nils-herrmann/clean_scirepeval huggingface dataset:
Sales Forecasting for Retail Chains

This paper presents a use case of data mining for sales forecasting in retail demand and sales prediction. In particular, the Extreme Gradient Boosting algorithm is used to design a prediction model to accurately estimate probable sales for retail outlets of a major European Pharmacy retailing company. The forecast of potential sales is based on a mixture of temporal and economical features including prior sales data, store promotions, retail competitors, school and state holidays, location and accessibility of the store as well as the time of year. The model building process was guided by common sense reasoning and by analytic knowledge discovered during data analysis and definitive conclusions were drawn. The performances of the XGBoost predictor were compared with those of more traditional regression algorithms like Linear Regression and Random Forest Regression. Findings not only reveal that the XGBoost algorithm outperforms the traditional modeling approaches with regard to prediction accuracy, but it also uncovers new knowledge that is hidden in data which help in building a more robust feature set and strengthen the sales prediction model.
A temporal modal defeasible logic for formalizing social commitments in dialogue and argumentation models

In this paper, we extend a temporal defeasible logic with a modal operator Committed to formalize commitments that agents undertake as a consequence of communicative actions (speech acts) during dialogues. We represent commitments as modal sentences. The defeasible dual of the modal operator Committed is a modal operator called Exempted. The logical setting makes the social-commitment based semantics of speech acts verifiable and practical; it is possible to detect if, and when, a commitment is violated and/or complied with. One of the main advantages of the proposed system is that it allows for capturing the nonmonotonic behavior of the commitments induced by the relevant speech acts.
Partial discharge diagnostics and electrical equipment insulation condition assessment

Partial discharge (PD) measurement has long been used as a test to evaluate different insulation system designs, and as a quality control test for new equipment. However, in the past 20 years, PD measurement has been widely applied to diagnose the condition of the electrical insulation in operating apparatus such as switchgear, transformers, cables, as well as motor and generator stator windings. Improvements in the capabilities as well the lower cost of sensors, electronics and memory is partly the reason for the increased popularity of PD diagnostics. Another reason has been the development of methods-including the use of ultrawide band detection-to improve the reliability of the PD measurement in the presence of noise. In addition, rapid progress is being made in automated pattern recognition techniques that also helps to suppress noise. This paper reviews the various PD measurement technologies that have been specifically developed to improve PD diagnostic methods, and outlines how they have been implemented for stators, cables, transformers and switchgear. Areas for further research are also presented.
A honey bees mating optimization algorithm for the open vehicle routing problem

Honey Bees Mating Optimization algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Open Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Open Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm and the Expanding Neighborhood Search (ENS) algorithm. Two set of benchmark instances is used in order to test the proposed algorithm. The results obtained for both sets are very satisfactory. More specifically, in the fourteen instances proposed by Christofides, the average quality is 0.35% when a hierarchical objective function is used, where, first, the number of vehicles is minimized and, afterwards, the total travel distance is minimized and the average quality is 0.42% when only the travel distance is minimized, while for the eight instances proposed by Li et al. when a hierarchical objective function is used the average quality is 0.21%.
Color QR Codes: Increased Capacity Via Per-Channel Data Encoding and Interference Cancellation

Monochrome 2-D barcodes have recently become extremely popular in mobile imaging applications. To accommodate higher data rates, we present a methodology for extending these barcodes to color. We use independent per channel data encoding in cyan, magenta, and yellow print colorant channels and decode the information in the complementary red, green, and blue channels used in capture, effectively increasing the data rate by a factor of three. To overcome unavoidable inter-channel interference, our framework utilizes adaptive thresholding and model-based interference cancellation at the decoder which are facilitated by an intelligent barcode design that allows estimation of parameters required for thresholding and cancellation. We present the framework by constructing and evaluating the performance for a color version of the QR code, the most commonly deployed monochrome barcode in mobile applications. Experimental results demonstrate that the framework is effective and allows recovery of embedded data with low bit error rates, which can be readily handled by the built in error correction within the QR code
AVANT-GUARD: scalable and vigilant switch flow management in software-defined networks

Among the leading reference implementations of the Software Defined Networking (SDN) paradigm is the OpenFlow framework, which decouples the control plane into a centralized application. In this paper, we consider two aspects of OpenFlow that pose security challenges, and we propose two solutions that could address these concerns. The first challenge is the inherent communication bottleneck that arises between the data plane and the control plane, which an adversary could exploit by mounting a "control plane saturation attack" that disrupts network operations. Indeed, even well-mined adversarial models, such as scanning or denial-of-service (DoS) activity, can produce more potent impacts on OpenFlow networks than traditional networks. To address this challenge, we introduce an extension to the OpenFlow data plane called "connection migration", which dramatically reduces the amount of data-to-control-plane interactions that arise during such attacks. The second challenge is that of enabling the control plane to expedite both detection of, and responses to, the changing flow dynamics within the data plane. For this, we introduce "actuating triggers" over the data plane's existing statistics collection services. These triggers are inserted by control layer applications to both register for asynchronous call backs, and insert conditional flow rules that are only activated when a trigger condition is detected within the data plane's statistics module. We present Avant-Guard, an implementation of our two data plane extensions, evaluate the performance impact, and examine its use for developing more scalable and resilient SDN security services.
Text summarization using enhanced MMR technique

Now a day when huge amount of documents and web contents are available, so reading of full content is somewhat difficult. Summarization is a way to give abstract form of large document so that the moral of the document can be communicated easily. Current research in automatic summarization is dominated by some effective, yet naive approaches: summarization through extraction, summarization through Abstraction and multi-document summarization. These techniques are used to building a summary of a document. Although there are a number of techniques implemented for the summarization of text for the single document or for the online web data or for any language. Here in this paper we are implemented an efficient technique for text summarization to reduce the computational cost and time and also the storage capacity.
CONCEPTUAL REQUIREMENTS FOR THE AUTOMATIC RECONSTRUCTION OF BUILDING INFORMATION MODELS FROM UNINTERPRETED 3 D MODELS

A multitude of new applications is quickly emerging in the field of Building Information Models (BIM). BIM models describe buildings with respect to their spatial and especially semantic and thematic characteristics. Since BIM models are manually created during the planning and construction phase, they are only available for newly planned or recently constructed buildings. In order to apply the new applications to already existing buildings, methods for the acquisition of BIM models for built-up sites are required. Primary data source are 3D geometry models obtained from surveying, CAD, or computer graphics. Automation of this process is highly desirable, but faces a range of specific problems setting the bar very high for a reconstruction process. This paper discusses these problems and identifies consequential requirements on reconstruction methods. Above, a two-step strategy for BIM model reconstruction is proposed which incorporates CityGML as an intermediate layer between 3D graphics models and IFC/BIM models.
Intrinsic video and applications

We present a method to decompose a video into its intrinsic components of reflectance and shading, plus a number of related example applications in video editing such as segmentation, stylization, material editing, recolorization and color transfer. Intrinsic decomposition is an ill-posed problem, which becomes even more challenging in the case of video due to the need for temporal coherence and the potentially large memory requirements of a global approach. Additionally, user interaction should be kept to a minimum in order to ensure efficiency. We propose a probabilistic approach, formulating a Bayesian Maximum a Posteriori problem to drive the propagation of clustered reflectance values from the first frame, and defining additional constraints as priors on the reflectance and shading. We explicitly leverage temporal information in the video by building a causal-anticausal, coarse-to-fine iterative scheme, and by relying on optical flow information. We impose no restrictions on the input video, and show examples representing a varied range of difficult cases. Our method is the first one designed explicitly for video; moreover, it naturally ensures temporal consistency, and compares favorably against the state of the art in this regard.
A Multi-task Deep Network for Person Re-identification

Person re-identification (ReID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as a binary classification task or a ranking task in current person ReID approaches. In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) that makes use of their own advantages and jointly optimize the two tasks simultaneously for person ReID. To the best of our knowledge, we are the first to integrate both tasks in one network to solve the person ReID. We show that our proposed architecture significantly boosts the performance. Furthermore, deep architecture in general requires a sufficient dataset for training, which is usually not met in person ReID. To cope with this situation, we further extend the MTDnet and propose a cross-domain architecture that is capable of using an auxiliary set to assist training on small target sets. In the experiments, our approach outperforms most of existing person ReID algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS and PRID2011, which clearly demonstrates the effectiveness of the proposed approach.
Centroidal Voronoi Tessellations: Applications and Algorithms

A centroidal Voronoi tessellation is a Voronoi tessellation whose generating points are the centroids (centers of mass) of the corresponding Voronoi regions. We give some applications of such tessellations to problems in image compression, quadrature, finite difference methods, distribution of resources, cellular biology, statistics, and the territorial behavior of animals. We discuss methods for computing these tessellations, provide some analyses concerning both the tessellations and the methods for their determination, and, finally, present the results of some numerical experiments.
Combinatory Categorial Grammar

Combinatory Categorial Grammar (CCG) is a radically lexicalized theory of grammar in which all language-specific information, including the linear order of heads, arguments, and adjuncts, is specified in the lexicon, from which it is projected onto sentences by language-independent universal typedependent combinatory rules of low “slightly non-context-free” expressive power, applying to strictly adjacent phonologically-realised categories. Syntactic and phonological derivation are isomorphic, and are synchronously coupled with semantic composition in a purely type-dependent rule-to-rule relation.
Word sense disambiguation using WordNet and the Lesk algorithm

Word sense disambiguation is the process of automatically clarifying the meaning of a word in its context. It has drawn much interest in the last decade and much improved results are being obtained. In this paper we take the so-called Lesk approach. In our case, definitions of the senses of the words to be disambiguated, as well as of the ten surrounding nouns, adjectives and verbs, are derived and enriched using the WordNet lexical database. Two possible implications of this project could be that the results are dependent on the characteristics of a test document and on the characteristics of glosses, which needs to be further investigated. The average precision performed worse (0.45) than baseline precision (0.60) which was based on always selecting the most frequent sense. However, the presented approach has several limitations: a small sample, and a big number of fine senses in WordNet, many of which are not that distinguishable from each other. The future work would include experimenting with different variations of the approach.
CLEF 2017 eRisk Overview: Early Risk Prediction on the Internet: Experimental Foundations

This paper provides an overview of eRisk 2017. This was the first year that this lab was organized at CLEF. The main purpose of eRisk was to explore issues of evaluation methodology, effectiveness metrics and other processes related to early risk detection. Early detection technologies can be employed in different areas, particularly those related to health and safety. The first edition of eRisk had two possible ways to participate: a pilot task on early risk detection of depression, and a workshop open to the submission of papers related to the topics of the lab.
Tibiofemoral movement 1: the shapes and relative movements of the femur and tibia in the unloaded cadaver knee.

In six unloaded cadaver knees we used MRI to determine the shapes of the articular surfaces and their relative movements. These were confirmed by dissection. Medially, the femoral condyle in sagittal section is composed of the arcs of two circles and that of the tibia of two angled flats. The anterior facets articulate in extension. At about 20 degrees the femur 'rocks' to articulate through the posterior facets. The medial femoral condyle does not move anteroposteriorly with flexion to 110 degrees. Laterally, the femoral condyle is composed entirely, or almost entirely, of a single circular facet similar in radius and arc to the posterior medial facet. The tibia is roughly flat. The femur tends to roll backwards with flexion. The combination during flexion of no anteroposterior movement medially (i.e., sliding) and backward rolling (combined with sliding) laterally equates to internal rotation of the tibia around a medial axis with flexion. About 5 degrees of this rotation may be obligatory from 0 degrees to 10 degrees flexion; thereafter little rotation occurs to at least 45 degrees. Total rotation at 110 degrees is about 20 degrees, most if not all of which can be suppressed by applying external rotation to the tibia at 90 degrees.
Efficient parallel kNN joins for large data in MapReduce

In data mining applications and spatial and multimedia databases, a useful tool is the kNN join, which is to produce the k nearest neighbors (NN), from a dataset S, of every point in a dataset R. Since it involves both the join and the NN search, performing kNN joins efficiently is a challenging task. Meanwhile, applications continue to witness a quick (exponential in some cases) increase in the amount of data to be processed. A popular model nowadays for large-scale data processing is the shared-nothing cluster on a number of commodity machines using MapReduce . Hence, how to execute kNN joins efficiently on large data that are stored in a MapReduce cluster is an intriguing problem that meets many practical needs. This work proposes novel (exact and approximate) algorithms in MapReduce to perform efficient parallel kNN joins on large data. We demonstrate our ideas using Hadoop. Extensive experiments in large real and synthetic datasets, with tens or hundreds of millions of records in both R and S and up to 30 dimensions, have demonstrated the efficiency, effectiveness, and scalability of our methods.
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weaklysupervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pretraining of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved stateof-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse but still acceptable performance when compared to the single language model, while benefiting from better generalization properties across languages.
Single Shot Text Detector with Regional Attention

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN-based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allowing the detector to work reliably on multi-scale and multi-orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 benchmark, advancing the state-of-the-art results in [18, 28].
Differentially Private K-Means Clustering

There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support various data mining tasks. In this paper we study the effectiveness of the two approaches on differentially private k-means clustering. We develop techniques to analyze the empirical error behaviors of the existing interactive and non-interactive approaches. Based on the analysis, we propose an improvement of DPLloyd which is a differentially private version of the Lloyd algorithm. We also propose a non-interactive approach EUGkM which publishes a differentially private synopsis for k-means clustering. Results from extensive and systematic experiments support our analysis and demonstrate the effectiveness of our improvement on DPLloyd and the proposed EUGkM algorithm.
Electricity-producing bacterial communities in microbial fuel cells.

Microbial fuel cells (MFCs) are not yet commercialized but they show great promise as a method of water treatment and as power sources for environmental sensors. The power produced by these systems is currently limited, primarily by high internal (ohmic) resistance. However, improvements in the system architecture will soon result in power generation that is dependent on the capabilities of the microorganisms. The bacterial communities that develop in these systems show great diversity, ranging from primarily delta-Proteobacteria that predominate in sediment MFCs to communities composed of alpha-, beta-, gamma- or delta-Proteobacteria, Firmicutes and uncharacterized clones in other types of MFCs. Much remains to be discovered about the physiology of these bacteria capable of exocellular electron transfer, collectively defined as a community of "exoelectrogens". Here, we review the microbial communities found in MFCs and the prospects for this emerging bioenergy technology.
General problem solving with category theory

This paper proposes a formal cognitive framework for problem solving based on category theory. We introduce cognitive categories, which are categories with exactly one morphism between any two objects. Objects in these categories are interpreted as states and morphisms as transformations between states. Moreover, cognitive problems are reduced to the specification of two objects in a cognitive category: an outset (i.e. the current state of the system) and a goal (i.e. the desired state). Cognitive systems transform the target system by means of generators and evaluators. Generators realize cognitive operations over a system by grouping morphisms, whilst evaluators group objects as a way to generalize outsets and goals to partially defined states. Meta-cognition emerges when the whole cognitive system is self-referenced as sub-states in the cognitive category, whilst learning must always be considered as a meta-cognitive process to maintain consistency. Several examples grounded in basic AI methods are provided as well.
The Wnt signaling pathway in development and disease.

Tight control of cell-cell communication is essential for the generation of a normally patterned embryo. A critical mediator of key cell-cell signaling events during embryogenesis is the highly conserved Wnt family of secreted proteins. Recent biochemical and genetic analyses have greatly enriched our understanding of how Wnts signal, and the list of canonical Wnt signaling components has exploded. The data reveal that multiple extracellular, cytoplasmic, and nuclear regulators intricately modulate Wnt signaling levels. In addition, receptor-ligand specificity and feedback loops help to determine Wnt signaling outputs. Wnts are required for adult tissue maintenance, and perturbations in Wnt signaling promote both human degenerative diseases and cancer. The next few years are likely to see novel therapeutic reagents aimed at controlling Wnt signaling in order to alleviate these conditions.