nils-herrmann/clean_scirepeval huggingface dataset:
Synthesis of Reactive Protocols for Vehicle-to-Vehicle Communication

We present a synthesis method for communication protocols for active safety applications that satisfy certain formal specifications on quality of service requirements. The protocols are developed to provide reliable communication services for automobile active safety applications. The synthesis method transforms a specification into a distributed implementation of senders and receivers that together satisfy the quality of service requirements by transmitting messages over an unreliable medium. We develop a specification language and an execution model for the implementations, and demonstrate the viability of our method by developing a protocol for a traffic scenario in which a car runs a red light at a busy intersection.
Truncated Importance Sampling

Importance sampling is a fundamental Monte Carlo technique. It involves generating a sample from a proposal distribution in order to estimate some property of a target distribution. Importance sampling can be highly sensitive to the choice of proposal distribution, and fails if the proposal distribution does not sufficiently well approximate the target. Procedures that involve truncation of large importance sampling weights are shown theoretically to improve on standard importance sampling by being less sensitive to the proposal distribution and having lower mean squared estimation error. Consistency is shown under weak conditions, and optimal truncation rates found under more specific conditions. Truncation at rate n1/2 is shown to be a good general choice. An adaptive truncation threshold, based on minimizing an unbiased risk estimate, is also presented. As an example, truncation is found to be effective for calculating the likelihood of partially observed multivariate diffusions. It is demonstrated as a component of a sequential importance sampling scheme for a continuous time population disease model. Truncation is most valuable for computationally intensive, multidimensional situations in which finding a proposal distribution that is everywhere a good approximation to the target distribution is challenging.
Implementation of Reverse Vending Machine Based on FPGA

Department of Electronics and Communication DAV Institute of Engineering and Technology, Jalandhar, Punjab, India ___________________________________________________________________________________  In these modern days, Reverse Vending Machine is very popular to Korea, Japan, England, Britain, Greece and China.  Vending Machine saves time, employee work and also save human’s energy. This paper describes about the Reverse Vending Machine which works after inserting the empty beverage container into it. The project of Reverse Vending Machine (RVM) is useless without the cooperation of consumer. This is very attractive and motivated prize for the consumer through the RVM i.e. Refund. This paper addresses the implementation of Reverse Vending Machine based on FPGA. RVM is based on that process in which a machine accepts empty remaining containers and gives output according to the number of container. In this Paper, RVM supports empty containers, coins, snacks, chocolate, cold-drink etc. The algorithm for it is implemented in VHDL and simulated using Xilinx FPGA Spartan 3 development board.
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection

The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects goodquality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the sourcelanguage NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data.
An Architecture to Support Autonomic Slice Networking

Network slices combine resource virtualization with the isolation level required by future 5G applications. In addition, the use of monitoring and data analytics help to maintain the required network performance, while reducing total cost of ownership. In this paper, an architecture to enable autonomic slice networking is presented. Extended nodes make local decisions close to network devices, whereas centralized domain systems collate and export metered data transparently to customer controllers, all of them leveraging customizable and isolated data analytics processes. Discovered knowledge can be applied for both proactive and reactive network slice reconfiguration, triggered either by service providers or customers, thanks to the interaction with state-of-the-art software-defined networking controllers and planning tools. The architecture is experimentally demonstrated by means of a complex use case for a multidomain multilayer multiprotocol label switching (MPLS)-over-optical network. In particular, the use case consists of the following observe–analyze–act loops: 1) proactive network slice rerouting after bit error rate (BER) degradation detection in a lightpath supporting a virtual link (vlink); 2) reactive core network restoration after optical link failure; and 3) reactive network slice rerouting after the degraded lightpath is restored. The proposed architecture is experimentally validated on a distributed testbed connecting premises in UPC (Spain) and CNIT (Italy).
Reproductive outcomes after medical and surgical management of ectopic pregnancy.

Ectopic pregnancies have a negative impact on future fertility. Prompt diagnosis is paramount to preserve tubal function and reproductive potential. Expectant, medical, and surgical management of ectopic pregnancies have similar efficacy in properly selected patients. Medical management has emerged as a safe alternative to surgery and holds promise for preservation of future fertility. Laparoscopic salpingostomy or salpingectomy remains the preferred means of surgical removal of ectopic pregnancies. The most predictive factor of future fertility is the health of the contralateral tube.
Evaluation of Fuzzy K-Means And K-Means Clustering Algorithms In Intrusion Detection Systems

According to the growth of the Internet technology, there is a need to develop strategies in order to maintain security of system. One of the most effective techniques is Intrusion Detection System (IDS). This system is created to make a complete security in a computerized system, in order to pass the Intrusion system through the firewall, antivirus and other security devices detect and deal with it. The Intrusion detection techniques are divided into two groups which includes supervised learning and unsupervised learning. Clustering which is commonly used to detect possible attacks is one of the branches of unsupervised learning. Fuzzy sets play an important role to reduce spurious alarms and Intrusion detection, which have uncertain quality.This paper investigates k-means fuzzy and k-means algorithm in order to recognize Intrusion detection in system which both of the algorithms use clustering method.
Dynamic selection of classifiers - A comprehensive review

This work presents a literature review of multiple classifier systems based on the dynamic selection of classifiers. First, it briefly reviews some basic concepts and definitions related to such a classification approach and then it presents the state of the art organized according to a proposed taxonomy. In addition, a two-step analysis is applied to the results of the main methods reported in the literature, considering different classification problems. The first step is based on statistical analyses of the significance of these results. The idea is to figure out the problems for which a significant contribution can be observed in terms of classification performance by using a dynamic selection approach. The second step, based on data complexity measures, is used to investigate whether or not a relation exists between the possible performance contribution and the complexity of the classification problem. From this comprehensive study, we observed that, for some classification problems, the performance contribution of the dynamic selection approach is statistically significant when compared to that of a single-based classifier. In addition, we found evidence of a relation between the observed performance contribution and the complexity of the classification problem. These observations allow us to suggest, from the classification problem complexity, that further work should be done to predict whether or not to use a dynamic selection approach. & 2014 Elsevier Ltd. All rights reserved.
Early detection of plant faults by using machine learning

To detect fault early is very important for safety. This paper proposes a method to detect plant faults early. For the early detections, to classify whether the signals is an early sign of faults or not in subtle signal difference is necessary. To the classification, this paper uses SVM (Support Vector Machine), which is one of a powerful classification technique of machine learning. Data in abnormal state of plants is obtained a few moments later after a fault occurs, hence in early stage of faults, the data of abnormal state is not available. Only measured data at normal state of plants is available in learning stage of the method. For a classification of such cases where only one side of data, which is from normal state, available, one class SVM is useful. In addition, to classify a very subtle signal as a signal of abnormal state, a high generalization ability is necessary for SVM. To get the high ability, a new kernel function, the generalized Gaussian function is used. To show the effectiveness of the method given in this paper, a simulation example of a water level control experimental plant is given.
Optimization of Watt’s Six-bar Linkage to Generate Straight and Parallel Leg Motion

This paper considers optimal synthesis of a special type of four-bar linkages. Combination of this optimal four-bar linkage with on of it’s cognates and elimination of two redundant cognates will result in a Watt’s six-bar mechanism, which generates straight and parallel motion. This mechanism can be utilized for legged machines. The advantage of this mechanism is that the leg remains straight during it’s contact period and because of it’s parallel motion, the legs can be as wide as desired to increase contact area and decrease the number of legs required to keep body’s stability statically and dynamically. “Genetic algorithm” optimization method is used to find optimal lengths. It is especially useful for problems like the coupler curve equation which are completely nonlinear or extremely difficult to solve.
Defective neural crest migration revealed by a Zebrafish model of Alx1-related frontonasal dysplasia.

Frontonasal dysplasia (FND) refers to a class of midline facial malformations caused by abnormal development of the facial primordia. The term encompasses a spectrum of severities but characteristic features include combinations of ocular hypertelorism, malformations of the nose and forehead and clefting of the facial midline. Several recent studies have drawn attention to the importance of Alx homeobox transcription factors during craniofacial development. Most notably, loss of Alx1 has devastating consequences resulting in severe orofacial clefting and extreme microphthalmia. In contrast, mutations of Alx3 or Alx4 cause milder forms of FND. Whilst Alx1, Alx3 and Alx4 are all known to be expressed in the facial mesenchyme of vertebrate embryos, little is known about the function of these proteins during development. Here, we report the establishment of a zebrafish model of Alx-related FND. Morpholino knock-down of zebrafish alx1 expression causes a profound craniofacial phenotype including loss of the facial cartilages and defective ocular development. We demonstrate for the first time that Alx1 plays a crucial role in regulating the migration of cranial neural crest (CNC) cells into the frontonasal primordia. Abnormal neural crest migration is coincident with aberrant expression of foxd3 and sox10, two genes previously suggested to play key roles during neural crest development, including migration, differentiation and the maintenance of progenitor cells. This novel function is specific to Alx1, and likely explains the marked clinical severity of Alx1 mutation within the spectrum of Alx-related FND.
Further evidence that anxiety and memory are regionally dissociated within the hippocampus

The hippocampus has been implicated in the regulation of anxiety and memory processes. Nevertheless, the precise contribution of its ventral (VH) and dorsal (DH) division in these issues still remains a matter of debate. The Trial 1/2 protocol in the elevated plus-maze (EPM) is a suitable approach to assess features associated with anxiety and memory. Information about the spatial environment on initial (Trial 1) exploration leads to a subsequent increase in open-arm avoidance during retesting (Trial 2). The objective of the present study was to investigate whether transient VH or DH deactivation by lidocaine microinfusion would differently interfere with the performance of EPM-naive and EPM-experienced rats. Male Wistar rats were bilaterally-implanted with guide cannulas aimed at the VH or the DH. One-week after surgery, they received vehicle or lidocaine 2.0% in 1.0 microL (0.5 microL per side) at pre-Trial 1, post-Trial 1 or pre-Trial 2. There was an increase in open-arm exploration after the intra-VH lidocaine injection on Trial 1. Intra-DH pre-Trial 2 administration of lidocaine also reduced the open-arm avoidance. No significant changes were observed in enclosed-arm entries, an EPM index of general exploratory activity. The cautious exploration of potentially dangerous environment requires VH functional integrity, suggesting a specific role for this region in modulating anxiety-related behaviors. With regard to the DH, it may be preferentially involved in learning and memory since the acquired response of inhibitory avoidance was no longer observed when lidocaine was injected pre-Trial 2.
Acute toxoplasmosis in squirrel monkeys (Saimiri sciureus) in Mexico.

Toxoplasma gondii causes fatal multisystemic disease in New World primates, with respiratory failure and multifocal necrotic lesions. Although cases and outbreaks of toxoplasmosis have been described, there are few genotyping studies and none has included parasite load quantification. In this article, we describe two cases of lethal acute toxoplasmosis in squirrel monkeys (Saimiri sciureus) of Mexico city. The main pathological findings included pulmonary edema, interstitial pneumonia, hepatitis and necrotizing lymphadenitis, and structures similar to T. gondii tachyzoites observed by histopathology in these organs. Diagnosis was confirmed by immunohistochemistry, transmission electron microscopy and both end point and real time PCR. The load was between <14 and 23 parasites/mg tissue. Digestion of the SAG3 gene amplicon showed similar bands to type I reference strains. These are the first cases of toxoplasmosis in primates studied in Mexico, with clinical features similar to others reported in Israel and French Guiana, although apparently caused by a different T. gondii variant.
Application of learning analytics using clustering data Mining for Students’ disposition analysis

Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student’s performance using Disposition analysis. In this project, K-means clustering data mining technique is used to obtain clusters which are further mapped to find the important features of a learning context. Relationships between these features are identified to assess the student’s performance.
Privacy preserving data mining — ‘A state of the art’

The process of extraction of interesting patterns or knowledge from the bulk of data refers to the data mining technique. “It is the process of discovering meaningful, new correlation patterns and trends through non-trivial extraction of implicit, previously unknown information from large amount of data stored in repositories using pattern recognition as well as statistical and mathematical techniques”. Due to the wide deployment of Internet and information technology, storage and processing of data technologies, the ever-growing privacy concern has been a major issue in data mining for information sharing. This gave rise to a new path in research, known as Privacy Preserving Data Mining (PPDM). The literature paper discusses various privacy preserving data mining algorithms and provide a wide analyses for the representative techniques for privacy preserving data mining along with their merits and demerits. The paper describes an overview of some of the well-known PPDM algorithms. Most of the algorithms are usually a modification of a well-known data-mining algorithm along with some privacy preserving techniques. This paper also focuses on the problems and directions for the future research here. The paper finally discusses the comparative analysis of some well-known privacy preservation techniques that are used. This paper is intended to be a summary and an overview of PPDM.
A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model

We describe an accelerated hardware neuron being capable of emulating the adaptive exponential integrate-and-fire neuron model. Firing patterns of the membrane stimulated by a step current are analyzed in transistor level simulations and in silicon on a prototype chip. The neuron is destined to be the hardware neuron of a highly integrated wafer-scale system reaching out for new computational paradigms and opening new experimentation possibilities. As the neuron is dedicated as a universal device for neuroscientific experiments, the focus lays on parameterizability and reproduction of the analytical model.
Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems Feilong

The commoditization of high-performance networking has sparked research interest in the RDMA capability of this hardware. One-sided RDMA primitives, in particular, have generated substantial excitement due to the ability to directly access remote memory from within an application without involving the TCP/IP stack or the remote CPU. This paper considers how to leverage RDMA to improve the analytical performance of parallel database systems. To shuffle data efficiently using RDMA, one needs to consider a complex design space that includes (1) the number of open connections, (2) the contention for the shared network interface, (3) the RDMA transport function, and (4) how much memory should be reserved to exchange data between nodes during query processing. We contribute six designs that capture salient trade-offs in this design space. We comprehensively evaluate how transport-layer decisions impact the query performance of a database system for different generations of InfiniBand. We find that a shuffling operator that uses the RDMA Send/Receive transport function over the Unreliable Datagram transport service can transmit data up to 4× faster than an RDMA-capable MPI implementation in a 16-node cluster. The response time of TPC-H queries improves by as much as 2×.
Key Technologies and System Trade-offs for Detection and Localization of Amateur Drones

The use of amateur drones is expected to significantly increase over the upcoming years. However, regulations do not allow such drones to fly over all areas, in addition to typical altitude limitations. As a result, there is an urgent need for amateur drone surveillance solutions. These solutions should include means of accurate detection, classification, and localization of the unwanted drones in a no-fly zone. In this article, we give an overview of promising techniques for modulation classification and signal-strength-based localization of amateur drones by using surveillance drones. By introducing a generic altitude-dependent propagation model, we show how detection and localization performance depend on the altitude of surveillance drones. Particularly, our simulation results show a 25 dB reduction in the minimum detectable power or 10 times coverage enhancement of a surveillance drone by flying at the optimum altitude. Moreover, for a target no-fly zone, the location estimation error of an amateur drone can be remarkably reduced by optimizing the positions of the surveillance drones. Finally, we conclude the article with a general discussion about the future work and possible challenges in aerial surveillance systems.
High-Resolution Angle Estimation for an Automotive FMCW Radar Sensor

This paper introduces the application of high-resolution angle estimation algorithms for a 77GHz automotive long range radar sensor. Highresolution direction of arrival (DOA) estimation is important for future safety systems. Using FMCW principle, major challenges discussed in this paper are small number of snapshots, correlation of the signals, and antenna mismatches. Simulation results allow analysis of these effects and help designing the sensor. Road traffic measurements show superior DOA resolution and the feasibility of high-resolution angle estimation.
Eight Kinetically Stable but Thermodynamically Activated Molecules that Power Cell Metabolism.

Contemporary analyses of cell metabolism have called out three metabolites: ATP, NADH, and acetyl-CoA, as sentinel molecules whose accumulation represent much of the purpose of the catabolic arms of metabolism and then drive many anabolic pathways. Such analyses largely leave out how and why ATP, NADH, and acetyl-CoA  at the molecular level play such central roles. Yet, without those insights into why cells accumulate them and how the enabling properties of these key metabolites power much of cell metabolism, the underlying molecular logic remains mysterious. Four other metabolites, S-adenosylmethionine, carbamoyl phosphate, UDP-glucose, and Δ2-isopentenyl-PP play similar roles in using group transfer chemistry to drive otherwise unfavorable biosynthetic equilibria. This review provides the underlying chemical logic to remind how these seven key molecules function as mobile packets of cellular currencies for phosphoryl transfers (ATP), acyl transfers (acetyl-CoA, carbamoyl-P), methyl transfers (SAM), prenyl transfers (IPP), glucosyl transfers (UDP-glucose), and electron and ADP-ribosyl transfers (NAD(P)H/NAD(P)+) to drive metabolic transformations in and across most primary pathways. The eighth key metabolite is molecular oxygen (O2), thermodynamically activated for reduction by one electron path, leaving it kinetically stable to the vast majority of organic cellular metabolites.