nils-herrmann/clean_scirepeval huggingface dataset:
FADM: DDoS Flooding Attack Detection and Mitigation System in Software-Defined Networking

Distributed Denial-of-Service (DDoS) flooding attack is one of the most serious threats to network security. Software-Defined Networking (SDN) has recently emerged as a new network management platform, and its centralized control architecture brings many new opportunities for defending against network attacks. In this paper, we propose FADM, an efficient and lightweight framework to detect and mitigate DDoS attacks in SDN. Firstly, the network traffic information is collected through the SDN controller and sFlow agents. Then an entropy-based method is used to measure network features, and the SVM classifier is applied to identify network anomalies. By adopting these methods together, the timeliness and accuracy of attack detection are effectively improved. To keep the major network functionality working, we propose an efficient attack mitigation mechanism based on the white-list and traffic migration. By introducing the mitigation agent to the network, attack traffic can be timely blocked while benign traffic can be forwarded as usual, which prevents the controller resources from being exhausted and ensures that legitimate users can access the network normally. The experimental results show that multiple DDoS attacks can be accurately detected and effectively mitigated by FADM, which enables the network to recover in a short time.
BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.
Lightweight Management of Resource-Constrained Sensor Devices in Internet of Things

It is predicted that billions of intelligent devices and networks, such as wireless sensor networks (WSNs), will not be isolated but connected and integrated with computer networks in future Internet of Things (IoT). In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management (DM) entities to remotely monitor and control devices without consuming significant resources. In this paper, we propose a lightweight RESTful Web service (WS) approach to enable device management of wireless sensor devices. Specifically, motivated by the recent development of IPv6-based open standards for accessing wireless resource-constrained networks, we consider to implement IPv6 over low-power wireless personal area network (6LoWPAN)/routing protocol for low power and lossy network (RPL)/constrained application protocol (CoAP) protocols on sensor devices and propose a CoAP-based DM solution to allow easy access and management of IPv6 sensor devices. By developing a prototype cloud system, we successfully demonstrate the proposed solution in efficient and effective management of wireless sensor devices.
An Analysis of Buck Converter Efficiency in PWM / PFM Mode with Simulink

This technical paper takes a study into efficiency comparison between PWM and PFM control modes in DC-DC buck converters. Matlab Simulink Models are built to facilitate the analysis of various effects on power loss and converting efficiency, including different load conditions, gate switching frequency, setting of voltage and current thresholds, etc. From efficiency vs. load graph, a best switching frequency is found to achieve a good efficiency throughout the wide load range. This simulation point is then compared to theoretical predictions, justifying the effectiveness of computer based simulation. Efficiencies at two different control modes are compared to verify the improvement of PFM scheme.
Blockchain — Literature survey

In the modern world that we live in everything is gradually shifting towards a more digitized outlook. From teaching methods to online transactions, every small aspect of our life is given a more technological touch. In such a case “money” is not left behind either. An approach towards digitizing money led to the creation of “bitcoin”. Bitcoin is the most efficient crypto-currency so far in the history of digital currency. Ruling out the interference of any third party which monitors the cash flow, Bitcoin is a decentralized form of online currency and is widely accepted for internet transactions all over the world. The need for digital money would be extensive in the near future.
Mining Optimized Association Rules with Categorical and Numeric Attributes

ÐMining association rules on large data sets has received considerable attention in recent years. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial, and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes. Optimized association rules are permitted to contain uninstantiated attributes and the problem is to determine instantiations such that either the support or confidence of the rule is maximized. In this paper, we generalize the optimized association rules problem in three ways: 1) association rules are allowed to contain disjunctions over uninstantiated attributes, 2) association rules are permitted to contain an arbitrary number of uninstantiated attributes, and 3) uninstantiated attributes can be either categorical or numeric. Our generalized association rules enable us to extract more useful information about seasonal and local patterns involving multiple attributes. We present effective techniques for pruning the search space when computing optimized association rules for both categorical and numeric attributes. Finally, we report the results of our experiments that indicate that our pruning algorithms are efficient for a large number of uninstantiated attributes, disjunctions, and values in the domain of the attributes.
The CL-SciSumm Shared Task 2018: Results and Key Insights

This overview describes the official results of the CL-SciSumm Shared Task 2018 – the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. This year, the dataset comprised 60 annotated sets of citing and reference papers from the open access research papers in the CL domain. The Shared Task was organized as a part of the 41 Annual Conference of the Special Interest Group in Information Retrieval (SIGIR), held in  Arbor, USA in July 2018. We compare the participating systems in terms of two evaluation metrics. The annotated dataset and evaluation scripts can be accessed and used by the community from: https://github.com/WING-NUS/scisumm-corpus.
The Democratization of Personal Consumer Loans? Determinants of Success in Online Peer-to-peer Loan Auctions

Online peer-to-peer (P2P) loan auctions enable individual consumers to borrow from, and lend money to, one another directly. We study the borrowerand auction-related determinants of funding success in these auctions by conceptualizing auction decision variables (loan amount, starting interest rate, duration) as mediators between borrower characteristics such as their demographic attributes, financial strength, and effort prior to the auction, and the likelihood of funding success. Borrower characteristics are also treated as moderators of the effects of auction decision variables on funding success. The results of our empirical study, conducted using a database of 5,370 completed P2P loan auctions, provide support for the proposed conceptual framework, and reveal a strong democratization of lending practices in P2P loan auctions. Although demographic attributes such as race and gender affect likelihood of the auction’s funding success, their effects are small when compared to those of borrowers’ financial strength and effort when listing and publicizing the auction. These results are substantially different from the documented discriminatory practices of regulated financial institutions in the US, suggesting that individual lenders bid more rationally when their own investment money is at stake. The paper concludes with specific suggestions to borrowers to increase their chances of receiving funding in P2P loan auctions, and a discussion of future research opportunities.
Privacy Preserving OLAP

We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (randomized) in order to preserve privacy before it is integrated at the server. We develop formal notions of privacy obtained from data perturbation and show that our perturbation provides guarantees against privacy breaches. We develop and analyze algorithms for reconstructing counts of subcubes over perturbed data. We also evaluate the tradeoff between privacy guarantees and reconstruction accuracy and show the practicality of our approach.
Novel Online Methods for Time Series Segmentation

To efficiently and effectively mine massive amounts of data in the time series, approximate representation of the data is one of the most commonly used strategies. Piecewise linear approximation is such an approach, which represents a time series by dividing it into segments and approximating each segment with a straight line. In this paper, we first propose a new segmentation criterion that improves computing efficiency. Based on this criterion, two novel online piecewise linear segmentation methods are developed, the feasible space window method and the stepwise feasible space window method. The former usually produces much fewer segments and is faster and more reliable in the running time than other methods. The latter can reduce the representation error with fewer segments. It achieves the best overall performance on the segmentation results compared with other methods. Extensive experiments on a variety of real-world time series have been conducted to demonstrate the advantages of our methods.
Metamaterial-Based Low-Profile Broadband Mushroom Antenna

A metamaterial-based broadband low-profile mushroom antenna is presented. The proposed antenna is formed using an array of mushroom cells and a ground plane, and fed by a microstrip line through a slot cut onto the ground plane. With the feeding slot right underneath the center gap between the mushroom cells, the dual resonance modes are excited simultaneously for the radiation at boresight. A transmission-line model integrated with the dispersion relation of a composite right/left-handed mushroom structure is applied to analyze the modes. The proposed dielectric-filled (ε<sub>r</sub>=3.38) mushroom antenna with a low profile of 0.06λ<sub>0</sub> ( λ<sub>0</sub> is the operating wavelength in free space) and a ground plane of 1.10λ<sub>0</sub>×1.10λ<sub>0</sub> attains 25% measured bandwidth with(|S<sub>11</sub>| <; - 10dB) 9.9-dBi average gain at 5-GHz band. Across the bandwidth, the antenna efficiency is greater than 76%, and cross-polarization levels are less than -20 dB.
HfO2-Based OxRAM Devices as Synapses for Convolutional Neural Networks

In this paper, the use of HfO2-based oxide-based resistive memory (OxRAM) devices operated in binary mode to implement synapses in a convolutional neural network (CNN) is studied. We employed an artificial synapse composed of multiple OxRAM cells connected in parallel, thereby providing synaptic efficacies. Electrical characterization results show that the proposed HfO2-based OxRAM technology offers good electrical properties in terms of endurance (>108 cycles), speed (<;10 ns), and low energy (<;10 pJ), and thus being well suited for neuromorphic applications. A device physical model is developed in order to study the variability of the resistance as a function of the stochastic position of oxygen vacancies in 3-D. Finally, the proposed binary OxRAM synapse has been used for CNN system-level simulations. High accuracy (recognition rate > 98%) is demonstrated for a complex visual pattern recognition application. We demonstrated that the resistance variability and the reduced memory window of the OxRAM cells when operated at extremely low programming conditions (<;10 pJ per switching event) have a small impact on the performances of proposed OxRAM-based CNN (recognition rate 94%).
Data-Confined HTML5 Applications

Rich client-side applications written in HTML5 proliferate diverse platforms such as mobile devices, commodity PCs, and the web platform. These client-side HTML5 applications are increasingly accessing sensitive data, including users’ personal and social data, sensor data, and capability-bearing tokens. To fulfill their security and privacy guarantees, these applications need to maintain certain data-confinement invariants. These invariants are not explicitly stated in today’s HTML5 applications and are enforced using ad-hoc mechanisms. The complexity of web applications, coupled with hard-to-analyze client-side languages, leads to low-assurance data-confinement mechanisms in which the whole application needs to be in the TCB to ensure data-confinement in-
Wrist MR arthrography: how, why, when.

MR imaging of the wrist frequently represents a diagnostic challenge for radiologists because of the complex anatomy of this joint, small size of its components, and little known pathologic conditions. MR arthrography combines the advantages of conventional MR imaging and arthrography by improving the visualization of small intra-articular abnormalities. This article reviews the current role of MR arthrography in the evaluation of wrist joint disorders considering the relevant aspects of anatomy, techniques, and applications.
GSM Based Home Automation System UsingApp-Inventor for Android Mobile Phone

Nowadays, the remote Home Automation turns out to be more and more significant and appealing. It improves the value of our lives by automating various electrical appliances or instruments. This paper describes GSM (Global System Messaging) based secured device control system using App Inventor for Android mobile phones. App Inventor is a latest visual programming platform for developing mobile applications for Android-based smart phones. The Android Mobile Phone Platform becomes more and more popular among software developers, because of its powerful capabilities and open architecture. It is a fantastic platform for the real world interface control, as it offers an ample of resources and already incorporates a lot of sensors. No need to write programming codes to develop apps in the App Inventor, instead it provides visual design interface as the way the apps looks and use blocks of interlocking components to control the app’s behaviour. The App Inventor aims to make programming enjoyable and accessible to novices.
Combined long short-term memory based network employing wavelet coefficients for MI-EEG recognition

Motor Imagery Electroencephalography (MI-EEG) plays an important role in brain computer interface (BCI) based rehabilitation robot, and its recognition is the key problem. The Discrete Wavelet Transform (DWT) has been applied to extract the time-frequency features of MI-EEG. However, the existing EEG classifiers, such as support vector machine (SVM), linear discriminant analysis (LDA) and BP network, did not make full use of the time sequence information in time-frequency features, the resulting recognition performance were not very ideal. In this paper, a Long Short-Term Memory (LSTM) based recurrent Neural Network (RNN) is integrated with Discrete Wavelet Transform (DWT) to yield a novel recognition method, denoted as DWT-LSTM. DWT is applied to analyze the each channel of MI-EEG and extract its effective wavelet coefficients, representing the time-frequency features. Then a LSTM based RNN is used as a classifier for the patten recognition of observed MI-EEG data. Experiments are conducted on a publicly available dataset, and the 5-fold cross validation experimental results show that DWT-LSTM yields relatively higher classification accuracies compared to the existing approaches. This is helpful for the further research and application of RNN in processing of MI-EEG.
An integrated process for game balancing

Game balancing is a recurring problem that currently requires a lot of manual work, usually following a game designer's intuition or rules-of-thumb. To what extent can or should the balancing process be automated? We establish a process model that integrates both manual and automated balancing approaches. Artificial agents are employed to automatically assess the desirability of a game. We demonstrate the feasibility of implementing the model and analyze the resulting solutions from its application to a simple video game.
Numerical Synthesis of Six-Bar Linkages for Mechanical Computation

This paper presents a design procedure for six-bar linkages that use eight accuracy points to approximate a specified input-output function. In the kinematic synthesis of linkages, this is known as the synthesis of a function generator to perform mechanical computation. Our formulation uses isotropic coordinates to define the loop equations of the Watt II, Stephenson II, and Stephenson III six-bar linkages. The result is 22 polynomial equations in 22 unknowns that are solved using the polynomial homotopy software Bertini. The bilinear structure of the system yields a polynomial degree of 705,432. Our first run of Bertini generated 92,736 nonsingular solutions, which were used as the basis of a parameter homotopy solution. The algorithm was tested on the design of the Watt II logarithmic function generator patented by Svoboda in 1944. Our algorithm yielded his linkage and 64 others, in 129 minutes of parallel computation on a Mac Pro with 12⇥2.93 GHz processors. Three additional examples are provided as well.
Short-Text Clustering using Statistical Semantics

Short documents are typically represented by very sparse vectors, in the space of terms. In this case, traditional techniques for calculating text similarity results in measures which are very close to zero, since documents even the very similar ones have a very few or mostly no terms in common. In order to alleviate this limitation, the representation of short-text segments should be enriched by incorporating information about correlation between terms. In other words, if two short segments do not have any common words, but terms from the first segment appear frequently with terms from the second segment in other documents, this means that these segments are semantically related, and their similarity measure should be high. Towards achieving this goal, we employ a method for enhancing document clustering using statistical semantics. However, the problem of high computation time arises when calculating correlation between all terms. In this work, we propose the selection of a few terms, and using these terms with the Nystr\"om method to approximate the term-term correlation matrix. The selection of the terms for the Nystr\"om method is performed by randomly sampling terms with probabilities proportional to the lengths of their vectors in the document space. This allows more important terms to have more influence on the approximation of the term-term correlation matrix and accordingly achieves better accuracy.
Error detecting and error correcting codes

When a message is transmitted, it has the potential to get scrambled by noise. This is certainly true of voice messages, and is also true of the digital messages that are sent to and from computers. Now even sound and video are being transmitted in this manner. A digital message is a sequence of 0’s and 1’s which encodes a given message. More data will be added to a given binary message that will help to detect if an error has been made in the transmission of the message; adding such data is called an error-detecting code. More data may also be added to the original message so that errors made in transmission may be detected, and also to figure out what the original message was from the possibly corrupt message that was received. This type of code is an error-correcting code.
The Impact of Interactive Touchscreens on Physics Education in Upper Secondary School – A systematic literature review

Interactive touchscreens such as tablet PCs (TPC) and interactive whiteboards (IWB) are becoming more and more common in classrooms around the world. To date, very little research has been conducted on the impact of the new technology on physics education. This systematic literature review aims to investigate research on what impact tablet PCs and interactive whiteboards might have on the education in upper Secondary School. The review was performed in response to the following questions: 1. What is the influence of IWBs and TPCs on students’ active participation in physics education? 2. How can an IWB or TPC improve students’ learning about physics concepts? 3. How can educational research on touchscreen technology help inform effective teaching strategies in physics education? To respond to the questions of the study, relevant research about interactive whiteboards and/or tablet PCs was consulted and analysed. Twelve articles were located, mainly through the ERIC and Scopus databases, but also through Google Scholar. The included articles reported empirical research about physics education with interactive whiteboards or tablet PCs. The results from the articles indicate that interactive touchscreens might help improve learners’ active participation in physics education. Interactive whiteboards can, for example, be used to display interactive simulations during group work, something students are found to appreciate and easily engage in. A tablet PC can be used in the same way, but also allows students to receive anonymous support and feedback from the teacher during class which seems to be beneficial for learning. Results show that it is possible to improve students’ understanding of physics concepts by using interactive whiteboards or tablet PCs. However, further research is required to compare results from students using touch technology and students taught in traditional manner to be able to draw any general conclusions about observed learning effects.
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements

This paper reports our submissions to the Semantic Textual Similarity (STS) task in SemEval 2013 (Task 6). We submitted three Support Vector Regression (SVR) systems in core task, using 6 types of similarity measures, i.e., string similarity, number similarity, knowledge-based similarity, corpus-based similarity, syntactic dependency similarity and machine translation similarity. Our third system with different training data and different feature sets for each test data set performs the best and ranks 35 out of 90 runs. We also submitted two systems in typed task using string based measure and Named Entity based measure. Our best system ranks 5 out of 15 runs.