nils-herrmann/clean_scirepeval huggingface dataset:
Interactive Evolution of Particle Systems for Computer Graphics and Animation

Interactive Evolutionary Computation (IEC) creates the intriguing possibility that a large variety of useful content can be produced quickly and easily for practical computer graphics and gaming applications. To show that IEC can produce such content, this paper applies IEC to particle system effects, which are the de facto method in computer graphics for generating fire, smoke, explosions, electricity, water, and many other special effects. While particle systems are capable of producing a broad array of effects, they require substantial mathematical and programming knowledge to produce. Therefore, efficient particle system generation tools are required for content developers to produce special effects in a timely manner. This paper details the design, representation, and animation of particle systems via two IEC tools called NEAT Particles and NEAT Projectiles. Both tools evolve artificial neural networks (ANN) with the NeuroEvolution of Augmenting Topologies (NEAT) method to control the behavior of particles. NEAT Particles evolves general-purpose particle effects, whereas NEAT Projectiles specializes in evolving particle weapon effects for video games. The primary advantage of this NEAT-based IEC approach is to decouple the creation of new effects from mathematics and programming, enabling content developers without programming knowledge to produce complex effects. Furthermore, it allows content designers to produce a broader range of effects than typical development tools. Finally, it acts as a concept generator, allowing content creators to interactively and efficiently explore the space of possible effects. Both NEAT Particles and NEAT Projectiles demonstrate how IEC can evolve useful content for graphical media and games, and are together a step toward the larger goal of automated content generation.
A Multi-Modal Neuro-Physiological Study of Phishing Detection and Malware Warnings

Detecting phishing attacks (identifying fake vs. real websites) and heeding security warnings represent classical user-centered security tasks subjected to a series of prior investigations. However, our understanding of user behavior underlying these tasks is still not fully mature, motivating further work concentrating at the neuro-physiological level governing the human processing of such tasks.
 We pursue a comprehensive three-dimensional study of phishing detection and malware warnings, focusing not only on what users' task performance is but also on how users process these tasks based on: (1) neural activity captured using Electroencephalogram (EEG) cognitive metrics, and (2) eye gaze patterns captured using an eye-tracker. Our primary novelty lies in employing multi-modal neuro-physiological measures in a single study and providing a near realistic set-up (in contrast to a recent neuro-study conducted inside an fMRI scanner). Our work serves to advance, extend and support prior knowledge in several significant ways. Specifically, in the context of phishing detection, we show that users do not spend enough time analyzing key phishing indicators and often fail at detecting these attacks, although they may be mentally engaged in the task and subconsciously processing real sites differently from fake sites. In the malware warning tasks, in contrast, we show that users are frequently reading, possibly comprehending, and eventually heeding the message embedded in the warning.
 Our study provides an initial foundation for building future mechanisms based on the studied real-time neural and eye gaze features, that can automatically infer a user's "alertness" state, and determine whether or not the user's response should be relied upon.
An Eight-Way Radial Switch Based on SIW Power Divider

This paper presents a single-pole eight-throw switch, based on an eight-way power divider, using substrate integrate waveguide(SIW) technology. Eight sectorial-lines are formed by inserting radial slot-lines on the top plate of SIW power divider. Each sectorial-line can be controlled independently with high level of isolation. The switching is accomplished by altering the capacitance of the varactor on the line, which causes different input impedances to be seen at a central probe to each sectorial line. The proposed structure works as a switching circuit and an eight-way power divider depending on the bias condition. The change in resonant frequency and input impedance are estimated by adapting a tapered transmission line model. The detailed design, fabrication, and measurement are discussed.
A 1000-word vocabulary, speaker-independent, continuous live-mode speech recognizer implemented in a single FPGA

The Carnegie Mellon In Silico Vox project seeks to move best-quality speech recognition technology from its current software-only form into a range of efficient all-hardware implementations. The central thesis is that, like graphics chips, the application is simply too performance hungry, and too power sensitive, to stay as a large software application. As a first step in this direction, we describe the design and implementation of a fully functional speech-to-text recognizer on a single Xilinx XUP platform. The design recognizes a 1000 word vocabulary, is speaker-independent, recognizes continuous (connected) speech, and is a "live mode" engine, wherein recognition can start as soon as speech input appears. To the best of our knowledge, this is the most complex recognizer architecture ever fully committed to a hardware-only form. The implementation is extraordinarily small, and achieves the same accuracy as state-of-the-art software recognizers, while running at a fraction of the clock speed.
Single incision endoscope-assisted surgery for sagittal craniosynostosis

The objective of this study is to present the novel technique and associated results of a single-incision endoscope-assisted procedure for the treatment of sagittal craniosynostosis. We retrospectively reviewed the charts of infants who underwent single-incision endoscope-assisted sagittal craniectomy for craniosynostosis at our institution. Demographic data collected included patient age, blood loss, operative time, pre- and post-operative hemoglobin, pre- and post-operative cephalic index (CI), and hospital length of stay. Seven consecutive infants underwent surgery for sagittal craniosynostosis using a single-incision endoscopic technique. Average operative time was 87 (±10.5) minutes. Average blood loss was 32 (±13.5) cubic centimeters (cc). Post-operative hemoglobin was an average of 7.1 (±0.2) g/dL. No patients required a blood transfusion intra-operatively or in the post-operative setting. Dural tears were encountered in one patient. The average hospital length of stay was 1.4 (±1.1) days. Difference between pre- and post-operative CI was 8.4 % (±3.5; p < 0.05). We demonstrate the novel use of a single-incision technique for endoscope-assisted sagittal craniosynostosis correction that improves upon the classically described surgical procedure by decreasing invasiveness, while allowing for excellent clinical outcomes.
Pelee: A Real-Time Object Detection System on Mobile Devices

There has been a rising interest in running high-quality Convolutional Neural Network (CNN) models under strict constraints on memory and computational budget. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and NASNet-A. However, all these architectures are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. Meanwhile, there are few studies that combine efficient models with fast object detection algorithms. This research tries to explore the design of an efficient CNN architecture for both image classification tasks and object detection tasks. We propose an efficient architecture named PeleeNet, which is built with conventional convolution instead. On ImageNet ILSVRC 2012 dataset, our proposed PeleeNet achieves a higher accuracy by 0.6% and 11% lower computational cost than MobileNet, the state-of-the-art efficient architecture. It is also important to point out that PeleeNet is of only 66% of the size of MobileNet and 1/49 size of VGG. We then propose a real-time object detection system on mobile devices. We combine PeleeNet with Single Shot MultiBox Detector (SSD) method and optimize the architecture for fast speed. Meanwhile, we port SSD to iOS and provide an optimized code implementation. Our proposed detection system, named Pelee, achieves 70.9% mAP on PASCAL VOC2007 dataset at the speed of 17 FPS on iPhone 6s and 23.6 FPS on iPhone 8. Compared to TinyYOLOv2, the most widely used computational efficient object detection system, our proposed Pelee is more accurate (70.9% vs. 57.1%), 2.88 times lower in computational cost and 2.92 times smaller in model size.
Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks

As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of “block mining” by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network, which is constrained by the computation resource and deployed as an infrastructure for decentralized data management applications. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multidemand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multidemand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider. Yutao Jiao, Ping Wang, Dusit Niyato and Kongrath Suankaewmanee are with School of Computer Science and Engineering, Nanyang Technological University, Singapore. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. ar X iv :1 80 4. 09 96 1v 2 [ cs .G T ] 2 8 A pr 2 01 8
Antibacterial and Antitumor Activities of Biscoumarin and Dihydropyran Derivatives

A novel series of biscoumarin (1-4) and dihydropyran (5-13) derivatives were synthesized via a one-pot multicomponent condensation reaction and evaluated for antibacterial and antitumor activity in vitro. The X-ray crystal structure analysis of four representative compounds, 3, 7, 9 and 11, confirmed the structures of these compounds. Compounds 1-4 showed the most potent antitumor activity among the total 13 derivatives; especially for compounds 1 and 2, they also emerged as promising antibacterial members with better antibacterial activity. In addition, the results of density functional theory (DFT) showed that compared with compounds 3 and 4, biscoumarins 1 and 2 had lower intramolecular hydrogen bonds (HB) energy in their structures.
Hierarchical loss for classification

Failing to distinguish between a sheepdog and a skyscraper should be worse and penalized more than failing to distinguish between a sheepdog and a poodle; after all, sheepdogs and poodles are both breeds of dogs. However, existing metrics of failure (so-called “loss” or “win”) used in textual or visual classification/recognition via neural networks seldom view a sheepdog as more similar to a poodle than to a skyscraper. We define a metric that, inter alia, can penalize failure to distinguish between a sheepdog and a skyscraper more than failure to distinguish between a sheepdog and a poodle. Unlike previously employed possibilities, this metric is based on an ultrametric tree associated with any given tree organization into a semantically meaningful hierarchy of a classifier’s classes.
Part-invariant Model for Music Generation and Harmonization

Automatic music generation has been gaining more attention in recent years. Existing approaches, however, are mostly ad hoc to specific rhythmic structures or instrumentation layouts, and lack music-theoretic rigor in their evaluations. In this paper, we present a neural language (music) model that tries to model symbolic multi-part music. Our model is part-invariant, i.e., it can process/generate any part (voice) of a music score consisting of an arbitrary number of parts, using a single trained model. For better incorporating structural information of pitch spaces, we use a structured embedding matrix to encode multiple aspects of a pitch into a vector representation. The generation is performed by Gibbs Sampling. Meanwhile, our model directly generates note spellings to make outputs human-readable. We performed objective (grading) and subjective (listening) evaluations by recruiting music theorists to compare the outputs of our algorithm with those of music students on the task of bassline harmonization (a traditional pedagogical task). Our experiment shows that errors of our algorithm and students are differently distributed, and the range of ratings for generated pieces overlaps with students’ to varying extents for our three provided basslines. This experiment suggests some future research directions.
Walter Thiel's Embalming Method: Review of Solutions and Applications in Different Fields of Biomedical Research

Walter Thiel developed the method that enables preservation of the body with natural colors in 1992. It consists in the application of an intravascular injection formula, and maintaining the corps submerged for a determinate period of time in the immersion solution in the pool. After immersion, it is possible to maintain the corps in a hermetically sealed container, thus avoiding dehydration outside the pool. The aim of this work was to review the Thiel method, searching all scientific articles describing this technique from its development point of view, and application in anatomy and morphology teaching, as well as in clinical and su rgic l practice. Most of these studies were carried out in Europe. We used PubMed, Ebsco and Embase databases with the terms “Thiel cadaver”, “Thiel embalming”, “Thiel embalming method” and we searched for papers that cited Thiel`s work. In comparison with methods commonly used with high concentrations of formaldehyde, this method lacks the emanation of noxious or irritating gases; gives the corps important passive joint mobility without stiffness; maintaining color, flexibility and tissue plasticity at a level e quivalent to that of a living body. Furthermore, it allows vascular repletion at the capillary level. All this makes for great advantage over the f rmalinfixed and fresh material. Its multiple uses are applicable in anatomy teaching and research; teaching for undergraduates (prose ction and dissection) and for training in surgical techniques for graduates and specialists (laparoscopies, arthroscopies, endoscopies).
Feasibility study for non-invasive blood glucose monitoring

The present work investigates the feasibility of a non invasive blood glucose monitoring system using an antenna placed in a cuff and wrapped around the wrist. The technique is based on the fact that glucose levels affect the dielectric properties of blood. Using a realistic tissue model of a human hand, simulations have been performed with HFSS to obtain the antenna input impedance. The resonant frequency is seen to shift with changes in blood glucose levels. Based on our previous work of tissue characterization, an analytical technique can be developed to relate this frequency shift to the permittivity and conductivity of blood, from which the glucose levels are determined.
Overview of fault diagnosis methods for Discrete Event Systems

February 2013 Accepted 26 July 2013 Available online 11 October 2013 Fault diagnosis of Discrete Event Systems has become an active research area in recent years. The research activity in this area is driven by the needs of many different application domains such as manufacturing, process control, control systems, transportation, communication networks, software engineering, and others. The aim of this paper is to review the state-of the art of methods and techniques for fault diagnosis of Discrete Event Systems based on models that include faulty behaviour. Theoretical and practical issues related to model description tools, diagnosis processing structure, sensor selection, fault representation and inference are discussed.
Word Sense Induction Using Graphs of Collocations

Word Sense Induction (WSI) is the task of identifying the different senses (uses) of a target word in a given text. Traditional graph-based approaches create and then cluster a graph, in which each vertex corresponds to a word that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated words. In contrast, in our approach each vertex corresponds to a collocation that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated collocations. A smoothing technique is applied to identify more edges between vertices and the resulting graph is then clustered. Our evaluation under the framework of SemEval-2007 WSI task shows the following: (a) our approach produces less sense-conflating clusters than those produced by traditional graph-based approaches, (b) our approach outperforms the existing state-of-the-art results.
Morphology-based and sub-word language modeling for Turkish speech recognition

We explore morphology-based and sub-word language modeling approaches proposed for morphologically rich languages, and evaluate and contrast them for Turkish broadcast news transcription task. In addition, as a morphology-based model, we improve our previously proposed morphology-integrated model for automatic speech recognition. This model is built by composing the finite-state transducer of the morphological parser with a language model over lexical morphemes. This approach provides a morphology-integrated search network with an unlimited vocabulary, generating only valid word forms while reducing the out-of-vocabulary rate and hence improving the word error rate. We also analyze the effect of morpho-tactics and morphological disambiguation on the speech recognition accuracy for the morphology-integrated model. The improved morphology-integrated model performs better than statistically derived sub-word models with added benefit of generating morpho-syntactic and semantic features.
Kinematic Design of a Dynamic Brace for Measurement of Head/Neck Motion

Head drop is a symptom commonly seen in patients with amyotrophic lateral sclerosis. These patients usually experience neck pain and have difficulty in swallowing and breathing. Static neck braces are used in current treatment. These braces, however, immobilize the head in a single configuration, which causes muscle atrophy. This letter presents the design of a dynamic neck brace for the first time in the literature, which can both measure and potentially assist in the head motion of the human user. This letter introduces the brace design method and validates its capability to perform measurements. The brace is designed based on kinematics data collected from a healthy individual via a motion capture system. A pilot study was conducted to evaluate the wearability of the brace and the accuracy of measurements with the brace. This study recruited ten participants who performed a series of head motions. The results of this human study indicate that the brace is wearable by individuals who vary in size, the brace allows nearly $70\%$ of the overall range of head rotations, and the sensors on the brace give accurate motion of the head with an error of under  $5^{\circ }$ when compared to a motion capture system. We believe that this neck brace can be a valid and accurate measurement tool for human head motion. This brace will be a big improvement in the available technologies to measure head motion as these are currently done in the clinic using hand-held protractors in two orthogonal planes.
A gentle introduction to mutual recursion

Recursion is an important topic in computer science curricula. It is related to the acquisition of competences regarding problem decomposition, functional abstraction and the concept of induction. In comparison with direct recursion, mutual recursion is considered to be more complex. Consequently, it is generally addressed superficially in CS1/2 programming courses and textbooks. We show that, when a problem is approached appropriately, not only can mutual recursion be a powerful tool, but it can also be easy to understand and fun. This paper provides several intuitive and attractive algorithms that rely on mutual recursion, and which have been designed to help strengthen students' ability to decompose problems and apply induction. Furthermore, we show that a solution based on mutual recursion may be easier to design, prove and comprehend than other solutions based on direct recursion. We have evaluated the use of these algorithms while teaching recursion concepts. Results suggest that mutual recursion, in comparison with other types of recursion, is not as hard as it seems when: (1) determining the result of a (mathematical) function call, and, most importantly, (2) designing algorithms for solving simple problems.
Protocol state fuzzing of an OpenVPN

Virtual Private Network is a widely used technology for secure data transmission. The purpose of a VPN is to provide a secure of way of transferring sensitive data between two or more parties over an insecure channel. Flaws in the implementations of security protocols are some of the most serious security problems. This paper describes a popular VPN solution, OpenVPN, as well as methodology used to infer state machines from a security protocol, using largely automated fuzzing techniques. If a vulnerability is found, an attacker may remotely exploit vulnerable systems over the Internet. State machines can be used to specify possible sequences of sent and received messages in different states of protocol. Learning techniques allow the automatic inference of the behavior of a protocol implementation as a state machine. Additionally, fuzzing is a well known and effective testing method which allows discovering different flaws within the implementations. Combining automatic state machine inference and protocol fuzzing, it is possible to produce a universal state machine which is a good representation of the implemented protocol structure. Manually inspecting these state machines allows for a straightforward way to possibly find bugs, inaccuracies or vulnerabilities in the implementation.