nils-herrmann/clean_scirepeval huggingface dataset:
Extracting semantic relations from query logs

In this paper we study a large query log of more than twenty million queries with the goal of extracting the semantic relations that are implicitly captured in the actions of users submitting queries and clicking answers. Previous query log analyses were mostly done with just the queries and not the actions that followed after them. We first propose a novel way to represent queries in a vector space based on a graph derived from the query-click bipartite graph. We then analyze the graph produced by our query log, showing that it is less sparse than previous results suggested, and that almost all the measures of these graphs follow power laws, shedding some light on the searching user behavior as well as on the distribution of topics that people want in the Web. The representation we introduce allows to infer interesting semantic relationships between queries. Second, we provide an experimental analysis on the quality of these relations, showing that most of them are relevant. Finally we sketch an application that detects multitopical URLs.
BER of IEEE 802.11ad OFDM radios vs. carrier frequency in real 60 GHz indoor channels

Multi-gigabit 60 GHz radios are expected to match QoS requirements of modern multimedia applications. Several published standards were defined based on performance evaluations over standard channel models. Unfortunately, those models, and most models available in the literature, do not take into account the behavior of 60 GHz channels at different carrier frequencies, thus no guidelines are provided for the selection of the best suitable frequency band for a given service. This paper analyzes the impact of changes in multipath profiles, due to both frequency and distance, on the BER performance achieved by IEEE 802.11ad 60 GHz radios. Our analysis is based on real experimental channel impulse responses recorded through an indoor measurement campaign in seven sub-bands from 54 to 65 GHz with a break at 60 GHz at distances from 1 to 5 m. The small-scale fading is modeled by Rician distributions with K-factors extracted from experimental data, which are shown to give good agreement with the empirical distributions. A strong dependence of performance on both frequency and distance due to the sole multipath is observed, which calls for an appropriate selection of the best suitable frequency band according to the service required by the current session over the 802.11ad link.
Methylotrophic methanogenic Thermoplasmata implicated in reduced methane emissions from bovine rumen.

Rumen methanogens are major sources of anthropogenic methane emissions, and these archaea are targets in strategies aimed at reducing methane emissions. Here we show that the poorly characterised Thermoplasmata archaea in bovine rumen are methylotrophic methanogens and that they are reduced upon dietary supplementation with rapeseed oil in lactating cows. In a metatranscriptomic survey, Thermoplasmata 16S rRNA and methyl-coenzyme M reductase (mcr) transcripts decreased concomitantly with mRNAs of enzymes involved in methanogenesis from methylamines that were among the most abundant archaeal transcripts, indicating that these Thermoplasmata degrade methylamines. Their methylotrophic methanogenic lifestyle was corroborated by in vitro incubations, showing enhanced growth of these organisms upon methylamine supplementation paralleled by elevated methane production. The Thermoplasmata have a high potential as target in future strategies to mitigate methane emissions from ruminant livestock. Our findings and the findings of others also indicate a wider distribution of methanogens than previously anticipated.
PIkit: A New Kernel-Independent Processor-Interconnect Rootkit

The goal of rootkit is often to hide malicious software running on a compromised machine. While there has been significant amount of research done on different rootkits, we describe a new type of rootkit that is kernel-independent – i.e., no aspect of the kernel is modified and no code is added to the kernel address space to install the rootkit. In this work, we present PIkit – Processor-Interconnect rootkit that exploits the vulnerable hardware features within multi-socket servers that are commonly used in datacenters and high-performance computing. In particular, PIkit exploits the DRAM address mapping table structure that determines the destination node of a memory request packet in the processorinterconnect. By modifying this mapping table appropriately, PIkit enables access to victim’s memory address region without proper permission. Once PIkit is installed, only user-level code or payload is needed to carry out malicious activities. The malicious payload mostly consists of memory read and/or write instructions that appear like “normal” user-space memory accesses and it becomes very difficult to detect such malicious payload. We describe the design and implementation of PIkit on both an AMD and an Intel x86 multi-socket servers that are commonly used. We discuss different malicious activities possible with PIkit and limitations of PIkit, as well as possible software and hardware solutions to PIkit.
Towards a Visual Turing Challenge

As language and visual understanding by machines progresse s rapidly, we are observing an increasing interest in holistic architectures t hat ightly interlink both modalities in a joint learning and inference process. This t rend has allowed the community to progress towards more challenging and open tas ks and refueled the hope at achieving the old AI dream of building machines that c ould pass a turing test in open domains. In order to steadily make progress towa rds this goal, we realize that quantifying performance becomes increasingly dif ficult. Therefore we ask how we can precisely define such challenges and how we can eval uate different algorithms on this open tasks? In this paper, we summarize an d discuss such challenges as well as try to give answers where appropriate optio ns are available in the literature. We exemplify some of the solutions on a recently presented dataset of question-answering task based on real-world indoor images that establishes a visual turing challenge. Finally, we argue despite the succes s of unique ground-truth annotation, we likely have to step away from carefully curat ed dataset and rather rely on ’social consensus’ as the main driving force to creat e suitable benchmarks. Providing coverage in this inherently ambiguous output spa ce is an emerging challenge that we face in order to make quantifiable progress in th is area.
Extraction with Semi-supervised Neural Tagging

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.
Design of reconfigurable fractal antenna using pin diode switch for wireless applications

In this article, a frequency reconfigurable fractal patch antenna using pin diodes is proposed and studied. The antenna structure has been designed on FR-4 low-cost substrate material of relative permittivity εr = 4.4, with a compact volume of 30×30×0.8 mm3. The bandwidth and resonance frequency of the antenna design will be increased when we exploit the fractal iteration on the patch antenna. This antenna covers some service bands such as: WiMAX, m-WiMAX, WLAN, C-band and X band applications. The simulation of the proposed antenna is carried out using CST microwave studio. The radiation pattern and S parameter are further presented and discussed.
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images

We propose a simple and efficient method for exploiting synthetic images when training a Deep Network to predict a 3D pose from an image. The ability of using synthetic images for training a Deep Network is extremely valuable as it is easy to create a virtually infinite training set made of such images, while capturing and annotating real images can be very cumbersome. However, synthetic images do not resemble real images exactly, and using them for training can result in suboptimal performance. It was recently shown that for exemplar-based approaches, it is possible to learn a mapping from the exemplar representations of real images to the exemplar representations of synthetic images. In this paper, we show that this approach is more general, and that a network can also be applied after the mapping to infer a 3D pose: At run-time, given a real image of the target object, we first compute the features for the image, map them to the feature space of synthetic images, and finally use the resulting features as input to another network which predicts the 3D pose. Since this network can be trained very effectively by using synthetic images, it performs very well in practice, and inference is faster and more accurate than with an exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for 3D object pose estimation from color images, and the NYU dataset for 3D hand pose estimation from depth maps. We show that it allows us to outperform the state-of-the-art on both datasets.
Quantum dot-fluorescent protein FRET probes for sensing intracellular pH.

Intracellular pH (pH(i)) plays a critical role in the physiological and pathophysiological processes of cells, and fluorescence imaging using pH-sensitive indicators provides a powerful tool to assess the pH(i) of intact cells and subcellular compartments. Here we describe a nanoparticle-based ratiometric pH sensor, comprising a bright and photostable semiconductor quantum dot (QD) and pH-sensitive fluorescent proteins (FPs), exhibiting dramatically improved sensitivity and photostability compared to BCECF, the most widely used fluorescent dye for pH imaging. We found that Förster resonance energy transfer between the QD and multiple FPs modulates the FP/QD emission ratio, exhibiting a >12-fold change between pH 6 and 8. The modularity of the probe enables customization to specific biological applications through genetic engineering of the FPs, as illustrated by the altered pH range of the probe through mutagenesis of the fluorescent protein. The QD-FP probes facilitate visualization of the acidification of endosomes in living cells following polyarginine-mediated uptake. These probes have the potential to enjoy a wide range of intracellular pH imaging applications that may not be feasible with fluorescent proteins or organic fluorophores alone.
An Entropy Analysis Based Intrusion Detection System for Controller Area Network in Vehicles

Dozens of Electronic Control Units (ECUs) can be found on modern vehicles for safety and driving assistance. These ECUs also introduce new security vulnerabilities as recent attacks have been reported by plugging the in-vehicle system or through wireless access. In this paper, we focus on the security of the Controller Area Network (CAN), which is a standard for communication among ECUs. CAN bus by design does not have sufficient security features to protect it from insider or outsider attacks. Intrusion detection system (IDS) is one of the most effective ways to enhance vehicle security on the insecure CAN bus protocol. We propose a new IDS based on the entropy of the identifier bits in CAN messages. The key observation is that all the known CAN message injection attacks need to alter the CAN ID bits and analyzing the entropy of such bits can be an effective way to detect those attacks. We collected real CAN messages from a vehicle (2016 Ford Fusion) and performed simulated message injection attacks. The experimental results showed that our entropy based IDS can successfully detect all the injection attacks without disrupting the communication on CAN.
Mottling score predicts survival in septic shock

Experimental and clinical studies have identified a crucial role of microcirculation impairment in severe infections. We hypothesized that mottling, a sign of microcirculation alterations, was correlated to survival during septic shock. We conducted a prospective observational study in a tertiary teaching hospital. All consecutive patients with septic shock were included during a 7-month period. After initial resuscitation, we recorded hemodynamic parameters and analyzed their predictive value on mortality. The mottling score (from 0 to 5), based on mottling area extension from the knees to the periphery, was very reproducible, with an excellent agreement between independent observers [kappa = 0.87, 95% CI (0.72–0.97)]. Sixty patients were included. The SOFA score was 11.5 (8.5–14.5), SAPS II was 59 (45–71) and the 14-day mortality rate 45% [95% CI (33–58)]. Six hours after inclusion, oliguria [OR 10.8 95% CI (2.9, 52.8), p = 0.001], arterial lactate level [<1.5 OR 1; between 1.5 and 3 OR 3.8 (0.7–29.5); >3 OR 9.6 (2.1–70.6), p = 0.01] and mottling score [score 0–1 OR 1; score 2–3 OR 16, 95% CI (4–81); score 4–5 OR 74, 95% CI (11–1,568), p < 0.0001] were strongly associated with 14-day mortality, whereas the mean arterial pressure, central venous pressure and cardiac index were not. The higher the mottling score was, the earlier death occurred (p < 0.0001). Patients whose mottling score decreased during the resuscitation period had a better prognosis (14-day mortality 77 vs. 12%, p = 0.0005). The mottling score is reproducible and easy to evaluate at the bedside. The mottling score as well as its variation during resuscitation is a strong predictor of 14-day survival in patients with septic shock.
Systematic literature review on user interfaces of autonomous cars: Liabilities and responsibilities

There is a growing interest in developing user interfaces of self-driving cars or automated vehicles which are also known as driverless cars. The literature on Self-driving cars has steadily been growing. However, there has not been any effort to systematically select, review, and synthesize the literature on this topic. A systematic literature review is conducted that reports different perspectives of autonomous cars like liabilities and responsibilities of these Self-driving cars in case of accidents, ethics related to decision making, potentials and drawbacks of using Self-driving cars, people's perspective about them and challenges faced by them. Data has been extracted from different papers to identify various aspects of self-driving cars as discussed above. More recent and current findings are considered to deal with these research issues. Review's and findings presented in this paper are expected to help researchers and practitioners to understand the Self-driving cars from various perspectives.
Geriatric Infectious Diseases: Current Concepts on Diagnosis and Management.

New information on infectious diseases in older adults has become available in the past 20 years. In this review, in-depth discussions on the general problem of geriatric infectious diseases (epidemiology, pathogenesis, age-related host defenses, clinical manifestations, diagnostic approach); diagnosis and management of bacterial pneumonia, urinary tract infection, and Clostridium difficile infection; and the unique challenges of diagnosing and managing infections in a long-term care setting are presented.
NISAN: network information service for anonymization networks

Network information distribution is a fundamental service for any anonymization network. Even though anonymization and information distribution about the network are two orthogonal issues, the design of the distribution service has a direct impact on the anonymization. Requiring each node to know about all other nodes in the network (as in Tor and AN.ON -- the most popular anonymization networks) limits scalability and offers a playground for intersection attacks. The distributed designs existing so far fail to meet security requirements and have therefore not been accepted in real networks.
 In this paper, we combine probabilistic analysis and simulation to explore DHT-based approaches for distributing network information in anonymization networks. Based on our findings we introduce NISAN, a novel approach that tries to scalably overcome known security problems. It allows for selecting nodes uniformly at random from the full set of all available peers, while each of the nodes has only limited knowledge about the network. We show that our scheme has properties similar to a centralized directory in terms of preventing malicious nodes from biasing the path selection. This is done, however, without requiring to trust any third party. At the same time our approach provides high scalability and adequate performance. Additionally, we analyze different design choices and come up with diverse proposals depending on the attacker model. The proposed combination of security, scalability, and simplicity, to the best of our knowledge, is not available in any other existing network information distribution system.
Privacy Protection: p-Sensitive k-Anonymity Property

In this paper, we introduce a new privacy protection property called p-sensitive k-anonymity. The existing kanonymity property protects against identity disclosure, but it fails to protect against attribute disclosure. The new introduced privacy model avoids this shortcoming. Two necessary conditions to achieve p-sensitive kanonymity property are presented, and used in developing algorithms to create masked microdata with p-sensitive k-anonymity property using generalization and suppression.
Dual-band, dual-polarization, interleaved cross-dipole and cavity-backed disc elements phased array antenna

In ship, submarine, or airborne satellite communication or multi-function radar operations, dual-band phased array antennas with dual-linear or circular polarizations are needed. We present a dual-band/dual-polarization phased array design using a interleaved cross-dipole radiator and a cavity-backed disk radiator in the same lattice structure. The low band radiator is a disk radiator sitting on top of a dielectric puck in a cavity and the high band radiator is the cross dipole element printed on a low-K dielectric layer above the disk-cavity plane. Dual-linear or circular polarizations can be achieved using these two elements to cover two separate bands with about a 2:1 ratio. This is a relatively low profile, compact and rigid array. Good impedance match for both bands can be obtained over a wide scan volume.
A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method

In this paper, we propose four new variants of the backpropagation algorithm to improve the generalization ability for feedforward neural networks. The basic idea of these methods stems from the Group Lasso concept which deals with the variable selection problem at the group level. There are two main drawbacks when the Group Lasso penalty has been directly employed during network training. They are numerical oscillations and theoretical challenges in computing the gradients at the origin. To overcome these obstacles, smoothing functions have then been introduced by approximating the Group Lasso penalty. Numerical experiments for classification and regression problems demonstrate that the proposed algorithms perform better than the other three classical penalization methods, Weight Decay, Weight Elimination, and Approximate Smoother, on both generalization and pruning efficiency. In addition, detailed simulations based on a specific data set have been performed to compare with some other common pruning strategies, which verify the advantages of the proposed algorithm. The pruning abilities of the proposed strategy have been investigated in detail for a relatively large data set, MNIST, in terms of various smoothing approximation cases.
Contextual Code Completion Using Machine Learning

Large projects such as kernels, drivers and libraries follow a code style, and have recurring patterns. In this project, we explore learning based code recommendation, to use the project context and give meaningful suggestions. Using word vectors to model code tokens, and neural network based learning techniques, we are able to capture interesting patterns, and predict code that that cannot be predicted by a simple grammar and syntax based approach as in conventional IDEs. We achieve a total prediction accuracy of 56.0% on Linux kernel, a C project, and 40.6% on Twisted, a Python networking library.
Today's Tidbit: VoltDB

TO CLOUD TIDBITS! In each issue, I'll be looking at a different “tidbit” of technology that I consider unique or eye-catching and of particular interest to IEEE Cloud Computing readers. Today's tidbit is VoltDB, a new cloud database. This system caught my eye for several reasons. First, it's the latest database designed by Michael Stonebraker, the database pioneer best known for Ingres, PostgreSQL, Illustra, Streambase, and more recently, Vertica. But interestingly, in this goaround, Stonebraker declared that he has thrown “all previous database architecture out the window” and “started over with a complete rewrite.”1 What's resulted is something totally different from every other database-including all the columnand table-oriented NoSQL systems. Moreover, VoltDB claims a 50 to 100x speed improvement over other relational database management systems (RDBMSs) and NoSQL systems. It sounds too good to be true. What we have is nothing short of a whole class of SQL, as compared to the “NoSQL” compromises detailed above. This “total rearchitecture,” called NewSQL, supports 100 percent in memory operation, supports SQL and stored procedures, and has a loosely coupled scale-out capability perfectly matched to cloud computing platforms. Wait a minute! That doesn't sound possible. That's precisely why I thought it made for a perfect tidbit.
Genomes on the Edge: Programmed Genome Instability in Ciliates

Ciliates are an ancient and diverse group of microbial eukaryotes that have emerged as powerful models for RNA-mediated epigenetic inheritance. They possess extensive sets of both tiny and long noncoding RNAs that, together with a suite of proteins that includes transposases, orchestrate a broad cascade of genome rearrangements during somatic nuclear development. This Review emphasizes three important themes: the remarkable role of RNA in shaping genome structure, recent discoveries that unify many deeply diverged ciliate genetic systems, and a surprising evolutionary "sign change" in the role of small RNAs between major species groups.
Monocular 3D Human Pose Estimation by Predicting Depth on Joints

This paper aims at estimating full-body 3D human poses from monocular images of which the biggest challenge is the inherent ambiguity introduced by lifting the 2D pose into 3D space. We propose a novel framework focusing on reducing this ambiguity by predicting the depth of human joints based on 2D human joint locations and body part images. Our approach is built on a two-level hierarchy of Long Short-Term Memory (LSTM) Networks which can be trained end-to-end. The first level consists of two components: 1) a skeleton-LSTM which learns the depth information from global human skeleton features; 2) a patch-LSTM which utilizes the local image evidence around joint locations. The both networks have tree structure defined on the kinematic relation of human skeleton, thus the information at different joints is broadcast through the whole skeleton in a top-down fashion. The two networks are first pre-trained separately on different data sources and then aggregated in the second layer for final depth prediction. The empirical e-valuation on Human3.6M and HHOI dataset demonstrates the advantage of combining global 2D skeleton and local image patches for depth prediction, and our superior quantitative and qualitative performance relative to state-of-the-art methods.