nils-herrmann/clean_scirepeval huggingface dataset:
Assessment of chronic pain coping strategies

We made an adaptation of the Coping Strategies Questionnaire (CSQ) to the Spanish population. This measure, the most used in its scope, was developed by Rosenstiel and Keefe in 1983.  205 participants coming from Primary Health Care and pain clinics made up the sample. More than half suffered migraine and chronic tension-type headache; the rest, fibromyalgia, low back pain, arthrosis or arthritis.  Factor analyses explained 59% of the total variance, on an 8-factor structure that converged into a 2-factor structure. In the 8-factor solution the novelty was the diversification of mental-non-mental distraction strategies, and religious-non-religious hope strategies. In the 2-factor solution the novelty was the grouping according to the efficacy of the coping. All the CSQ factors showed inner consistency and construct validity. Thus, unadaptive coping strategies were related to negative, anxious and depressed self-talk, related to lack of control and perceived self-efficacy, and related to many pain behaviors. On the contrary it happened with adaptive coping strategies. In addition, the diagnosis of pain was related to the utilization and effectiveness of coping strategies. Conclussions. CSQ is shown to be a reliable and valid measure of coping strategies in chronic pain in the Spanish population, showing the difference between theoretical and empirical factor structures again.
Subjective Probability : A Judgment of Representativeness

This paper explores a heuristic-representativeness-according to which the subjective probability of an event, or a sample, is determined by the degree to which it: (i) is similar in essential characteristics to its parent population; and (ii) reflects the salient features of the process by which it is generated. This heuristic is explicated in a series of empirical examples demonstrating predictable and systematic errors in the evaluation of uncertain events. In particular, since sample size does not represent any property of the population, it is expected to have little or no effect on judgment of likelihood. This prediction is confirmed in studies showing that subjective sampling distributions and posterior probability judgments are determined by the most salient characteristic of the sample (e.g., proportion, mean) without regard to the size of the sample. The present heuristic approach is contrasted with the normative (Bayesian) approach to the analysis of the judgment of uncertainty.
Space/Time Trade-offs in Hash Coding with Allowable Errors

In this paper trade-offs among certain computational factors in hash coding are analyzed. The paradigm problem considered is that of testing a series of messages one-by-one for membership in a given set of messages. Two new hash-coding methods are examined and compared with a particular conventional hash-coding method. The computational factors considered are the size of the hash area (space), the time required to identify a message as a nonmember of the given set (reject time), and an allowable error frequency.
The new methods are intended to reduce the amount of space required to contain the hash-coded information from that associated with conventional methods. The reduction in space is accomplished by exploiting the possibility that a small fraction of errors of commission may be tolerable in some applications, in particular, applications in which a large amount of data is involved and a core resident hash area is consequently not feasible using conventional methods.
In such applications, it is envisaged that overall performance could be improved by using a smaller core resident hash area in conjunction with the new methods and, when necessary, by using some secondary and perhaps time-consuming test to “catch” the small fraction of errors associated with the new methods. An example is discussed which illustrates possible areas of application for the new methods.
Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.
Secure Overlay Cloud Storage with Access Control and Assured Deletion

We can now outsource data backups off-site to third-party cloud storage services so as to reduce data management costs. However, we must provide security guarantees for the outsourced data, which is now maintained by third parties. We design and implement FADE, a secure overlay cloud storage system that achieves fine-grained, policy-based access control and file assured deletion. It associates outsourced files with file access policies, and assuredly deletes files to make them unrecoverable to anyone upon revocations of file access policies. To achieve such security goals, FADE is built upon a set of cryptographic key operations that are self-maintained by a quorum of key managers that are independent of third-party clouds. In particular, FADE acts as an overlay system that works seamlessly atop today's cloud storage services. We implement a proof-of-concept prototype of FADE atop Amazon S3, one of today's cloud storage services. We conduct extensive empirical studies, and demonstrate that FADE provides security protection for outsourced data, while introducing only minimal performance and monetary cost overhead. Our work provides insights of how to incorporate value-added security features into today's cloud storage services.
PARIS: Probabilistic Alignment of Relations, Instances, and Schema

One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present paris, an approach for the automatic alignment of ontologies. paris aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows paris to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90 % in experiments with some of the world’s largest ontologies.
Recurrent Neural Word Segmentation with Tag Inference

In this paper, we present a Long Short-TermMemory (LSTM) based model for the task of Chinese Weibo word segmentation. The model adopts a LSTM layer to capture long-range dependencies in sentence and learn the underlying patterns. In order to infer the optimal tag path, we introduce a transition score matrix for jumping between tags of successive characters. Integrated with some unsupervised features, the performance of the model is further improved. Finally, our model achieves a weighted F1-score of 0.8044 on close track, 0.8298 on the semi-open track.
Constraint-based recommender systems: technologies and research issues

Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid-1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.
Practical Image-Based Relighting and Editing with Spherical-Harmonics and Local Lights

We present a practical technique for image-based relighting under environmental illumination which greatly reduces the number of required photographs compared to traditional techniques, while still achieving high quality editable relighting results. The proposed method employs an optimization procedure to combine spherical harmonics, a global lighting basis, with a set of local lights. Our choice of lighting basis captures both low and high frequency components of typical surface reflectance functions while generating close approximations to the ground truth with an order of magnitude less data. This technique benefits the acquisition process by reducing the number of required photographs, while simplifying the modification of reflectance data and enabling artistic lighting edits for post-production effects. Here, we demonstrate two desirable lighting edits, modifying light intensity and angular width, employing the proposed lighting basis.
Exploring IT-enabled innovation: A new paradigm?

In this paper we report on findings from ten case studies of successful IT-enabled innovation covering a wide range of organisations and projects. They were carried out as an exploratory study to identify practices that contribute to innovation and specifically consider the implications of a focus on innovation for earlier work on benefits-led approaches to IT. We identified a number of practices from earlier work on benefits-led approaches to IT that contribute to IT-enabled innovation. We also identified organizations developing a new practice of ideation drawing on ‘crowd sourcing’ and exploiting social media technologies. Existing principles and practices for benefits realization can contribute to innovation, but the practices have to be applied in different ways according to the context. This amounts to a third paradigm, different from previous paradigms of technology implementation and a planned approach to benefits realization.
Seeing what the system thinks you know: visualizing evidence in an open learner model

User knowledge levels in adaptive learning systems can be assessed based on user interactions that are interpreted as Knowledge Indicating Events (KIE). Such an approach makes complex inferences that may be hard to understand for users, and that are not necessarily accurate. We present MyExperiences, an open learner model designed for showing the users the inferences about them, as well as the underlying data. MyExperiences is one of the first open learner models based on tree maps. It constitutes an example of how research into open learner models and information visualization can be combined in an innovative way.
Search Result Diversification in Short Text Streams

We consider the problem of search result diversification for streams of short texts. Diversifying search results in short text streams is more challenging than in the case of long documents, as it is difficult to capture the latent topics of short documents. To capture the changes of topics and the probabilities of documents for a given query at a specific time in a short text stream, we propose a dynamic Dirichlet multinomial mixture topic model, called D2M3, as well as a Gibbs sampling algorithm for the inference. We also propose a streaming diversification algorithm, SDA, that integrates the information captured by D2M3 with our proposed modified version of the PM-2 (Proportionality-based diversification Method -- second version) diversification algorithm. We conduct experiments on a Twitter dataset and find that SDA statistically significantly outperforms state-of-the-art non-streaming retrieval methods, plain streaming retrieval methods, as well as streaming diversification methods that use other dynamic topic models.
Knowledge transfer in learning to recognize visual objects classes

Learning to recognize of object classes is one of the most important functionalities of vision. It is estimated that humans are able to learn tens of thousands of visual categori es in their life. Given the photometric and geometric variabilities displayed by objects as well as the high degree of intra-clas s variabilities, we hypothesize that humans achieve such a fe at by using knowledge and information cumulated throughout the learning process. In recent years, a handful of pioneering p apers have applied various forms of knowledge transfer algorithms to the problem of learning object classes. We first review some o f these papers by loosely grouping them into three categories : transfer through prior parameters, transfer through shared features or parts, and transfer through contextual information. In the second half of the paper, we detail a recent algorithm proposed by the author. This incremental learning scheme us es information from object classes previously learned in the f orm of prior models to train a new object class model. Training images can be presented in an incremental way. We present experimental results tested with this model on a large numbe r of object categories.
The reviewing of object files: Object-specific integration of information

A series of experiments explored a form of object-specific priming. In all experiments a preview field containing two or more letters is followed by a target letter that is to be named. The displays are designed to produce a perceptual interpretation of the target as a new state of an object that previously contained one of the primes. The link is produced in different experiments by a shared location, by a shared relative position in a moving pattern, or by successive appearance in the same moving frame. An object-specific advantage is consistently observed: naming is facilitated by a preview of the target, if (and in some cases only if) the two appearances are linked to the same object. The amount and the object specificity of the preview benefit are not affected by extending the preview duration to 1 s, or by extending the temporal gap between fields to 590 ms. The results are interpreted in terms of a reviewing process, which is triggered by the appearance of the target and retrieves just one of the previewed items. In the absence of an object link, the reviewing item is selected at random. We develop the concept of an object file as a temporary episodic representation, within which successive states of an object are linked and integrated.
Research of Workflow Access Control Strategy based on Trust

The traditional workflow access control strategy has often found to be inadequate to detect and restrain malicious behavior effectively. With the aim to solve this problem, this paper presents a new workflow access control model based on trust, and a new access control strategy with an authorization process. This strategy introduces user behavior evaluation, trust computation and role hierarchy into role access control strategy. Through the trust computation of user behavior, it can dynamically adjust user's role and permissions, realizing the dynamic authorization process. Theory analysis and simulation experiments show that this access control strategy is more sensitive in dynamic authorization, and it has fine-grained trust computation. Also this strategy can detect malicious behaviors in time, effectively restraining malicious behavior harming the system so enhancing the security of the system.
Automatic Readability Classification of Crowd-Sourced Data based on Linguistic and Information-Theoretic Features

This paper presents a classifier of text readability based on information-theoretic features. The classifier was developed based on a linguistic approach to readability that explores lexical, syntactic and semantic features. For this evaluation we extracted a corpus of 645 articles from Wikipedia together with their quality judgments. We show that information-theoretic features perform as well as their linguistic counterparts even if we explore several linguistic levels at once.
Total Colorings Of Degenerate Graphs

A total coloring of a graph G is a coloring of all elements of G, i.e. vertices and edges, such that no two adjacent or incident elements receive the same color. A graph G is sdegenerate for a positive integer s if G can be reduced to a trivial graph by successive removal of vertices with degree ≤ s. We prove that an s-degenerate graph G has a total coloring with ∆+1 colors if the maximum degree ∆ of G is sufficiently large, say ∆≥4s+3. Our proof yields an efficient algorithm to find such a total coloring. We also give a lineartime algorithm to find a total coloring of a graph G with the minimum number of colors if G is a partial k-tree, that is, the tree-width of G is bounded by a fixed integer k.
Monitoring of Micro-sleep and Sleepiness for the Drivers Using EEG Signal

................................................................................................................................................... 3 ACKNOWLEDGEMENT ................................................................................................................................. 5 LIST OF FIGURES .......................................................................................................................................... 8 LIST OF TABLES ............................................................................................................................................ 9 LIST OF ABBREVIATIONS ............................................................................................................................ 10 INTRODUCTION ......................................................................................................................................... 11 BACKGROUND ........................................................................................................................................... 13 METHODS .................................................................................................................................................. 17 PRE-PROCESSING ................................................................................................................................... 20 FEATURE EXTRACTION ........................................................................................................................... 24 SUPPORT VECTOR MACHINE ................................................................................................................. 31 RELATED WORK ......................................................................................................................................... 41 Extraction of Feature Information in EEG Signal ................................................................................... 41 Sleepiness and health problems in drivers who work driving some kind of vehicle with nocturnal and shift works ............................................................................................................................................. 43 SVM Regression Training with SMO ...................................................................................................... 48
CPLR: Collaborative pairwise learning to rank for personalized recommendation

Compared with explicit feedback data, implicit feedback data is easier to be collected and more widespread. However, implicit feedback data is also more difficult to be analyzed due to the lack of negative examples. Bayesian personalized ranking (BPR) is a well-known algorithm for personalized recommendation on implicit feedback data due to its high performance. However, it (1) treats all the unobserved feedback the same as negative examples which may be just caused by unseen, (2) treats all the observed feedback the same as positive examples which may be just caused by noisy action, and (3) assumes the preferences of users are independent which is difficult to achieve in reality. To solve all these problems, we propose a novel personalized recommendation algorithm called collaborative pairwise learning to rank (CPLR), which considers the influence between users on the preferences for both items with observed feedback and items without. To take these information into consideration, we try to optimize a generalized AUC instead of the standard AUC used in BPR. CPLR can be seen as a generalized BPR. Besides BPR, several extension algorithms of BPR, like social BPR (SBPR) and group preference based BPR (GBPR), are special cases of CPLR. Extensive experiments demonstrate the promise of our approach in comparison with traditional collaborative filtering methods and state-of-the-art pairwise learning to rank algorithms. Compared with the performance of baseline algorithms on five real-world data sets, the improvements of CPLR are over 17%, 23% and 22% for Pre@5, MAP and NDCG, respectively.
TCA: An Efficient Two-Mode Meta-Heuristic Algorithm for Combinatorial Test Generation (T)

Covering arrays (CAs) are often used as test suites for combinatorial interaction testing to discover interaction faults of real-world systems. Most real-world systems involve constraints, so improving algorithms for covering array generation (CAG) with constraints is beneficial. Two popular methods for constrained CAG are greedy construction and meta-heuristic search. Recently, a meta-heuristic framework called two-mode local search has shown great success in solving classic NPhard problems. We are interested whether this method is also powerful in solving the constrained CAG problem. This work proposes a two-mode meta-heuristic framework for constrained CAG efficiently and presents a new meta-heuristic algorithm called TCA. Experiments show that TCA significantly outperforms state-of-the-art solvers on 3-way constrained CAG. Further experiments demonstrate that TCA also performs much better than its competitors on 2-way constrained CAG.
An Intelligent Approach for Anti-Spoofing in a Multimodal Biometric System

Biometric systems are vulnerable to certain type of attacks at various points in the biometric model. A spoofing attack which is submitting a stolen, copied biometric trait to the sensor to gain unauthorized access to the biometric system is one among them. Multimodal biometric systems are designed to increase the accuracy of the biometric system, but they are more vulnerable to spoofing attacks than a unimodal biometric system. The existing approaches for anti-spoofing do not consider multiple biometric traits and also have a high false acceptance rate. The proposed method is designed to overcome spoofing in a multimodal biometric system that uses a combination of face, fingerprint and iris images. The extracted biometric features are fused and fed to a convolution neural network that employs deep learning to detect spoofed features from real features. The proposed method gives better results than existing anti-spoofing methods.  Multimodal Biometrics, Anti-spoofing, Biometric feature extraction, Biometric feature fusion, Convolution Neural Network.
GIFT: A Real-Time and Scalable 3D Shape Search Engine

Projective analysis is an important solution for 3D shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different view points. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, thus cannot satisfy the basic requirement of scalability for search engines. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: (1) efficient projection and view feature extraction using GPU acceleration, (2) the first inverted file, referred as F-IF, is utilized to speed up the procedure of multi-view matching, (3) the second inverted file (S-IF), which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based reranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines GPU acceleration and Inverted File (Twice), as GIFT. Besides its high efficiency, GIFT also outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.