nils-herrmann/clean_scirepeval huggingface dataset:
Class Proportion Estimation with Application to Multiclass Anomaly Rejection

This work addresses two classification problems that fall under the heading of domain adaptation, wherein the distributions of training and testing examples differ. The first problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set given labeled examples of each class. Compared to previous work on this problem, our approach has the novel feature that it does not require labeled training data from one of the classes. This property allows us to address the second domain adaptation problem, namely, multiclass anomaly rejection. Here, the goal is to design a classifier that has the option of assigning a “reject” label, indicating that the instance did not arise from a class present in the training data. We establish consistent learning strategies for both of these domain adaptation problems, which to our knowledge are the first of their kind. We also implement the class proportion estimation technique and demonstrate its performance on several benchmark data sets.
Virtual Reality in Psychotherapy: Review

Virtual reality (VR) has recently emerged as a potentially effective way to provide general and specialty health care services, and appears poised to enter mainstream psychotherapy delivery. Because VR could be part of the future of clinical psychology, it is critical to all psychotherapists that it be defined broadly. To ensure appropriate development of VR applications, clinicians must have a clear understanding of the opportunities and challenges it will provide in professional practice. This review outlines the current state of clinical research relevant to the development of virtual environments for use in psychotherapy. In particular, the paper focuses its analysis on both actual applications of VR in clinical psychology and how different clinical perspectives can use this approach to improve the process of therapeutic change.
CodeHint: dynamic and interactive synthesis of code snippets

There are many tools that help programmers find code fragments, but most are inexpressive and rely on static information. We present a new technique for synthesizing code that is dynamic (giving accurate results and allowing programmers to reason about concrete executions), easy-to-use (supporting a wide range of correctness specifications), and interactive (allowing users to refine the candidate code snippets). Our implementation, which we call CodeHint, generates and evaluates code at runtime and hence can synthesize real-world Java code that involves I/O, reflection, native calls, and other advanced language features. We have evaluated CodeHint in two user studies and show that its algorithms are efficient and that it improves programmer productivity by more than a factor of two.
Analysis of operating system diversity for intrusion tolerance

One of the key benefits of using intrusion-tolerant systems is the possibility of ensuring correct behavior in the presence of attacks and intrusions. These security gains are directly dependent on the components exhibiting failure diversity. To what extent failure diversity is observed in practical deployment depends on how diverse are the components that constitute the system. In this paper, we present a study with operating system’s (OS’s) vulnerability data from the NIST National Vulnerability Database (NVD). We have analyzed the vulnerabilities of 11 different OSs over a period of 18 years, to check how many of these vulnerabilities occur in more than one OS. We found this number to be low for several combinations of OSs. Hence, although there are a few caveats on the use of NVD data to support definitive conclusions, our analysis shows that by selecting appropriate OSs, one can preclude (or reduce substantially) common vulnerabilities from occurring in the replicas of the intrusion-tolerant system.
Modeling human performance of pen stroke gestures

This paper presents a quantitative human performance model of making single-stroke pen gestures within certain error constraints in terms of production time. Computed from the properties of Curves, Line segments, and Corners (CLC) in a gesture stroke, the model may serve as a foundation for the design and evaluation of existing and future gesture-based user interfaces at the basic motor control efficiency level, similar to the role of previous "laws of action" played to pointing, crossing or steering-based user interfaces. We report and discuss our experimental results on establishing and validating the CLC model, together with other basic empirical findings in stroke gesture production.
Animation of fracture by physical modeling

The breaking of solid objects, like glass or pottery, poses a complex problem for computer animation. We present our methods of using physical simulation to drive the animation of breaking objects. Breakage is obtaned in a three-dimensional flexible model as the limit of elastic behavior. This article describes three principal features of the model: a breakage model, a collision-detection/response scheme, and a geometric modeling method. We use networks of point masses connected by springs to represent physical objects that can bend and break. We present effecient collision-detection algorithms, appropriate for simulating the collisions between the various pieces that interact in breakage. The capability of modeling real objects is provided by a technique of building up composite structures from simple lattice models. We applied these methods to animate the breaking of a teapot and other dishware activities in the animationTipsy Turvy shown at Siggraph '89. Animation techniques that rely on physical simulation to control the motion of objects are discussed, and further topics for research are presented.
Graph evolution: Densification and shrinking diameters

How do real graphs evolve over time? What are normal growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network or in a very small number of snapshots; these include heavy tails for in- and out-degree distributions, communities, small-world phenomena, and others. However, given the lack of information about network evolution over long periods, it has been hard to convert these findings into statements about trends over time.
 Here we study a wide range of real graphs, and we observe some surprising phenomena. First, most of these graphs densify over time with the number of edges growing superlinearly in the number of nodes. Second, the average distance between nodes often shrinks over time in contrast to the conventional wisdom that such distance parameters should increase slowly as a function of the number of nodes (like O(log n) or O(log(log n)).
 Existing graph generation models do not exhibit these types of behavior even at a qualitative level. We provide a new graph generator, based on a forest fire spreading process that has a simple, intuitive justification, requires very few parameters (like the flammability of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study.
 We also notice that the forest fire model exhibits a sharp transition between sparse graphs and graphs that are densifying. Graphs with decreasing distance between the nodes are generated around this transition point.
 Last, we analyze the connection between the temporal evolution of the degree distribution and densification of a graph. We find that the two are fundamentally related. We also observe that real networks exhibit this type of relation between densification and the degree distribution.
Are privacy concerns a turn-off?: engagement and privacy in social networks

We describe survey results from a representative sample of 1,075 U. S. social network users who use Facebook as their primary network. Our results show a strong association between low engagement and privacy concern. Specifically, users who report concerns around sharing control, comprehension of sharing practices or general Facebook privacy concern, also report consistently less time spent as well as less (self-reported) posting, commenting and "Like"ing of content. The limited evidence of other significant differences between engaged users and others suggests that privacy-related concerns may be an important gate to engagement. Indeed, privacy concern and network size are the only malleable attributes that we find to have significant association with engagement. We manually categorize the privacy concerns finding that many are nonspecific and not associated with negative personal experiences. Finally, we identify some education and utility issues associated with low social network activity, suggesting avenues for increasing engagement amongst current users.
Personalized Interactive Narratives via Sequential Recommendation of Plot Points

In story-based games or other interactive systems, a drama manager (DM) is an omniscient agent that acts to bring about a particular sequence of plot points for the player to experience. Traditionally, the DM's narrative evaluation criteria are solely derived from a human designer. We present a DM that learns a model of the player's storytelling preferences and automatically recommends a narrative experience that is predicted to optimize the player's experience while conforming to the human designer's storytelling intentions. Our DM is also capable of manipulating the space of narrative trajectories such that the player is more likely to make choices that result in the recommended experience. Our DM uses a novel algorithm, called prefix-based collaborative filtering (PBCF), that solves the sequential recommendation problem to find a sequence of plot points that maximizes the player's rating of his or her experience. We evaluate our DM in an interactive storytelling environment based on choose-your-own-adventure novels. Our experiments show that our algorithms can improve the player's experience over the designer's storytelling intentions alone and can deliver more personalized experiences than other interactive narrative systems while preserving players' agency.
Implementation of the ArabiQA Question Answering System's components

—Most of the components of a Question Answering (QA) system are language-dependent. For this reason, it is really necessary to build such a system component by component, taking into consideration each of the target language peculiarities. In our case, we are interested in building an Arabic QA system (ArabiQA). In previous works, we have presented how we managed to implement an Arabic-oriented Passage Retrieval (PR) system and a Named Entities Recognition (NER) system for Arabic texts, two of the necessary components to build ArabiQA. In this work we present the different components that ArabiQA shall contain making a special focus on the Answer Extraction (AE) module. This module is required to achieve high precision, because it is responsable of extracting the different possible answers from the relevant passages retrieved by the PR module and it represents the last step before validating the extracted answers and present them to the user. The obtained results show that this approach helps significantly to tackle the AE task.
Is expanding retrieval a superior method for learning text materials?

Expanding retrieval practice refers to the idea that gradually increasing the spacing interval between repeated tests ought to promote optimal long-term retention. Belief in the superiority of this technique is widespread, but empirical support is scarce. In addition, virtually all research on expanding retrieval has examined the learning of word pairs in paired-associate tasks. We report two experiments in which we examined the learning of text materials with expanding and equally spaced retrieval practice schedules. Subjects studied brief texts and recalled them in an initial learning phase. We manipulated the spacing of the repeated recall tests and examined final recall 1 week later. Overall we found that (1) repeated testing enhanced retention more than did taking a single test, (2) testing with feedback (restudying the passages) produced better retention than testing without feedback, but most importantly (3) there were no differences between expanding and equally spaced schedules of retrieval practice. Repeated retrieval enhanced long-term retention, but how the repeated tests were spaced did not matter.
A 4.2-W 10-GHz GaN MMIC Doherty Power Amplifier

This paper describes the design and performance of an X-band GaN monolithic microwave integrated circuit (MMIC) Doherty power amplifier (DPA) in a 0.15 μm gate length GaN on SiC process. The measured output power is greater than 36dBm at peak PAE of 47% at 10 GHz. Gain flatness of ±0.1 dB around 9.2 dB is obtained at up to 25dBm input power. The PAE at 6 and 10 dB backoff is 41% and 31% respectively. For a 10-Mbps OQPSK signal, the ACPR at 10MHz is >30 dBc at maximum output power and >33 dBc at 10 dB backoff. This combination of linearity and efficiency at backoff represents state of the art for an X-band DPA. Limitations of supply modulation in DPAs to further extend output power back-off are highlighted in the discussion.
Combining neural network model with seasonal time series ARIMA model

This paper proposes a hybrid forecasting model, which combines the seasonal time series ARIMA (SARIMA) and the neural network back propagation (BP) models, known as SARIMABP. This model was used to forecast two seasonal time series data of total production value for Taiwan machinery industry and the soft drink time series. The forecasting performance was compared among four models, i.e., the SARIMABP and SARIMA models and the two neural network models with differenced and deseasonalized data, respectively. Among these methods, the mean square error (MSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the SARIMABP model were the lowest. The SARIMABP model was also able to forecast certain significant turning points of the test time series. D 2002 Elsevier Science Inc. All rights reserved.
Edge Weight Prediction in Weighted Signed Networks

Weighted signed networks (WSNs) are networks in which edges are labeled with positive and negative weights. WSNs can capture like/dislike, trust/distrust, and other social relationships between people. In this paper, we consider the problem of predicting the weights of edges in such networks. We propose two novel measures of node behavior: the goodness of a node intuitively captures how much this node is liked/trusted by other nodes, while the fairness of a node captures how fair the node is in rating other nodes' likeability or trust level. We provide axioms that these two notions need to satisfy and show that past work does not meet these requirements for WSNs. We provide a mutually recursive definition of these two concepts and prove that they converge to a unique solution in linear time. We use the two measures to predict the edge weight in WSNs. Furthermore, we show that when compared against several individual algorithms from both the signed and unsigned social network literature, our fairness and goodness metrics almost always have the best predictive power. We then use these as features in different multiple regression models and show that we can predict edge weights on 2 Bitcoin WSNs, an Epinions WSN, 2 WSNs derived from Wikipedia, and a WSN derived from Twitter with more accurate results than past work. Moreover, fairness and goodness metrics form the most significant feature for prediction in most (but not all) cases.
Software development cost estimation approaches - A survey

This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertise-based techniques, learning-oriented techniques, dynamics-based models, regression-based models, and composite-Bayesian techniques for integrating expertisebased and regression-based models. Experience to date indicates that neural-net and dynamics-based techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates.
Large scale image annotation: learning to rank with joint word-image embeddings

Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at k of the ranked list of annotations for a given image and learning a low-dimensional joint embedding space for both images and annotations. Our method both outperforms several baseline methods and, in comparison to them, is faster and consumes less memory. We also demonstrate how our method learns an interpretable model, where annotations with alternate spellings or even languages are close in the embedding space. Hence, even when our model does not predict the exact annotation given by a human labeler, it often predicts similar annotations, a fact that we try to quantify by measuring the newly introduced “sibling” precision metric, where our method also obtains excellent results.
When does start-up innovation spur the gale of creative destruction ?

This paper examines the determinants of commercialization strategy for start-up innovators. We examine whether the returns on innovation are earned through product market competition as opposed to cooperation with more established firms (either through licensing, strategic alliances or outright acquisition). Our key hypotheses are that the relative returns to cooperation are increasing in (a) the control of intellectual property rights, (b) low transaction costs and (c) the cost of the sunk assets associated with product market entry. We find support for these ideas using a novel dataset of the commercialization strategies of start-up innovators. The results suggest that the procompetitive benefits of start-up innovation – the gale of creative destruction – depends on the severity of imperfections in the “market for ideas.” Journal of Economic Literature Classification Numbers: L10, L14, O31 and O32.
Privometer: Privacy protection in social networks

The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests self-sanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user's friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook.
Ensemble Semantics for Large-scale Unsupervised Relation Extraction

Discovering significant types of relations from the web is challenging because of its open nature. Unsupervised algorithms are developed to extract relations from a corpus without knowing the relations in advance, but most of them rely on tagging arguments of predefined types. Recently, a new algorithm was proposed to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar (“synonymous”) relation instances because of the sparseness of features. In this paper, we present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm incorporates various knowledge sources which we will show to be very effective for unsupervised extraction. Moreover, it explicitly disambiguates polysemous relation phrases and groups synonymous ones. While maintaining approximately the same precision, the algorithm achieves significant improvement on recall compared to the previous method. It is also very efficient. Experiments on a realworld dataset show that it can handle 14.7 million relation instances and extract a very large set of relations from the web.
Are the Dorsal and Ventral Hippocampus Functionally Distinct Structures?

One literature treats the hippocampus as a purely cognitive structure involved in memory; another treats it as a regulator of emotion whose dysfunction leads to psychopathology. We review behavioral, anatomical, and gene expression studies that together support a functional segmentation into three hippocampal compartments: dorsal, intermediate, and ventral. The dorsal hippocampus, which corresponds to the posterior hippocampus in primates, performs primarily cognitive functions. The ventral (anterior in primates) relates to stress, emotion, and affect. Strikingly, gene expression in the dorsal hippocampus correlates with cortical regions involved in information processing, while genes expressed in the ventral hippocampus correlate with regions involved in emotion and stress (amygdala and hypothalamus).
A survey of cloud computing data integrity schemes: Design challenges, taxonomy and future trends

8 Cloud computing has gained tremendous popularity in recent years. By outsourcing computation and 9 storage requirements to public providers and paying for the services used, customers can relish upon the 10 advantages of this new paradigm. Cloud computing provides with a comparably lower-cost, scalable, a 11 location-independent platform for managing clients’ data. Compared to a traditional model of computing, 12 which uses dedicated in-house infrastructure, cloud computing provides unprecedented benefits regarding 13 cost and reliability. Cloud storage is a new cost-effective paradigm that aims at providing high 14 availability, reliability, massive scalability and data sharing. However, outsourcing data to a cloud service 15 provider introduces new challenges from the perspectives of data correctness and security. Over the years, 16 many data integrity schemes have been proposed for protecting outsourced data. This paper aims to 17 enhance the understanding of security issues associated with cloud storage and highlights the importance 18 of data integrity schemes for outsourced data. In this paper, we have presented a taxonomy of existing 19 data integrity schemes use for cloud storage. A comparative analysis of existing schemes is also provided 20 along with a detailed discussion on possible security attacks and their mitigations. Additionally, we have 21 discussed design challenges such as computational efficiency, storage efficiency, communication 22 efficiency, and reduced I/O in these schemes. Furthermore; we have highlighted future trends and open 23 issues, for future research in cloud storage security. 24