nils-herrmann/clean_scirepeval huggingface dataset:
Efficient mutation analysis by propagating and partitioning infected execution states

Mutation analysis evaluates a testing technique by measur- ing how well it detects seeded faults (mutants). Mutation analysis is hampered by inherent scalability problems — a test suite is executed for each of a large number of mutants. Despite numerous optimizations presented in the literature, this scalability issue remains, and this is one of the reasons why mutation analysis is hardly used in practice. Whereas most previous optimizations attempted to stati- cally reduce the number of executions or their computational overhead, this paper exploits information available only at run time to further reduce the number of executions. First, state infection conditions can reveal — with a single test execution of the unmutated program — which mutants would lead to a different state, thus avoiding unnecessary test executions. Second, determining whether an infected execution state propagates can further reduce the number of executions. Mutants that are embedded in compound expressions may infect the state locally without affecting the outcome of the compound expression. Third, those mutants that do infect the state can be partitioned based on the resulting infected state — if two mutants lead to the same infected state, only one needs to be executed as the result of the other can be inferred. We have implemented these optimizations in the Major mu- tation framework and empirically evaluated them on 14 open source programs. The optimizations reduced the mutation analysis time by 40% on average.
MR-based synthetic CT generation using a deep convolutional neural network method.

Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images.



The proposed method builds upon recent developments of deep learning and convolutional neural networks in the computer vision literature. The proposed DCNN model has 27 convolutional layers interleaved with pooling and unpooling layers and 35 million free parameters, which can be trained to learn a direct end-to-end mapping from MR images to their corresponding CTs. Training such a large model on our limited data is made possible through the principle of transfer learning and by initializing model weights from a pretrained model. Eighteen brain tumor patients with both CT and T1-weighted MR images are used as experimental data and a sixfold cross-validation study is performed. Each sCT generated is compared against the real CT image of the same patient on a voxel-by-voxel basis. Comparison is also made with respect to an atlas-based approach that involves deformable atlas registration and patch-based atlas fusion.



The proposed DCNN method produced a mean absolute error (MAE) below 85 HU for 13 of the 18 test subjects. The overall average MAE was 84.8 ± 17.3 HU for all subjects, which was found to be significantly better than the average MAE of 94.5 ± 17.8 HU for the atlas-based method. The DCNN method also provided significantly better accuracy when being evaluated using two other metrics: the mean squared error (188.6 ± 33.7 versus 198.3 ± 33.0) and the Pearson correlation coefficient(0.906 ± 0.03 versus 0.896 ± 0.03). Although training a DCNN model can be slow, training only need be done once. Applying a trained model to generate a complete sCT volume for each new patient MR image only took 9 s, which was much faster than the atlas-based approach.



A DCNN model method was developed, and shown to be able to produce highly accurate sCT estimations from conventional, single-sequence MR images in near real time. Quantitative results also showed that the proposed method competed favorably with an atlas-based method, in terms of both accuracy and computation speed at test time. Further validation on dose computation accuracy and on a larger patient cohort is warranted. Extensions of the method are also possible to further improve accuracy or to handle multi-sequence MR images.
Cooperative gestures: multi-user gestural interactions for co-located groupware

Multi-user, touch-sensing input devices create opportunities for the use of cooperative gestures -- multi-user gestural interactions for single display groupware. Cooperative gestures are interactions where the system interprets the gestures of more than one user as contributing to a single, combined command. Cooperative gestures can be used to enhance users' sense of teamwork, increase awareness of important system events, facilitate reachability and access control on large, shared displays, or add a unique touch to an entertainment-oriented activity. This paper discusses motivating scenarios for the use of cooperative gesturing and describes some initial experiences with CollabDraw, a system for collaborative art and photo manipulation. We identify design issues relevant to cooperative gesturing interfaces, and present a preliminary design framework. We conclude by identifying directions for future research on cooperative gesturing interaction techniques.
Classification of CT brain images based on deep learning networks

While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information rooted in both 2D slices and 3D blocks of CT images and an elaborated hand-crated approach of 3D KAZE.
Book2Movie: Aligning video scenes with book chapters

Film adaptations of novels often visually display in a few shots what is described in many pages of the source novel. In this paper we present a new problem: to align book chapters with video scenes. Such an alignment facilitates finding differences between the adaptation and the original source, and also acts as a basis for deriving rich descriptions from the novel for the video clips. We propose an efficient method to compute an alignment between book chapters and video scenes using matching dialogs and character identities as cues. A major consideration is to allow the alignment to be non-sequential. Our suggested shortest path based approach deals with the non-sequential alignments and can be used to determine whether a video scene was part of the original book. We create a new data set involving two popular novel-to-film adaptations with widely varying properties and compare our method against other text-to-video alignment baselines. Using the alignment, we present a qualitative analysis of describing the video through rich narratives obtained from the novel.
On Model Discovery For Hosted Data Science Projects

Alongside developing systems for scalable machine learning and collaborative data science activities, there is an increasing trend toward publicly shared data science projects, hosted in general or dedicated hosting services, such as GitHub and DataHub. The artifacts of the hosted projects are rich and include not only text files, but also versioned datasets, trained models, project documents, etc. Under the fast pace and expectation of data science activities, model discovery, i.e., finding relevant data science projects to reuse, is an important task in the context of data management for end-to-end machine learning. In this paper, we study the task and present the ongoing work on ModelHub Discovery, a system for finding relevant models in hosted data science projects. Instead of prescribing a structured data model for data science projects, we take an information retrieval approach by decomposing the discovery task into three major steps: project query and matching, model comparison and ranking, and processing and building ensembles with returned models. We describe the motivation and desiderata, propose techniques, and present opportunities and challenges for model discovery for hosted data science projects.
Novel design technique of address Decoder for SRAM

Address Decoder is an important digital block in SRAM which takes up to half of the total chip access time and significant part of the total SRAM power in normal read/write cycle. To design address decoder need to consider two objectives, first choosing the optimal circuit technique and second sizing of their transistors. Novel address decoder circuit is presented and analysed in this paper. Address decoder using NAND-NOR alternate stages with predecoder and replica inverter chain circuit is proposed and compared with traditional and universal block architecture, using 90nm CMOS technology. Delay and power dissipation in proposed decoder is 60.49% and 52.54% of traditional and 82.35% and 73.80% of universal block architecture respectively.
Optimal Portfolio Selection using Regularization Marine Carrasco and Nérée Noumon

The mean-variance principle of Markowitz (1952) for portfolio selection gives disappointing results once the mean and variance are replaced by their sample counterparts. The problem is amplied when the number of assets is large and the sample covariance is singular or nearly singular. In this paper, we investigate four regularization techniques to stabilize the inverse of the covariance matrix: the ridge, spectral cut-o¤, Landweber-Fridman and LARS Lasso. These four methods involve a tuning parameter that needs to be selected. The main contribution is to derive a data-driven method for selecting the tuning parameter in an optimal way, i.e. in order to minimize a quadratic loss function measuring the distance between the estimated allocation and the optimal one. The cross-validation type criterion takes a similar form for the four regularization methods. Preliminary simulations show that regularizing yields a higher out-of-sample performance than the sample based Markowitz portfolio and often outperforms the 1 over N equal weights portfolio. We thank Raymond Kan, Bruce Hansen, and Marc Henry for their helpful comments.
Tagging Your Tweets: A Probabilistic Modeling of Hashtag Annotation in Twitter

The adoption of hashtags in major social networks including Twitter, Facebook, and Google+ is a strong evidence of its importance in facilitating information diffusion and social chatting. To understand the factors (e.g., user interest, posting time and tweet content) that may affect hashtag annotation in Twitter and to capture the implicit relations between latent topics in tweets and their corresponding hashtags, we propose two PLSA-style topic models to model the hashtag annotation behavior in Twitter. Content-Pivoted Model (CPM) assumes that tweet content guides the generation of hashtags while Hashtag-Pivoted Model (HPM) assumes that hashtags guide the generation of tweet content. Both models jointly incorporate user, time, hashtag and tweet content in a probabilistic framework. The PLSA-style models also enable us to verify the impact of social factor on hashtag annotation by introducing social network regularization in the two models. We evaluate the proposed models using perplexity and demonstrate their effectiveness in two applications: retrospective hashtag annotation and related hashtag discovery. Our results show that HPM outperforms CPM by perplexity and both user and time are important factors that affect model performance. In addition, incorporating social network regularization does not improve model performance. Our experimental results also demonstrate the effectiveness of our models in both applications compared with baseline methods.
GPU Cluster for High Performance Computing

Inspired by the attractive Flops/dollar ratio and the incredible growth in the speed of modern graphics processing units (GPUs), we propose to use a cluster of GPUs for high performance scientific computing. As an example application, we have developed a parallel flow simulation using the lattice Boltzmann model (LBM) on a GPU cluster and have simulated the dispersion of airborne contaminants in the Times Square area of New York City. Using 30 GPU nodes, our simulation can compute a 480x400x80 LBM in 0.31second/step, a speed which is 4.6 times faster than that of our CPU cluster implementation. Besides the LBM, we also discuss other potential applications of the GPU cluster, such as cellular automata, PDE solvers, and FEM.
Discovering key concepts in verbose queries

Current search engines do not, in general, perform well with longer, more verbose queries. One of the main issues in processing these queries is identifying the key concepts that will have the most impact on effectiveness. In this paper, we develop and evaluate a technique that uses query-dependent, corpus-dependent, and corpus-independent features for automatic extraction of key concepts from verbose queries. We show that our method achieves higher accuracy in the identification of key concepts than standard weighting methods such as inverse document frequency. Finally, we propose a probabilistic model for integrating the weighted key concepts identified by our method into a query, and demonstrate that this integration significantly improves retrieval effectiveness for a large set of natural language description queries derived from TREC topics on several newswire and web collections.
Predicting positive and negative links in online social networks

We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.
Reflecting upon Feelings: An fMRI Study of Neural Systems Supporting the Attribution of Emotion to Self and Other

Understanding one's own and other individual's emotional states is essential for maintaining emotional equilibrium and strong social bonds. Although the neural substrates supporting reflection upon one's own feelings have been investigated, no studies have directly examined attributions about the internal emotional states of others to determine whether common or distinct neural systems support these abilities. The present study sought to directly compare brain regions involved in judging one's own, as compared to another individual's, emotional state. Thirteen participants viewed mixed valence blocks of photos drawn from the International Affective Picture System while whole-brain fMRI data were collected. Preblock cues instructed participants to evaluate either their emotional response to each photo, the emotional state of the central figure in each photo, or (in a baseline condition) whether the photo was taken indoors or outdoors. Contrasts indicated (1) that both self and other judgments activated the medial prefrontal cortex (MPFC), the superior temporal gyrus, and the posterior cingulate/precuneus, (2) that self judgments selectively activated subregions of the MPFC and the left temporal cortex, whereas (3) other judgments selectively activated the left lateral prefrontal cortex (including Broca's area) and the medial occipital cortex. These results suggest (1) that self and other evaluation of emotion rely on a network of common mechanisms centered on the MPFC, which has been hypothesized to support mental state attributions in general, and (2) that medial and lateral PFC regions selectively recruited by self or other judgments may be involved in attention to, and elaboration of, internally as opposed to externally generated information.
Network security intrusion detection system based on incremental improved convolutional neural network model

With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.
Document Spanners: A Formal Approach to Information Extraction

An intrinsic part of information extraction is the creation and manipulation of relations extracted from text. In this article, we develop a foundational framework where the central construct is what we call a document spanner (or just spanner for short). A spanner maps an input string into a relation over the spans (intervals specified by bounding indices) of the string. The focus of this article is on the representation of spanners. Conceptually, there are two kinds of such representations. Spanners defined in a primitive representation extract relations directly from the input string; those defined in an algebra apply algebraic operations to the primitively represented spanners. This framework is driven by SystemT, an IBM commercial product for text analysis, where the primitive representation is that of regular expressions with capture variables.
 We define additional types of primitive spanner representations by means of two kinds of automata that assign spans to variables. We prove that the first kind has the same expressive power as regular expressions with capture variables; the second kind expresses precisely the algebra of the regular spanners—the closure of the first kind under standard relational operators. The core spanners extend the regular ones by string-equality selection (an extension used in SystemT). We give some fundamental results on the expressiveness of regular and core spanners. As an example, we prove that regular spanners are closed under difference (and complement), but core spanners are not. Finally, we establish connections with related notions in the literature.
Markov Modeling of Moving Target Defense Games

We introduce a Markov-model-based framework for Moving Target Defense (MTD) analysis. The framework allows modeling of a broad range of MTD strategies, provides general theorems about how the probability of a successful adversary defeating an MTD strategy is related to the amount of time/cost spent by the adversary, and shows how a multilevel composition of MTD strategies can be analyzed by a straightforward combination of the analysis for each one of these strategies. Within the proposed framework we define the concept of security capacity which measures the strength or effectiveness of an MTD strategy: the security capacity depends on MTD specific parameters and more general system parameters. We apply our framework to two concrete MTD strategies.
Detecting Auto Insurance Fraud by Data Mining Techniques

The paper presents fraud detection method to predict and analyze fraud patterns from data. To generate classifiers, we apply the Naïve Bayesian Classification, and Decision Tree-Based algorithms. A brief description of the algorithm is provided along with its application in detecting fraud. The same data is used for both the techniques. We analyze and interpret the classifier predictions. The model prediction is supported by Bayesian Naïve Visualization, Decision Tree visualization, and Rule-Based Classification. We evaluate techniques to solve fraud detection in automobile insurance.
Implications of Loyalty Program Membership and Service Experiences for Customer Retention and Value

Companies that offer loyalty reward programs believe that their programs have a long-run positive effect on customer evaluations and behavior. However, if loyalty rewards programs increase relationship durations and usage levels, customers will be increasingly exposed to the complete spectrum of service experiences, including experiences that may cause customers to switch to another service provider. Using cross-sectional, time-series data from a worldwide financial services company that offers a loyalty reward program, this article investigates the conditions under which a loyalty rewards program will have a positive effect on customer evaluations, behavior, and repeat purchase intentions. The results show that members in the loyalty reward program overlook or discount negative evaluations of the company vis-à-vis competition. One possible reason could be that members of the loyalty rewards program perceive that they are getting better quality and service for their price or, in other words, “good value.”