nils-herrmann/clean_scirepeval huggingface dataset:
Learning Phrase-Based Spelling Error Models from Clickthrough Data

This paper explores the use of clickthrough data for query spelling correction. First, large amounts of query-correction pairs are derived by analyzing users' query reformulation behavior encoded in the clickthrough data. Then, a phrase-based error model that accounts for the transformation probability between multi-term phrases is trained and integrated into a query speller system. Experiments are carried out on a human-labeled data set. Results show that the system using the phrase-based error model outperforms significantly its baseline systems.
RankTopic: Ranking Based Topic Modeling

Topic modeling has become a widely used tool for document management due to its superior performance. However, there are few topic models distinguishing the importance of documents on different topics. In this paper, we investigate how to utilize the importance of documents to improve topic modeling and propose to incorporate link based ranking into topic modeling. Specifically, topical pagerank is used to compute the topic level ranking of documents, which indicates the importance of documents on different topics. By retreating the topical ranking of a document as the probability of the document involved in corresponding topic, a generalized relation is built between ranking and topic modeling. Based on the relation, a ranking based topic model Rank Topic is proposed. With Rank Topic, a mutual enhancement framework is established between ranking and topic modeling. Extensive experiments on paper citation data and Twitter data are conducted to compare the performance of Rank Topic with that of some state-of-the-art topic models. Experimental results show that Rank Topic performs much better than some baseline models and is comparable with the state-of-the-art link combined relational topic model (RTM) in generalization performance, document clustering and classification by setting a proper balancing parameter. It is also demonstrated in both quantitative and qualitative ways that topics detected by Rank Topic are more interpretable than those detected by some baseline models and still competitive with RTM.
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon

Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining. Tweets are often informal, unstructured and lacking labeled data such as categories and ratings, making it challenging for product opinion mining. In this paper, we propose an LDA-based opinion model named Twitter Opinion Topic Model (TOTM) for opinion mining and sentiment analysis. TOTM leverages hashtags, mentions, emoticons and strong sentiment words that are present in tweets in its discovery process. It improves opinion prediction by modeling the target-opinion interaction directly, thus discovering target specific opinion words, neglected in existing approaches. Moreover, we propose a new formulation of incorporating sentiment prior information into a topic model, by utilizing an existing public sentiment lexicon. This is novel in that it learns and updates with the data. We conduct experiments on 9 million tweets on electronic products, and demonstrate the improved performance of TOTM in both quantitative evaluations and qualitative analysis. We show that aspect-based opinion analysis on massive volume of tweets provides useful opinions on products.
Evolution of swarm robotics systems with novelty search

Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task—aggregation, and a more challenging task—sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.
Driving Like a Human : Imitation Learning for Path Planning using Convolutional Neural Networks

Human-like path planning is still a challenging task for automated vehicles. Imitation learning can teach these vehicles to learn planning from human demonstration. In this work, we propose to formulate the planning stage as a convolutional neural network (CNN). Thus, we can employ well established CNN techniques to learn planning from imitation. With the proposed method, we train a network for planning in complex traffic situations from both simulated and real world data. The resulting planning network exhibits human-like path generation.
Continuous Realtime Gesture Following and Recognition

We present a HMM based system for real-time gesture analysis. The system outputs continuously parameters relative to the gesture time progression and its likelihood. These parameters are computed by comparing the performed gesture with stored reference gestures. The method relies on a detailed modeling of multidimensional temporal curves. Compared to standard HMM systems, the learning procedure is simplified using prior knowledge allowing the system to use a single example for each class. Several applications have been developed using this system in the context of music education, music and dance performances and interactive installation. Typically, the estimation of the time progression allows for the synchronization of physical gestures to sound files by time stretching/compressing audio buffers or videos.
Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification

Crowdsourcing is an arising collaborative approach applicable among many other applications to the area of language and speech processing. In fact, the use of crowdsourcing was already applied in the field of speech processing with promising results. However, only few studies investigated the use of crowdsourcing in computational paralinguistics. In this contribution, we propose a novel evaluator for crowdsourced-based ratings termed Weighted Trustability Evaluator (WTE) which is computed from the rater-dependent consistency over the test questions. We further investigate the reliability of crowdsourced annotations as compared to the ones obtained with traditional labelling procedures, such as constrained listening experiments in laboratories or in controlled environments. This comparison includes an in-depth analysis of obtainable classification performances. The experiments were conducted on the Speaker Likability Database (SLD) already used in the INTERSPEECH Challenge 2012, and the results lend further weight to the assumption that crowdsourcing can be applied as a reliable annotation source for computational paralinguistics given a sufficient number of raters and suited measurements of their reliability.
Biologically Inspired Mobile Robot Vision Localization

We present a robot localization system using biologically inspired vision. Our system models two extensively studied human visual capabilities: (1) extracting the ldquogistrdquo of a scene to produce a coarse localization hypothesis and (2) refining it by locating salient landmark points in the scene. Gist is computed here as a holistic statistical signature of the image, thereby yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, which efficiently directs the time-consuming landmark-identification process toward the most likely candidate locations in the image. The gist features and salient regions are then further processed using a Monte Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments-building complex (38.4 m times 54.86 m area, 13 966 testing images), vegetation-filled park (82.3 m times 109.73 m area, 26 397 testing images), and open-field park (137.16 m times 178.31 m area, 34 711 testing images)-each with its own challenges. The system is able to localize, on average, within 0.98, 2.63, and 3.46 m, respectively, even with multiple kidnapped-robot instances.
Global Models for the Orientation Field of Fingerprints: An Approach Based on Quadratic Differentials

Quadratic differentials naturally define analytic orientation fields on planar surfaces. We propose to model orientation fields of fingerprints by specifying quadratic differentials. Models for all fingerprint classes such as arches, loops and whorls are laid out. These models are parametrised by few, geometrically interpretable parameters which are invariant under Euclidean motions. We demonstrate their ability in adapting to given, observed orientation fields, and we compare them to existing models using the fingerprint images of the NIST Special Database 4. We also illustrate that these model allow for extrapolation into unobserved regions. This goes beyond the scope of earlier models for the orientation field as those are restricted to the observed planar fingerprint region. Within the framework of quadratic differentials we are able to verify analytically Penrose's formula for the singularities on a palm [L. S. Penrose, "Dermatoglyphics"'  1969]. Potential applications of these models are the use of their parameters as indices of large fingerprint databases, as well as the definition of intrinsic coordinates for single fingerprint images.
Mutational landscape and significance across 12 major cancer types

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.
Adversarial Model for Radio Frequency Identification

Radio Frequency Identification (RFID) systems aim to identify objects in open environments with neither physical nor visual contact. They consist of transponders inserted into objects, of readers, and usually of a database which contains information about the objects. The key point is that authorised readers must be able to identify tags without an adversary being able to trace them. Traceability is often underestimated by advocates of the technology and sometimes exaggerated by its detractors. Whatever the true picture, this problem is a reality when it blocks the deployment of this technology and some companies, faced with being boycotted, have already abandoned its use. Using cryptographic primitives to thwart the traceability issues is an approach which has been explored for several years. However, the research carried out up to now has not provided satisfactory results as no universal formalism has been defined. In this paper, we propose an adversarial model suitable for RFID environments. We define the notions of existential and universal untraceability and we model the access to the communication channels from a set of oracles. We show that our formalisation fits the problem being considered and allows a formal analysis of the protocols in terms of traceability. We use our model on several well-known RFID protocols and we show that most of them have weaknesses and are vulnerable to traceability.
Bitcoin's Security Model Revisited

We revisit the fundamental question of Bitcoin's security against double spending attacks. While previous work has bounded the probability that a transaction is reversed, we show that no such guarantee can be effectively given if the attacker can choose when to launch the attack. Other approaches that bound the cost of an attack have erred in considering only limited attack scenarios, and in fact it is easy to show that attacks may not cost the attacker at all. We therefore provide a different interpretation of the results presented in previous papers and correct them in several ways. We provide different notions of the security of transactions that provide guarantees to different classes of defenders: merchants who regularly receive payments, miners, and recipients of large one-time payments. We additionally consider an attack that can be launched against lightweight clients, and show that these are less secure than their full node counterparts and provide the right strategy for defenders in this case as well. Our results, overall, improve the understanding of Bitcoin's security guarantees and provide correct bounds for those wishing to safely accept transactions.
Leaf shape based plant species recognition

Plant has plenty use in foodstuff, medicine and industry. And it is also vitally important for environmental protection. However, it is an important and difficult task to recognize plant species on earth. Designing a convenient and automatic recognition system of plants is necessary and useful since it can facilitate fast classifying plants, and understanding and managing them. In this paper, a leaf database from different plants is firstly constructed. Then, a new classification method, referred to as move median centers (MMC) hypersphere classifier, for the leaf database based on digital morphological feature is proposed. The proposed method is more robust than the one based on contour features since those significant curvature points are hard to find. Finally, the efficiency and effectiveness of the proposed method in recognizing different plants is demonstrated by experiments.
CLARINET: WAN-Aware Optimization for Analytics Queries

Recent work has made the case for geo-distributed analytics, where data collected and stored at multiple datacenters and edge sites world-wide is analyzed in situ to drive operational and management decisions. A key issue in such systems is ensuring low response times for analytics queries issued against geo-distributed data. A central determinant of response time is the query execution plan (QEP). Current query optimizers do not consider the network when deriving QEPs, which is a key drawback as the geo-distributed sites are connected via WAN links with heterogeneous and modest bandwidths, unlike intra-datacenter networks. We propose CLARINET, a novel WAN-aware query optimizer. Deriving a WAN-aware QEP requires working jointly with the execution layer of analytics frameworks that places tasks to sites and performs scheduling. We design efficient heuristic solutions in CLARINET to make such a joint decision on the QEP. Our experiments with a real prototype deployed across EC2 datacenters, and large-scale simulations using production workloads show that CLARINET improves query response times by ≥ 50% compared to state-of-the-art WAN-aware task placement and scheduling.
Geometric skinning with approximate dual quaternion blending

Skinning of skeletally deformable models is extensively used for real-time animation of characters, creatures and similar objects. The standard solution, linear blend skinning, has some serious drawbacks that require artist intervention. Therefore, a number of alternatives have been proposed in recent years. All of them successfully combat some of the artifacts, but none challenge the simplicity and efficiency of linear blend skinning. As a result, linear blend skinning is still the number one choice for the majority of developers. In this article, we present a novel skinning algorithm based on linear combination of dual quaternions. Even though our proposed method is approximate, it does not exhibit any of the artifacts inherent in previous methods and still permits an efficient GPU implementation. Upgrading an existing animation system from linear to dual quaternion skinning is very easy and has a relatively minor impact on runtime performance.
Cybersecurity of SCADA Systems: Vulnerability assessment and mitigation

There has been a growing concern over the cyber security of Supervisory Control and Data Acquisition (SCADA) Systems due to the fast increasing connectivity of power grids with the information and communication systems. Although energy control centers are normally highly secured, the cyber-power network is large and complex. Potential intrusions can be launched from many different places in many different ways. Therefore, the complexity of the overall cyber-physical system vulnerability assessment and mitigation is very high.
Composite sketch recognition via deep network - a transfer learning approach

Sketch recognition is one of the integral components used by law enforcement agencies in solving crime. In recent past, software generated composite sketches are being preferred as they are more consistent and faster to construct than hand drawn sketches. Matching these composite sketches to face photographs is a complex task because the composite sketches are drawn based on the witness description and lack minute details which are present in photographs. This paper presents a novel algorithm for matching composite sketches with photographs using transfer learning with deep learning representation. In the proposed algorithm, first the deep learning architecture based facial representation is learned using large face database of photos and then the representation is updated using small problem-specific training database. Experiments are performed on the extended PRIP database and it is observed that the proposed algorithm outperforms recently proposed approach and a commercial face recognition system.
Dynamic Network Embedding by Modeling Triadic Closure Process

Network embedding, which aims to learn the lowdimensional representations of vertices, is an important task and has attracted considerable research efforts recently. In real world, networks, like social network and biological networks, are dynamic and evolving over time. However, almost all the existing network embedding methods focus on static networks while ignore network dynamics. In this paper, we present a novel representation learning approach, DynamicTriad, to preserve both structural information and evolution patterns of a given network. The general idea of our approach is to impose triad, which is a group of three vertices and is one of the basic units of networks. In particular, we model how a closed triad, which consists of three vertices connected with each other, develops from an open triad that has two of three vertices not connected with each other. This triadic closure process is a fundamental mechanism in the formation and evolution of networks, thereby makes our model being able to capture the network dynamics and to learn representation vectors for each vertex at different time steps. Experimental results on three real-world networks demonstrate that, compared with several state-of-the-art techniques, DynamicTriad achieves substantial gains in several application scenarios. For example, our approach can effectively be applied and help to identify telephone frauds in a mobile network, and to predict whether a user will repay her loans or not in a loan network.
Adaptive Semi-Supervised Learning with Discriminative Least Squares Regression

Semi-supervised learning plays a significant role in multi-class classification, where a small number of labeled data are more deterministic while substantial unlabeled data might cause large uncertainties and potential threats. In this paper, we distinguish the label fitting of labeled and unlabeled training data through a probabilistic vector with an adaptive parameter, which always ensures the significant importance of labeled data and characterizes the contribution of unlabeled instance according to its uncertainty. Instead of using traditional least squares regression (LSR) for classification, we develop a new discriminative LSR by equipping each label with an adjustment vector. This strategy avoids incorrect penalization on samples that are far away from the boundary and simultaneously facilitates multi-class classification by enlarging the geometrical distance of instances belonging to different classes. An efficient alternative algorithm is exploited to solve the proposed model with closed form solution for each updating rule. We also analyze the convergence and complexity of the proposed algorithm theoretically. Experimental results on several benchmark datasets demonstrate the effectiveness and superiority of the proposed model for multi-class classification tasks.