nils-herrmann/clean_scirepeval huggingface dataset:
Actor-Oriented Design of Embedded Hardware and Software Systems

In this paper, we argue that model-based design and platform-based design are two views of the same thing. A platform is a set of designs. Model-based design is about using platforms with useful modeling properties to specify designs, and then synthesizing implementations from these specifications. A design language, such as SystemC or VHDL, defines a platform. Any valid expression in the language is an element of the set. Viewed from below, the language provides an abstraction of implementation capabilities. Viewed from above, the language provides a set of constraints together with benefits that flow from those constraints. It provides a conceptual framework within which a design is crafted. An actor-oriented platform lies above the program-level platforms that are widely used today. It orthogonalizes the actor definition language and the actor assembly language, enabling domain-polymorphic actor definitions, multiple models of computation, and hierarchical heterogeneity. Actor-oriented platforms offer compelling modeling properties. Synthesis into program-level descriptions is possible. We illustrate these concepts by describing a design framework built on Ptolemy II.
Query Optimization over Crowdsourced Data

Deco is a comprehensive system for answering declarative queries posed over stored relational data together with data obtained ondemand from the crowd. In this paper we describe Deco’s costbased query optimizer, building on Deco’s data model, query language, and query execution engine presented earlier. Deco’s objective in query optimization is to find the best query plan to answer a query, in terms of estimated monetary cost. Deco’s query semantics and plan execution strategies require several fundamental changes to traditional query optimization. Novel techniques incorporated into Deco’s query optimizer include a cost model distinguishing between “free” existing data versus paid new data, a cardinality estimation algorithm coping with changes to the database state during query execution, and a plan enumeration algorithm maximizing reuse of common subplans in a setting that makes reuse challenging. We experimentally evaluate Deco’s query optimizer, focusing on the accuracy of cost estimation and the efficiency of plan enumeration.
A Multiarmed Bandit Incentive Mechanism for Crowdsourcing Demand Response in Smart Grids

Demand response is a critical part of renewable integration and energy cost reduction goals across the world. Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand. Our work is inspired by a novel connection we make to crowdsourcing mechanisms. The proposed mechanism incorporates realistic features of the demand response problem including time varying and quadratic cost function. The mechanism marries auctions, that allow users to report their preferences, with online algorithms, that allow distribution companies to learn user-specific parameters. We show that MAB-MDR is dominant strategy incentive compatible, individually rational, and achieves sublinear regret. Such mechanisms can be effectively deployed in smart grids using new information and control architecture innovations and lead to welcome savings in energy costs.
Pastry: Scalable, distributed object location and routing for large-scale peer-to-

This paper presents the design and evaluation of Pastry, a sc al ble, distributed object location and routing scheme for wide-ar a peer-to-peer applications. Pastry performs application-level routing and ob ject location in a potentially very large overlay network of nodes connected via the Int rnet. It can be used to support a wide range of peer-to-peer applications li ke g obal data storage, global data sharing, and naming. An insert operation in Pastry stores an object at a user-defin ed number of diverse nodes within the Pastry network. A lookup operation reliabl y retrieves a copy of the requested object if one exists. Moreover, a lookup is usu ally routed to the node nearest the client issuing the lookup (by some measure of pro ximity), among the nodes storing the requested object. Pastry is completely de centralized, scalable, and self-configuring; it automatically adapts to the arriva l, departure and failure of nodes. Experimental results obtained with a prototype implementa tion on a simulated network of up to 100,000 nodes confirm Pastry’s scalability, its ability to selfconfigure and adapt to node failures, and its good network loc ality properties.
Image sequence enhancement using sub-pixel displacements

Given a sequence of images taken from a moving camera, they are registered with sub-pixel accuracy in respect to translation and rotation. The sub-pixel registration enables image enhancement in respect to improved resolution and noise cleaning. Both the registration and the enhancement procedures are described. The methods are particulary useful for image sequences taken from an aircraft or satellite where images in a sequence differ mostly by translation and rotation. In these cases the process results in images that are stable, clean, and sharp.
A breakthrough novel method to resolve the drug and target interference problem in immunogenicity assays.

Biological matrix interference in detection and quantitation immunoassays remains a major challenge in the field of bioanalysis. For example, circulating drug may interfere with the detection of anti-drug antibodies (ADA) and drug target, or ADA may interfere with quantitation of drug levels in PK/TK analysis. Monoclonal antibody drug interference, especially for human IgG4 drugs, presents an additional challenge for ADA analysis due to its longer half-life and higher dose. Assay tolerance to such interference may depend on assay platform and reagents. Various approaches have been used to improve drug tolerance in ADA analysis but limited success was observed. We have developed a breakthrough novel method that uses Precipitation and Acid dissociation (PandA) to overcome drug interference in the ADA assay. The method principle is based on four components for detection of total ADA (free ADA and drug bound ADA) in the presence of drug in patient samples: (1) use excess drug to saturate free ADA to form drug bound ADA as drug:ADA complexes, (2) precipitate the complex using an agent such as PEG, (3) acid dissociate ADA from drug and immobilize (capture) free ADA (and free drug) under acidic conditions (without neutralization) onto a large capacity surface, and (4) detect free ADA (not the captured drug) using specific anti-human Ig detection reagent. In this manuscript, we are describing case studies for three humanized monoclonal antibodies (an IgG1 and two IgG4 drugs). The three drug specific PandA ADA assays resulted in complete recovery of ADA in samples containing drug levels in excess of those expected in patients, in contrast to the commonly used acid dissociation approach in ECL bridging assays. This breakthrough novel method shows significant improvement over the current approaches. In fact, the drug interference or under detecting of ADA in all three cases was eliminated. This assay principle could be used not only for ADA assays but also PK and biomarker (drug target) analysis in the presence of interference factors.
Multimodal Emotion Recognition for One-Minute-Gradual Emotion Challenge

The continuous dimensional emotion modelled by arousal and valence can depict complex changes of emotions. In this paper, we present our works on arousal and valence predictions for One-Minute-Gradual (OMG) Emotion Challenge. Multimodal representations are first extracted from videos using a variety of acoustic, video and textual models and support vector machine (SVM) is then used for fusion of multimodal signals to make final predictions. Our solution achieves Concordant Correlation Coefficient (CCC) scores of 0.397 and 0.520 on arousal and valence respectively for the validation dataset, which outperforms the baseline systems with the best CCC scores of 0.15 and 0.23 on arousal and valence by a large margin.
Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence

Breast cancer (BC) is the most common cancer in women, affecting about 10% of all women at some stages of their life. In recent years, the incidence rate keeps increasing and data show that the survival rate is 88% after five years from diagnosis and 80% after 10 years from diagnosis . Early prediction of breast cancer is one of the most crucial works in the follow-up process. Data mining methods can help to reduce the number of false positive and false negative decisions [2,3]. Consequently, new methods such as knowledge discovery in databases (KDD) has become a popular research tool for medical researchers who try to identify and exploit patterns and relationships among large number of variables, and predict the outcome of a disease using historical cases stored in datasets [4].
An effective defensive node against jamming attacks in sensor networks

Jamming is defined as the act of intentionally directing electromagnetic energy towards a communication system to disrupt or prevent signal transmission. In the context of wireless sensor networks (WSNs), jamming is the type of attack which interferes with the radio frequencies used by sensor nodes and may be viewed as a special case of denial of service (DoS) attacks. Herein, we outline the possible jamming attacks a WSN may encounter. The main contribution of this paper is the outline of the design specifications of a prototype node ‘Ares’ that effectively defends jamming attacks. Our focus is on frequency hopping spread spectrum (FHSS) and direct sequence spread spectrum (DSSS), two of the most effective countermeasures against jamming. The main novel aspect of Ares is that it uses a hybrid FHSS–DSSS approach to defend jamming attacks. We suggest the use of a specific FHSS technique in 5 GHz band with 51 frequency channels wherein the channel sequence is generated using a key (which derives from a secret word), known only to the sink and the sensor nodes, as a seed. Each channel uses DSSS modulation with 16-bit pseudo noise (PN) code. The PN code derives from the same key used for FHSS channel generation. The pre-eminence of our method against alternative anti-jamming techniques is demonstrated through extensive simulation tests.
Ontology Learning from Text: A Survey of Methods

After the vision of the Semantic Web was broadcasted at the turn of the millennium, ontology became a synonym for the solution to many problems concerning the fact that computers do not understand human language: if there were an ontology and every document were marked up with it and we had agents that would understand the markup, then computers would finally be able to process our queries in a really sophisticated way. Some years later, the success of Google shows us that the vision has not come true, being hampered by the incredible amount of extra work required for the intellectual encoding of semantic mark-up – as compared to simply uploading an HTML page. To alleviate this acquisition bottleneck, the field of ontology learning has since emerged as an important sub-field of ontology engineering. It is widely accepted that ontologies can facilitate text understanding and automatic processing of textual resources. Moving from words to concepts not only mitigates data sparseness issues, but also promises appealing solutions to polysemy and homonymy by finding non-ambiguous concepts that may map to various realizations in – possibly ambiguous – words. Numerous applications using lexical-semantic databases like WordNet (Miller, 1990) and its non-English counterparts, e.g. EuroWordNet (Vossen, 1997) or CoreNet (Choi and Bae, 2004) demonstrate the utility of semantic resources for natural language processing. Learning semantic resources from text instead of manually creating them might be dangerous in terms of correctness, but has undeniable advantages: Creating resources for text processing from the texts to be processed will fit the semantic component neatly and directly to them, which will never be possible with general-purpose resources. Further, the cost per entry is greatly reduced, giving rise to much larger resources than an advocate of a manual approach could ever afford. On the other hand, none of the methods used today are good enough for creating semantic resources of any kind in a completely unsupervised fashion, albeit automatic methods can facilitate manual construction to a large extent. The term ontology is understood in a variety of ways and has been used in philosophy for many centuries. In contrast, the notion of ontology in the field of computer science is younger – but almost used as inconsistently, when it comes to the details of the definition. The intention of this essay is to give an overview of different methods that learn ontologies or ontology-like structures from unstructured text. Ontology learning from other sources, issues in description languages, ontology editors, ontology merging and ontology evolving transcend the scope of this article. Surveys on ontology learning from text and other sources can be found in Ding and Foo (2002) and Gómez-Pérez
Analysis of Language Style Self-Presentational and Relational Concerns Revealed Through the Managing Impressions and Relationships on Facebook :

The merging of audiences in social media and the variety of participation structures they present, including different audience sizes and interaction targets, pose questions about how people respond to these new communication situations. This research examined self-presentational and relational concerns through the analysis of language styles on Facebook. The authors collected a corpus of status updates, wall posts, and private messages from 79 participants. These messages varied in certain characteristics of language style, revealing differences in underlying self-presentational and relational concerns based on the publicness and directedness of the interaction. Positive emotion words correlated with self-reported self-presentational concerns in status updates, suggesting a strategic use of sharing positive emotions in public and nondirected communication via status updates. Verbal immediacy correlated with partner familiarity in wall posts but not in private messages, suggesting that verbal immediacy cues serve as markers to differentiate between more and less familiar partners in public wall posts.
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games

Evaluating agent performance when outcomes are stochastic and agents use randomized strategies can be challenging when there is limited data available. The variance of sampled outcomes may make the simple approach of Monte Carlo sampling inadequate. This is the case for agents playing heads-up no-limit Texas hold’em poker, where manmachine competitions typically involve multiple days of consistent play by multiple players, but still can (and sometimes did) result in statistically insignificant conclusions. In this paper, we introduce AIVAT, a low variance, provably unbiased value assessment tool that exploits an arbitrary heuristic estimate of state value, as well as the explicit strategy of a subset of the agents. Unlike existing techniques which reduce the variance from chance events, or only consider game ending actions, AIVAT reduces the variance both from choices by nature and by players with a known strategy. The resulting estimator produces results that significantly outperform previous state of the art techniques. It was able to reduce the standard deviation of a Texas hold’em poker man-machine match by 85% and consequently requires 44 times fewer games to draw the same statistical conclusion. AIVAT enabled the first statistically significant AI victory against professional poker players in no-limit hold’em. Furthermore, the technique was powerful enough to produce statistically significant results versus individual players, not just an aggregate pool of the players. We also used AIVAT to analyze a short series of AI vs human poker tournaments, producing statistical significant results with as few as 28 matches.
Support vector machines for spam categorization

We study the use of support vector machines (SVM's) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM's performed best when using binary features. For both data sets, boosting trees and SVM's had acceptable test performance in terms of accuracy and speed. However, SVM's had significantly less training time.
Intercell Busbar Design for Copper Electrowinning

The design of intercell bars is of primary relevance to control electrowinning costs. Two intercell technologies for designing these busbars are presently available: current source (CS) or voltage source. For this research project, we selected CS based on tankhouse operational data that consistently show better performance than its counterpart. Both technologies can be implemented in dogbone or central busbar configurations. Dogbone was chosen because of the better contact distance and larger heatsink area. The proposed design ensures hot swapability. Conventional busbars suffer premature aging, corrosion, and contact deterioration. Industrial samples show these phenomena. Multiphysics finite-element method modeling highlights the origin of this accelerated aging. This work provides design guidelines for S-shaped connectors to overcome previous design flaws. For completeness, the paper includes a design example for an industrial site. The resulting design surpasses standard design practices and secures hot-spot free operation.
The influence of futures markets on real time price stabilization in electricity markets

Markets can interact with power systems in ways that can render an otherwise stable market and an otherwise stable power system into an unstable overall system. This unstable system will be characterized not only by fluctuating prices that do not settle to constant values, but, more worrisome, it creates the possibility of inducing slow electromechanical oscillations if left unchecked. This will tend to happen as a result of "price chasing" on the part of suppliers that can react (and over-react) to changing system prices. This paper examines the role that futures markets may have on the clearing prices and on altering the volatility and potential instability of real time prices and generator output.
W-net: Bridged U-net for 2D Medical Image Segmentation

In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is close to ground truth and will cause oscillation during training. To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size. Meanwhile, we propose a new loss function to accelerate the learning process and a combination of different activation functions to improve the network performance. Our experimental results suggest that our network is comparable or superior to state-of-the-art methods.
Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza

Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013-2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.
Adapted unstructured LBM for flow simulation on curved surfaces

Flow motion on curved surfaces of arbitrary topology is an interesting visual effect but a complex dynamics to simulate. In this paper, we introduce a novel and effective way to model such dynamics. We propose a technique that adapts a recently emerged computational fluid dynamics (CFD) model, unstructured lattice Boltzmann model (Unstructured LBM), from the 2D unstructured meshes to the 3D surface meshes. Unlike previous methods in modeling flows on surfaces, which start from the macroscopic point of view and modify the Navier Stokes solvers for the curved surfaces, our method is based on the microscopic kinetic equations for discrete particle distribution functions. All computations on the surface mesh only involve the information within local neighborhoods. This model lends itself the following advantages: (i) simplicity and explicit parallelism in computation, (ii) great capability in handling complex interactions, such as the interactions between flow and boundaries and the interactions of multiple-component fluids; (iii) no need of global surface parameterization which may cause strong distortions; (iv) capability of being applied to meshes with arbitrary connectivity.
Be Appropriate and Funny: Automatic Entity Morph Encoding

Internet users are keen on creating different kinds of morphs to avoid censorship, express strong sentiment or humor. For example, in Chinese social media, users often use the entity morph “方便面 (Instant Noodles)” to refer to “周永康 (Zhou Yongkang)” because it shares one character “康 (Kang)” with the well-known brand of instant noodles “康师傅 (Master Kang)”. We developed a wide variety of novel approaches to automatically encode proper and interesting morphs, which can effectively pass decoding tests 1.