nils-herrmann/clean_scirepeval huggingface dataset:
FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FMLbased Dynamic Assessment Agent (FDAA), and we present an FML-based Human–Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook’s Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, realtime simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google’s Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.
Mining topic-level influence in heterogeneous networks

Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. Understanding influence can benefit various applications such as viral marketing, recommendation, and information retrieval. However, most existing works on social influence analysis have focused on verifying the existence of social influence. Few works systematically investigate how to mine the strength of direct and indirect influence between nodes in heterogeneous networks.
 To address the problem, we propose a generative graphical model which utilizes the heterogeneous link information and the textual content associated with each node in the network to mine topic-level direct influence. Based on the learned direct influence, a topic-level influence propagation and aggregation algorithm is proposed to derive the indirect influence between nodes. We further study how the discovered topic-level influence can help the prediction of user behaviors. We validate the approach on three different genres of data sets: Twitter, Digg, and citation networks. Qualitatively, our approach can discover interesting influence patterns in heterogeneous networks. Quantitatively, the learned topic-level influence can greatly improve the accuracy of user behavior prediction.
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning

Convolution neural networks (CNNs) are the heart of deep learning applications. Recent works PRIME  and ISAAC  demonstrated the promise of using resistive random access memory (ReRAM) to perform neural computations in memory. We found that training cannot be efficiently supported with the current schemes. First, they do not consider weight update and complex data dependency in training procedure. Second, ISAAC attempts to increase system throughput with a very deep pipeline. It is only beneficial when a large number of consecutive images can be fed into the architecture. In training, the notion of batch (e.g. 64) limits the number of images can be processed consecutively, because the images in the next batch need to be processed based on the updated weights. Third, the deep pipeline in ISAAC is vulnerable to pipeline bubbles and execution stall. In this paper, we present PipeLayer, a ReRAM-based PIM accelerator for CNNs that support both training and testing. We analyze data dependency and weight update in training algorithms and propose efficient pipeline to exploit inter-layer parallelism. To exploit intra-layer parallelism, we propose highly parallel design based on the notion of parallelism granularity and weight replication. With these design choices, PipeLayer enables the highly pipelined execution of both training and testing, without introducing the potential stalls in previous work. The experiment results show that, PipeLayer achieves the speedups of 42.45x compared with GPU platform on average. The average energy saving of PipeLayer compared with GPU implementation is 7.17x.
Curriculum is not enough: the educational theory and research foundation of the exploring computer science professional development model

In recent years, the computer science education community has shown strong commitment to broadening participation in computing in K-12 classrooms. Educational research highlights the critical role of professional development in supporting teachers to attract and effectively teach underrepresented students in computing. In this paper we present the Exploring Computer Science (ECS) professional development model and the research on which it is based. We also present findings about the impact of ECS professional development on teachers' practice. As computing education initiatives become increasingly concerned with scaling up from a regional to a nationwide presence, it is important to consider how the essential components of effective professional development can drive this reform.
A survey on the role of negation in sentiment analysis

This paper presents a survey on the role of negationin sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis. We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects, such as level of representation used for sentiment analysis, negation word detection and scope of negation. We will also discuss limits and challenges of negation modeling on that task.
Differentially Private Frequent Sequence Mining

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively prune those unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose <italic>PFS</italic><inline-formula><tex-math notation="LaTeX"> $^2$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="xu-ieq1-2601106.gif"/></alternatives></inline-formula>, a novel differentially private FSM algorithm. It is the first algorithm that supports the general gap-constrained FSM in the context of differential privacy. The gap constraints in FSM can be used to limit the mining results to a controlled set of frequent sequences. In our <italic>PFS</italic><inline-formula><tex-math notation="LaTeX">$^2$ </tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="xu-ieq2-2601106.gif"/></alternatives></inline-formula> algorithm, the core is to utilize sample databases to prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which candidate sequences are potentially frequent. To improve the accuracy of such private estimations, a gap-aware sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to calibrate the amount of noise required by differential privacy, a gap-aware sensitivity computation method is proposed to obtain the sensitivity of the local support computations with different gap constraints. Furthermore, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our <italic>PFS </italic><inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="xu-ieq3-2601106.gif"/></alternatives></inline-formula> algorithm is <inline-formula> <tex-math notation="LaTeX">$\epsilon$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="xu-ieq4-2601106.gif"/> </alternatives></inline-formula>-differentially private. Extensive experiments on real datasets illustrate that our <italic>PFS</italic><inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="xu-ieq5-2601106.gif"/></alternatives></inline-formula> algorithm can privately find frequent sequences with high accuracy.
Dlib-ml: A Machine Learning Toolkit

There are many excellent toolkits which provide support for developing machine learning software in Python, R, Matlab, and similar environments. Dlib-m l is an open source library, targeted at both engineers and research scientists, which aims to pro vide a similarly rich environment for developing machine learning software in the C++ language. T owards this end, dlib-ml contains an extensible linear algebra toolkit with built in BLAS supp ort. It also houses implementations of algorithms for performing inference in Bayesian networks a nd kernel-based methods for classification, regression, clustering, anomaly detection, and fe atur ranking. To enable easy use of these tools, the entire library has been developed with contract p rogramming, which provides complete and precise documentation as well as powerful debugging too ls.
Algebricks: a data model-agnostic compiler backend for big data languages

A number of high-level query languages, such as Hive, Pig, Flume, and Jaql, have been developed in recent years to increase analyst productivity when processing and analyzing very large datasets. The implementation of each of these languages includes a complete, data model-dependent query compiler, yet each involves a number of similar optimizations. In this work, we describe a new query compiler architecture that separates language-specific and data model-dependent aspects from a more general query compiler backend that can generate executable data-parallel programs for shared-nothing clusters and can be used to develop multiple languages with different data models. We have built such a data model-agnostic query compiler substrate, called Algebricks, and have used it to implement three different query languages --- HiveQL, AQL, and XQuery --- to validate the efficacy of this approach. Experiments show that all three query languages benefit from the parallelization and optimization that Algebricks provides and thus have good parallel speedup and scaleup characteristics for large datasets.
The leadership skills strataplex : Leadership skill requirements across organizational levels

Leadership scholars have called for additional research on leadership skill requirements and how those requirements vary by organizational level. In this study, leadership skill requirements are conceptualized as being layered (strata) and segmented (plex), and are thus described using a strataplex. Based on previous conceptualizations, this study proposes a model made up of four categories of leadership skill requirements: Cognitive skills, Interpersonal skills, Business skills, and Strategic skills. The model is then tested in a sample of approximately 1000 junior, midlevel, and senior managers, comprising a full career track in the organization. Findings support the “plex” element of the model through the emergence of four leadership skill requirement categories. Findings also support the “strata” portion of the model in that different categories of leadership skill requirements emerge at different organizational levels, and that jobs at higher levels of the organization require higher levels of all leadership skills. In addition, although certain Cognitive skill requirements are important across organizational levels, certain Strategic skill requirements only fully emerge at the highest levels in the organization. Thus a strataplex proved to be a valuable tool for conceptualizing leadership skill requirements across organizational levels.
A Search Engine for Mathematical Formulae

We present a search engine for mathematical formulae. The MathWebSearch system harvests the web for content representations (currently MathML and OpenMath) of formulae and indexes them with substitution tree indexing, a technique originally developed for accessing intermediate results in automated theorem provers. For querying, we present a generic language extension approach that allows constructing queries by minimally annotating existing representations. First experiments show that this architecture results in a scalable application.
Sparse Compositional Metric Learning

We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algorithms have several advantages over existing methods in the literature: a much smaller number of parameters to be estimated and a principled way to generalize learned metrics to new testing data points. To analyze the approach theoretically, we derive a generalization bound that justifies the sparse combination. Empirically, we evaluate our algorithms on several datasets against state-of-theart metric learning methods. The results are consistent with our theoretical findings and demonstrate the superiority of our approach in terms of classification performance and scalability.
Designing 1V Op Amps Using Standard Digital CMOS Technology

This paper addresses the difficulty of designing 1-V capable analog circuits in standard digital complementary metal–oxide–semiconductor (CMOS) technology. Design techniques for facilitating 1-V operation are discussed and 1-V analog building block circuits are presented. Most of these circuits use the bulk-driving technique to circumvent the metal– oxide–semiconductor field-effect transistor turn-on (threshold) voltage requirement. Finally, techniques are combined within a 1-V CMOS operational amplifier with rail-to-rail input and output ranges. While consuming 300 W, the 1-V rail-to-rail CMOS op amp achieves 1.3-MHz unity-gain frequency and 57 phase margin for a 22-pF load capacitance.
FPGA Implementation of a Single-Precision Floating-Point Multiply-Accumulator with Single-Cycle Accumulation

This paper describes an FPGA implementation of a single-precision floating-point multiply-accumulator (FPMAC) that supports single-cycle accumulation while maintaining high clock frequencies. A non-traditional internal representation reduces the cost of mantissa alignment within the accumulator. The FPMAC is evaluated on an Altera Stratix III FPGA.
Energy management systems: state of the art and emerging trends

The electric grid is radically evolving and transforming into the smart grid, which is characterized by improved energy efficiency and manageability of available resources. Energy management (EM) systems, often integrated with home automation systems, play an important role in the control of home energy consumption and enable increased consumer participation. These systems provide consumers with information about their energy consumption patterns and help them adopt energy-efficient behavior. The new generation EM systems leverage advanced analytics and communication technologies to offer consumers actionable information and control features, while ensuring ease of use, availability, security, and privacy. In this article, we present a survey of the state of the art in EM systems, applications, and frameworks. We define a set of requirements for EM systems and evaluate several EM systems in this context. We also discuss emerging trends in this area.
Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics

An overview of privacy enhancing technologies in the era of big data analytics FINAL 1.0 PUBLIC DECEMBER 2015 The European Union Agency for Network and Information Security (ENISA) is a centre of network and information security expertise for the EU, its member states, the private sector and Europe's citizens. ENISA works with these groups to develop advice and recommendations on good practice in information security. It assists EU member states in implementing relevant EU legislation and works to improve the resilience of Europe's critical information infrastructure and networks. ENISA seeks to enhance existing expertise in EU member states by supporting the development of cross-border communities committed to improving network and information security throughout the EU. More information about ENISA and its work can be found at www.enisa.europa.eu. Editors European Union Agency for Network and Information Security ENISA responsible officers: Athena Bourka, Prokopios Drogkaris For contacting the authors please use isdp@enisa.europa.eu. For media enquiries about this paper, please use press@enisa.europa.eu. Acknowledgements We would like to thank Gwendal Le Grand (CNIL) for his support and advice during the project. Acknowledgements should also be given to Stefan Schiffner (ENISA) for his help and support in producing this document. Legal notice Notice must be taken that this publication represents the views and interpretations of the authors and editors, unless stated otherwise. This publication should not be construed to be a legal action of ENISA or the ENISA bodies unless adopted pursuant to the Regulation (EU) No 526/2013. This publication does not necessarily represent state-of the-art and ENISA may update it from time to time. Third-party sources are quoted as appropriate. ENISA is not responsible for the content of the external sources including external websites referenced in this publication. This publication is intended for information purposes only. It must be accessible free of charge. Neither ENISA nor any person acting on its behalf is responsible for the use that might be made of the information contained in this publication.
Secure Virtual Machine Execution under an Untrusted Management OS

Virtualization is a rapidly evolving technology that can be used to provide a range of benefits to computing systems, including improved resource utilization, software portability, and reliability. For security-critical applications, it is highly desirable to have a small trusted computing base (TCB), since it minimizes the surface of attacks that could jeopardize the security of the entire system. In traditional virtualization architectures, the TCB for an application includes not only the hardware and the virtual machine monitor (VMM), but also the whole management operating system (OS) that contains the device drivers and virtual machine (VM) management functionality. For many applications, it is not acceptable to trust this management OS, due to its large code base and abundance of vulnerabilities. In this paper, we address the problem of providing a secure execution environment on a virtualized computing platform under the assumption of an untrusted management OS. We propose a secure virtualization architecture that provides a secure run-time environment, network interface, and secondary storage for a guest VM. The proposed architecture significantly reduces the TCB of security-critical guest VMs, leading to improved security in an untrusted management environment. We have implemented a prototype of the proposed approach using the Xen virtualization system, and demonstrated how it can be used to facilitate secure remote computing services. We evaluate the performance penalties incurred by the proposed architecture, and demonstrate that the penalties are minimal.
Predicting Computer System Failures Using Support Vector Machines

Mitigating the impact of computer failure is possible if accurate failure predictions are provided. Resources, applications, and services can be scheduled around predicted failure and limit the impact. Such strategies are especially important for multi-computer systems, such as compute clusters, that experience a higher rate failure due to the large number of components. However providing accurate predictions with sufficient lead time remains a challenging problem. This paper describes a new spectrum-kernel Support Vector Machine (SVM) approach to predict failure events based on system log files. These files contain messages that represent a change of system state. While a single message in the file may not be sufficient for predicting failure, a sequence or pattern of messages may be. The approach described in this paper will use a sliding window (sub-sequence) of messages to predict the likelihood of failure. The a frequency representation of the message sub-sequences observed are then used as input to the SVM. The SVM then associates the messages to a class of failed or non-failed system. Experimental results using actual system log files from a Linux-based compute cluster indicate the proposed spectrum-kernel SVM approach has promise and can predict hard disk failure with an accuracy of 73% two days in advance.
Leadership Effectiveness in Global Virtual Teams

The trend toward physically dispersed work groups has necessitated a fresh inquiry into the role and nature of team leadership in virtual settings. To accomplish this, we assembled thirteen culturally diverse global teams from locations in Europe, Mexico, and the United States, assigning each team a project leader and task to complete. The findings suggest that effective team leaders demonstrate the capability to deal with paradox and contradiction by performing multiple leadership roles simultaneously (behavioral complexity). Specifically, we discovered that highly effective virtual team leaders act in a mentoring role and exhibit a high degree of understanding (empathy) toward other team members. At the same time, effective leaders are also able to assert their authority without being perceived as overbearing or inflexible. Finally, effective leaders are found to be extremely effective at providing regular, detailed, and prompt communication with their peers and in articulating role relationships (responsibilities) among the virtual team members. This study provides useful insights for managers interested in developing global virtual teams, as well as for academics interested in pursuing virtual team research. 8 KAYWORTH AND LEIDNER
GPS enabled employee registration and attendance tracking system

GPS enables a management to maintain Staff attendance and employee registration through mobile application, this application facilitates the staffs to login through mobile phone and track other staff members' whereabouts through mobile phone. In the present scenario manual registration through biometric systems is commonly in practice. The staff will be kept on informed about their attendance constantly by the admin when they login and log out so that the staff can keep a track on their attendance by using this application. The admin can track the location of any staff member using latitude, longitude and IMSI number.