nils-herrmann/clean_scirepeval huggingface dataset:
Compositional Distributional Semantics with Compact Closed Categories and Frobenius Algebras

The provision of compositionality in distributional models of meaning, where a word is represented as a vector of co-occurrence counts with every other word in the vocabulary, offers a solution to the fact that no text corpus, regardless of its size, is capable of providing reliable co-occurrence statistics for anything but very short text constituents. The purpose of a compositional distributional model is to provide a function that composes the vectors for the words within a sentence, in order to create a vectorial representation that reflects its meaning. Using the abstract mathematical framework of category theory, Coecke, Sadrzadeh and Clark showed that this function can directly depend on the grammatical structure of the sentence, providing an elegant mathematical counterpart of the formal semantics view. The framework is general and compositional but stays abstract to a large extent. This thesis contributes to ongoing research related to the above categorical model in three ways: Firstly, I propose a concrete instantiation of the abstract framework based on Frobenius algebras (joint work with Sadrzadeh). The theory improves shortcomings of previous proposals, extends the coverage of the language, and is supported by experimental work that improves existing results. The proposed framework describes a new class of compositional models that find intuitive interpretations for a number of linguistic phenomena. Secondly, I propose and evaluate in practice a new compositional methodology which explicitly deals with the different levels of lexical ambiguity (joint work with Pulman). A concrete algorithm is presented, based on the separation of vector disambiguation from composition in an explicit prior step. Extensive experimental work shows that the proposed methodology indeed results in more accurate composite representations for the framework of Coecke et al. in particular and every other class of compositional models in general. As a last contribution, I formalize the explicit treatment of lexical ambiguity in the context of the categorical framework by resorting to categorical quantum mechanics (joint work with Coecke). In the proposed extension, the concept of a distributional vector is replaced with that of a density matrix, which compactly represents a probability distribution over the potential different meanings of the specific word. Composition takes the form of quantum measurements, leading to interesting analogies between quantum physics and linguistics.
Ripple: Home Automation for Research Data Management

Exploding data volumes and acquisition rates, plus ever more complex research processes, place significant strain on research data management processes. It is increasingly common for data to flow through pipelines comprised of dozens of different management, organization, and analysis steps distributed across multiple institutions and storage systems. To alleviate the resulting complexity, we propose a home automation approach to managing data throughout its lifecycle, in which users specify via high-level rules the actions that should be performed on data at different times and locations. To this end, we have developed Ripple, a responsive storage architecture that allows users to express data management tasks via a rules notation. Ripple monitors storage systems for events, evaluates rules, and uses serverless computing techniques to execute actions in response to these events. We evaluate our solution by applying Ripple to the data lifecycles of two real-world projects, in astronomy and light source science, and show that it can automate many mundane and cumbersome data management processes.
Cooperative Resource Management in Cloud-Enabled Vehicular Networks

Cloud-enabled vehicular networks are a new paradigm to improve the quality of vehicular services, which have drawn considerable attention in industry and academia. In this paper, we consider the resource management and sharing problem for bandwidth and computing resources to support mobile applications in cloud-enabled vehicular networks. In such an environment, cloud service providers (SPs) can cooperate to form coalitions to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory for cooperation among cloud SPs to share their idle resources. As a result, the resources can be better utilized, and the QoS for users can be improved. Numerical results indicate that our scheme can improve resource utilization and increase by 75% the QoS of the applications compared with that without cooperation. Moreover, the higher service cost of cooperation brings negative effect on coalition formation. The higher cooperation willingness of cloud SPs and the lower service cost support more service applications.
aBCD18 - An advanced 0.18um BCD technology for PMIC application

We present a new advanced 0.18um BCD(Bipolar-CMOS-DMOS) technology with the key features being a 40V HV-MOS and an SSTC(Sidewall Selective Transistor Cell) type EEPROM as well as complimentary available analog devices such as a high gain BJT, 4fF/um2 MIM capacitor, and 10k Ω/sq. poly resistor. To reduce device area and enhance latch up immunity, a 15um depth deep trench isolation process has been developed, which will help to significantly reduce the chip size.
LKIM : The Linux Kernel Integrity

509 he Linux Kernel Integrity Measurer (LKIM) is a next-generation technology for the detection of malicious modifications to a running piece of software. Unlike traditional antivirus systems, LKIM does not rely on a database of known malware signatures; instead, LKIM uses a precise model of expected program behavior to verify the consistency of critical data structures at runtime. APL and the Research Directorate of the National Security Agency (NSA) developed the LKIM prototype and are now working to transition the technology to a variety of critical government applications.  The Linux Kernel Integrity Measurer
MACHINE LEARNING IN APPLIED ECONOMETRICS Deriving personal income drivers with randomized decision forests

In this paper I explore a modern field of research in applied econometrics: machine learning and the estimation of synthetic treatment effects. Data generation is currently on an exponential growth path: smart phones, social media and networks of interconnected devices are generating information at an unprecedented pace. The size, structure and velocity of these information streams vary to a great extent. The field of econometrics is also evolving: classic econometric models can lead to biased results with big data and will not scale up to modern data sets. I propose the wellperforming Random Forests algorithm for use in econometrics. To adjust this method for causal analysis, recent theory on causal decision trees is explored. The proposed framework is then tested by estimating personal income drivers for the top 1% in U.S. population. The data used is the American Community Survey 5year sample consisting of approximately 20 million rows. It appears that high income is in fact driven by four core factors: education, experience, working hours and gender. To rank these predictors, a synthetic treatment effect simulation is run. I find that investing in education after a master’s degree has a significant positive effect in the likelihood of high income. Additionally, it appears that the negative gender income effect for females can be undone with a combination of work experience and exceptional workethic.
Is face recognition really a Compressive Sensing problem?

Compressive Sensing has become one of the standard methods of face recognition within the literature. We show, however, that the sparsity assumption which underpins much of this work is not supported by the data. This lack of sparsity in the data means that compressive sensing approach cannot be guaranteed to recover the exact signal, and therefore that sparse approximations may not deliver the robustness or performance desired. In this vein we show that a simple £2 approach to the face recognition problem is not only significantly more accurate than the state-of-the-art approach, it is also more robust, and much faster. These results are demonstrated on the publicly available YaleB and AR face datasets but have implications for the application of Compressive Sensing more broadly.
Matching service strategies, business models and modular business processes

In recent decades, supply chains have diverged and new types of services and operators have emerged in the logistics sector. The purpose of this paper is to focus on analyzing service strategies and service-related business models, as well as their modular business processes in logistic services. The aims are to describe these three levels and to match strategic service positioning with business models and modular business processes. Different types of services are analyzed and the analyses are conducted on both the industry and corporate levels.  The theoretical focus of the paper illustrates frameworks for service strategy, service positioning, business models, and business processes. The corporate level approach aims to describe the efficiency and quality of services and their processes, while the industry level approach focuses on service strategies in an industry and on the organization of business, i.e. business models. A case study is used to illustrate the strategic level divergence in logistic services and to match this with the business model framework and the business process approach.  The findings show that a match exists between service strategy, business models, and operational level business processes. Standardization, service productization and modularization of services, and also service production structures are useful tools for efficient service production and output.  Companies are currently examining new roles in supply chains and the logistics market. For management, the frameworks presented facilitate analysis of the different options available for the firm in terms of strategic positioning, structural business model portfolio, and modular business processes. Based on the theoretical frameworks, it is possible to evaluate past developments and also predict the future of services.
Learning from Positive and Unlabeled Examples with Different Data Distributions

We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive examples in the positive set P and the positive examples in the unlabeled set U are generated from the same distribution. This assumption may be violated in practice. For example, one wants to collect all printer pages from the Web. One can use the printer pages from one site as the set P of positive pages and use product pages from another site as U. One wants to classify the pages in U into printer pages and non-printer pages. Although printer pages from the two sites have many similarities, they can also be quite different because different sites often present similar products in different styles and have different focuses. In such cases, existing methods perform poorly. This paper proposes a novel technique A-EM to deal with the problem. Experiment results with product page classification demonstrate the effectiveness of the proposed technique.
Fame for sale: Efficient detection of fake Twitter followers

Fake followers are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere—hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel Class A classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers.
A synthesis technique for grounded theory data analysis.

The purposes of this paper are to examine the issues surrounding current changes in grounded theory (GT) research methods and to explicate an innovative synthesis technique to GT data analysis.



In recent years there has been a steady rise in the number of published research reports that use the GT method. However, this growing body of GT literature has been criticized for its lack of adherence to the method as explicated by its originators, Glaser and Strauss.



Recent and past literature that explicates, describes, and discusses GT methods is reviewed. A synthesis technique for grounded theory data analysis was developed to analyse qualitative data collected for a grounded theory study on caregiving. This synthesis technique was derived from the works of four grounded theorists (Kathy Charmaz, Mark Chesler, Juliet Corbin and Anselm Strauss).



The lack of clarity and the inconsistencies surrounding GT analysis, as reported in the literature, resulted in the development of a synthesis technique based on the works of the aforementioned-grounded theorists. The product was a synthesis approach that included analytical steps from each of these authors.



This synthesis approach increased understanding and enhanced clarity of GT data analysis techniques. This paper illustrates how integration of works by noted qualitative scholars is an appropriate and effective means to advance the discourse on data analysis for GT research studies.
An Adaptive Dataset for the Evaluation of Android Malware Detection Techniques

Android is currently the leading mobile operating system in the world. The huge number of Android devices attracts developers to create applications for it. However, it also attracts attackers that collect sensitive data or make money. This problem has led many researchers to propose malware detection systems and custom versions of Android that can help users against malicious activities. Evaluating these systems is a crucial part of malware prevention research. However, recent datasets that cover different kinds of benign and malicious applications to evaluate the malware detection techniques are often not available. With thousands of newly released applications every day and different new malicious activities discovered, it is difficult to keep malicious application datasets up to date. This paper introduces a recent and adaptive dataset that includes 5,000 applications from different malware categories that can be used by the research community. The applications are selected from more than 5 million applications. To show how the dataset can be used, we deploy a popular malware analysis platform and generate detailed reports on all the applications in an automated way. We also provide the steps to update the dataset and perform the analysis automatically on the updated set of samples. We believe that the adaptiveness of the dataset and the automatic analysis process will help researchers save time in preparing their datasets and focus more on the detection techniques.
Practical regular expression matching free of scalability and performance barriers

http://dx.doi.org/10.1016/j.comcom.2014.08.005 0140-3664/ 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author at: Department of Automation, Tsinghua University, Beijing, China. E-mail addresses: wang-kai09@mails.tsinghua.edu.cn (K. Wang), fu-z13@mails. tsinghua.edu.cn (Z. Fu), huxh10@mails.tsinghua.edu.cn (X. Hu), junl@tsinghua.edu. cn (J. Li). Kai Wang a,b,⇑, Zhe Fu , Xiaohe Hu , Jun Li b,c
Multitask Compressive Sensing

Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N << M this is performed under the assumption that u is sparse in the basis represented by the matrix Psi RMtimesM. It has been demonstrated that with appropriate design of the compressive measurements used to define v, the decompressive mapping vrarru may be performed with error ||u-u||2 2 having asymptotic properties analogous to those of the best adaptive transform-coding algorithm applied in the basis Psi. The mapping vrarru constitutes an inverse problem, often solved using l1 regularization or related techniques. In most previous research, if L > 1 sets of compressive measurements {vi}i=1,L are performed, each of the associated {ui}i=1,Lare recovered one at a time, independently. In many applications the L ldquotasksrdquo defined by the mappings virarrui are not statistically independent, and it may be possible to improve the performance of the inversion if statistical interrelationships are exploited. In this paper, we address this problem within a multitask learning setting, wherein the mapping vrarru for each task corresponds to inferring the parameters (here, wavelet coefficients) associated with the desired signal vi, and a shared prior is placed across all of the L tasks. Under this hierarchical Bayesian modeling, data from all L tasks contribute toward inferring a posterior on the hyperparameters, and once the shared prior is thereby inferred, the data from each of the L individual tasks is then employed to estimate the task-dependent wavelet coefficients. An empirical Bayesian procedure for the estimation of hyperparameters is considered; two fast inference algorithms extending the relevance vector machine (RVM) are developed. Example results on several data sets demonstrate the effectiveness and robustness of the proposed algorithms.
Frequency compensation techniques using current buffers

When attempting to stabilize a multi-stage amplifier, the introduction of a Miller capacitor may create a feed-forward path and Right-Half-Plane (RHP) zero, jeopardizing the stability of the control loop. In addition to a nulling resistor, a voltage buffer or a current buffer can be placed in series with the Miller capacitor to obviate the feed-forward path and introduce a Left-Half-Plane (LHP) zero. A LHP zero, placed in its proper location, can cancel the effect of a pole and/or an RHP zero, boosting the phase margin, and improving large-signal stability. This tutorial starts with the basics of frequency compensation. We explain some of the complex details in analyzing a wide range of compensation networks for multi-stage amplifiers. We introduce frequency compensation techniques using current buffers. In addition to the popular common-gate (or cascode) transistor topology used as a positive current buffer, we detail the recent technique of using current mirror as an inverting current buffer. Simple design equations and design examples of frequency compensation using current buffers are detailed. The tutorial concludes by introducing an effective method of using a series resistor for accurate placement of current buffer LHP zeros.
mOS: A Reusable Networking Stack for Flow Monitoring Middleboxes

Stateful middleboxes, such as intrusion detection systems and application-level firewalls, have provided key functionalities in operating modern IP networks. However, designing an efficient middlebox is challenging due to the lack of networking stack abstraction for TCP flow processing. Thus, middlebox developers often write the complex flow management logic from scratch, which is not only prone to errors, but also wastes efforts for similar functionalities across applications. This paper presents the design and implementation of mOS, a reusable networking stack for stateful flow processing in middlebox applications. Our API allows developers to focus on the core application logic instead of dealing with low-level packet/flow processing themselves. Under the hood, it implements an efficient event system that scales to monitoring millions of concurrent flow events. Our evaluation demonstrates that mOS enables modular development of stateful middleboxes, often significantly reducing development efforts represented by the source lines of code, while introducing little performance overhead in multi-10Gbps network environments.
A New Approach of copy move Forgery Detection using Rigorous Preprocessing and Feature Extraction

these days, advanced pictures are being used in an extensive variety of uses and for numerous reasons. They additionally assume an imperative part in the capacity and exchange of visual data, particularly the mystery ones. With this far reaching utilization of advanced pictures, notwithstanding the expanding number of devices and programming of computerized pictures altering, it has turned out to be anything but difficult to control and change the real data of the picture. In this way, it has turned out to be important to check the credibility and the respectability of the picture by utilizing present day and advanced methods, which add to examination and comprehension of the pictures’ substance, and after that ensure their trustworthiness. There are many sorts of picture imitation, the most critical and prominent sort is called duplicate glue fabrication, which utilizes a similar picture during the time spent falsification. This sort of fraud is utilized for one of two things, first to cover a protest or scene by replicating the region of the picture and gluing it on another zone of a similar picture. In this paper we have presented a new approach of copy move forgery detection. proposed scheme uses Oriented FAST and rotated BRIEF(ORB) alternative of scale invariant feature transform (SIFT) technique which is integrated with modified local contrast modification-contrast limited adaptive histogram equalization(LCM-CLAHE). Experimental results shows that proposed scheme is more promising in terms of false positive rate(FPR) and true positive rate(TPR) compare to state of the art techniques.  Image Processing, Image Enhancement, Histogram Equalization, SIFT, TPR, FPR, Copy move forgery, ORB
Authentication anomaly detection: a case study on a virtual private network

The authentication logs on a network can provide a trove of information for discovering potential anomalies in login attempts. Using such logs collected by a production Virtual Private Network device over a period of 15 months, we generate a diurnal model of network accesses. These models are used to detect anomalous authentications, which merit further investigation by a security analyst. We intend that this work will dramatically reduce the amount time spent by analysts identifying anomalous events and allow them to focus on in-depth analysis of these anomalies. Our work makes two contributions: a novel approach of mining authentication data, and the use of geographic distance as a metric to evaluate Virtual Private Network connections. We demonstrate the success of our model using real-world case analysis.