nils-herrmann/clean_scirepeval huggingface dataset:
A comprehensive survey of Network Function Virtualization

Today’s networks are filled with a massive and ever-growing variety of network functions that coupled with proprietary devices, which leads to network ossification and difficulty in network management and service provision. Network Function Virtualization (NFV) is a promising paradigm to change such situation by decoupling network functions from the underlying dedicated hardware and realizing them in the form of software, which are referred to as Virtual Network Functions (VNFs). Such decoupling introduces many benefits which include reduction of Capital Expenditure (CAPEX) and Operation Expense (OPEX), improved flexibility of service provision, etc. In this paper, we intend to present a comprehensive survey on NFV, which starts from the introduction of NFV motivations. Then, we explain the main concepts of NFV in terms of terminology, standardization and history, and how NFV differs from traditional middlebox based network. After that, the standard NFV architecture is introduced using a bottom up approach, based on which the corresponding use cases and solutions are also illustrated. In addition, due to the decoupling of network functionalities and hardware, people’s attention is gradually shifted to the VNFs. Next, we provide an extensive and in-depth discussion on state-of-the-art VNF algorithms including VNF placement, scheduling, migration, chaining and multicast. Finally, to accelerate the NFV deployment and avoid pitfalls as far as possible, we survey the challenges faced by NFV and the trend for future directions. In particular, the challenges are discussed from bottom up, which include hardware design, VNF deployment, VNF life cycle control, service chaining, performance evaluation, policy enforcement, energy efficiency, reliability and security, and the future directions are discussed around the current trend towards network softwarization.
The Impact of Quantum Computing on Present Cryptography

The aim of this paper is to elucidate the implications of quantum computing in present cryptography and to introduce the reader to basic post-quantum algorithms. In particular the reader can delve into the following subjects: present cryptographic schemes (symmetric and asymmetric), differences between quantum and classical computing, challenges in quantum computing, quantum algorithms (Shor’s and Grover’s), public key encryption schemes affected, symmetric schemes affected, the impact on hash functions, and post quantum cryptography. Specifically, the section of Post-Quantum Cryptography deals with different quantum key distribution methods and mathematicalbased solutions, such as the BB84 protocol, lattice-based cryptography, multivariate-based cryptography, hash-based signatures and code-based cryptography. quantum computers; post-quantum cryptography; Shor’s algorithm; Grover’s algorithm; asymmetric cryptography; symmetric cryptography
Knowledge Sharing Behaviour in the Public Sector : the Business Process Management Perspectives

The purpose of this paper is to demonstrate the preliminary analysis on the knowledge sharing behaviour of the public sectors in terms of individual attitudes, organisational environment, reward system and information and communication technology (ICT) within the system. The paper looks into the perspective of business process management which embedded in a knowledge sharing behaviour. This research-inprogress intends to use both quantitative and qualitative methods (triangulation) because we believe that it will be appropriate to answer the research questions better. In the Phase One, consists of the questionnaire that uses to measure perceptions, beliefs, reactions, and attitudes related to knowledge sharing. The unit of analysis using middle to senior management in the public sector as knowledge is said to be captured more systematically at this level. Phase Two will be carried out after analysis of data from the questionnaire survey is completed. The researcher intends to undertake an in-depth exploration of issues arising from Phase One. Hence a semi-structured interview will be carried out with small group of people. Top management officials will be selected from within the same sample for this exercise. The research design in this study hopefully will answer all the research questions significantly. The preliminary findings will provide a basis for proposing a structured framework for further data collection and analysis.
Clustering Data Streams

W e study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time. The data stream model i s relevant to new classes of applications involving massive data sets, such as web click stream analysis and multimedia data analysis. W e give constant-factor approximation algorithms for the k-Median problem in the data stream model of computation in a single pass. W e also show negative results implying that our algorithms cannot be improved in a certain sense.
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images

In Tables 2 and 3, we present the results of our eight models and three baselines compared to current state-of-the-art under the two metric of MedErr and Acc π 6 respectively. As we mentioned in the paper, we run all experiments three times and report the mean and standard deviation (in brackets) across these three trials. We also show figures of images where we obtain the least pose estimation error and the most pose estimation error for every object category using one run of model MG+. As can be seen from Figs. , we make the most error under three conditions: (i) when the objects are really blurry (very small in pixel size in the original image), (ii) the shape of the object is uncommon (possibly very few examples seen during training) and (iii) the pose of a test image is very different from common poses observed during training. The first condition is best observed in the bad cases for categories aeroplane and car where almost all the images shown are very blurry. The second condition is best observed in categories boat and chair where the bad cases contain uncommon boats and chairs. The third condition is best observed in categories bottle and tvmonitor where the bad images are in very different poses compared to the best images. We also present the performance of our models MG and MG+ across different object categories of the Pascal3D+ dataset during ablation experiments in Tables 4-11. These are detailed tables for the results shown in Tables 3 and 4 of the main paper and an overview of the experiments is shown below.
Low Rank Spectral Network Alignment

Network alignment or graph matching is the classic problem of finding matching vertices between two graphs with applications in network de-anonymization and bioinformatics. There exist a wide variety of algorithms for it, but a challenging scenario for all of the algorithms is aligning two networks without any information about which nodes might be good matches. In this case, the vast majority of principled algorithms demand quadratic memory in the size of the graphs. We show that one such method—the recently proposed and theoretically grounded EigenAlign algorithm—admits a novel implementation which requires memory that is linear in the size of the graphs. The key step to this insight is identifying low-rank structure in the node-similarity matrix used by EigenAlign for determining matches. With an exact, closed-form low-rank structure, we then solve a maximum weight bipartite matching problem on that low-rank matrix to produce the matching between the graphs. For this task, we show a new, a-posteriori, approximation bound for a simple algorithm to approximate a maximum weight bipartite matching problem on a low-rank matrix. The combination of our two new methods then enables us to tackle much larger network alignment problems than previously possible and to do so quickly. Problems that take hours with existing methods take only seconds with our new algorithm. We thoroughly validate our low-rank algorithm against the original EigenAlign approach. We also compare a variety of existing algorithms on problems in bioinformatics and social networks. Our approach can also be combined with existing algorithms to improve their performance and speed.
Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes

In this paper we extend neural Turing machine (NTM) into a dynamic neural Turing machine (D-NTM) by introducing a trainable memory addressing scheme. This scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies including both linear and nonlinear ones. We implement the D-NTM with both soft, differentiable and hard, non-differentiable read/write mechanisms. We investigate the mechanisms and effects for learning to read and write to a memory through experiments on Facebook bAbI tasks using both a feedforward and GRU-controller. The D-NTM is evaluated on a set of the Facebook bAbI tasks and shown to outperform NTM and LSTM baselines.
GEO-CONQUESTING: COMPETITIVE LOCATIONAL TARGETING OF MOBILE PROMOTIONS

As consumers spend more time on their mobile devices, a focal retailer’s natural approach is to target potential customers in close proximity to its own location. Yet focal (own) location targeting may cannibalize profits on infra-marginal sales. This study demonstrates the effectiveness of competitive locational targeting, the practice of promoting to consumers near a competitor’s location. The analysis is based on a randomized field experiment in which mobile promotions were sent to customers at three similar shopping areas (competitive, focal, and benchmark locations). The results show that competitive locational targeting can take advantage of heightened demand that a focal retailer would not otherwise capture. Competitive locational targeting produced increasing returns to promotional discount depth, whereas targeting the focal location produced decreasing returns to deep discounts, indicating saturation effects and profit cannibalization. These findings are important for marketers, who can use competitive locational targeting to generate incremental sales without cannibalizing profits. While the experiment focuses on the effects of unilateral promotions, it represents the first step in understanding the competitive implications of mobile marketing technologies.
Design and Validation of a Synchronous Reluctance Motor With Single Tooth Windings

This paper presents for the first time the analysis and experimental validation of a six-slot four-pole synchronous reluctance motor with nonoverlapping fractional slot-concentrated windings. The machine exhibits high torque density and efficiency due to its high fill factor coils with very short end windings, facilitated by a segmented stator and bobbin winding of the coils. These advantages are coupled with its inherent robustness and low cost. The topology is presented as a logical step forward in advancing synchronous reluctance machines that have been universally wound with a sinusoidally distributed winding. The paper presents the motor design, performance evaluation through finite element studies and validation of the electromagnetic model, and thermal specification through empirical testing. It is shown that high performance synchronous reluctance motors can be constructed with single tooth wound coils, but considerations must be given regarding torque quality and the d-q axis inductances.
Scaling-up resistive synaptic arrays for neuro-inspired architecture: Challenges and prospect

The crossbar array architecture with resistive synaptic devices is attractive for on-chip implementation of weighted sum and weight update in the neuro-inspired learning algorithms. This paper discusses the design challenges on scaling up the array size due to non-ideal device properties and array parasitics. Circuit-level mitigation strategies have been proposed to minimize the learning accuracy loss in a large array. This paper also discusses the peripheral circuits design considerations for the neuro-inspired architecture. Finally, a circuit-level macro simulator is developed to explore the design trade-offs and evaluate the overhead of the proposed mitigation strategies as well as project the scaling trend of the neuro-inspired architecture.
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer

If components (denoted by horizontal and vertical axis in Figure 2a) are correlated, then samples (points in Figure 2a) are in a non-spherical shape, then eigenvalues are mutually different. Hence correlation leads to non-uniformity of eigenvalues. Since the eigenvectors are orthogonal by design, it suffices to focus on eigenvalues only. To reduce correlation, we encourage the eigenvalues to be uniform (Figure 2b). Rotation does not affect eigenvalues or uncorrelation. For a component matrix A and rotation matrix R, A>A equals to A>R>RA and they have the same eigendecomposition (say UEU>). Ensuring the eigenvalue matrix E is close to identity implies the latent components are rotations of the orthonormal (and hence uncorrelated) eigenvectors.
Dependence language model for information retrieval

This paper presents a new dependence language modeling approach to information retrieval. The approach extends the basic language modeling approach based on unigram by relaxing the independence assumption. We integrate the linkage of a query as a hidden variable, which expresses the term dependencies within the query as an acyclic, planar, undirected graph. We then assume that a query is generated from a document in two stages: the linkage is generated first, and then each term is generated in turn depending on other related terms according to the linkage. We also present a smoothing method for model parameter estimation and an approach to learning the linkage of a sentence in an unsupervised manner. The new approach is compared to the classical probabilistic retrieval model and the previously proposed language models with and without taking into account term dependencies. Results show that our model achieves substantial and significant improvements on TREC collections.
Beyond citations: Scholars' visibility on the social Web

Traditionally, scholarly impact and visibility have been measured by counting publications and citations in the scholarly literature. However, increasingly scholars are also visible on the Web, establishing presences in a growing variety of social ecosystems. But how wide and established is this presence, and how do measures of social Web impact relate to their more traditional counterparts? To answer this, we sampled 57 presenters from the 2010 Leiden STI Conference, gathering publication and citations counts as well as data from the presenters’ Web “footprints.” We found Web presence widespread and diverse: 84% of scholars had homepages, 70% were on LinkedIn, 23% had public Google Scholar profiles, and 16% were on Twitter. For sampled scholars’ publications, social reference manager bookmarks were compared to Scopus and Web of Science citations; we found that Mendeley covers more than 80% of sampled articles, and that Mendeley bookmarks are significantly correlated (r=.45) to Scopus citation counts.
Toward unified DevOps model

DevOps community advocates collaboration between development and operations staff during software deployment. However this collaboration may cause a conceptual deficit. This paper proposes a Unified DevOps Model (UDOM) in order to overcome the conceptual deficit. Firstly, the origin of conceptual deficit is discussed. Secondly, UDOM model is introduced that includes three sub-models: application and data model, workflow execution model and infrastructure model. UDOM model can help to scale down deployment time, mitigate risk, satisfy customer requirements, and improve productivity. Finally, this paper can be a roadmap for standardization DevOps terminologies, concepts, patterns, cultures, and tools.
Peruse and Profit: Estimating the Accelerability of Loops

There exist a multitude of execution models available today for a developer to target. The choices vary from general purpose processors to fixed-function hardware accelerators with a large number of variations in-between. There is a growing demand to assess the potential benefits of porting or rewriting an application to a target architecture in order to fully exploit the benefits of performance and/or energy efficiency offered by such targets. However, as a first step of this process, it is necessary to determine whether the application has characteristics suitable for acceleration.
 In this paper, we present Peruse, a tool to characterize the features of loops in an application and to help the programmer understand the amenability of loops for acceleration. We consider a diverse set of features ranging from loop characteristics (e.g., loop exit points) and operation mixes (e.g., control vs data operations) to wider code region characteristics (e.g., idempotency, vectorizability). Peruse is language, architecture, and input independent and uses the intermediate representation of compilers to do the characterization. Using static analyses makes Peruse scalable and enables analysis of large applications to identify and extract interesting loops suitable for acceleration. We show analysis results for unmodified applications from the SPEC CPU benchmark suite, Polybench, and HPC workloads.
 For an end-user it is more desirable to get an estimate of the potential speedup due to acceleration. We use the workload characterization results of Peruse as features and develop a machine-learning based model to predict the potential speedup of a loop when off-loaded to a fixed function hardware accelerator. We use the model to predict the speedup of loops selected by Peruse and achieve an accuracy of 79%.
Learn from experience: Probabilistic prediction of perception performance to avoid failure

Despite significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging time-varying environments. When these systems are used for autonomous decision-making, such as in self-driving vehicles, the impact of their mistakes can be catastrophic. As such, it is important to characterize the performance of the system and predict when and where it may fail in order to take appropriate action. While similar in spirit to the idea of introspection, this work introduces a new paradigm for predicting the likely performance of a robot’s perception system based on past experience in the same workspace. In particular, we propose two models that probabilistically predict perception performance from observations gathered over time. While both approaches are place-specific, the second approach additionally considers appearance similarity when incorporating past observations. We evaluate our method in a classical decision-making scenario in which the robot must choose when and where to drive autonomously in 60 km of driving data from an urban environment. Results demonstrate that both approaches lead to fewer false decisions (in terms of incorrectly offering or denying autonomy) for two different detector models, and show that leveraging visual appearance within a state-of-the-art navigation framework increases the accuracy of our performance predictions.
Architectural layout planning using genetic algorithms

This paper presents a model which generates architectural layout for a single flat having regular shaped spaces; Bedroom, Bathroom, Kitchen, Balcony, Living and Dining Room. Using constraints at two levels; Topological (Adjacency, Compactness, Vaastu, and Open and Closed face constraints) and Dimensional (Length to Width ratio constraint), Genetic Algorithms have been used to generate the topological arrangement of spaces in the layout and further if required, feasibility have been dimensionally analyzed. Further easy evacuation form the selected layout in case of adversity has been proposed using Dijkstra's Algorithm. Later the proposed model has been tested for efficiency using various test cases. This paper also presents a classification and categorization of various problems of space planning.
Agile Challenges in Practice: A Thematic Analysis

As agile is maturing and becoming more widely adopted, it is important that researchers are aware of the challenges faced by practitioners and organisations. We undertook a thematic analysis of 193 agile challenges collected at a series of agile conferences and events during 2013 and 2014. Participants were mainly practitioners and business representatives along with some academics. The challenges were thematically analysed by separate authors, synthesised, and a list of seven themes and 27 sub-themes was agreed. Themes were Organisation, Sustainability, Culture, Teams, Scale, Value and Claims and Limitations. We compare our findings against previous attempts to identify and categorise agile challenges. While most themes have persisted we found a shift of focus towards sustainability, business engagement and transformation, as well as claims and limitations. We identify areas for further research and a need for innovative methods of conveying academic research to industry and industrial problems to academia.
Nonlinear Principal Component Analysis Using Autoassociative Neural Networks

Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA, like PCA, is used to identify and remove correlations among problem variables as an aid to dimensionality reduction, visualization, and exploratory data analysis. While PCA identifies only linear correlations between variables, NLPCA uncovers both linear and nonlinear correlations, without restriction on the character of the nonlinearities present in the data. NLPCA operates by training a feedforward neural network to perform the identity mapping, where the network inputs are reproduced at the output layer. The network contains an internal “bottleneck” layer (containing fewer nodes than input or output layers), which forces the network to develop a compact representation of the input data, and two additional hidden layers. The NLPCA method is demonstrated using time-dependent, simulated batch reaction data. Results show that NLPCA successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system parameters.
Ashamed and Afraid: A Scoping Review of the Role of Shame in Post-Traumatic Stress Disorder (PTSD)

Despite considerable progress in the treatment of post-traumatic stress disorder (PTSD), a large percentage of individuals remain symptomatic following gold-standard therapies. One route to improving care is examining affective disturbances that involve other emotions beyond fear and threat. A growing body of research has implicated shame in PTSD's development and course, although to date no review of this specific literature exists. This scoping review investigated the link between shame and PTSD and sought to identify research gaps.



A systematic database search of PubMed, PsycInfo, Embase, Cochrane, and CINAHL was conducted to find original quantitative research related to shame and PTSD.



Forty-seven studies met inclusion criteria. Review found substantial support for an association between shame and PTSD as well as preliminary evidence suggesting its utility as a treatment target. Several design limitations and under-investigated areas were recognized, including the need for a multimodal assessment of shame and more longitudinal and treatment-focused research.



This review provides crucial synthesis of research to date, highlighting the prominence of shame in PTSD, and its likely relevance in successful treatment outcomes. The present review serves as a guide to future work into this critical area of study.