nils-herrmann/clean_scirepeval huggingface dataset:
Detecting adversarial advertisements in the wild

In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful advertisements. In this paper, we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because both false positives and false negatives have high cost, our deployed system uses a tiered strategy combining automated and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge, and independently assessing the effectiveness of our system.
From Partition-Based Clustering to Density-Based Clustering: Fast Find Clusters With Diverse Shapes and Densities in Spatial Databases

Spatial data clustering has played an important role in the knowledge discovery in spatial databases. However, due to the increasing volume and diversity of data, conventional spatial clustering methods are inefficient even on moderately large data sets, and usually fail to discover clusters with diverse shapes and densities. To address these challenges, we propose a two-phase clustering method named KMDD (clustering by combining K-means with density and distance-based method) to fast find clusters with diverse shapes and densities in spatial databases. In the first phase, KMDD uses a partition-based algorithm (K-means) to cluster the data set into several relatively small spherical or ball-shaped subclusters. After that, each subcluster is given a local density; to merge subclusters, KMDD utilizes the idea that genuine cluster cores are characterized by a higher density than their neighbor subclusters and by a relatively large distance from subclusters with higher densities. Extensive experiments on both synthetic and real-world data sets demonstrate that the proposed algorithm has a near-linear time complexity with respect to the data set size and dimension, and has the capability to find clusters with diverse shapes and densities.
Policy-based non-interactive outsourcing of computation using multikey FHE and CP-ABE

We consider the problem of outsourced computation that operates on encrypted inputs supplied by multiple independent parties. To facilitate fine-grained access control, it would be desirable if each party could encrypt her input under an appropriate access policy. Moreover, a party should only be authorized to decrypt the result of a computation performed on a set of encrypted inputs if his credentials satisfy the composition of all input policies. There has been limited success so far achieving homomorphic encryption in the functional setting; that is, for primitives such as Ciphertext-Policy Attribute Based Encryption (CP-ABE) and Identity Based Encryption (IBE). We introduce a new primitive that captures homomorphic encryption with support for access policies and policy composition. We then present a generic construction using CP-ABE and multikey Fully-Homomorphic encryption (FHE). Furthermore, we show that a CP-ABE scheme that is homomorphic for circuits of polylogarithmic depth in some parameter m implies a CP-ABE scheme that is homomorphic for circuits of arity m and unbounded depth.
Analysis of sentiments & polarity computation of opinions

In the current era of social media, opinion mining shows a remarkable significance in information retrieval and web data analysis. This new research domain becomes important as the use of social media has increased to next fold. Users here generate the content, which is in the form of emotions, comments that can be positive or negative, an individual's own view point etc. Using the social networking sites (e.g. Facebook®, twitter®), multi-media sharing sites (e.g. YouTube®, Flickr®), blogs and rich web applications as the usage of Web 2.0 increases, user can exchange or share their opinion. In this paper a detailed analysis is conducted that thoroughly discuss the domain and commonly used classification techniques to assist future research in this new emerging area. These techniques are used for Opinion Mining and Sentiment Analysis.
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses

Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health – much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.
Trust Region Policy Optimization

In this article, we describe a method for optimizing control policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified scheme, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is effective for optimizing large nonlinear policies such as neural networks. Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters.
Joint learning of ontology and semantic parser from text

Semantic parsing methods are used for capturing and representing semantic meaning of text. Meaning representation capturing all the concepts in the text may not always be available or may not be sufficiently complete. Ontologies provide a structured and reasoning-capable way to model the content of a collection of texts. In this work, we present a novel approach to joint learning of ontology and semantic parser from text. The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees. Both, the grammar and the semantic trees are used to learn the ontology on several levels – classes, instances, taxonomic and non-taxonomic relations. The approach was evaluated on the first sentences of Wikipedia pages describing people.
Constant Communication ORAM with Small Blocksize

There have been several attempts recently at using homomorphic encryption to increase the efficiency of Oblivious RAM protocols. One of the most successful has been Onion ORAM, which achieves O(1) communication overhead with polylogarithmic server computation. However, it has two drawbacks. It requires a large block size of B = Ω(log6 N) with large constants. Moreover, while it only needs polylogarithmic computation complexity, that computation consists mostly of expensive homomorphic multiplications. In this work, we address these problems and reduce the required block size to Ω(log4 N). We remove most of the homomorphic multiplications while maintaining O(1) communication complexity. Our idea is to replace their homomorphic eviction routine with a new, much cheaper permute-and-merge eviction which eliminates homomorphic multiplications and maintains the same level of security. In turn, this removes the need for layered encryption that Onion ORAM relies on and reduces both the minimum block size and server computation.
A Survey of Recent Results in Networked Control Systems

Networked control systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. We review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies
Shape matching and object recognition using shape contexts

This paper presents my work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation. In this paper, I propose shape detection using a feature called shape context. Shape context describes all boundary points of a shape with respect to any single boundary point. Thus it is descriptive of the shape of the object. Object recognition can be achieved by matching this feature with a priori knowledge of the shape context of the boundary points of the object. Experimental results are promising on handwritten digits, trademark images.
Recurrence networks — a novel paradigm for nonlinear time series analysis

This paper presents a new approach for analysing the structural properties of time series from complex systems. Starting from the concept of recurrences in phase space, the recurrence matrix of a time series is interpreted as the adjacency matrix of an associated complex network, which links different points in time if the considered states are closely neighboured in phase space. In comparison with similar network-based techniques the new approach has important conceptual advantages, and can be considered as a unifying framework for transforming time series into complex networks that also includes other existing methods as special cases. It has been demonstrated here that there are fundamental relationships between many topological properties of recurrence networks and different nontrivial statistical properties of the phase space density of the underlying dynamical system. Hence, this novel interpretation of the recurrence matrix yields new quantitative characteristics (such as average path length, clustering coefficient, or centrality measures of the recurrence network) related to the dynamical complexity of a time series, most of which are not yet provided by other existing methods of nonlinear time series analysis. 6 Author to whom any correspondence should be addressed. New Journal of Physics 12 (2010) 033025 1367-2630/10/033025+40$30.00 © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft
Stable Proportional-Derivative Controllers

In computer animation, the proportional-derivative (PD) controller is a common technique for tracking characters' motion. A new formulation of the PD controller-stable PD (SPD)-allows arbitrarily high gains, even at large time steps. The key is to determine joint forces and torques while taking into account the character's positions and velocities in the next time step. SPD is stable even when combined with a physics simulator that uses simple Euler integration. SPD controllers have a variety of uses, including motion tracking in a physics simulator, keyframe interpolation with secondary motion, and constraint satisfaction for simulation.
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure. Learning under this family of models can be conducted using a carefully crafted particle MCMC algorithm. This scheme is computationally efficient and yet allows for a fully Bayesian treatment of the problem. Compared to conventional system identification tools or existing learning methods, we show competitive performance and reliable quantification of uncertainties in the model.
A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics

Metrics for structured data have received an increasing interest in the machine learning community. Graphs provide a natural representation for structured data, but a lot of operations on graphs are computationally intractable. In this article, we present a polynomial-time algorithm that computes a maximum common subgraph of two outerplanar graphs. The algorithm makes use of the block-and-bridge preserving subgraph isomorphism, which has significant efficiency benefits and is also motivated from a chemical perspective. We focus on the application of learning structure-activity relationships, where the task is to predict the chemical activity of molecules. We show how the algorithm can be used to construct a metric for structured data and we evaluate this metric and more generally also the block-and-bridge preserving matching operator on 60 molecular datasets, obtaining state-of-the-art results in terms of predictive performance and efficiency.
Classification of Spreadsheet Errors

This paper describes a framework for a systematic classification of spreadsheet errors. This classification or taxonomy of errors is aimed at facilitating analysis and comprehension of the different types of spreadsheet errors. It is far more comprehensive than any presented or published before. The taxonomy is an outcome of a thorough investigation of the widespread problem of spreadsheet errors and an analysis of specific types of these errors. This paper also contains a clear description of the various elements and categories of the classification. It is also accompanied and supported by appropriate examples.
Multi-robot SLAM using ceiling vision

In this paper we present a new vision-based SLAM approach for multi-robot formulation. For a cooperative map reconstruction, the robots have to know each other's relative poses, but estimating these at the start of operation puts a limit on real applications. In our study, the robots start the single SLAM with their own global coordinate, and merge their maps during the operation by detecting the overlapped region of their maps. The robots automatically recognize the occurrence of map overlapping by matching their current frame with the maps built by other robots. With the robust data association technique from the ceiling-vision based SLAM, the proposed algorithm robustly detects the overlapping regions and estimates the accurate transformations for map alignment. In our experiment, we have verified that our algorithm successfully enables the multi-robot SLAM without any initial correspondence or encounter of robots.
Modeling Interestingness with Deep Neural Networks

This paper presents a deep semantic similarity model (DSSM), a special type of deep neural networks designed for text analysis, for recommending target documents to be of interest to a user based on a source document that she is reading. We observe, identify, and detect naturally occurring signals of interestingness in click transitions on the Web between source and target documents, which we collect from commercial Web browser logs. The DSSM is trained on millions of Web transitions, and maps source-target document pairs to feature vectors in a latent space in such a way that the distance between source documents and their corresponding interesting targets in that space is minimized. The effectiveness of the DSSM is demonstrated using two interestingness tasks: automatic highlighting and contextual entity search. The results on large-scale, real-world datasets show that the semantics of documents are important for modeling interestingness and that the DSSM leads to significant quality improvement on both tasks, outperforming not only the classic document models that do not use semantics but also state-of-the-art topic models.
Social Collaborative Viewpoint Regression with Explainable Recommendations

A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction.
 In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To this end, we use so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations. In addition, such viewpoints can be used as explanations. We apply a Gibbs EM sampler to infer posterior distributions of sCVR. Experiments conducted on three large benchmark datasets show the effectiveness of our proposed method for predicting item ratings and for generating explanations.
The WHOI micro-modem: an acoustic communications and navigation system for multiple platforms

The micro-modem is a compact, low-power, underwater acoustic communications and navigation subsystem. It has the capability to perform low-rate frequency-hopping frequency-shift keying (FH-FSK), variable rate phase-coherent keying (PSK), and two different types of long base line navigation, narrow-band and broadband. The system can be configured to transmit in four different bands from 3 to 30 kHz, with a larger board required for the lowest frequency. The user interface is based on the NMEA standard, which is a serial port specification. The modem also includes a simple built-in networking capability which supports up to 16 units in a polled or random-access mode and has an acknowledgement capability which supports guaranteed delivery transactions. The paper contains a detailed system description and results from several tests are also presented
Chronos : Serverless Multi-User Chat Over NDN

Multi-user applications are commonly implemented using a centralized server. This paper presents a new design for multi-user chat applications (Chronos) that works in a distributed, serverless fashion over Named Data Networking. In Chronos, all participants share their views by exchanging the cryptographic digests of the chat room data set. A newly generated message causes a change of the digest at the message originator, which leads to retrieving the new data by all other participants in an efficient way and resynchronization of chat room views. Chronos does not have a single point of failure and eliminates traffic concentration problem of server-based implementations. We use simulations to evaluate and compare Chronos with a traditional serverbased chat room implementation. Our results demonstrate Chronos’ robustness and efficiency in data dissemination. Chronos’ approach of replacing centralized servers by distributed data synchronization can be applied to a variety of distributed applications to simplify design and ease deployment.
A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization

The Teaching-Learning-Based Optimization (TLBO) algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning method for mobile robots is presented, which is based on an improved TLBO algorithm called Nonlinear Inertia Weighted Teaching-Learning-Based Optimization (NIWTLBO) algorithm in our previous work. Firstly, the NIWTLBO algorithm is introduced. Then, a new map model of the path between start-point and goal-point is built by coordinate system transformation. Lastly, utilizing the NIWTLBO algorithm, the objective function of the path is optimized; thus, a global optimal path is obtained. The simulation experiment results show that the proposed method has a faster convergence rate and higher accuracy in searching for the path than the basic TLBO and some other algorithms as well, and it can effectively solve the optimization problem for mobile robot global path planning.
Maximizing Throughput of Overprovisioned HPC Data Centers Under a Strict Power Budget

Building future generation supercomputers while constraining their power consumption is one of the biggest challenges faced by the HPC community. For example, US Department of Energy has set a goal of 20 MW for an exascale (1018 flops) supercomputer. To realize this goal, a lot of research is being done to revolutionize hardware design to build power efficient computers and network interconnects. In this work, we propose a software-based online resource management system that leverages hardware facilitated capability to constrain the power consumption of each node in order to optimally allocate power and nodes to a job. Our scheme uses this hardware capability in conjunction with an adaptive runtime system that can dynamically change the resource configuration of a running job allowing our resource manager to re-optimize allocation decisions to running jobs as new jobs arrive, or a running job terminates.
 We also propose a performance modeling scheme that estimates the essential power characteristics of a job at any scale. The proposed online resource manager uses these performance characteristics for making scheduling and resource allocation decisions that maximize the job throughput of the supercomputer under a given power budget. We demonstrate the benefits of our approach by using a mix of jobs with different power-response characteristics. We show that with a power budget of 4.75 MW, we can obtain up to 5.2X improvement in job throughput when compared with the SLURM scheduling policy that is power-unaware. We corroborate our results with real experiments on a relatively small scale cluster, in which we obtain a 1.7X improvement.
k-Anonymously Private Search over Encrypted Data

In this paper we compare the performance of various homomorphic encryption methods on a private search scheme that can achieve k-anonymity privacy. To make our benchmarking fair, we use open sourced cryptographic libraries which are written by experts and well scrutinized. We find that Goldwasser-Micali encryption achieves good enough performance for practical use, whereas fully homomorphic encryptions are much slower than partial ones like Goldwasser-Micali and Paillier.
Efficient Canny Edge Detection Using a GPU

Recent GPUs, which have many processing units connected with a global memory, can be used for general purpose parallel computation. Users can develop parallel programs running on GPUs using programming architecture called CUDA (Compute Unified Device Architecture). The main contribution of this paper is to implement a Canny edge detection algorithm on CUDA. The experimental result shows that our implementation of Canny edge detection algorithm on CUDA achieves a speedup factor of 61 over a conventional software implementation.
A Hardware-Assisted Realtime Attack on A5/2 Without Precomputations

A5/2 is a synchronous stream cipher that is used for protecting GSM communication. Recently, some powerful attacks [2,10] on A5/2 have been proposed. In this contribution we enhance the ciphertext-only attack  by Barkan, Biham, and Keller by designing special-purpose hardware for generating and solving the required systems of linear equations. For realizing the LSE solver component, we use an approach recently introduced in  describing a parallelized hardware implementation of the Gauss-Jordan algorithm. Our hardware-only attacker immediately recovers the initial secret state of A5/2 which is sufficient for decrypting all frames of a session using a few ciphertext frames without any precomputations and memory. More precisely, in contrast to  our hardware architecture directly attacks the GSM speech channel (TCH/FS and TCH/EFS). It requires 16 ciphertext frames and completes the attack in about 1 second. With minor changes also input from other GSM channels (e.g., SDCCH/8) can be used to mount the attack.