nils-herrmann/clean_scirepeval huggingface dataset:
A Dependency-Based Neural Reordering Model for Statistical Machine Translation

In machine translation (MT) that involves translating between two languages with significant differences in word order, determining the correct word order of translated words is a major challenge. The dependency parse tree of a source sentence can help to determine the correct word order of the translated words. In this paper, we present a novel reordering approach utilizing a neural network and dependency-based embeddings to predict whether the translations of two source words linked by a dependency relation should remain in the same order or should be swapped in the translated sentence. Experiments on Chinese-to-English translation show that our approach yields a statistically significant improvement of 0.57 BLEU point on benchmark NIST test sets, compared to our prior state-of-theart statistical MT system that uses sparse dependency-based reordering features.
From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods

Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organizations admit compromises on a daily basis. Many misuse and anomaly based intrusion detection systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this paper uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communication.
Preventing Location-Based Identity Inference in Anonymous Spatial Queries

The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users.
Decision Making Under Uncertainty in Visualisation

Decision making under uncertainty can lead to irrational behaviour; such errors are often being referred to as cognitive biases. Related work in this area has tended to focus on the human’s analytic and sensemaking processes. This paper puts forward a novel perspective on this, proposing that some cognitive biases can also occur in the process of viewing visualisations. Consequently, this source of error may have a negative impact on decision making. This paper presents examples of situations where cognitive biases in visualisation can occur and outlines a future user study to investigate the anchoring and adjustment cognitive biases in visualisation.
Deblurring Face Images with Exemplars

The human face is one of the most interesting subjects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblurring methods are not able to achieve satisfying results on blurry face images. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel estimation. When there is not much texture in the blurry image (e.g., face images), existing methods are less effective as only few edges can be used for kernel estimation. Moreover, recent methods are usually jeopardized by selecting ambiguous edges, which are imaged from the same edge of the object after blur, for kernel estimation due to local edge selection strategies. In this paper, we address these problems of deblurring face images by exploiting facial structures. We propose a maximum a posteriori (MAP) deblurring algorithm based on an exemplar dataset, without using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.
A Fast Calibration Method for Triaxial Magnetometers

This paper presents a novel iterative calibration algorithm for triaxial magnetometers. The proposed algorithm estimates and compensates the effects of deterministic interference parameters using only nine distinct measurements. The results of our extensive simulations and empirical evaluations confirm that the proposed method outperforms conventional ellipsoid fitting based models both in terms of accuracy and reliability even in the presence of a moderate wideband noise. The algorithm also achieves considerably faster convergence, which makes it suitable for real-time applications. The algorithm performance is also shown to be independent of the initial guesses of the interference parameters.
From a Set of Shapes to Object Discovery

This paper presents an approach to object discovery in a give n unlabeled image set, based on mining repetitive spatial config urations of image contours. Contours that similarly deform from one image to a n ther are viewed as collaborating, or, otherwise, conflicting. This is captu red by a graph over all pairs of matching contours, whose maximum a posteriori mult icoloring assignment is taken to represent the shapes of discovered objects. Multicoloring is conducted by our new Coordinate Ascent Swendsen-Wang cut (CASW ). CASW uses the Metropolis-Hastings (MH) reversible jumps to probabil istically sample graph edges, and color nodes. CASW extends SW cut by introducing a r e ularization in the posterior of multicoloring assignments that prevent s the MH jumps to arrive at trivial solutions. Also, CASW seeks to learn paramet ers of the posterior via maximizing a lower bound of the MH acceptance rate. This s peeds up multicoloring iterations, and facilitates MH jumps from local mi nima. On benchmark datasets, we outperform all existing approaches to unsuper vised object discovery.
Enterprise Software System Integration : An Architectural Perspective

The present thesis is concerned with enterprise software systems of companies in the Swedish electricity industry, an industry that for the past few years has been exposed to a fairly tumultuous change process as a consequence of legislative reforms. Previously, the business operations of electric utilities – as well as those of most companies in the computerized world – were supported by a number of isolated software systems performing specific tasks. In recent years, these systems have been extended, and more importantly, integrated into a company-wide system in its own right, in this thesis referred to as the enterprise software system. As enterprise software systems have evolved, so has a need for strategies, methods and techniques for their management. Enterprise software system management involves a number of concerns; of these concerns, software integration is one of the most prominent. The discipline of software architecture is concerned with the modeling of large-scale structures of software systems. While the generated models are employed for a number of purposes , their perhaps most significant function is to serve as a base for reasoning about the represented system. Several methods for analysis of architectures have been proposed in recent years, and although software architecture analysis has displayed considerable success for a number of systems, enterprise software systems have to a large extent been ignored. In the empirical context of the Scandinavian electricity industry, this thesis explores the applicability of software architecture analysis to enterprise software system integration. Two conceptually different architectural analysis methods – deduction-and induction-based approaches – are considered, as well as the engineering process in which architectural analysis is performed. As a result of the investigations, the thesis proposes a modified process for architectural analysis, presents an evaluation of deduction-based analysis methods , and proposes an adaptation of induction-based analysis methods to the enterprise software system context.
Predicting the fix time of bugs

Two important questions concerning the coordination of development effort are which bugs to fix first and how long it takes to fix them. In this paper we investigate empirically the relationships between bug report attributes and the time to fix. The objective is to compute prediction models that can be used to recommend whether a new bug should and will be fixed fast or will take more time for resolution. We examine in detail if attributes of a bug report can be used to build such a recommender system. We use decision tree analysis to compute and 10-fold cross validation to test prediction models. We explore prediction models in a series of empirical studies with bug report data of six systems of the three open source projects Eclipse, Mozilla, and Gnome. Results show that our models perform significantly better than random classification. For example, fast fixed Eclipse Platform bugs were classified correctly with a precision of 0.654 and a recall of 0.692. We also show that the inclusion of postsubmission bug report data of up to one month can further improve prediction models.
Resolution of autoimmune progesterone dermatitis after treatment with oral contraceptives.

OCP: oral contraceptive pill CASE REPORT A 20-year-old woman presented with a 6-year history of recurrent erythematous papules and plaques on the trunk. The eruption recurred cyclically, beginning 5 days before menses and resolving 1 to 2 days after the menstrual period. The lesions were asymptomatic, and there were no associated systemic symptoms. She had not previously sought treatment. Her medical history was unremarkable, and she was not taking any medications. Examination found widespread erythematous and edematous papules and nonscaly plaques, on the chest, back, and lower neck . Lesions were uniform in color, ranging in size from 3mm to 4 cm in diameter. A biopsy specimen was taken for histopathologic evaluation . Results of a punch biopsy of the skin showed dermal perivascular lymphohistiocytic inflammation, with rare eosinophils. An intradermal progesterone test read at 20 minutes was positive (progesterone, 50 mg/mL at a dilution of 1:10 in aqueous solution elicited a 10-mm wheal vs a 4-mm wheal for the glycerine control). The patient was started on levonorgestrel/ ethinyl estradiol, 0.1 mg to 20 g daily. There was a marked reduction in the number of lesions during her next menstrual period. By the third menstrual cycle, after beginning oral contraceptive pills (OCPs), no lesions appeared perimenstrually. The patient remained clear for another 4 months while taking Levonorgestrel/Ethinyl Estradiol 0.1 mg to 20 g. She then discontinued treatment because of the loss of health insurance. Despite discontinuing OCPs, lesions have not returned in the 8 months since the medication was stopped.
A unified API gateway for high availability clusters

High-availability (HA) clusters are widely used to provide high availability services. Recently, many HA cluster solutions and products have been proposed or developed by different organizations. However, each HA cluster system has its own administrative tool and application programming interface (API). Besides, vendor lock-in makes customers dependent on the specific vendors' own high availability clusters. Therefore, it is very complicated to simultaneously manage various HA clusters. To solve this problem, a novel SOA-based Unified API Gateway for high-availability Clusters (UAGC) is proposed in this paper. Under UAGC, cluster services are conveniently managed in a unified way, which is independent of platforms or programming languages. Six web service interfaces are implemented in UAGC to cover most cluster functions. A UAGC-based web service (UAGCService) is implemented with WCF. The experimental results show that UAGCService has good performances.
Analyzing Posture and Affect in Task-Oriented Tutoring

Intelligent tutoring systems research aims to produce systems that meet or exceed the effectiveness of one on one expert human tutoring. Theory and empirical study suggest that affective states of the learner must be addressed to achieve this goal. While many affective measures can be utilized, posture offers the advantages of non intrusiveness and ease of interpretation. This paper presents an accurate posture estimation algorithm applied to a computer mediated tutoring corpus of depth recordings. Analyses of posture and session level student reports of engagement and cognitive load identified significant patterns. The results indicate that disengagement and frustration may coincide with closer postural positions and more movement, while focused attention and less frustration occur with more distant, stable postural positions. It is hoped that this work will lead to intelligent tutoring systems that recognize a greater breadth of affective expression through channels of posture and gesture.
DroidLink: Automated Generation of Deep Links for Android Apps

The mobile application (app) has become the main entrance to access the Internet on handheld devices. Unlike the Web where each webpage has a global URL to reach directly, a specific “content page” of an app can be opened only by exploring the app with several operations from the landing page. The interoperability between apps is quite fixed and thus limits the value-added “linked data” between apps. Recently, deep link has been proposed to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier (URI). However, implementing deep link for mobile apps requires a lot of manual efforts by app developers, which can be very error-prone and time-consuming. In this paper, we propose DroidLink to automatically generating deep links for existing Android apps. We design a deep link model suitable for automatic generation. Then we explore the transition of pages and build a navigation graph based on static and dynamic analysis of Android apps. Next, we realize an updating mechanism that keeps on revisiting the target app and discover new pages, and thus generates deep links for every single page of the app. Finally, we repackage the app with deep link supports, but requires no additional deployment requirements. We generate deep links for some popular apps and demonstrate the feasibility of DroidLink.
Ichthyosis congenita, harlequin fetus type: a case report.

Ichthyosis is a very heterogeneous family of skin disorders with harlequin ichthyosis being the most severe genetic form. It is a rare autosomal recessive condition, characterized by dry, severely thickened skin with large plates of hyperkeratotic scale, separated by deep fissures. Infants are very susceptible to metabolic abnormalities and infections. They usually do not survive for very long, but several long term survivals have been noted. The vast majority of affected individuals are homozygous for mutations in the ABCA12 gene, which cause a deficiency of the epidermal lipid transporter, resulting in hyperkeratosis and abnormal barrier function. We report a case of a newborn with harlequin ichthyosis, born to unrelated parents, who had a favorable evolution with topical treatment and intensive care.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature. We extend the framework of natural policy gradient and propose to optimize both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region; hence we call our method Actor Critic using Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this is the first scalable trust region natural gradient method for actor-critic methods. It is also the method that learns non-trivial tasks in continuous control as well as discrete control policies directly from raw pixel inputs. We tested our approach across discrete domains in Atari games as well as continuous domains in the MuJoCo environment. With the proposed methods, we are able to achieve higher rewards and a 2to 3-fold improvement in sample efficiency on average, compared to previous state-of-the-art on-policy actor-critic methods. Code is available at https://github.com/openai/baselines.
The Practical Use of BI Visualizations in Decision-making

Master Degree Project in Accounting, 30 credits University: University of Gothenburg, School of Business, Economics and Law Semester: Spring 2017 Author: Kevin Larsson & Philip Kaber Supervisor:  The Practical Use of BI Visualizations in Decision-making  Due to the constantly altering business environment, firms are trying to find ways of combining information technology (IT) and decision-making in order to grasp information of the complex and dynamic business environment. As a solution to this strenuousness; business intelligence (BI) systems serve to conduct more efficient and reliable decisions by the means of visualizations. One existing gap in the research of BI concerns the sociotechnical effects involved in the decision-making, in which knowledge of how BI visualizations are used and may be used in practice to support the decision-making have remained limited.  The research aim of the study is to investigate how BI visualizations are and may be used to support strategic and operational decisions. Further, the study addresses how BI visualizations are used in various decision contexts.  The following questions are addressed in order to achieve the research aim: (1) How are and may BI visualizations be used in strategic and operational decisions? (1a) Which visual elements are used depending on the task and decision category? (2) How are BI visualizations used in different decision contexts?  A qualitative study was made through semi-structured interviews. In-depth interviews with six people from the purchasing company were conducted in order to understand how BI and visualizations are and may be used. Two people from the consultancy company were interviewed in order to understand how BI and visualizations may be used.  The primary purpose of BI systems in the purchasing company is to support decision-making from a strategic perspective by providing the business with new insights and monitor business processes. However, the findings in this study also suggest that BI visualizations are used for operational errands. Strategic decisions are primary based on graphical representations, whereas operational decisions are based on data derived from tables and additional calculations in Excel. By creating more interactive, initiative, and humanizing visualizations, the end-users may discover new patterns of information and enhance the cognitive fit without being overwhelmed by data. BI visualizations may also be used in a larger extent to detect errors in data. The usage depends on the decision situation, in which decisions made in the fact-based contexts domains tend to be based on intuitions and BI visualizations, before responding to the business problem. This differs from the pattern-based management domains, in which actions are done prior to the analysis as there is lack of data. Further the findings indicate that several people are often involved in the decisions. The mental model of the respondents is not entirely fit in a cognitive aspect.
Modelling Sarcasm in Twitter, a Novel Approach

Automatic detection of figurative language is a challenging task in computational linguistics. Recognising both literal and figurative meaning is not trivial for a machine and in some cases it is hard even for humans. For this reason novel and accurate systems able to recognise figurative languages are necessary. We present in this paper a novel computational model capable to detect sarcasm in the social network Twitter (a popular microblogging service which allows users to post short messages). Our model is easy to implement and, unlike previous systems, it does not include patterns of words as features. Our seven sets of lexical features aim to detect sarcasm by its inner structure (for example unexpectedness, intensity of the terms or imbalance between registers), abstracting from the use of specific terms.
Dual Band-Notched Square Monopole Antenna for Ultrawideband Applications

This letter proposes a novel printed monopole antenna for ultrawideband applications with dual band-notch function. The antenna consists of a square radiating patch with a pair of L-shaped slits, and an E-shaped slot and a ground plane with a V-shaped protruded strip, which provides a wide usable fractional bandwidth of more than 140% (2.89-17.83 GHz). In order to generate single band-notch characteristics, we use two L-shaped slits in the radiating patch. By adding an E-shaped slot in the center of the radiating patch, a dual band-notch function is achieved. Also, by inserting a V-shaped protruded in the ground plane, additional resonances are excited, and hence much wider impedance bandwidth can be produced, especially at the higher band. The measured results reveal that the presented dual band-notch monopole antenna offers a very wide bandwidth with two notched bands, covering all the 5.2/5.8-GHz WLAN, 3.5/5.5-GHz WiMAX and 4-GHz C-bands. The designed antenna has a small size area of 10 × 16 mm2, which has a size reduction of 35% with respect to the previous similar antenna.
Algorithms in HElib

HElib is a software library that implements homomorphic encryption (HE), specifically the Brakerski-Gentry-Vaikuntanathan (BGV) scheme, focusing on effective use of the Smart-Vercauteren ciphertext packing techniques and the Gentry-Halevi-Smart optimizations. The underlying cryptosystem serves as the equivalent of a “hardware platform” for HElib, in that it defines a set of operations that can be applied homomorphically, and specifies their cost. This “platform” is a SIMD environment (somewhat similar to Intel SSE and the like), but with unique cost metrics and parameters. In this report we describe some of the algorithms and optimization techniques that are used in HElib for data movement, linear algebra, and other operations over this “platform.”