nils-herrmann/clean_scirepeval huggingface dataset:
New Algorithms for Enumerating All Maximal Cliques

In this paper, we consider the problems of generating all maximal (bipartite) cliques in a given (bipartite) graph G = (V, E) with n vertices and m edges. We propose two algorithms for enumerating all maximal cliques. One runs with O(M(n)) time delay and in O(n) space and the other runs with O(∆) time delay and in O(n + m) space, where ∆ denotes the maximum degree of G, M(n) denotes the time needed to multiply two n×n matrices, and the latter one requires O(nm) time as a preprocessing. For a given bipartite graph G, we propose three algorithms for enumerating all maximal bipartite cliques. The first algorithm runs with O(M(n)) time delay and in O(n) space, which immediately follows from the algorithm for the nonbipartite case. The second one runs with O(∆) time delay and in O(n + m) space, and the last one runs with O(∆) time delay and in O(n + m + N∆) space, where N denotes the number of all maximal bipartite cliques in G and both algorithms require O(nm) time as a preprocessing. Our algorithms improve upon all the existing algorithms, when G is either dense or sparse. Furthermore, computational experiments show that our algorithms for sparse graphs have significantly good performance for graphs which are generated randomly and appear in real-world problems.
Bi-Lingual Text to Speech Synthesis System for Urdu and Sindhi

Text to Speech Synthesis along with the Speech Recognition is widely used throughout the world to enhance the accessibility of the information and enable even the disabled persons to interact with the computers in order to get the potential benefit from this high-tech revolution. In this paper we introduce a bi-lingual novel algorithm for the synthesis of Urdu and Sindhi language text. The devised bi-lingual algorithm uses knowledge based approach along with the hybrid rule based and concatenative acoustic methods to provide efficient and accurate conversion of Urdu and Sindhi text into the high quality speech. The algorithm has been implemented in the VB programming language with a GUI based interface. The proposed system works with high accuracy and has a great potential to be used for variety of applications. The system is versatile enough and can be used for speech recognition also.
Efficient analytical fragility function fitting using dynamic structural analysis

Estimation of fragility functions using dynamic structural analysis is an important step in a number of seismic assessment procedures. This paper discusses the applicability of statistical inference concepts for fragility function estimation, describes appropriate fitting approaches for use with various structural analysis strategies, and studies how to fit fragility functions while minimizing the required number of structural analyses. Illustrative results show that multiple stripe analysis produces more efficient fragility estimates than incremental dynamic analysis for a given number of structural analyses, provided that some knowledge of the building’s capacity is available prior to analysis so that relevant portions of the fragility curve can be approximately identified. This finding has other benefits, as the multiple stripe analysis approach allows for different ground motions to be used for analyses at varying intensity levels, to represent the differing characteristics of low intensity and high intensity shaking. The proposed assessment approach also provides a framework for evaluating alternate analysis procedures that may arise in the future.
Automatic linearization of nonlinear skinning

Linear blending is a very popular skinning technique for virtual characters, even though it does not always generate realistic deformations. Recently, nonlinear blending techniques (such as dual quaternions) have been proposed in order to improve upon the deformation quality of linear skinning. The trade-off consists of the increased vertex deformation time and the necessity to redesign parts of the 3D engine. In this paper, we demonstrate that any nonlinear skinning technique can be approximated to an arbitrary degree of accuracy by linear skinning, using just a few samples of the nonlinear blending function (virtual bones). We propose an algorithm to compute this linear approximation in an automatic fashion, requiring little or no interaction with the user. This enables us to retain linear skinning at the core of our 3D engine without compromising the visual quality or character setup costs.
GaN MMIC PAs for E-Band (71 GHz - 95 GHz) Radio

High data rate E-band (71 GHz- 76 GHz, 81 GHz - 86 GHz, 92 GHz - 95 GHz) communication systems will benefit from power amplifiers that are more than twice as powerful than commercially available GaAs pHEMT MMICs. We report development of three stage GaN MMIC power amplifiers for E-band radio applications that produce 500 mW of saturated output power in CW mode and have > 12 dB of associated power gain. The output power density from 300 mum output gate width GaN MMICs is seven times higher than the power density of commercially available GaAs pHEMT MMICs in this frequency range.
Two retinotopic visual areas in human lateral occipital cortex.

We describe two visual field maps, lateral occipital areas 1 (LO1) and 2 (LO2), in the human lateral occipital cortex between the dorsal part of visual area V3 and visual area V5/MT+. Each map contained a topographic representation of the contralateral visual hemifield. The eccentricity representations were shared with V1/V2/V3. The polar angle representation in LO1 extended from the lower vertical meridian (at the boundary with dorsal V3) through the horizontal to the upper vertical meridian (at the boundary with LO2). The polar angle representation in LO2 was the mirror-reversal of that in LO1. LO1 and LO2 overlapped with the posterior part of the object-selective lateral occipital complex and the kinetic occipital region (KO). The retinotopy and functional properties of LO1 and LO2 suggest that they correspond to two new human visual areas, which lack exact homologues in macaque visual cortex. The topography, stimulus selectivity, and anatomical location of LO1 and LO2 indicate that they integrate shape information from multiple visual submodalities in retinotopic coordinates.
Social interactions in P2P lending

Access to capital in the form of credit through money lending requires that the lender to be able to measure the risk of repayment for a given return. In ancient times money lending needed to occur between known parties or required collateral to secure the loan. In the modern era of banking institutions provide loans to individuals who meet a qualification test. Grameen Bank in Bangladesh has demonstrated that small poor communities benefited from the "microcredit" financial innovation, which allowed a priori non-bankable entrepreneurs to engage in self-employment projects. Online P2P (Peer to Peer) lending is considered an evolution of the microcredit concept, and reflects the application of its principles into internet communities. Internet ventures like Prosper.com, Zopa or Lendingclub.com, provide the means for lenders and borrowers to meet, interact and define relationships as part of social groups. This paper measures the influence of social interactions in the risk evaluation of a money request; with special focus on the impact of one-to-one and one-to-many relationships. The results showed that fostering social features increases the chances of getting a loan fully funded, when financial features are not enough to construct a differentiating successful credit request. For this task, a model-based clustering method was applied on actual P2P Lending data provided by Prosper.com.
Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing

The increase in the number and diversity of smart objects has raised substantial cybersecurity challenges due to the recent exponential rise in the occurrence and sophistication of attacks. Although cloud computing has transformed the world of business in a dramatic way, its centralization hammers the application of distributed services such as security mechanisms for IoT applications. The new and emerging IoT applications require novel cybersecurity controls, models, and decisions distributed at the edge of the network. Despite the success of the existing cryptographic solutions in the traditional Internet, factors such as system development flaws, increased attack surfaces, and hacking skills have proven the inevitability of detection mechanisms. The traditional approaches such as classical machine-learning-based attack detection mechanisms have been successful in the last decades, but it has already been proven that they have low accuracy and less scalability for cyber-attack detection in massively distributed nodes such as IoT. The proliferation of deep learning and hardware technology advancement could pave a way to detecting the current level of sophistication of cyber-attacks in edge networks. The application of deep networks has already been successful in big data areas, and this indicates that fog-tothings computing can be the ultimate beneficiary of the approach for attack detection because a massive amount of data produced by IoT devices enable deep models to learn better than shallow algorithms. In this article, we propose a novel distributed deep learning scheme of cyber-attack detection in fog-to-things computing. Our experiments show that deep models are superior to shallow models in detection accuracy, false alarm rate, and scalability.
Augmenting Software Architectures with Physical Components

This paper presents an extension of existing software architecture tools to model physical systems, their interconnections, and the interactions between physical and cyber components. We introduce a new cyber-physical system (CPS) architectural style to support the construction of architectural descriptions of complete systems and to serve as the reference context for analysis and evaluation of design alternatives using existing model-based tools. The implementation of the CPS architectural style in AcmeStudio includes behavioral annotations on components and connectors using either finite state processes (FSP) or linear hybrid automata (LHA) with plug-ins to perform behavior analysis. The application of the CPS architectural style is illustrated for the STARMAC quadrotor.
OrigamiBot-I: A thread-actuated origami robot for manipulation and locomotion

This paper presents OrigamiBot-I, a thread-actuated origami robot, to demonstrate a physical application of an origami design for robotic manipulation and locomotion. The selected design can generate twisting and bending motions by pulling, pushing, or torsional force applied to the origami structure. Thread-based actuation also enables various shapes and motions by using different numbers of threads and routing them through different paths. The kinematics for each twisting and bending motions based on estimated parameters is derived. To evaluate potential use of origami for real-world applications and identify structural weaknesses, preliminary stiffness and durability testing was conducted. For physical demonstrations of robotic manipulation and locomotion, OrigamiBot-I was equipped with four independently-routed threads, where each thread is controlled by a geared DC motor. The robot successfully demonstrated its simple manipulation and locomotion capabilities.
Attitudes towards science : a review of the literature and its implications

This article offers a review of the major literature about attitudes to science and its implications over the past 20 years. It argues that the continuing decline in numbers choosing to study science at the point of choice requires a research focus on students’ attitudes to science if the nature of the problem is to be understood and remediated. Starting from a consideration of what is meant by attitudes to science, it considers the problems inherent to their measurement, what is known about students’ attitudes towards science and the many factors of influence such as gender, teachers, curricula, cultural and other variables. The literature itself points to the crucial importance of gender and the quality of teaching. Given the importance of the latter we argue that there is a greater need for research to identify those aspects of science teaching that make school science engaging for pupils. In particular, a growing body of research on motivation offers important pointers to the kind of classroom environment and activities that might raise pupils’ interest in studying school science and a focus for future research.
A file provenance system

A file provenance system supports the automatic collection and management of provenance i.e. the complete processing history of a data object. File system level provenance provides functionality unavailable in the existing provenance systems. In this paper, we discuss the design objectives for a flexible and efficient file provenance system and then propose the design of such a system, called FiPS. We design FiPS as a thin stackable file system for capturing provenance in a portable manner. FiPS can capture provenance at various degrees of granularity, can transform provenance records into secure information, and can direct the resulting provenance data to various persistent storage systems.
An Improved $LLC$ Resonant Inverter for Induction-Heating Applications With Asymmetrical Control

This paper proposes a modified LLC resonant load configuration of a full-bridge inverter for induction-heating applications. The LLC load configuration is a combination of a series inductor, a matching transformer, and an inductor and a capacitor connected in parallel. The output power is controlled using the asymmetrical voltage-cancellation technique. With the use of a phase-locked loop control, the operating frequency is automatically adjusted to maintain a small constant lagging phase angle under load-parameter variation during the heating process. The proposed configuration has the benefit of smaller inductance and inherent short-circuit protection capability in case a short circuit occurs at the induction coil or from transformer saturation. The validity of the proposed method is verified through computer simulation and hardware experiment at an operating frequency range of 108.7-110.6 kHz.
Mirror mirror on the wall… An intelligent multisensory mirror for well-being self-assessment

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.
Lane detection and tracking using a new lane model and distance transform

Lane detection is a significant component of driver assistance systems. Highway-based lane departure warning solutions are in the market since the mid-1990s. However, improving and generalizing vision-based lane detection remains to be a challenging task until recently. Among various lane detection methods developed, strong lane models, based on the global assumption of lane shape, have shown robustness in detection results, but are lack of flexibility to various shapes of lane. On the contrary, weak lane models will be adaptable to different shapes, as well as to maintain robustness. Using a typical weak lane model, particle filtering of lane boundary points has been proved to be a robust way to localize lanes. Positions of boundary points are directly used as the tracked states in the current research. This paper introduces a new weak lane model with this particle filter-based approach. This new model parameterizes the relationship between points of left and right lane boundaries, and can be used to detect all types of lanes. Furthermore, a modified version of an Euclidean distance transform is applied on an edge map to provide information for boundary point detection. In comparison to an edge map, properties of this distance transform support improved lane detection, including a novel initialization and tracking method. This paper fully explains how the application of this distance transform greatly facilitates lane detection and tracking. Two lane tracking methods are also discussed while focusing on efficiency and robustness, respectively. Finally, the paper reports about experiments on lane detection and tracking, and comparisons with other methods.
Measuring cognitive load

The paper ‘Cognitive load predicts point-of-care ultrasound simulator performance’ by Aldekhyl, Cavalcanti, and Naismith, in this issue of Perspectives on Medical Education , is an important paper that adds to work on cognitive load theory and medical education [2–4]. The implications of the findings of this paper extend substantially beyond the confines of medical practice that is the focus of the work. In this commentary, I will discuss issues associated with obtaining measures of cognitive load independently of content task performance during instruction. I will begin with a brief history of attempts to provide independent measures of cognitive load. In the 1980s, cognitive load was used as a theoretical construct to explain experimental results with very little attempt to directly measure load . The theory was used to predict differential learning using particular instructional designs. Randomized controlled trials were run to test the predictions and if the hypothesized results were obtained they were attributed to cognitive load factors. The distinction between extraneous and intrinsic cognitive load had not been specified but the results were due to what was called and continues to be called extraneous cognitive load. Cognitive load was an assumed rather than a measured construct. At that time, the only attempt to provide an independent indicator of load was to use computational models [6] with quantitative differences between models used as cognitive load proxies. The first rating scale measure of cognitive load was introduced in the early 1990s by Fred Paas . The Paas scale continues to be the most popular measure of cognitive load and was used by Aldekhyl et al. to validate alternative measures of load. It is very easy to use and requires no more than a minute or so of a participant’s time. Used primarily to measure extraneous cognitive load it has repeatedly indicated that instructional designs hypothesized to decrease
Unified Power Control for PMSG-Based WECS Operating Under Different Grid Conditions

A unified power control strategy is proposed for the permanent magnet synchronous generator-based wind energy conversion system (WECS) operating under different grid conditions. In the strategy, the generator-side converter is used to control the dc-link voltage and the grid-side converter is responsible for the control of power flow injected into the grid. The generator-side controller has inherent damping capability of the torsional oscillations caused by drive-train characteristics. The grid-side control is utilized to satisfy the active and reactive current (power) requirements defined in the grid codes, and at the same time mitigates the current distortions even with unsymmetrical grid fault. During grid faults, the generator-side converter automatically reduces the generator current to maintain the dc voltage and the resultant generator acceleration is counteracted by pitch regulation. Compared with the conventional strategy, the dc chopper, which is intended to assist the fault ride through of the WECS, can be eliminated if the proposed scheme is employed. Compared with the variable-structured control scheme, the proposed strategy has quicker and more precise power responses, which is beneficial to the grid recovery. The simulation results verify the effectiveness of the proposed strategy.
Feature Weighting for Improved Classifier Robustness

There are often discrepancies between the learning sample and the evaluation environment, be it natural or adversarial. It is therefore desirable that classifiers are robust, i.e., not very sensitive to changes in data distribution. In this paper, we introduce a new methodology to measure the lower bound of classifier robustness under adversarial attack and show that simple averaged classifiers can improve classifier robustness significantly. In addition, we propose a new feature reweighting technique that ameliorates the performance and robustness of standard classifiers at at most twice the computational cost. We verify our claims in content based email spam classification experiments on some public and private datasets.
BlenSor: Blender Sensor Simulation Toolbox

This paper introduces a novel software package for the simulation of various types of range scanners. The goal is to provide researchers in the elds of obstacle detection, range data segmentation, obstacle tracking or surface reconstruction with a versatile and powerful software package that is easy to use and allows to focus on algorithmic improvements rather than on building the software framework around it. The simulation environment and the actual simulations can be e ciently distributed with a single compact le. Our proposed approach facilitates easy regeneration of published results, hereby highlighting the value of reproducible research.
Twin Peaks: A software platform for heterogeneous computing on general-purpose and graphics processors

Modern processors are evolving into hybrid, heterogeneous processors with both CPU and GPU cores used for general purpose computation. Several languages such as Brook, CUDA, and more recently OpenCL are being developed to fully harness the potential of these processors. These languages typically involve the control code running on the CPU and the performance-critical, data-parallel kernel code running on the GPUs.
 In this paper, we present Twin Peaks, a software platform for heterogeneous computing that executes code originally targeted for GPUs efficiently on CPUs as well. This permits a more balanced execution between the CPU and GPU, and enables portability of code between these architectures and to CPU-only environments. We propose several techniques in the runtime system to efficiently utilize the caches and functional units present in CPUs. Using OpenCL as a canonical language for heterogeneous computing, and running several experiments on real hardware, we show that our techniques enable GPGPU-style code to execute efficiently on multicore CPUs with minimal runtime overhead. These results also show that for maximum performance, it is beneficial for applications to utilize both CPUs and GPUs as accelerator targets.
Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel

This paper describes a new computational finance approach. This approach combines pattern recognition techniques with an evolutionary computation kernel applied to financial markets time series in order to optimize trading strategies. Moreover, for pattern matching a template-based approach is used in order to describe the desired trading patterns. The parameters for the pattern templates, as well as, for the decision making rules are optimized using a genetic algorithm kernel. The approach was tested considering actual data series and presents a robust profitable trading strategy which clearly beats the market, S&P 500 index, reducing the investment risk significantly.