nils-herrmann/clean_scirepeval huggingface dataset:
Experimenting at scale with google chrome's SSL warning

Web browsers show HTTPS authentication warnings (i.e., SSL warnings) when the integrity and confidentiality of users' interactions with websites are at risk. Our goal in this work is to decrease the number of users who click through the Google Chrome SSL warning. Prior research showed that the Mozilla Firefox SSL warning has a much lower click-through rate (CTR) than Chrome. We investigate several factors that could be responsible: the use of imagery, extra steps before the user can proceed, and style choices. To test these factors, we ran six experimental SSL warnings in Google Chrome 29 and measured 130,754 impressions.
Why Buy When You Can Rent? - Bribery Attacks on Bitcoin-Style Consensus

The Bitcoin cryptocurrency introduced a novel distributed consensus mechanism relying on economic incentives. While a coalition controlling a majority of computational power may undermine the system, for example by double-spending funds, it is often assumed it would be incentivized not to attack to protect its long-term stake in the health of the currency. We show how an attacker might purchase mining power (perhaps at a cost premium) for a short duration via bribery. Indeed, bribery can even be performed in-band with the system itself enforcing the bribe. A bribing attacker would not have the same concerns about the long-term health of the system, as their majority control is inherently short-lived. New modeling assumptions are needed to explain why such attacks have not been observed in practice. The need for all miners to avoid short-term profits by accepting bribes further suggests a potential tragedy of the commons which has not yet been analyzed.
A Federated Architecture for Information Management

An approach to the coordinated sharing and interchange of computerized information is described emphasizing partial, controlled sharing among autonomous databases. Office information systems provide a particularly appropriate context for this type of information sharing and exchange. A federated database architecture is described in which a collection of independent database systems are united into a loosely coupled federation in order to share and exchange information. A federation consists of components (of which there may be any number) and a single federal dictionary. The components represent individual users, applications, workstations, or other components in an office information system. The federal dictionary is a specialized component that maintains the topology of the federation and oversees the entry of new components. Each component in the federation controls its interactions with other components by means of an export schema and an import schema. The export schema specifies the information that a component will share with other components, while the import schema specifies the nonlocal information that a component wishes to manipulate. The federated architecture provides mechanisms for sharing data, for sharing transactions (via message types) for combining information from several components, and for coordinating activities among autonomous components (via negotiation). A prototype implementation of the federated database mechanism is currently operational on an experimental basis.
Printing Food

This paper examines the possible applications of food as a raw material in freeform fabrication, and provides several demonstrations of edible three-dimensional objects. The use of edible materials offers several advantages: First, it opens the door to the application of SFF technology in custom food industry, such as manufacturing of complex confections with specialized geometries and intricate material compositions. For pedagogical purposes, edible materials provide an easily accessible, nontoxic and low cost way to experiment with rapid prototyping techniques using educational systems such as Fab@Home. For more traditional SFF technologies, food materials with appropriate rheological properties can serve as sacrificial, bio-degradable, bio-compatible or recyclable materials for structural support and draft-printing. We have used the Fab@Home personal fabrication system to produce multi-material edible 3D objects with cake frosting, chocolate, processed cheese, and peanut butter. These are just indicative of the range of potential edible materials and applications.
Accelerating Bitcoin's Transaction Processing. Fast Money Grows on Trees, Not Chains

Bitcoin is a potentially disruptive new crypto-currency based on a decentralized opensource protocol which is gradually gaining popularity. Perhaps the most important question that will affect Bitcoin’s success, is whether or not it will be able to scale to support the high volume of transactions required from a global currency system. We investigate the restrictions on the rate of transaction processing in Bitcoin as a function of both the bandwidth available to nodes and the network delay, both of which lower the efficiency of Bitcoin’s transaction processing. The security analysis done by Bitcoin’s creator Satoshi Nakamoto  assumes that block propagation delays are negligible compared to the time between blocks—an assumption that does not hold when the protocol is required to process transactions at high rates. We improve upon the original analysis and remove this assumption. Using our results, we are able to give bounds on the number of transactions per second the protocol can handle securely. Building on previously published measurements by Decker and Wattenhofer , we show these bounds are currently more restrictive by an order of magnitude than the bandwidth needed to stream all transactions. We additionally show how currently planned improvements to the protocol, namely the use of transaction hashes in blocks (instead of complete transaction records), will dramatically alleviate these restrictions. Finally, we present an easily implementable modification to the way Bitcoin constructs its main data structure, the blockchain, that immensely improves security from attackers, especially when the network operates at high rates. This improvement allows for further increases in the number of transactions processed per second. We show that with our proposed modification, significant speedups can be gained in confirmation time of transactions as well. The block generation rate can be securely increased to more than one block per second – a 600 fold speedup compared to today’s rate, while still allowing the network to processes many transactions per second.
Drone-assisted public safety wireless broadband network

A nationwide interoperable public safety wireless broadband network is being planned by the First Responder Network Authority (FirstNet) under the auspices of the United States government. The public safety network shall provide the needed wireless coverage in the wake of an incident or a disaster. This paper proposes a drone-assisted multi-hop device-to-device (D2D) communication scheme as a means to extend the network coverage over regions where it is difficult to deploy a landbased relay. The resource are shared using either time division or frequency division scheme. Efficient algorithms are developed to compute the optimal position of the drone for maximizing the data rate, which are shown to be highly effective via simulations.
Cognitive support by language visualization: A case study with hindi language

The main objective of this research is to give a cognitive support to a person for understanding Hindi language text using automatic text visualization (ATV). ATV is especially useful for the persons with learning disabilities (LD). This paper focuses on the background and complexity of the problem. The impact on comprehension through visualized text over reading the text is discussed. The architecture of Preksha — a Hindi text visulizer — is given and several illustrative examples of scenes generated using it are given.
A new zero-ripple switching dc-to-dc converter and integrated magnetics

A new switching dc-to-dc converter is synthesized which consists of the least number of storage elements (inductive and capacitive) and switches, and yet truly emulates the ideally desired dc-to-dc transformer having both input and output currents as pure dc quantities with no ripple. This result was facilitated by implementation of a new concept termed integrated magnetics, which leads in some special switching structures to the integration of otherwise independent and separate magnetic components (inductors and transformers) into a single magnetic circuit.
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods

In this paper we propose a novel gradient algorithm to learn a policy from an expert’s observed behavior assuming that the expert behaves optimally with respect to some unknown reward function of a Markovian Decision Problem. The algorithm’s aim is to find a reward function such that the resulting optimal policy matches well the expert’s observed behavior. The main difficulty is that the mapping from the parameters to policies is both nonsmooth and highly redundant. Resorting to subdifferentials solves the first difficulty, while the second one is overcome by computing natural gradients. We tested the proposed method in two artificial domains and found it to be more reliable and efficient than some previous methods.
CHAOS: A Data Stream Analysis Architecture for Enterprise Applications

In this paper, we describe the design of our architecture for Continuous, Heterogeneous Analysis Over Streams, aka CHAOS that combines stream processing, approximation techniques, mining, complex event processing and visualization. CHAOS, with the novel concept of Computational Stream Analysis Cube, provides an effective, scalable platform for near real time processing of business and enterprise streams. We describe our approach with a real data center temperature analysis application.
Model-free Probabilistic Movement Primitives for physical interaction

Physical interaction in robotics is a complex problem that requires not only accurate reproduction of the kinematic trajectories but also of the forces and torques exhibited during the movement. We base our approach on Movement Primitives (MP), as MPs provide a framework for modelling complex movements and introduce useful operations on the movements, such as generalization to novel situations, time scaling, and others. Usually, MPs are trained with imitation learning, where an expert demonstrates the trajectories. However, MPs used in physical interaction either require additional learning approaches, e.g., reinforcement learning, or are based on handcrafted solutions. Our goal is to learn and generate movements for physical interaction that are learned with imitation learning, from a small set of demonstrated trajectories. The Probabilistic Movement Primitives (ProMPs) framework is a recent MP approach that introduces beneficial properties, such as combination and blending of MPs, and represents the correlations present in the movement. The ProMPs provides a variable stiffness controller that reproduces the movement but it requires a dynamics model of the system. Learning such a model is not a trivial task, and, therefore, we introduce the model-free ProMPs, that are learning jointly the movement and the necessary actions from a few demonstrations. We derive a variable stiffness controller analytically. We further extent the ProMPs to include force and torque signals, necessary for physical interaction. We evaluate our approach in simulated and real robot tasks.
Scalable Clustering of Time Series with U-Shapelets

A recently introduced primitive for time series data mining, unsupervised shapelets (u-shapelets), has demonstrated significant potential for time series clustering. In contrast to approaches that consider the entire time series to compute pairwise similarities, the u-shapelets technique allows considering only relevant subsequences of time series. Moreover, u-shapelets allow us to bypass the apparent chicken-and-egg paradox of defining relevant with reference to the clustering itself. U-shapelets have several advantages over rival methods. First, they are defined even when the time series are of different lengths; for example, they allow clustering datasets containing a mixture of single heartbeats and multi-beat ECG recordings. Second, u-shapelets mitigate sensitivity to irrelevant data such as noise, spikes, dropouts, etc. Finally, u-shapelets demonstrated ability to provide additional insights into the data. Unfortunately, the state-ofthe-art algorithms for u-shapelets search are intractable and so their advantages have only been demonstrated on tiny datasets. We propose a simple approach to speed up a ushapelet discovery by two orders of magnitude, without any significant loss in clustering quality.
Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.
Semantic Representation and Recognition of Continued and Recursive Human Activities

This paper describes a methodology for automated recognition of complex human activities. The paper proposes a general framework which reliably recognizes high-level human actions and human-human interactions. Our approach is a description-based approach, which enables a user to encode the structure of a high-level human activity as a formal representation. Recognition of human activities is done by semantically matching constructed representations with actual observations. The methodology uses a context-free grammar (CFG) based representation scheme as a formal syntax for representing composite activities. Our CFG-based representation enables us to define complex human activities based on simpler activities or movements. Our system takes advantage of both statistical recognition techniques from computer vision and knowledge representation concepts from traditional artificial intelligence. In the low-level of the system, image sequences are processed to extract poses and gestures. Based on the recognition of gestures, the high-level of the system hierarchically recognizes composite actions and interactions occurring in a sequence of image frames. The concept of hallucinations and a probabilistic semantic-level recognition algorithm is introduced to cope with imperfect lower-layers. As a result, the system recognizes human activities including ‘fighting’ and ‘assault’, which are high-level activities that previous systems had difficulties. The experimental results show that our system reliably recognizes sequences of complex human activities with a high recognition rate.
Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus

Robotic systems for disease detection in greenhouses are expected to improve disease control, increase yield, and reduce pesticide application. We present a robotic detection system for combined detection of two major threats of greenhouse bell peppers: Powdery mildew (PM) and Tomato spotted wilt virus (TSWV). The system is based on a manipulator, which facilitates reaching multiple detection poses. Several detection algorithms are developed based on principal component analysis (PCA) and the coefficient of variation (CV). Tests ascertain the system can successfully detect the plant and reach the detection pose required for PM (along the side of the plant), yet it has difficulties in reaching the TSWV detection pose (above the plant). Increasing manipulator work-volume is expected to solve this issue. For TSWV, PCA-based classification with leaf vein removal, achieved the highest classification accuracy (90%) while the accuracy of the CV methods was also high (85% and 87%). For PM, PCA-based pixel-level classification was high (95.2%) while leaf condition classification accuracy was low (64.3%) since it was determined based on the upper side of the leaf while disease symptoms start on its lower side. Exposure of the lower side of the leaf during detection is expected to improve PM condition detection.
Prediction and Classification Using String Matching

This paper introduces a simple dynamic programming algorithm for performing text prediction. The algorithm is based on the KnuthMorris-Pratt string matching algorithm. It is well established that there is a close relationship between the tasks of prediction, compression, and classification. A compression technique called Prediction by Partial Matching (PPM) is very similar to the algorithm introduced in this paper. However, most variants of PPM have a higher space complexity and are significantly more difficult to implement. The algorithm is evaluated on a text classification task and outperforms several existing classification techniques.
Pairwise geometric matching for large-scale object retrieval

Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images, and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in individual correspondences. In this paper, we consider the pairwise geometric relations between correspondences and propose a strategy to incorporate these relations at significantly reduced computational cost, which makes it suitable for large-scale object retrieval. In addition, we combine the information on geometric relations from both the individual correspondences and pairs of correspondences to further improve the verification accuracy. Experimental results on three reference datasets show that the proposed approach results in a substantial performance improvement compared to the existing methods, without making concessions regarding computational efficiency.
An Experiment in Software Prototyping Productivity ∗

We describe the results of an experiment in which several conventional programming languages, together with the functional language Haskell, were used to prototype a Naval Surface Warfare Center (NSWC) requirement for a Geometric Region Server. The resulting programs and development metrics were reviewed by a committee chosen by the Navy. The results indicate that the Haskell prototype took significantly less time to develop and was considerably more concise and easier to understand than the corresponding prototypes written in several different imperative languages, including Ada and C++. ∗This work was supported by the Advanced Research Project Agency and the Office of Naval Research under Arpa Order 8888, Contract N00014-92-
SSGAN: Secure Steganography Based on Generative Adversarial Networks

How to design a secure steganography method is the problem that researchers have always been concerned about. Traditionally, the steganography method is designed in a heuristic way which does not take into account the detection side (steganalysis) fully and automatically. In this paper, we propose a new strategy that generates more suitable and secure covers for steganography with adversarial learning scheme, named SSGAN. The proposed architecture has one generative network called G, and two discriminative networks called D and S, among which the former evaluates the visual quality of the generated images for steganography and the latter assesses their suitableness for information hiding. Different from the existing work, we use WGAN instead of GAN for the sake of faster convergence speed, more stable training, and higher quality images, and also re-design the S net with more sophisticated steganalysis network. The experimental results prove the effectiveness of the proposed method.
Automatic Detection Of Circular Objects By Ellipse Growing

We present a new method for automatically detecting circular objects in images: we detect an osculating circle to an elliptic arc using a Hough transform, iteratively deforming it into an ellipse, removing outlier pixels, and searching for a separate edge. The voting space is restricted to one and two dimensions for efficiency, and special weighting schemes are introduced to enhance the accuracy. We demonstrate the effectiveness of our method using real images. Finally, we apply our method to the calibration of a turntable for 3-D object shape reconstruction.
Real-Coded Memetic Algorithms with Crossover Hill-Climbing

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.
Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

We describe the neural-network training framework used in the Kaldi speech recognition toolkit, which is geared towards training DNNs with large amounts of training data using multiple GPU-equipped or multicore machines. In order to be as hardwareagnostic as possible, we needed a way to use multiple machines without generating excessive network traffic. Our method is to average the neural network parameters periodically (typically every minute or two), and redistribute the averaged parameters to the machines for further training. Each machine sees different data. By itself, this method does not work very well. However, we have another method, an approximate and efficient implementation of Natural Gradient for Stochastic Gradient Descent (NG-SGD), which seems to allow our periodic-averaging method to work well, as well as substantially improving the convergence of SGD on a single machine.