nils-herrmann/clean_scirepeval huggingface dataset:
Seriously, get off my cloud! Cross-VM RSA Key Recovery in a Public Cloud

It has been six years since Ristenpart et al. [29] demonstrated the viability of co-location and provided the first concrete evidence for sensitive information leakage on a commercial cloud. We show that co-location can be achieved and detected by monitoring the last level cache in public clouds. More significantly, we present a full-fledged attack that exploits subtle leakages to recover RSA decryption keys from a colocated instance. We target a recently patched Libgcrypt RSA implementation by mounting Cross-VM Prime and Probe cache attacks in combination with other tests to detect co-location in Amazon EC2. In a preparatory step, we reverse engineer the unpublished nonlinear slice selection function for the 10 core Intel Xeon processor which significantly accelerates our attack (this chipset is used in Amazon EC2). After co-location is detected and verified, we perform the Prime and Probe attack to recover noisy keys from a carefully monitored Amazon EC2 VM running the aforementioned vulnerable libgcrypt library. We subsequently process the noisy data and obtain the complete 2048-bit RSA key used during encryption. This work reaffirms the privacy concerns and underlines the need for deploying stronger isolation techniques in public clouds. Amazon EC2, Co-location Detection, RSA key recovery, Resource Sharing, Prime and Probe
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections

The availability of map interfaces and location-aware devices makes a growing amount of unstructured, geo-referenced information available on the Web. This type of information can be valuable not only for browsing, finding and making sense of individual items, but also in aggregate form to help understand data trends and features. In particular, over twenty million geo-referenced photos are now available on Flickr, a photo-sharing website - the first major collection of its kind. These photos are often associated with user-entered unstructured text labels (i.e., tags). We show how we analyze the tags associated with the geo-referenced Flickr images to generate aggregate knowledge in the form of "representative tags" for arbitrary areas in the world. We use these tags to create a visualization tool, World Explorer, tha tcan help expose the content of the data, using a map interface to display the derived tags and the original photo items. We perform a qualitative evaluation of World Explorer that outlines the visualization's benefits in browsing this type of content. We provide insights regarding the aggregate versus individual-item requirements in browsing digital geo-referenced material.
Expert Systems and Probabilistic Network Models

Follow up what we will offer in this article about expert systems and probabilistic network models. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.
65GHz Doppler Sensor with On-Chip Antenna in 0.18μm SiGe BiCMOS

A single-chip 65GHz Doppler radar transceiver with on-chip patch antenna is reported. Implemented in a production 0.18mum SiGe BiCMOS process, it features a differential output transmit power of 4.3dBm, 16.5dB single-ended down-conversion gain and a double-sideband noise figure of 12.8dB. The radar includes a 65GHz 2-stage cascode LNA with S 11<-15dB at 50-94GHz and 14dB gain at 65GHz, a double-balanced down-convert mixer, a SiGe HBT IF amplifier with low 1/f noise, a VCO and a 65GHz output buffer. The LO is provided by an integrated varactor-tuned 61-67GHz VCO optimized for low phase noise. The patch antenna is designed to be impedance-matched for dual-band operation at 62.8 and 64.6GHz. The use of lumped inductors in all blocks and a vertically-stacked transformer for single-ended to differential conversion in the receive path help reduce the transceiver area to 1mm times 1mm
DWCMM: The Data Warehouse Capability Maturity Model

Data Warehouses and Business Intelligence have become popular fields of research in recent years. Unfortunately, in daily practice many Data Warehouse and Business Intelligence solutions still fail to help organizations make better decisions and increase their profitability, due to intransparent complexities and project interdependencies. In addition, emerging application domains such as Mobile Learning & Analytics heavily depend on a wellstructured data foundation with a longitudinally prepared architecture. Therefore, this research presents the Data Warehouse Capability Maturity Model (DWCMM) which encompasses both technical and organizational aspects involved in developing a Data Warehouse environment. The DWCMM can be used to help organizations assess their current Data Warehouse solution and provide them with guidelines for future improvements. The DWCMM consists of a maturity matrix and a maturity assessment questionnaire with 60 questions. The DWCMM has been evaluated empirically through expert interviews and case studies. We conclude that the DWCMM can be successfully applied in practice and that organizations can intelligibly utilize the DWCMM as a quickscan instrument to jumpstart their Data Warehouse and Business Intelligence improvement processes.
New local edge binary patterns for image retrieval

A new method, called local edge binary patterns (LEBP), is introduced in the paper, which takes the advantages of local binary patterns and local edges into account. Furthermore, several extensions to LEBP are also discussed in detail. Center-symmetric local binary pattern (CS-LBP) and direction local binary pattern (D-LBP) are chosen as examples to prove the performance of the new method on two commonly used texture databases. Experimental results show that LEBP can greatly improve the performance of the traditional local binary patterns in texture image retrieval.
Requirements for Safe Robots : Measurements , Analysis and New Insights

Physical human–robot interaction and cooperation has become a topic of increasing importance and of major focus in robotics research. An essential requirement of a robot designed for high mobility and direct interaction with human users or uncertain environments is that it must in no case pose a threat to the human. Until recently, quite a few attempts were made to investigate real-world threats via collision tests and use the outcome to considerably improve safety during physical human–robot interaction. In this paper, we give an overview of our systematic evaluation of safety in human–robot interaction, covering various aspects of the most significant injury mechanisms. In order to quantify the potential injury risk emanating from such a manipulator, impact tests with the DLR-Lightweight Robot III were carried out using standard automobile crash test facilities at the German Automobile Club (ADAC). Based on these tests, several industrial robots of different weight have been evaluated and the influence of the robot mass and velocity have been investigated. The evaluated non-constrained impacts would only partially capture the nature of human–robot safety. A possibly constrained environment and its effect on the resulting human injuries are discussed and evaluated from different perspectives. As well as such impact tests and simulations, we have analyzed the problem of the quasi-static constrained impact, which could pose a serious threat to the human even for low-inertia robots under certain circumstances. Finally, possible injuries relevant in robotics are summarized and systematically classified. Physical Human-Robot Interaction, Simulation, Flexible Arms, Mechanics, Design and Control, Force Control The International  No. 00, Xxxxxxxx 2009,  2009.  http://www.sagepub.co.uk/journalsPermissions.nav Figures 1–10, 12–18 appear in color online: http://ijr.sagepub.com
Low-Cost, High Resolution X-Band Laboratory Radar System for Synthetic Aperture Radar Applications

Entry into the field of radar cross section measurements or synthetic aperture radar (SAR) algorithm development is often difficult due to the cost of high-end precision pulsed IF or other precision radar test instruments. A low-cost entry-level alternative was developed in order to provide an intermediate step between high-end high precision radar systems and ad-hoc spare parts systems. The system developed is a frequency modulated continuous wave radar utilizing homodyne radar architecture. Transmit chirp covers 8 GHz to 10.5 GHz with 18 dBm of transmit power. Due to the fairly wide transmit bandwidth; this radar is capable of better than 12 inches of range resolution. The dynamic range of this system was measured to be 60 dB. Such a low-cost, high resolution X-band laboratory radar system could be utilized as a linear rail SAR, inverse SAR, or for motion compensation experiments
Ninja on a Plane: Automatic Discovery of Physical Planes for Augmented Reality Using Visual SLAM

Most work in visual augmented reality (AR) employs predefined markers or models that simplify the algorithms needed for sensor positioning and augmentation but at the cost of imposing restrictions on the areas of operation and on interactivity. This paper presents a simple game in which an AR agent has to navigate using real planar surfaces on objects that are dynamically added to an unprepared environment.  even if they are outside the camera's current field of view.
Marketing actions can modulate neural representations of experienced pleasantness.

Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates.
A Dual-Band Circularly Polarized Aperture-Coupled Stacked Microstrip Antenna for Global Positioning Satellite

This paper describes the design and testing of an aperture-coupled circularly polarized antenna for global positioning satellite (GPS) applications. The antenna operates at both the L1 and L2 frequencies of 1575 and 1227 MHz, which is required for differential GPS systems in order to provide maximum positioning accuracy. Electrical performance, lowprofile, and cost were equally important requirements for this antenna. The design procedure is discussed, and measured results are presented. Results from a manufacturing sensitivity analysis are also included.
Planes Detection for Robust Localization and Mapping in RGB-D SLAM Systems

This paper describes an extension of the popular simultaneous localisation and mapping system (RGB-D SLAM) introduced by Endres et al. In . RGB-D SLAM uses a moving RGB-D sensor (i.e. A Kinect) to incrementally produce a graph-based camera pose trajectory along with a global 3D map of the environment composed of potentially millions of 3D points. The goal here is to produce a global 3D map which is not only composed of millions of 3D points but also 3D planes as indoor scenes are mostly composed of planar features such as floor, walls, desks and other furnitures. These planes are directly detected within the depth images provided by the sensor and therefore tend to "regularize" the noise and missing data in depth images during the whole RGB-D SLAM process. This process represents a first step towards a more semantic approach of RGB-D SLAM.
The Case Research Strategy in Studies of Information Systems

This article defines and discusses one of these qualitative methods--the case research strategy. Suggestions are provided for researchers who wish to undertake research employing this approach. Criteria for the evaluation of case research are established and several characteristics useful for categorizing the studies are identified. A sample of papers drawn from information systems journals is reviewed. The paper concludes with examples of research areas that are particularly wellsuited to investigation using the case research approach.
Brain basis of early parent-infant interactions: psychology, physiology, and in vivo functional neuroimaging studies.

Parenting behavior critically shapes human infants' current and future behavior. The parent-infant relationship provides infants with their first social experiences, forming templates of what they can expect from others and how to best meet others' expectations. In this review, we focus on the neurobiology of parenting behavior, including our own functional magnetic resonance imaging (fMRI) brain imaging experiments of parents. We begin with a discussion of background, perspectives and caveats for considering the neurobiology of parent-infant relationships. Then, we discuss aspects of the psychology of parenting that are significantly motivating some of the more basic neuroscience research. Following that, we discuss some of the neurohormones that are important for the regulation of social bonding, and the dysregulation of parenting with cocaine abuse. Then, we review the brain circuitry underlying parenting, proceeding from relevant rodent and nonhuman primate research to human work. Finally, we focus on a study-by-study review of functional neuroimaging studies in humans. Taken together, this research suggests that networks of highly conserved hypothalamic-midbrain-limbic-paralimbic-cortical circuits act in concert to support aspects of parent response to infants, including the emotion, attention, motivation, empathy, decision-making and other thinking that are required to navigate the complexities of parenting. Specifically, infant stimuli activate basal forebrain regions, which regulate brain circuits that handle specific nurturing and caregiving responses and activate the brain's more general circuitry for handling emotions, motivation, attention, and empathy--all of which are crucial for effective parenting. We argue that an integrated understanding of the brain basis of parenting has profound implications for mental health.
The basic building blocks and evolution of CRISPR-CAS systems.

CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) is an adaptive immunity system in bacteria and archaea that functions via a distinct self/non-self recognition mechanism that involves unique spacers homologous with viral or plasmid DNA and integrated into the CRISPR loci. Most of the Cas proteins evolve under relaxed purifying selection and some underwent dramatic structural rearrangements during evolution. In many cases, CRISPR-Cas system components are replaced either by homologous or by analogous proteins or domains in some bacterial and archaeal lineages. However, recent advances in comparative sequence analysis, structural studies and experimental data suggest that, despite this remarkable evolutionary plasticity, all CRISPR-Cas systems employ the same architectural and functional principles, and given the conservation of the principal building blocks, share a common ancestry. We review recent advances in the understanding of the evolution and organization of CRISPR-Cas systems. Among other developments, we describe for the first time a group of archaeal cas1 gene homologues that are not associated with CRISPR-Cas loci and are predicted to be involved in functions other than adaptive immunity.
Stakeholders in Explainable AI

There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable. However, there is no general consensus over what is meant by ‘explainable’ and ‘interpretable’. In this paper, we argue that this lack of consensus is due to there being several distinct stakeholder communities. We note that, while the concerns of the individual communities are broadly compatible, they are not identical, which gives rise to different intents and requirements for explainability/interpretability. We use the software engineering distinction between validation and verification, and the epistemological distinctions between knowns/unknowns, to tease apart the concerns of the stakeholder communities and highlight the areas where their foci overlap or diverge. It is not the purpose of the authors of this paper to ‘take sides’ — we count ourselves as members, to varying degrees, of multiple communities — but rather to help disambiguate what stakeholders mean when they ask ‘Why?’ of an AI.
Health Information Management System for Elderly Health Sector: A Qualitative Study in Iran.

There are increasing change and development of information in healthcare systems. Given the increase in aging population, managers are in need of true and timely information when making decision.



The aim of this study was to investigate the current status of the health information management system for the elderly health sector in Iran.



This qualitative study was conducted in two steps. In the first step, required documents for administrative managers were collected using the data gathering form and observed and reviewed by the researcher. In the second step, using an interview guide, the required information was gathered through interviewing experts and faculty members. The convenience, purposeful and snowball sampling methods were applied to select interviewees and the sampling continued until reaching the data saturation point. Finally, notes and interviews were transcribed and content analysis was used to analyze them.



The results of the study showed that there was a health information management system for the elderly health sector in Iran. However, in all primary health care centers the documentation of data was done manually; the data flow was not automated; and the analysis and reporting of data are also manually. Eventually, decision makers are provided with delayed information.



It is suggested that the steward of health in Iran, the ministry of health, develops an appropriate infrastructure and finally puts a high priority on the implementation of the health information management system for elderly health sector in Iran.
A Game of Things: Strategic Allocation of Security Resources for IoT

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost, as many IoT devices may not have sufficient resources to host expensive security tools. In this paper, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security tools. We model our problem according to game theory, and provide a Pareto-optimal solution, in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack, are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies.
Securing digital identities in the cloud by selecting an apposite Federated Identity Management from SAML, OAuth and OpenID Connect

Access to computer systems and the information held on them, be it commercially or personally sensitive, is naturally, strictly controlled by both legal and technical security measures. One such method is digital identity, which is used to authenticate and authorize users to provide access to IT infrastructure to perform official, financial or sensitive operations within organisations. However, transmitting and sharing this sensitive information with other organisations over insecure channels always poses a significant security and privacy risk. An example of an effective solution to this problem is the Federated Identity Management (FIdM) standard adopted in the cloud environment. The FIdM standard is used to authenticate and authorize users across multiple organisations to obtain access to their networks and resources without transmitting sensitive information to other organisations. Using the same authentication and authorization details among multiple organisations in one federated group, it protects the identities and credentials of users in the group. This protection is a balance, mitigating security risk whilst maintaining a positive experience for users. Three of the most popular FIdM standards are Security Assertion Markup Language (SAML), Open Authentication (OAuth), and OpenID Connect (OIDC). This paper presents an assessment of these standards considering their architectural design, working, security strength and security vulnerability, to cognise and ascertain effective usages to protect digital identities and credentials. Firstly, it explains the architectural design and working of these standards. Secondly, it proposes several assessment criteria and compares functionalities of these standards based on the proposed criteria. Finally, it presents a comprehensive analysis of their security vulnerabilities to aid in selecting an apposite FIdM. This analysis of security vulnerabilities is of great significance because their improper or erroneous deployment may be exploited for attacks.
Understanding inverse document frequency: on theoretical arguments for IDF

The term weighting function known as IDF was proposed in 1972, and has since been extremely widely used, usually as part of a TF*IDF function. It is often described as a heuristic, and many papers have been written (some based on Shannon’s Information Theory) seeking to establish some theoretical basis for it. Some of these attempts are reviewed, and it is shown that the Information Theory approaches are problematic, but that there are good theoretical justifications of both IDF and TF*IDF in traditional probabilistic model of information retrieval.
A COMPRESSED SENSING VIEW OF UNSUPERVISED TEXT EMBEDDINGS , BAG-OF-n-GRAMS , AND LSTM S

Low-dimensional vector embeddings, computed using LSTMs or simpler techniques, are a popular approach for capturing the “meaning” of text and a form of unsupervised learning useful for downstream tasks. However, their power is not theoretically understood. The current paper derives formal understanding by looking at the subcase of linear embedding schemes. Using the theory of compressed sensing we show that representations combining the constituent word vectors are essentially information-preserving linear measurements of Bag-of-n-Grams (BonG) representations of text. This leads to a new theoretical result about LSTMs: low-dimensional embeddings derived from a low-memory LSTM are provably at least as powerful on classification tasks, up to small error, as a linear classifier over BonG vectors, a result that extensive empirical work has thus far been unable to show. Our experiments support these theoretical findings and establish strong, simple, and unsupervised baselines on standard benchmarks that in some cases are state of the art among word-level methods. We also show a surprising new property of embeddings such as GloVe and word2vec: they form a good sensing matrix for text that is more efficient than random matrices, the standard sparse recovery tool, which may explain why they lead to better representations in practice.