nils-herrmann/clean_scirepeval huggingface dataset:
Investigating the interaction between personalities and the benefit of gamification

Many studies have confirmed the benefit of gamification on learners’ motivation. However, gamification may also demotivate some learners, or learners may focus on the gamification elements instead of the learning content. Some researchers have recommended building learner models that can be used to adapt gamification elements based on learners’ personalities. Building such a model requires a strong understanding of the relationship between gamification and personality. Existing empirical work has focused on measuring knowledge gain and learner preference. These findings may not be reliable because the analyses are based on learners who complete the study and because they rely on self-report from learners. This preliminary study explores a different approach by allowing learners to drop out at any time and then uses the number of students left as a proxy for motivation and engagement. Survival analysis is used to analyse the data. The results confirm the benefits of gamification and provide some pointers to how this varies with personality.
Personalizing Information Using Users' Online Social Networks: A Case Study of CiteULike

This paper aims to assess the feasibility of a new and less-focused type of online sociability (the watching network) as a useful information source for personalized recommendations. In this paper, we recommend scientific articles of interests by using the shared interests between target users and their watching connections. Our recommendations are based on one typical social bookmarking system, CiteULike. The watching network-based recommendations, which use a much smaller size of user data, produces suggestions that are as good as the conventional Collaborative Filtering technique. The results demonstrate that the watching network is a useful information source and a feasible foundation for information personalization. Furthermore, the watching network is substitutable for anonymous peers of the Collaborative Filtering recommendations. This study shows the expandability of social network-based recommendations to the new type of online social networks.
TreeSpirit: Illegal logging detection and alerting system using audio identification over an IoT network

Illegal logging has been identified as a major problem in the world, which may be minimized through effective monitoring of forest covered areas. In this paper, we propose and describe the initial steps to build a new three-tier architecture for Forest Monitoring based on Wireless Sensor Network and Chainsaw Noise Identification using a Neural Network. In addition to detection of chainsaw noises, we also propose methodologies to localize the origin of the chainsaw noise.
Secure Double-Layered Defense against HTTP-DDoS Attacks

A major cyber-security concern to date for webservers are Distributed Denial of Service (DDoS) attacks. Previously we proposed a novel overlay-based method consisting of distributed network of public servers (PS) for preparation, and access nodes (AN) for actual communication. The AN's performance is evaluated under difficult to detect HTTP(S)-DDoS attacks. Yet, attackers may attempt service denial by attacking the PS instead. The focus in this paper is on mitigating complex slow-requesting HTTP-DDoS attacks that target the PS. A proof-of-concept prototype is implemented with simplified countermeasures and tested. We report on the results of two experiments. Results suggest that the simple PS role can enable high mitigation factors of both high-rate and low-rate attack traffic per source, even with 10,000 unique attack sources per target PS, acting as a second layer of defense with the AN. Yet, with a cost of longer time to load the requested resource file in comparison to direct access.
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. This is achieved by explicitly encoding object presence, locations and appearances in the latent variables of the model. SQAIR retains all strengths of its predecessor, Attend, Infer, Repeat (AIR, Eslami et al., 2016), including learning in an unsupervised manner, and addresses its shortcomings. We use a moving multi-MNIST dataset to show limitations of AIR in detecting overlapping or partially occluded objects, and show how SQAIR overcomes them by leveraging temporal consistency of objects. Finally, we also apply SQAIR to real-world pedestrian CCTV data, where it learns to reliably detect, track and generate walking pedestrians with no supervision.
Coupled-fed antenna integrated with USB printed on variable PCB for WWAN/LTE mobile phone

A coupled-fed antenna design capable of covering eight-band WWAN/LTE operation in a smartphone and suitable to integrate with a USB connector is presented. The antenna comprises an asymmetric T-shaped monopole as a coupling feed and a radiator as well, and a coupled-fed loop strip shorted to the ground plane. The antenna generates a wide lower band to cover (824-960 MHz) for GSM850/900 operation and a very wide upper band of larger than 1 GHz to cover the GPS/GSM1800/1900/UMTS/LTE2300/2500 operation (1565-2690 MHz). The proposed antenna provides wideband operation and exhibits great flexible behavior. The antenna is capable of providing eight-band operation for nine different sizes of PCBs, and enhance impedance matching only by varying a single element length, L. Details of proposed antenna, parameters and performance are presented and discussed in this paper.
A Custom FPGA Processor for Physical Model Ordinary Differential Equation Solving

Models of physical systems, such as of human physiology or of chemical reactions, are typically comprised of numerous ordinary differential equations (ODEs). Today's designers commonly consider simulating physical models utilizing field-programmable gate arrays (FPGAs). This letter introduces a resource efficient custom processor-the differential equation processing element, or DEPE-specifically designed for efficient solution of ODEs on FPGAs, and also introduces its accompanying compilation tools. We show that a single DEPE on a Xilinx Virtex6 130T FPGA executes several physiological models faster than real-time while requiring only a few hundred FPGA lookup tables (LUTs). Experiments with a commercial high-level synthesis(HLS) tool show that while a single DEPE is 5-50× slower than HLS circuits, DEPE is 10-200 × smaller. We show that a single DEPE is only 10× slower than a relatively massive and costly 3 GHz Pentium 4 desktop processor for ODE solving, and its speed is also competitive with a 700 Mhz TI digital signal processor and an 450 Mhz ARM9 processor. DEPE is 4×-17× faster than a Xilinx MicroBlaze soft-core processor and 3 ×-6 × smaller. DEPE thus represents an excellent processing element for use by itself for small physical models, and in future parallel networks for larger models.
Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning

We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minimax duality of Bellman equations. The SPD methods update a few coordinates of the value and policy estimates as a new state transition is observed. These methods use small storage and has low computational complexity per iteration. The SPD methods find an absolute-optimal policy, with high probability, using O ( |S||A|σ (1−γ)6 2 ) iterations/samples for the infinite-horizon discounted-reward MDP and O ( |S||A|Hσ 2 ) for the finite-horizon MDP.
Assessing the effect of self-assembly ports in evolutionary swarm robotics

Self-assembly in swarm robotics is essential for a group of robots in achieving a common goal that is not possible to achieve by a single robot. Self-assembly also provides several advantages to swarm robotics. Some of these include versatility, scalability, re-configurability, cost-effectiveness, extended reliability, and capability for emergent phenomena. This work investigates the effect of self-assembly in evolutionary swarm robotics. Because of the lack of research literature within this paradigm, there are few comparisons of the different implementations of self-assembly mechanisms. This paper reports the influence of connection port configuration on evolutionary self-assembling swarm robots. The port configuration consists of the number and the relative positioning of the connection ports on each of the robot. Experimental results suggest that configuration of the connection ports can significantly impact the emergence of selfassembly in evolutionary swarm robotics.
Multi-class Open Set Recognition Using Probability of Inclusion

The perceived success of recent visual recognition approaches has largely been derived from their performance on classification tasks, where all possible classes are known at training time. But what about open set problems, where unknown classes appear at test time? Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under an assumption of incomplete class knowledge. In this paper, we formulate the problem as one of modeling positive training data at the decision boundary, where we can invoke the statistical extreme value theory. A new algorithm called the PI -SVM is introduced for estimating the unnormalized posterior probability of class inclusion.
Alcelaphine Herpesvirus-1 (Malignant Catarrhal Fever Virus) in Wildebeest Placenta: Genetic Variation of ORF50 and A9.5 Alleles

Alcelaphine herpesvirus-1 (AlHV-1), a causative agent of malignant catarrhal fever in cattle, was detected in wildebeest (Connochaetes taurinus) placenta tissue for the first time. Although viral load was low, the finding of viral DNA in over 50% of 94 samples tested lends support to the possibility that placental tissue could play a role in disease transmission and that wildebeest calves are infected in utero. Two viral loci were sequenced to examine variation among virus samples obtained from wildebeest and cattle: the ORF50 gene, encoding the lytic cycle transactivator protein, and the A9.5 gene, encoding a novel polymorphic viral glycoprotein. ORF50 was well conserved with six newly discovered alleles differing at only one or two base positions. In contrast, while only three new A9.5 alleles were discovered, these differed by up to 13% at the nucleotide level and up to 20% at the amino acid level. Structural homology searching performed with the additional A9.5 sequences determined in this study adds power to recent analysis identifying the four-helix bundle cytokine interleukin-4 (IL4) as the major homologue. The majority of MCF virus samples obtained from Tanzanian cattle and wildebeest encoded A9.5 polypeptides identical to the previously characterized A9.5 allele present in the laboratory maintained AlHV-1 C500 strain. This supports the view that AlHV-1 C500 is suitable for the development of a vaccine for wildebeest-associated MCF.
Securing web applications from injection and logic vulnerabilities: Approaches and challenges

Web applications are trusted by billions of users for performing day-to-day activities. Accessibility, availability and omnipresence of web applications have made them a prime target for attackers. A simple implementation flaw in the application could allow an attacker to steal sensitive information and perform adversary actions, and hence it is important to secure web applications from attacks. Defensive mechanisms for securing web applications from the flaws have received attention from both academia and industry.  The objective of this literature review is to summarize the current state of the art for securing web applications from major flaws such as injection and logic flaws. Though different kinds of injection flaws exist, the scope is restricted to SQL Injection (SQLI) and Cross-site scripting (XSS), since they are rated as the top most threats by different security consortiums.  The relevant articles recently published are identified from well-known digital libraries, and a total of 86 primary studies are considered. A total of 17 articles related to SQLI, 35 related to XSS and 34 related to logic flaws are discussed.  The articles are categorized based on the phase of software development life cycle where the defense mechanism is put into place. Most of the articles focus on detecting the flaws and preventing attacks against web applications.  Even though various approaches are available for securing web applications from SQLI and XSS, they are still prevalent due to their impact and severity. Logic flaws are gaining attention of the researchers since they violate the business specifications of applications. There is no single solution to mitigate all the flaws. More research is needed in the area of fixing flaws in the source code of applications.
The ISO/MPEG Unified Speech and Audio Coding Standard—Consistent High Quality for All Content Types and at All Bit Rates

MAX NEUENDORF,1 AES Member, MARKUS MULTRUS,1 AES Member, NIKOLAUS RETTELBACH1, GUILLAUME FUCHS1, JULIEN ROBILLIARD1, JÉRÉMIE LECOMTE1, STEPHAN WILDE1, STEFAN BAYER,10 AES Member, SASCHA DISCH1, CHRISTIAN HELMRICH10, ROCH LEFEBVRE,2 AES Member, PHILIPPE GOURNAY2, BRUNO BESSETTE2, JIMMY LAPIERRE,2 AES Student Member, KRISTOFER KJÖRLING3, HEIKO PURNHAGEN,3 AES Member, LARS VILLEMOES,3 AES Associate Member, WERNER  AES Member, ERIK SCHUIJERS4, KEI KIKUIRI5, TORU CHINEN6, TAKESHI NORIMATSU1, KOK SENG CHONG7, EUNMI OH,8 AES Member, MIYOUNG KIM8, SCHUYLER QUACKENBUSH,9 AES Fellow, AND BERNHARD GRILL1
Securing wireless communications of connected vehicles with artificial intelligence

This work applies artificial intelligence (AI) to secure wireless communications of Connected Vehicles. Vehicular Ad-hoc Network (VANET) facilitates exchange of safety messages for collision avoidance, leading to self-driving cars. An AI system continuously learns to augment its ability in discerning and recognizing its surroundings. Such ability plays a vital role in evaluating the authenticity and integrity of safety messages for cars driven by computers. Falsification of meter readings, disablement of brake function, and other unauthorized controls by spoofed messages injected into VANET emerge as security threats. Countermeasures must be considered at design stage, as opposed to afterthought patches, effectively against cyber-attacks. However, current standards oversubscribe security measures by validating every message circulating among Connected Vehicles, making VANET subject to denial-of-service (DoS) Attacks. This interdisciplinary research shows promising results by searching the pivot point to balance between message authentication and DoS prevention, making security measures practical for the real-world deployment of Connected Vehicles. Message authentication adopts Context-Adaptive Signature Verification strategy, applying AI filters to reduce both communication and computation overhead. Combining OMNET++, a data network simulator, and SUMO, a road traffic simulator, with Veins, an open source framework for VANET simulation, the study evaluates AI filters comparatively under various attacking scenarios. The results lead to an effective design choice of securing wireless communications for Connected Vehicles.
Zero-day Malware Detection based on Supervised Learning Algorithms of API call Signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k−Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO Normalized PolyKernel, SMO – PolyKernel, SMO – Puk, and SMORadial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. _____________________________  This paper appeared at the 9th Australasian Data Mining Conference (AusDM 2011), Ballarat, Australia. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 121, Peter Vamplew, Andrew Stranieri, Kok-Leong Ong, Peter Christen and Paul Kennedy, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.
Shape Modeling with Front Propagation: A Level Set Approach

Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This paper presents a new approach to shape modeling which retains some of the attractive features of existing methods, and overcomes some of their limitations. Our techniques can be applied to model arbitrarily complex shapes, which include shapes with significant protrusions, and to situations where no a priori assumption about the object's topology is made. A single instance of our model, when presented with an image having more than one object of interest, has the ability to split freely to represent each object. This method is based on the ideas developed by Osher and Sethian to model propagating solid/liquid interfaces with curvature-dependent speeds. The interface (front) is a closed, nonintersecting, hypersurface flowing along its gradient field with constant speed or a speed that depends on the curvature. It is moved by solving a "Hamilton-Jacobi" type equation written for a function in which the interface is a particular level set. A speed term synthesized from the image is used to stop the interface in the vicinity of object boundaries. The resulting equation of motion is solved by employing entropy-satisfying upwind finite difference schemes. We present a variety of ways of computing evolving front, including narrow bands, reinitializations, and different stopping criteria. The efficacy of the scheme is demonstrated with numerical experiments on some synthesized images and some low contrast medical images.  1 Supported in part by the Applied Mathematical Sciences Subprogram of the Office of Energy Research, U.S. Dept. of Energy under Contract DE-ACD3-76SD00098 and by the NSF ARPA under grant DMS-8919074. 2 Supported in part by NSF grant ECS-9210648.
PREFER: PREdiction Model for Financial Entity Relation

The Financial Entity Identification and Information Integration (FEIII) is a competition for the understanding relationships between financial entities. To predict competitor relation between two entities, there are three challenges - 1) relevant feature extraction from the various released dataset, 2) missing entity information handling and 3) imbalance of train data handling. To solve these challenges, we propose a model named PREFER which considering 1) relation trend and context feature extraction from the release dataset, 2) K-NN estimation with concept graph of knowledge bases (Probase), and 3) oversampling from the true labeled data. From the model, we increase 34% of F1-score compared to the baseline method.
A Matlab mesh generator for the two-dimensional finite element method

A new Matlab code for the generation of unstructured (3-node or 6-node) triangular meshes in two dimensions is described. The method is based on the Matlab mesh generator distmesh of Persson and Strang [ 2004]. As input, the code takes a signed distance function for the domain geometry. A mesh size function, for the spatial node distribution, is constructed using an approximate medial axis. As outputs, the code generates a 3-node or a 6-node triangular mesh with boundary data (edges and nodes). The approach presented consists of three steps: (1) an initial nodes placement is obtained using a probabilistic node distribution, (2) an iterative smoothing is performed assuming the presence of an attractive/repulsive internode force, and (3) a fast refinement procedure is performed for 6-node triangular meshes or large scale meshes.
Molecular characterisation of Haemoglobin Constant Spring and Haemoglobin Quong Sze with a Combine-Amplification Refractory Mutation System.

The interaction of the non-deletional α(+)-thalassaemia mutations Haemoglobin Constant Spring and Haemoglobin Quong Sze with the Southeast Asian double α-globin gene deletion results in non-deletional Haemoglobin H disease. Accurate detection of non-deletional Haemoglobin H disease, which is associated with severe phenotypes, is necessary as these mutations have been confirmed in the Malaysian population.



DNA from two families with Haemoglobin H disease was extracted from EDTA-anticoagulated whole blood and subjected to molecular analysis for α-thalassaemia. A duplex polymerase chain reaction was used to detect the Southeast Asian α-globin gene deletion. Polymerase chain reaction-restriction fragment length polymorphism analysis was then carried out to determine the presence of Haemoglobin Constant Spring and Haemoglobin Quong Sze. A combine-amplification refractory mutation system protocol was optimised and implemented for the rapid and specific molecular characterisation of Haemoglobin Constant Spring and Haemoglobin Quong Sze in a single polymerase chain reaction.



The combine-amplification refractory mutation system for Haemoglobin Constant Spring and Haemoglobin Quong Sze, together with the duplex polymerase chain reaction, provides accurate pre- and postnatal diagnosis of non-deletional Haemoglobin H disease and allows detailed genotype analyses using minimal quantities of DNA.