nils-herrmann/clean_scirepeval huggingface dataset:
Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing

Communication is an important factor for the big multi-agent world to stay organized and productive. Recently, the AI community has applied the Deep Reinforcement Learning (DRL) to learn the communication strategy and the control policy for multiple agents. However, when implementing the communication for real-world multi-agent applications, there is a more practical limited-bandwidth restriction, which has been largely ignored by the existing DRL-based methods. Specifically, agents trained by most previous methods keep sending messages incessantly in every control cycle; due to emitting too many messages, these methods are unsuitable to be applied to the real-world systems that have a limited bandwidth to transmit the messages. To handle this problem, we propose a gating mechanism to adaptively prune unprofitable messages. Results show that the gating mechanism can prune more than 80% messages with little damage to the performance. Moreover, our method outperforms several state-of-the-art DRL-based and rule-based methods by a large margin in both the real-world packet routing tasks and four benchmark tasks.
Microservices: Migration of a Mission Critical System

The microservices paradigm aims at changing the way in which software is perceived, conceived and designed. One of the foundational characteristics of this new promising paradigm, compared for instance to monolithic architectures, is scalability. In this paper, we present a real world case study in order to demonstrate how scalability is positively affected by reimplementing a monolithic architecture into microservices. The case study is based on the FX Core system, a mission critical system of Danske Bank, the largest bank in Denmark and one of the leading financial institutions in Northern Europe.
A reference model for learning analytics

Recently, there is an increasing interest in learning analytics in Technology Enhanced Learning (TEL). Generally, learning analytics deals with the development of methods that harness educational data sets to support the learning process. Learning analytics (LA) is a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics, and visualization. LA is also a field in which several related areas of research in TEL converge. These include academic analytics, action research, educational data mining, recommender systems, and personalized adaptive learning. In this paper, we investigate the connections between LA and these related fields. We describe a reference model for LA based on four dimensions, namely data and environments (what?), stakeholders (who?), objectives (why?), and methods (how?). We then review recent publications on LA and its related fields and map them to the four dimensions of the reference model. Furthermore, we identify various challenges and research opportunities in the area of LA in relation to each dimension.
Double-ridged horn antenna operating in 18–40 GHz range

The whole design cycle, including design, fabrication and characterization of a broadband double-ridged horn antenna equipped with a parabolic lens and a waveguide adapter is presented in this paper. A major goal of the presented work was to obtain high directivity with a flat phase characteristic within the main radiation lobe at an 18–40 GHz frequency range, so that the antenna can be applicable in a freespace material characterization setup.
Uncovering learning patterns in a MOOC through conformance alignments

Web-based learning is now offered in multiple forms. One of these is the phenomenon of Massive Open Online Courses (MOOCs). Several approaches in Learning Analytics (LA) attempt to analyze and explain students learning patterns in MOOCs. In addition to traditional data mining techniques, online surveys constitute another way used in LA for analyzing students’ learning habits in MOOCs. However, such an approach can be error-prone with data collection. Therefore, we adopt the use of process mining techniques. Process mining techniques provide more robust ways of extracting, analyzing and visualizing students’ activities trail. In this paper, we make use of alignment-based conformance checking to extract and analyse students’ learning patterns in MOOCs. The aim is to provide a guideline and demonstrate how process mining can provide critical insights in tems of students’ learning and quiz submissions behavior, their resulting performance and the correlation therein.
Finch: A System for Evolving Java (Bytecode)

The established approach in genetic programming (GP) involves the definition of functions and terminals appropriate to the problem at hand, after which evolution of expressions using these definitions takes place. We have recently developed a system, dubbed FINCH (Fertile Darwinian Bytecode Harvester), to evolutionarily improve actual, extant software, which was not intentionally written for the purpose of serving as a GP representation in particular, nor for evolution in general. This is in contrast to existing work that uses restricted subsets of the Java bytecode instruction set as a representation language for individuals in genetic programming. The ability to evolve Java programs will hopefully lead to a valuable new tool in the software engineer’s toolkit.
Implementation of a QPSK system on FPGA

The paper presents the design of a QPSK digital communication system and its implementation on FPGAs. The simulations of the system were made in Matlab/Simulink environment and System Generator, a tool used for FPGA designs. The system is made up of two Spartan 3E Starter Kit boards. The first is implemented to comport as a modulator and the second as a demodulator. The VHDL hardware description language was used to design each model. The local clock oscillator on the board operates at 50Mhz and used at 31,250 kHz for the carrier. The operating results were compared with the theorethical ones and were also evaluated by measurements.
Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy.

Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy.



Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions.



This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.
Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.
Modeling and Analyzing of Magnetic Field of Segmented Halbach Array Permanent Magnet Machine Considering Gap Between Segments

The Halbach array permanent magnet machine has several advantages. Most notably, it has more sinusoidal air-gap flux density distribution, higher air-gap flux density amplitude, and self-shielding magnetization property. In engineering, the segmented Halbach array is usually used considering the complexity of fabrication, and certain gap exists between segments inevitably. To analyze the effect of the gap between segments on air-gap flux density, an analytical model of the Halbach array permanent magnet machine considering the gap between segments is established in this paper. The finite element model is adopted to validate the established analytical model. Furthermore, effects of the parameters of the segmented Halbach array, including gap between segments, segment number per pole, and pole pair number on the fundamental amplitude, and waveform distortion factor of the radial component of air-gap flux density are analyzed using the established analytical model.
A SEM-neural network approach for predicting antecedents of m-commerce acceptance

Higher penetration of powerful mobile devices – especially smartphones – and high-speed mobile internet access are leading to better offer and higher levels of usage of these devices in commercial activities, especially among young generations. The purpose of this paper is to determine the key factors that influence consumers’ adoption of mobile commerce. The extended model incorporates basic TAM predictors, such as perceived usefulness and perceived ease of use, but also several external variables, such as trust, mobility, customization and customer involvement. Data was collected from 224 m-commerce consumers. First, structural equation modeling (SEM) was used to determine which variables had signif-
Flow and immersion in first-person shooters: measuring the player's gameplay experience

Researching experiential phenomena is a challenging undertaking, given the sheer variety of experiences that are described by gamers and missing a formal taxonomy: flow, immersion, boredom, excitement, challenge, and fun. These informal terms require scientific explanation, which amounts to providing measurable criteria for different experiential states. This paper reports the results of an experimental psychophysiological study investigating different traits of gameplay experience using subjective and objective measures. Participants played three Half-Life 2 game modifications while being measured with electroencephalography, electrocardiography, electromyography, galvanic skin response and eye tracking equipment. In addition, questionnaire responses were collected after each play session. A level designed for combat-oriented flow experience demonstrated measurable high-arousal positive affect emotions. The positive correlation between subjective and objective indicators of gameplay experience shows the great potential of the method presented here for providing real-time emotional profiles of gameplay that may be correlated with self-reported subjective descriptions.
ROS play a critical role in the differentiation of alternatively activated macrophages and the occurrence of tumor-associated macrophages

Differentiation to different types of macrophages determines their distinct functions. Tumor-associated macrophages (TAMs) promote tumorigenesis owing to their proangiogenic and immune-suppressive functions similar to those of alternatively activated (M2) macrophages. We report that reactive oxygen species (ROS) production is critical for macrophage differentiation and that inhibition of superoxide (O2−) production specifically blocks the differentiation of M2 macrophages. We found that when monocytes are triggered to differentiate, O2− is generated and is needed for the biphasic ERK activation, which is critical for macrophage differentiation. We demonstrated that ROS elimination by butylated hydroxyanisole (BHA) and other ROS inhibitors blocks macrophage differentiation. However, the inhibitory effect of ROS elimination on macrophage differentiation is overcome when cells are polarized to classically activated (M1), but not M2, macrophages. More importantly, the continuous administration of the ROS inhibitor BHA efficiently blocked the occurrence of TAMs and markedly suppressed tumorigenesis in mouse cancer models. Targeting TAMs by blocking ROS can be a potentially effective method for cancer treatment.
Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling Process of EEG Signals in Emotional Stress State

this paper proposes a new emotional stress recognition system using multi-modal bio-signals. Since electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research, it is used as the main signal. In order to choose the proper EEG channels we used the cognitive model of the brain under emotional stress. We designed an efficient acquisition protocol to acquire the EEG and psychophysiological signals under pictures induction environment (calm-neutral and negative-excited) for participants. Qualitative and quantitative evaluation of psychophysiological signals have been tried to select suitable segments of EEG signal for improving efficiency and performance of emotional stress recognition system. After pre-processing the signals, both Linear and nonlinear features were employed to extract the EEG parameters. Wavelet coefficients and chaotic invariants like fractal dimension by Higuchi's algorithm and correlation dimension were used to extract the characteristics of the EEG signal which showed that the classification accuracy in two emotional states was 82.7% using the Elman classifier. This is a great improvement in results compared with other similar published work.
Vision-based automated parking system

This paper describes an approach to overcome a situation of monitoring and managing a parking area using a vision based automated parking system. With the rapid increase of cars the need to find available parking space in the most efficient manner, to avoid traffic congestion in a parking area, is becoming a necessity in car park management. Current car park management is dependent on either human personnel keeping track of the available car park spaces or a sensor based system that monitors the availability of each car park space or the overall number of available car park spaces. In both situations, the information available was only the total number of car park spaces available and not the actual location available. In addition, the installation and maintenance cost of a sensor based system is dependent on the number of sensors used in a car park. This paper shows a vision based system that is able to detect and indicate the available parking spaces in a car park. The methods utilized to detect available car park spaces were based on coordinates to indicate the regions of interest and a car classifier. This paper shows that the initial work done here has an accuracy that ranges from 90% to 100% for a 4 space car park. The work done indicated that the application of a vision based car park management system would be able to detect and indicate the available car park spaces
Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5

Fine particulate matter ( $$\hbox {PM}_{2.5}$$ PM 2.5 ) has a considerable impact on human health, the environment and climate change. It is estimated that with better predictions, US$9 billion can be saved over a 10-year period in the USA (State of the science fact sheet air quality. http://www.noaa.gov/factsheets/new , 2012). Therefore, it is crucial to keep developing models and systems that can accurately predict the concentration of major air pollutants. In this paper, our target is to predict $$\hbox {PM}_{2.5}$$ PM 2.5 concentration in Japan using environmental monitoring data obtained from physical sensors with improved accuracy over the currently employed prediction models. To do so, we propose a deep recurrent neural network (DRNN) that is enhanced with a novel pre-training method using auto-encoder especially designed for time series prediction. Additionally, sensors selection is performed within DRNN without harming the accuracy of the predictions by taking advantage of the sparsity found in the network. The numerical experiments show that DRNN with our proposed pre-training method is superior than when using a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. The experiments confirm that when compared against the $$\hbox {PM}_{2.5}$$ PM 2.5 prediction system VENUS (National Institute for Environmental Studies. Visual Atmospheric Environment Utility System. http://envgis5.nies.go.jp/osenyosoku/ , 2014), our technique improves the accuracy of $$\hbox {PM}_{2.5}$$ PM 2.5 concentration level predictions that are being reported in Japan.
PortLand: a scalable fault-tolerant layer 2 data center network fabric

This paper considers the requirements for a scalable, easily manageable, fault-tolerant, and efficient data center network fabric. Trends in multi-core processors, end-host virtualization, and commodities of scale are pointing to future single-site data centers with millions of virtual end points. Existing layer 2 and layer 3 network protocols face some combination of limitations in such a setting: lack of scalability, difficult management, inflexible communication, or limited support for virtual machine migration. To some extent, these limitations may be inherent for Ethernet/IP style protocols when trying to support arbitrary topologies. We observe that data center networks are often managed as a single logical network fabric with a known baseline topology and growth model. We leverage this observation in the design and implementation of PortLand, a scalable, fault tolerant layer 2 routing and forwarding protocol for data center environments. Through our implementation and evaluation, we show that PortLand holds promise for supporting a ``plug-and-play" large-scale, data center network.
Learning Gaussian Networks

We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. Previ­ ous work has concentrated on metrics for do­ mains containing only discrete variables, un­ der the assumption that data represents a multinomial sample. In this paper, we ex­ tend this work, developing scoring metrics for domains containing only continuous variables under the assumption that continuous data is sampled from a multivariate normal distribu­ tion. Our work extends traditional statistical approaches for identifying vanishing regres­ sion coefficients in that we identify two im­ portant assumptions, called event equivalence and parameter modularity, that when com­ bined allow the construction of prior distri­ butions for multivariate normal parameters from a single prior Bayesian network speci­ fied by a user.
BackIP : Backpressure Routing in IPv 6-Based Wireless Sensor Networks

Wireless sensor networks are increasingly being deployed in real-world applications ranging from energy monitoring to water-level measurement. To better integrate with existing network infrastructure, they are being designed to communicate using IPv6. The current de-facto standard for routing in IPv6-based sensor networks is the shortest-path-based RPL, developed by the IETF 6LoWPaN working group. This paper describes BackIP, an alternative routing protocol for data collection in IPv6-based wireless sensor networks that is based on the backpressure paradigm. In a backpressure-based protocol, routing decisions can be made on-the-fly on a perpacket basis by nodes based on the current locally observed state, and prior work has shown that they can offer superior throughput performance and responsiveness to dynamic conditions compared to shortest-path routing protocols. We discuss a number of design decisions that are needed to enable backpressure routing to work in a scalable and efficient manner with IPv6. We implement and evaluate the performance of this protocol on a TinyOS-based real wireless sensor network testbed.
On the limits of PCI auto configuration and reuse in 4G/5G ultra dense networks

Increased demand for higher user throughput has led to deployment of multi-layer networks commonly called heterogeneous networks (Hetnets). Therein, small cells are deployed alongside traditional macro cells, in many cases on the same spectrum. Such scenarios complicate the configuration of network parameters such as the Physical Cell Identity (PCI). A number of approaches have as such been proposed to automate the allocation of PCIs in such scenarios. These approaches seek to address the two conflicting objectives for PCI assignment in a hetnet scenario: 1) the need for optimal performance by avoiding conflicts, against 2) the requirement to separate the different layers and avoid any need to share knowledge among the layers. However, as the density of small cells increases evolving the Hetnets into what are called Ultra Dense Networks (UDN), these approaches reach their limits. In this paper, we study the performance of the current PCI allocation strategies in such UDN scenarios and evaluate their break down points. Our results show that these strategies do not adequately address PCI allocation for the UDN scenario. Specifically, we observe that PCI assignment in one layer requires knowledge of the assignments in the other layer, otherwise the consequence is a very high count of PCI confusions.
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks

Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntaxaware versions on the English–German language pair.