nils-herrmann/clean_scirepeval huggingface dataset:
Why We Share : A Uses and Gratifications Approach to Privacy Regulation in Social Media Use

The contradiction between the stated preferences of social media users toward privacy and actual privacy behaviors has suggested a willingness to trade privacy regulation for social goals. This study employs data from a survey of 361 social media users, which collected data on privacy attitudes, online privacy strategies and behaviors, and the uses and gratifications that social media experiences bring. Using canonical correlation, it examines in detail how underlying dimensions of privacy concern relate to specific contexts of social media use, and how these contexts relate to various domains of privacyprotecting behaviors. In addition, this research identifies how specific areas of privacy concern relate to levels of privacy regulation, offering new insight into the privacy paradox. In doing so, this study lends greater nuance to how the dynamic of privacy and sociality is understood and enacted by users, and how privacy management and the motivations underlying media use intersect.
BEYOND BITCOIN: ISSUES IN REGULATING BLOCKCHAIN TRANSACTIONS

The buzz surrounding Bitcoin has reached a fever pitch. Yet in academic legal discussions, disproportionate emphasis is placed on bitcoins (that is, virtual currency), and little mention is made of blockchain technology—the true innovation behind the Bitcoin protocol. Simply, blockchain technology solves an elusive networking problem by enabling “trustless” transactions: value exchanges over computer networks that can be verified, monitored, and enforced without central institutions (for example, banks). This has broad implications for how we transact over electronic networks. This Note integrates current research from leading computer scientists and cryptographers to elevate the legal community’s understanding of blockchain technology and, ultimately, to inform policymakers and practitioners as they consider different regulatory schemes. An examination of the economic properties of a blockchainbased currency suggests the technology’s true value lies in its potential to facilitate more efficient digital-asset transfers. For example, applications of special interest to the legal community include more efficient document and authorship verification, title transfers, and contract enforcement. Though a regulatory patchwork around virtual currencies has begun to form, its careful analysis reveals much uncertainty with respect to these alternative applications.  The circulation of confidence is better than the circulation of money.
Editing plant genomes with CRISPR/Cas9.

CRISPR/Cas9 is a rapidly developing genome editing technology that has been successfully applied in many organisms, including model and crop plants. Cas9, an RNA-guided DNA endonuclease, can be targeted to specific genomic sequences by engineering a separately encoded guide RNA with which it forms a complex. As only a short RNA sequence must be synthesized to confer recognition of a new target, CRISPR/Cas9 is a relatively cheap and easy to implement technology that has proven to be extremely versatile. Remarkably, in some plant species, homozygous knockout mutants can be produced in a single generation. Together with other sequence-specific nucleases, CRISPR/Cas9 is a game-changing technology that is poised to revolutionise basic research and plant breeding.
Power-aware routing based on the energy drain rate for mobile ad hoc networks

Mobile ad hoc networks’ (MANETs) inherent power limitation makes power-awareness a critical requirement for MANET protocols. In this paper, we propose a new routing metric, the drain rate, which predicts the lifetime of a node as a function of current traffic conditions. We describe the Minimum Drain Rate (MDR) mechanism which uses a combination of the drain rate with remaining battery capacity to establish routes. MDR can be employed by any existing MANET routing protocol to achieve a dual goal: extend both nodal battery life and connection lifetime. Using the ns-2 simulator and the Dynamic Source Routing (DSR) protocol, we compared MDR to the Minimum Total Transmission Power Routing (MTPR) scheme and the Min-Max Battery Cost Routing (MMBCR) scheme and proved that MDR is the best approach to achieve the dual goal.
A Review of Learning with Deep Generative Models from perspective of graphical modeling

This document aims to provide a review on learning with deep generative models (DGMs), which is an highly-active area in machine learning and more generally, artificial intelligence. This review is not meant to be a tutorial, but when necessary, we provide self-contained derivations for completeness. This review has two features. First, though there are different perspectives to classify DGMs, we choose to organize this review from the perspective of graphical modeling, because the learning methods for directed DGMs and undirected DGMs are fundamentally different. Second, we differentiate model definitions from model learning algorithms, since different learning algorithms can be applied to solve the learning problem on the same model, and an algorithm can be applied to learn different models. We thus separate model definition and model learning, with more emphasis on reviewing, differentiating and connecting different learning algorithms. We also discuss promising future research directions. This review is by no means comprehensive as the field is evolving rapidly. The authors apologize in advance for any missed papers and inaccuracies in descriptions. Corrections and comments are highly welcome.
Pin-Loaded Circularly-Polarized Patch Antennas With Wide 3-dB Axial Ratio Beamwidth

Pin-loaded circularly polarized (CP) patch antennas with wide 3-dB axial ratio beamwidth (ARBW) are proposed in this paper. The ARBW of a CP patch antenna is mainly dependent on its electrical width at a frequency, while the patch size at resonance is fully controlled by the shunt inductive load. First of all, four shorting pins are introduced along the diagonals of the dual-fed square patch, so as to exhibit that the 3-dB ARBW can be significantly extended if the shorting position and pin radius are properly chosen. Next, the two degenerate modes in a single-fed CP patch antenna are slightly separated in resonant frequencies by loading another pair of shorting pins along the central line of the patch, so as to excite CP radiation. After extensive analysis is executed, two patch antennas with and without shorting pins are fabricated and measured. Simulated and measured results show good agreement, and both of them demonstrate that the 3-dB ARBW is tremendously improved from 50° to 140° or gets nearly 90° increment by means of the proposed method.
The impact of initial consumer trust on intentions to transact with a web site: a trust building model

This paper develops and tests a model of consumer trust in an electronic commerce vendor. Building consumer trust is a strategic imperative for web-based vendors because trust strongly influences consumer intentions to transact with unfamiliar vendors via the web. Trust allows consumers to overcome perceptions of risk and uncertainty, and to engage in the following three behaviors that are critical to the realization of a web-based vendor’s strategic objectives: following advice offered by the web vendor, sharing personal information with the vendor, and purchasing from the vendor’s web site. Trust in the vendor is defined as a multi-dimensional construct with two inter-related components—trusting beliefs (perceptions of the competence, benevolence, and integrity of the vendor), and trusting intentions—willingness to depend (that is, a decision to make oneself vulnerable to the vendor). Three factors are proposed for building consumer trust in the vendor: structural assurance (that is, consumer perceptions of the safety of the web environment), perceived web vendor reputation, and perceived web site quality. The model is tested in the context of a hypothetical web site offering legal advice. All three factors significantly influenced consumer trust in the web vendor. That is, these factors, especially web site quality and reputation, are powerful levers that vendors can use to build consumer trust, in order to overcome the negative perceptions people often have about the safety of the web environment. The study also demonstrates that perceived Internet risk negatively affects consumer intentions to transact with a web-based vendor. q 2002 Elsevier Science B.V. All rights reserved.
Emerging strategies in the prevention of domestic violence.

Responses to domestic violence have focused, to date, primarily on intervention after the problem has already been identified and harm has occurred. There are, however, new domestic violence prevention strategies emerging, and prevention approaches from the public health field can serve as models for further development of these strategies. This article describes two such models. The first involves public health campaigns that identify and address the underlying causes of a problem. Although identifying the underlying causes of domestic violence is difficult--experts do not agree on causation, and several different theories exist--these theories share some common beliefs that can serve as a foundation for prevention strategies. The second public health model can be used to identify opportunities for domestic violence prevention along a continuum of possible harm: (1) primary prevention to reduce the incidence of the problem before it occurs; (2) secondary prevention to decrease the prevalence after early signs of the problem; and (3) tertiary prevention to intervene once the problem is already clearly evident and causing harm. Examples of primary prevention include school-based programs that teach students about domestic violence and alternative conflict-resolution skills, and public education campaigns to increase awareness of the harms of domestic violence and of services available to victims. Secondary prevention programs could include home visiting for high-risk families and community-based programs on dating violence for adolescents referred through child protective services (CPS). Tertiary prevention includes the many targeted intervention programs already in place (and described in other articles in this journal issue). Early evaluations of existing prevention programs show promise, but results are still preliminary and programs remain small, locally based, and scattered throughout the United States and Canada. What is needed is a broadly based, comprehensive prevention strategy that is supported by sound research and evaluation, receives adequate public backing, and is based on a policy of zero tolerance for domestic violence.
Boxed Out: Blocking Cellular Interconnect Bypass Fraud at the Network Edge

The high price of incoming international calls is a common method of subsidizing telephony infrastructure in the developing world. Accordingly, international telephone system interconnects are regulated to ensure call quality and accurate billing. High call tariffs create a strong incentive to evade such interconnects and deliver costly international calls illicitly. Specifically, adversaries use VoIP-GSM gateways informally known as “simboxes” to receive incoming calls over wired data connections and deliver them into a cellular voice network through a local call that appears to originate from a customer’s phone. This practice is not only extremely profitable for simboxers, but also dramatically degrades network experience for legitimate customers, violates telecommunications laws in many countries, and results in significant revenue loss. In this paper, we present a passive detection technique for combating simboxes at a cellular base station. Our system relies on the raw voice data received by the tower during a call to distinguish errors in GSM transmission from the distinct audio artifacts caused by delivering the call over a VoIP link. Our experiments demonstrate that this approach is highly effective, and can detect 87% of real simbox calls in only 30 seconds of audio with no false positives. Moreover, we demonstrate that evading our detection across multiple calls is only possible with a small probability. In so doing, we demonstrate that fraud that degrades network quality and costs telecommunications billions of dollars annually can easily be detected and counteracted in real time.
Parallel field alignment for cross media retrieval

Cross media retrieval systems have received increasing interest in recent years. Due to the semantic gap between low-level features and high-level semantic concepts of multimedia data, many researchers have explored joint-model techniques in cross media retrieval systems. Previous joint-model approaches usually focus on two traditional ways to design cross media retrieval systems: (a) fusing features from different media data; (b) learning different models for different media data and fusing their outputs. However, the process of fusing features or outputs will lose both low- and high-level abstraction information of media data. Hence, both ways do not really reveal the semantic correlations among the heterogeneous multimedia data. In this paper, we introduce a novel method for the cross media retrieval task, named Parallel Field Alignment Retrieval (PFAR), which integrates a manifold alignment framework from the perspective of vector fields. Instead of fusing original features or outputs, we consider the cross media retrieval as a manifold alignment problem using parallel fields. The proposed manifold alignment algorithm can effectively preserve the metric of data manifolds, model heterogeneous media data and project their relationship into intermediate latent semantic spaces during the process of manifold alignment. After the alignment, the semantic correlations are also determined. In this way, the cross media retrieval task can be resolved by the determined semantic correlations. Comprehensive experimental results have demonstrated the effectiveness of our approach.
MACE: high-coverage and robust memory analysis for commodity operating systems

Memory forensic analysis collects evidence for digital crimes and malware attacks from the memory of a live system. It is increasingly valuable, especially in cloud computing. However, memory analysis on on commodity operating systems (such as Microsoft Windows) faces the following key challenges: (1) a partial knowledge of kernel data structures; (2) difficulty in handling ambiguous pointers; and (3) lack of robustness by relying on soft constraints that can be easily violated by kernel attacks. To address these challenges, we present MACE, a memory analysis system that can extract a more complete view of the kernel data structures for closed-source operating systems and significantly improve the robustness by only leveraging pointer constraints (which are hard to manipulate) and evaluating these constraint globally (to even tolerate certain amount of pointer attacks). We have evaluated MACE on 100 memory images for Windows XP SP3 and Windows 7 SP0. Overall, MACE can construct a kernel object graph from a memory image in just a few minutes, and achieves over 95% recall and over 96% precision. Our experiments on real-world rootkit samples and synthetic attacks further demonstrate that MACE outperforms other external memory analysis tools with respect to wider coverage and better robustness.
Blending SQL and NewSQL Approaches: Reference Architectures for Enterprise Big Data Challenges

As it becomes ever more pervasively engaged in data driven commerce, a modern enterprise becomes increasingly dependent upon reliable and high speed transaction services. At the same time it aspires to capitalize upon large inflows of information to draw timely business insights and improve business results. These two imperatives are frequently in conflict because of the widely divergent strategies that must be pursued: the need to bolster on-line transactional processing generally drives a business towards a small cluster of high-end servers running a mature, ACID compliant, SQL relational database, while high throughput analytics on massive and growing volumes of data favor the selection of very large clusters running non-traditional (NoSQL/NewSQL) databases that employ softer consistency protocols for performance and availability. This paper describes an approach in which the two imperatives are addressed by blending the two types (scale-up and scale-out) of data processing. It breaks down data growth that enterprises experience into three classes-Chronological, Horizontal, and Vertical, and picks out different approaches for blending SQL and NewSQL platforms for each class. To simplify application logic that must comprehend both types of data platforms, the paper describes two new capabilities: (a) a data integrator to quickly sift out updates that happen in an RDBMS and funnel them into a NewSQL database, and (b) extensions to the Hibernate-OGM framework that reduce the programming sophistication required for integrating HBase and Hive back ends with application logic designed for relational front ends. Finally the paper details several instances in which these approaches have been applied in real-world, at a number of software vendors with whom the authors have collaborated on design, implementation and deployment of blended solutions.
An ICP variant using a point-to-line metric

This paper describes PLICP, an ICP (iterative closest/corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual correspondences), and MBICP (Metric-Based ICP) by reproducing the experiments performed in Minguez et al. (2006). The experiments suggest that PLICP is more precise, and requires less iterations. However, it is less robust to very large initial displacement errors. The last part of the paper is devoted to purely algorithmic optimization of the correspondence search; this allows for a significant speed-up of the computation. The source code is available for download.
Grassmann discriminant analysis: a unifying view on subspace-based learning

In this paper we propose a discriminant learning framework for problems in which data consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we can make learning algorithms adapt naturally to the problems with linear invariant structures. We propose a unifying view on the subspace-based learning method by formulating the problems on the Grassmann manifold, which is the set of fixed-dimensional linear subspaces of a Euclidean space. Previous methods on the problem typically adopt an inconsistent strategy: feature extraction is performed in the Euclidean space while non-Euclidean distances are used. In our approach, we treat each sub-space as a point in the Grassmann space, and perform feature extraction and classification in the same space. We show feasibility of the approach by using the Grassmann kernel functions such as the Projection kernel and the Binet-Cauchy kernel. Experiments with real image databases show that the proposed method performs well compared with state-of-the-art algorithms.
Scene Graph Generation by Iterative Message Passing

Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel end-to-end model that generates such structured scene representation from an input image. Our key insight is that the graph generation problem can be formulated as message passing between the primal node graph and its dual edge graph. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods on the Visual Genome dataset as well as support relation inference in NYU Depth V2 dataset.
Probabilistic Evaluation of Optimal Location of Surge Arresters on EHV and UHV Networks Due to Switching and Lightning Surges

Switching surges are of primary importance in insulation coordination of extremely high voltage and ultra-high voltage networks. However, in regions of high lightning activity or high ground resistance insulation design, preferably, should be based on the risk of failure caused by lightning and switching surges and the probability of line outage, a combination of lightning and switching flashover rates (SSFOR). This paper describes an effective installation of transmission line arresters (TLAs) to obtain a better protection scheme (i.e., minimizing global risk to the network). As a consequence, protection costs are reduced in accordance with the costs of elements actually protected and the number of TLAs utilized. In order to accomplish this, a probabilistic method for calculating the lightning related failure and an artificial neural network for estimating the SSFOR are presented. A multicriteria optimization method based on a genetic algorithm is also developed to determine the optimum location of TLAs.
Alleviating Data Sparsity for Twitter Sentiment Analysis

Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-character limit. In this work we propose using two different sets of features to alleviate the data sparseness problem. One is the semantic feature set where we extract semantically hidden concepts from tweets and then incorporate them into classifier training through interpolation. Another is the sentiment-topic feature set where we extract latent topics and the associated topic sentiment from tweets, then augment the original feature space with these sentiment-topics. Experimental results on the Stanford Twitter Sentiment Dataset show that both feature sets outperform the baseline model using unigrams only. Moreover, using semantic features rivals the previously reported best result. Using sentimenttopic features achieves 86.3% sentiment classification accuracy, which outperforms existing approaches.
Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence

Threat intelligence is the provision of evidence-based knowledge about existing or potential threats. Benefits of threat intelligence include improved efficiency and effectiveness in security operations in terms of detective and preventive capabilities. Successful threat intelligence within the cyber domain demands a knowledge base of threat information and an expressive way to represent this knowledge. This purpose is served by the use of taxonomies, sharing standards, and ontologies.This paper introduces the Cyber Threat Intelligence (CTI) model, which enables cyber defenders to explore their threat intelligence capabilities and understand their position against the ever-changing cyber threat landscape. In addition, we use our model to analyze and evaluate several existing taxonomies, sharing standards, and ontologies relevant to cyber threat intelligence. Our results show that the cyber security community lacks an ontology covering the complete spectrum of threat intelligence. To conclude, we argue the importance of developing a multi-layered cyber threat intelligence ontology based on the CTI model and the steps should be taken under consideration, which are the foundation of our future work.