analysis using social media text. In particular, we collect a Twitter corpus of the descriptions of annoying behaviors using the #petpeeve hashtags. In the qualitative analysis, we study the language use in these tweets, with a special focus on the fine-grained categories and the geographic variation of the language. In quantitative analysis, we show that lexical and syntactic features are useful for automatic categorization of annoying behaviors, and frame-semantic features further boost the performance; that leveraging large lexical embeddings to create additional training instances significantly improves the lexical model; and incorporating frame-semantic embedding achieves the best overall performance.
Deep Colorization

This paper investigates into the colorization problem which converts a grayscale image to a colorful version. This is a very difficult problem and normally requires manual adjustment to achieve artifact-free quality. For instance, it normally requires human-labelled color scribbles on the grayscale target image or a careful selection of colorful reference images (e.g., capturing the same scene in the grayscale target image). Unlike the previous methods, this paper aims at a high-quality fully-automatic colorization method. With the assumption of a perfect patch matching technique, the use of an extremely large-scale reference database (that contains sufficient color images) is the most reliable solution to the colorization problem. However, patch matching noise will increase with respect to the size of the reference database in practice. Inspired by the recent success in deep learning techniques which provide amazing modeling of large-scale data, this paper re-formulates the colorization problem so that deep learning techniques can be directly employed. To ensure artifact-free quality, a joint bilateral filtering based post-processing step is proposed. Numerous experiments demonstrate that our method outperforms the state-of-art algorithms both in terms of quality and speed.
Keyword Search on Spatial Databases

Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability.
Asynchronous Complex Analytics in a Distributed Dataflow Architecture

Scalable distributed dataflow systems have recently experienced widespread adoption, with commodity dataflow engines such as Hadoop and Spark, and even commodity SQL engines routinely supporting increasingly sophisticated analytics tasks (e.g., support vector machines, logistic regression, collaborative filtering). However, these systems’ synchronous (often Bulk Synchronous Parallel) dataflow execution model is at odds with an increasingly important trend in the machine learning community: the use of asynchrony via shared, mutable state (i.e., data races) in convex programming tasks, which has—in a single-node context—delivered noteworthy empirical performance gains and inspired new research into asynchronous algorithms. In this work, we attempt to bridge this gap by evaluating the use of lightweight, asynchronous state transfer within a commodity dataflow engine. Specifically, we investigate the use of asynchronous sideways information passing (ASIP) that presents single-stage parallel iterators with a Volcano-like intra-operator iterator that can be used for asynchronous information passing. We port two synchronous convex programming algorithms, stochastic gradient descent and the alternating direction method of multipliers (ADMM), to use ASIPs. We evaluate an implementation of ASIPs within on Apache Spark that exhibits considerable speedups as well as a rich set of performance trade-offs in the use of these asynchronous algorithms.
A sub-100-milliwatt dual-core HOG accelerator VLSI for real-time multiple object detection

In this paper, a Histogram of Oriented Gradients (HOG) feature extraction accelerator for real-time multiple object detection is presented. The processor employs three techniques: a VLSI-oriented HOG algorithm with early classification in Support Vector Machine (SVM) classification, a dual core architecture for parallel feature extraction, and a detection-window-size scalable architecture with a reconfigurable MAC array for processing objects of different shapes. Early classification reduces the number of computations in SVM classification. The dual core architecture and the detection-window-size scalable architecture enable the processor to operate in several modes: high-speed mode, low-power mode, multiple object detection mode, and multiple shape object detection mode. These techniques expand the processor flexibility required for versatile application. The test chip was fabricated using 65 nm CMOS technology. The proposed architecture is designed to process HDTV resolution video (1920 × 1080 pixels) at 30 frames per second (fps). The performance of this accelerator is demonstrated on a pedestrian detection system.
Techno-economic analysis of PON architectures for FTTH deployments: Comparison between GPON,XGPON and NG-PON2 for a Greenfield operator

In this paper the cost for the deployment of a PON FTTH network is calculated in terms of NPV, IRR and payback period. A comparison is made between three PON technologies: GPON, XGPON and NG-PON2. Different scenarios regarding population density and bitrates are examined. It is shown that for high data rate scenarios in areas with high population density NG-PON2 can become a viable and future proof alternative to GPON and XGPON.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.
RAPID: A Fast Data Update Protocol in Erasure Coded Storage Systems for Big Data

Erasure codes are nowadays used extensively indistributed storage systems that handle big data, since theyoffer significant fault tolerance with low storage overhead. Eventhough erasure coded systems are space efficient, these involvehigher network bandwidth and computational complexity in theiroperations. In this paper, we present RAPID, a protocol for fastdata updates, which works by choosing a subset of code blocksfor updates and adapts the strength of the subset based on thepredicted number of failures. The proposal uses a predictionbased heuristic in which the set of failures that may happen inthe near future is represented as a function of past failures. Ahybrid protocol that uses both locking and buffering mechanismsis adopted in the solution to maintain the consistency on the dataand code blocks updates. Our experimental results demonstrateimprovement in the performance of data updates by 30% andthe failure prediction mechanism proposed shows an accuracy of 80%.
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge

While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.
An Impedance and Multi-Wavelength Near-Infrared Spectroscopy IC for Non-Invasive Blood Glucose Estimation

A multi-modal spectroscopy IC combining impedance spectroscopy (IMPS) and multi-wavelength near-infrared spectroscopy (mNIRS) is proposed for high precision non-invasive glucose level estimation. A combination of IMPS and mNIRS can compensate for the glucose estimation error to improve its accuracy. The IMPS circuit measures dielectric characteristics of the tissue using the RLC resonant frequency and the resonant impedance to estimate the glucose level. To accurately find resonant frequency, a 2-step frequency sweep sinusoidal oscillator (FSSO) is proposed: 1) 8-level coarse frequency switching (fSTEP = 9.4 kHz) in 10-76 kHz, and 2) fine analog frequency sweep in the range of 18.9 kHz. During the frequency sweep, the adaptive gain control loop stabilizes the output voltage swing (400 mVp-p). To improve accuracy of mNIRS, three wavelengths, 850 nm, 950 nm, and 1,300 nm, are used. For highly accurate glucose estimation, the measurement data of the IMPS and mNIRS are combined by an artificial neural network (ANN) in external DSP. The proposed ANN method reduces the mean absolute relative difference to 8.3% from 15% of IMPS, and 15-20% of mNIRS in 80-180 mg/dL blood glucose level. The proposed multi-modal spectroscopy IC occupies 12.5 mm 2 in a 0.18 μm 1P6M CMOS technology and dissipates a peak power of 38 mW with the maximum radiant emitting power of 12.1 mW.
On the Human–Machine Interaction of Unmanned Aerial System Mission Specialists

This paper surveys the human-machine interaction technologies supporting the Mission Specialist role in unmanned aerial systems (UASs). The Mission Specialist role is one of three formal human team member roles extracted from the UAS-related literature (the others are Flight Director and Pilot), but unlike the Pilot role, the interface needs have not been established. The interfaces used by 17 micro, small, medium altitude long endurance (MALE) and high altitude long endurance (HALE) platforms are examined to determine (1) what type of user interface technologies are present and/or available; (2) how the Mission Specialist currently or could interact with the user interface technology; and (3) what are the perceived positive and negative aspects of this user interface technology in the context of the UAS human-robot team roles. Micro and small UAVs pose significant user interface limitations for the Mission Specialist role and may produce unintentional interaction conflicts between the Mission Specialist role and the Pilot, potentially resulting in suboptimal performance and loss of robustness. The survey is expected to serve as a reference for future design and refinement of user interfaces for UAS and a foundation for better understanding human-robot interaction in UAS.