nils-herrmann/clean_scirepeval huggingface dataset:
Markov Chain-Based Trust Model for Analyzing Trust Value in Distributed Multicasting Mobile Ad Hoc Networks

The multicast mobile ad-hoc network (multicast MANET) is proposed for a sender sending packets to several receivers through a multicast session. Since members of a multicast group changes frequently, the issue of supporting secure authentication and authorization in multicast MANET becomes more critical than the network provided a fixed central authentication (CA) server. This work thus proposes a two-step secure authentication approach for the multicast MANET. First, a Markov chain trust model is proposed to determine the trust value (TV) for each one-hop neighbors. A nodepsilas trust value is analyzed from its previous trust manner in this group. The proposed trust model is proven as an ergodic continuous-time Markov chain model. Second, a node with the highest TV of a group will be selected as the CA server. For increasing reliability, the node with the second highest TV will be selected as the backup CA server that will take over CA when CA fails. The procedures of the secure authentication for group management are detailed. The security analysis of each procedure is analyzed to guarantee that the proposed approach achieves a secure reliable authentication in multicast MANET. Numerical results indicate that the analytical trust value of each mobile node is very close to that of simulation under various situations. The speed of the convergence of the analytical trust value shows that the analytical results are independent of initial values and the trust classes.
External Knowledge and Query Strategies in Active Learning: a Study in Clinical Information Extraction

This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that (1) amongst existing query strategies, the ones based on the classification model's confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and (2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.
Application of neural networks to inverse lens distortion modelling

The accurate and quick modelling of inverse lens distortion to rectify images or predict the image coordinates of real world objects has long been a challenge. This work investigates the use of artificial neural networks to perform this modelling. Several architectures are investigated and multiple methods of training the networks are used to achieve the best results. The error is expressed as a physical displacement on the imaging chip so that a fair comparison can be made between other published results which are based on cameras with different resolutions. It is shown that the novel application of locally optimised neural networks to residual lens calibration data yields an inverse distortion modelling that is highly competitive with prior published results.
LITE Kernel RDMA Support for Datacenter Applications

Recently, there is an increasing interest in building data-center applications with RDMA because of its low-latency, high-throughput, and low-CPU-utilization benefits. However, RDMA is not readily suitable for datacenter applications. It lacks a flexible, high-level abstraction; its performance does not scale; and it does not provide resource sharing or flexible protection. Because of these issues, it is difficult to build RDMA-based applications and to exploit RDMA's performance benefits.
 To solve these issues, we built LITE, a Local Indirection TiEr for RDMA in the Linux kernel that virtualizes native RDMA into a flexible, high-level, easy-to-use abstraction and allows applications to safely share resources. Despite the widely-held belief that kernel bypassing is essential to RDMA's low-latency performance, we show that using a kernel-level indirection can achieve both flexibility and low-latency, scalable performance at the same time. To demonstrate the benefits of LITE, we developed several popular datacenter applications on LITE, including a graph engine, a MapReduce system, a Distributed Shared Memory system, and a distributed atomic logging system. These systems are easy to build and deliver good performance. For example, our implementation of PowerGraph uses only 20 lines of LITE code, while outperforming PowerGraph by 3.5x to 5.6x.
Simulink based performance analysis of lead acid batteries with the variation of load current and temperature

Storage device in renewable energy applications plays an important role for efficient operation. For solar PV applications, storage device is an integral part during post sun shine hours to supply power to the load. For Solar Home System (SHS), approximately 60% of the total cost is due to storage battery and the battery needs replacement in every five years or so. If the performance of the battery can be enhanced, not only the cost of energy will come down appreciably but the reliability of the system will improve. Battery is needed in PV system to store the excess energy from the sun and release the power to the system again when it is needed. In other words, to enhance a system performance we have to increase the battery performance. In this paper, the parameters which play important role for optimum performance has been studied. The major two parameters that affect the battery performance are load current and temperature. It has been found that, when the discharge current is high, battery capacity reduces and the vice-versa. Again in case of temperature, when temperature increases more than the rated standard value of 25°C, capacity increases but the life time of the battery decreases. On the other hand, when the ambient temperature goes down, capacity of battery decreases but lifetime of the battery is increased. To analyse the effect of temperature and load current on battery performance, the standard equivalent circuit of battery is considered first and then the equivalent circuit is simulated using Simulink under MATLAB environment.
Dynamic topic models

A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed to carry out approximate posterior inference over the latent topics. In addition to giving quantitative, predictive models of a sequential corpus, dynamic topic models provide a qualitative window into the contents of a large document collection. The models are demonstrated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.
Detection of Fragmented Rectangular Enclosures in Very High Resolution Remote Sensing Images

We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance.
MDCube: a high performance network structure for modular data center interconnection

Shipping-container-based data centers have been introduced as building blocks for constructing mega-data centers. However, it is a challenge on how to interconnect those containers together with reasonable cost and cabling complexity, due to the fact that a mega-data center can have hundreds or even thousands of containers and the aggregate bandwidth among containers can easily reach tera-bit per second. As a new inner-container server-centric network architecture,  interconnects thousands of servers inside a container and provides high bandwidth support for typical traffic patterns. It naturally serves as a building block for mega-data center.
 In this paper, we propose MDCube, a high performance interconnection structure to scale BCube-based containers to mega-data centers. MDCube uses the high-speed uplink interfaces of the commodity switches in BCube containers to build the inter-container structure, reducing the cabling complexity greatly. MDCube puts its inter- and inner-container routing intelligences solely into servers to handle load-balance and fault-tolerance, thus directly leverages commodity instead of high-end switches to scale. Through analysis, we prove that MDCube has low diameter and high capacity. Both simulations and experiments in our testbed demonstrate the fault-tolerance and high network capacity of MDCube.
View security as the basis for data warehouse security

Access permissions in a data warehouse are currently managed in a separate world from the sources’ policies. The consequences are inconsistencies, slow response to change, and wasted administrative work. We present a different approach, which treats the sources’ exported tables and the warehouse as part of the same distributed database. Our main result is a way to control derived products by extending SQL grants rather than creating entirely new mechanisms. We provide a powerful, sound inference theory that derives permissions on warehouse tables (both materialized and virtual), making the system easier to administer and its applications more robust. We also propose a new permission construct suitable for views that filter data from mutually-suspicious parties.
Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification

The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.
Ubiquitous Smart Home System Using Android Application

This paper presents a flexible standalone, low cost smart home system, which is based on the Android app communicating with the micro-web server providing more than the switching functionalities. The Arduino Ethernet is used to eliminate the use of a personal computer (PC) keeping the cost of the overall system to a minimum while voice activation is incorporated for switching functionalities. Devices such as light switches, power plugs, temperature sensors, humidity sensors, current sensors, intrusion detection sensors, smoke/gas sensors and sirens have been integrated in the system to demonstrate the feasibility and effectiveness of the proposed smart home system. The smart home app is tested and it is able successfully perform the smart home operations such as switching functionalities, automatic environmental control and intrusion detection, in the later case where an email is generated and the siren goes on.
Feature-based aggregation and deep reinforcement learning: a survey and some new implementations

In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller “ aggregate ” Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural network-based reinforcement learning, thereby potentially leading to more effective policy improvement.
A cost analysis of RDL-first and mold-first fan-out wafer level packaging

Industry interest in fan-out wafer level packaging has been increasing steadily. This is partially because of the potential behind panel-based fan-out packaging, and partially because wafer level packaging may be fulfilling needs not met by interposer-based and 3D technologies. As is the case with any technology, there are variations within the fan-out wafer level packaging category. This paper will focus on two of the primary processes: RDL-first and mold-first (also called chip-first). While these process flows have many of the same activities, those activities are carried out in a different order, and there are a few key steps that will differ. Each process has unique challenges and benefits, and these will be explored and analyzed.
Compressed Nonparametric Language Modelling

Hierarchical Pitman-Yor Process priors are compelling methods for learning language models, outperforming point-estimate based methods. However, these models remain unpopular due to computational and statistical inference issues, such as memory and time usage, as well as poor mixing of sampler. In this work we propose a novel framework which represents the HPYP model compactly using compressed suffix trees. Then, we develop an efficient approximate inference scheme in this framework that has a much lower memory footprint compared to full HPYP and is fast in the inference time. The experimental results illustrate that our model can be built on significantly larger datasets compared to previous HPYP models, while being several orders of magnitudes smaller, fast for training and inference, and outperforming the perplexity of the state-of-the-art Modified Kneser-Ney countbased LM smoothing by up to 15%.
Diverse subpopulations of vesicles secreted by different intracellular mechanisms are present in exosome preparations obtained by differential ultracentrifugation

Exosomes are extracellular vesicles of 50 to 100 nm in diameter, released by many cell types. Exosomes are formed inside the cell in intracellular endosomal compartments and are secreted upon fusion of these compartments with the plasma membrane. Cells also secrete other types of membrane vesicles, for instance, by outward budding from the plasma membrane, and although some of them clearly differ from exosomes by their structural features (larger size), others are possibly more difficult to separate. Here, using Rab27a inhibition to modulate exosome secretion, we show the existence of at least 2 distinct populations of vesicles after purification by classical ultracentrifugation from mouse tumor cell conditioned medium. Rab27a inhibition lead to decreased vesicular secretion of some conventional markers of exosomes (CD63, Tsg101, Alix and Hsc70) but did not affect secretion of others (CD9 and Mfge8). By electron microscopy, CD9 was observed on vesicles of various sizes, ranging from 30 nm to more than 150 nm in diameter. Flotation onto sucrose gradients showed different proportions of CD63, CD9 and Mfge8 not only in fractions of densities classically described for exosomes (around 1.15 g/ml) but also in fractions of densities over 1.20 g/ml, indicating the presence of heterogenous vesicle populations. CD9 and Mfge8 were also found in large vesicles pelleted at low speed and can thus not be considered as specific components of endosome-derived vesicles. We propose that the most commonly used protocols for exosome preparations co-purify vesicles from endosomal and other origins, possibly the plasma membrane. Future work will be required to improve techniques for accurate purification and characterization of the different populations of extracellular vesicles.
A Novel Driving and Protection Circuit for Reverse-Blocking IGBT Used in Matrix Converter

A novel drive and protection circuit for reverse-blocking insulated gate bipolar transistor (RB-IGBT) is proposed in this paper. For the drive circuit, a dynamic current source is introduced to reduce the turn-on and turn-off transients. Meanwhile, the di/dt of the collector current and the dv/dt of the collector-emitter voltage are strictly restricted, so do the respective stresses. The drive circuit consists of a conventional push-pull driver and two controllable current sources-a current generator and a current sink. These two current sources work in switching transitions. For the protection circuit, a novel collector current detecting circuit suitable for RB-IGBT is proposed. This method detects the collector current by sensing collector-emitter voltage of the device. Further study shows that this method can be used to acquire the current signs in commutation transitions of matrix converter. A series of experiments has been carried out concerning the proposed drive and protection circuit and the experimental setup; results as well as detailed analysis are presented
Arabic language sentiment analysis on health services

The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naïve Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.
Cerebral mechanisms of word masking and unconscious repetition priming

We used functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) to visualize the cerebral processing of unseen masked words. Within the areas associated with conscious reading, masked words activated left extrastriate, fusiform and precentral areas. Furthermore, masked words reduced the amount of activation evoked by a subsequent conscious presentation of the same word. In the left fusiform gyrus, this repetition suppression phenomenon was independent of whether the prime and target shared the same case, indicating that case-independent information about letter strings was extracted unconsciously. In comparison to an unmasked situation, however, the activation evoked by masked words was drastically reduced and was undetectable in prefrontal and parietal areas, correlating with participants' inability to report the masked words.
Multilingual Word Embeddings using Multigraphs

We present a family of neural-network– inspired models for computing continuous word representations, specifically designed to exploit both monolingual and multilingual text. This framework allows us to perform unsupervised training of embeddings that exhibit higher accuracy on syntactic and semantic compositionality, as well as multilingual semantic similarity, compared to previous models trained in an unsupervised fashion. We also show that such multilingual embeddings, optimized for semantic similarity, can improve the performance of statistical machine translation with respect to how it handles words not present in the parallel data.
Editing Description Logic Ontologies with the Protégé OWL Plugin

The growing interest in the Semantic Web and the Web Ontology Language (OWL) will reveal the potential of Description Logics in industrial projects. The rich semantics of OWL provide powerful reasoning capabilities that help build, maintain and query domain models for many purposes. However, before OWL can unfold its full potential, user-friendly tools with a scalable architecture are required. We present the OWL Plugin, an extension of the Protégé ontology development environment, which can be used to define classes and properties, to edit logical class expressions, to invoke reasoners, and to link ontologies into the Semantic Web. We analyze some of the challenges for developers of Description Logic editors, and discuss some of our user interface design decisions.