nils-herrmann/clean_scirepeval huggingface dataset:
An occlusion-aware feature for range images

This paper presents a novel local feature for 3D range image data called `the line image'. It is designed to be highly viewpoint invariant by exploiting the range image to efficiently detect 3D occupancy, producing a representation of the surface, occlusions and empty spaces. We also propose a strategy for defining keypoints with stable orientations which define regions of interest in the scan for feature computation. The feature is applied to the task of object classification on sparse urban data taken with a Velodyne laser scanner, producing good results.
BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music

Fundamental frequency (F0) is one of the essential features in many acoustic related applications. Although numerous F0 detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F0 value among several F0 candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F0 detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.
SAR imaging via modern 2-D spectral estimation methods

This paper discusses the use of modern 2D spectral estimation algorithms for synthetic aperture radar (SAR) imaging. The motivation for applying power spectrum estimation methods to SAR imaging is to improve resolution, remove sidelobe artifacts, and reduce speckle compared to what is possible with conventional Fourier transform SAR imaging techniques. This paper makes two principal contributions to the field of adaptive SAR imaging. First, it is a comprehensive comparison of 2D spectral estimation methods for SAR imaging. It provides a synopsis of the algorithms available, discusses their relative merits for SAR imaging, and illustrates their performance on simulated and collected SAR imagery. Some of the algorithms presented or their derivations are new, as are some of the insights into or analyses of the algorithms. Second, this work develops multichannel variants of four related algorithms, minimum variance method (MVM), reduced-rank MVM (RRMVM), adaptive sidelobe reduction (ASR) and space variant apodization (SVA) to estimate both reflectivity intensity and interferometric height from polarimetric displaced-aperture interferometric data. All of these interferometric variants are new. In the interferometric contest, adaptive spectral estimation can improve the height estimates through a combination of adaptive nulling and averaging. Examples illustrate that MVM, ASR, and SVA offer significant advantages over Fourier methods for estimating both scattering intensity and interferometric height, and allow empirical comparison of the accuracies of Fourier, MVM, ASR, and SVA interferometric height estimates.
Beyond process monitoring: a proof-of-concept of event-driven business activity management

monitoring: a proof-of-concept of event-driven business activity management.  The purpose of this paper is to show how to employ complex event processing (CEP) for the observation and management of business processes. It proposes a conceptual architecture of BPM event producer, processor, and consumer and describes technical implications for the application with standard software in a perfect order scenario.  The authors discuss business process analytics as the technological background. The capabilities of CEP in a BPM context are outlined an architecture design is proposed. A sophisticated proof-of-concept demonstrates its applicability.  The results overcome the separation and data latency issues of process controlling, monitoring, and simulation. Distinct analyses of past, present, and future blur into a holistic real-time approach. The authors highlight the necessity for configurable event producer in BPM engines, process event support in CEP engines, a common process event format, connectors to visualizers, notifiers and return channels to the BPM engine.  Further research will thoroughly evaluate the approach in a variety of business settings. New concepts and standards for the architecture's building blocks will be needed to improve maintainability and operability.  Managers learn how CEP can yield insights into business processes' operations. The paper illustrates a path to overcome inflexibility, latency, and missing feedback mechanisms of current process modeling and control solutions. Software vendors might be interested in the conceptualization and the described needs for further development.  So far, there is no commercial CEP-based BPM solution which facilitates a round trip from insight to action as outlines. As major software vendors have begun developing solutions (BPM/BPA solutions), this paper will stimulate a debate between research and practice on suitable design and technology.
The Rise and Fall of Network Stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time

Trends change rapidly in today’s world, prompting this key question: What is the mechanism behind the emergence of new trends? By representing real-world dynamic systems as complex networks, the emergence of new trends can be symbolized by vertices that “shine.” That is, at a specific time interval in a network’s life, certain vertices become increasingly connected to other vertices. This process creates new high-degree vertices, i.e., network stars. Thus, to study trends, we must look at how networks evolve over time and determine how the stars behave. In our research, we constructed the largest publicly available network evolution dataset to date, which contains 38,000 real-world networks and 2.5 million graphs. Then, we performed the first precise wide-scale analysis of the evolution of networks with various scales. Three primary observations resulted: (a) links are most prevalent among vertices that join a network at a similar time; (b) the rate that new vertices join a network is a central factor in molding a network’s topology; and (c) the emergence of network stars (high-degree vertices) is correlated with fast-growing networks. We applied our learnings to develop a flexible network-generation model based on large-scale, real-world data. This model gives a better understanding of how stars rise and fall within networks, and is applicable to dynamic systems both in nature and society. Multimedia Links I Video I Interactive Data Visualization I Data I Code Tutorials
Motor Imagery in Mental Rotation: An fMRI Study

Twelve right-handed men performed two mental rotation tasks and two control tasks while whole-head functional magnetic resonance imaging was applied. Mental rotation tasks implied the comparison of different sorts of stimulus pairs, viz. pictures of hands and pictures of tools, which were either identical or mirror images and which were rotated in the plane of the picture. Control tasks were equal except that stimuli pairs were not rotated. Reaction time profiles were consistent with those found in previous research. Imaging data replicate classic areas of activation in mental rotation for hands and tools (bilateral superior parietal lobule and visual extrastriate cortex) but show an important difference in premotor area activation: pairs of hands engender bilateral premotor activation while pairs of tools elicit only left premotor brain activation. The results suggest that participants imagined moving both their hands in the hand condition, while imagining manipulating objects with their hand of preference (right hand) in the tool condition. The covert actions of motor imagery appear to mimic the "natural way" in which a person would manipulate the object in reality, and the activation of cortical regions during mental rotation seems at least in part determined by an intrinsic process that depends on the afforded actions elicited by the kind of stimuli presented.
A Ferrite LTCC-Based Monolithic SIW Phased Antenna Array

In this paper, we present a novel configuration for realizing monolithic substrate integrated waveguide (SIW)-based phased antenna arrays using Ferrite low-temperature cofired ceramic (LTCC) technology. Unlike the current common schemes for realizing SIW phased arrays that rely on surface-mount component (p-i-n diodes, etc.) for controlling the phase of the individual antenna elements, here the phase is tuned by biasing of the ferrite filling of the SIW. This approach eliminates the need for mounting of any additional RF components and enables seamless monolithic integration of phase shifters and antennas in SIW technology. As a proof of concept, a two-element slotted SIW-based phased array is designed, fabricated, and measured. The prototype exhibits a gain of 4.9 dBi at 13.2 GHz and a maximum E-plane beam-scanning of ±28° using external windings for biasing the phase shifters. Moreover, the array can achieve a maximum beam-scanning of ±19° when biased with small windings that are embedded in the package. This demonstration marks the first time a fully monolithic SIW-based phased array is realized in Ferrite LTCC technology and paves the way for future larger size implementations.
A What-and-Where fusion neural network for recognition and tracking of multiple radar emitters

A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is considered with position-specific information from active emitters in a scene. Type-specific parameters of the input pulse stream are fed to a neural network classifier trained on samples of data collected in the field. Meanwhile, a clustering algorithm is used to separate pulses from different emitters according to position-specific parameters of the input pulse stream. Classifier responses corresponding to different emitters are separated into tracks, or trajectories, one per active emitter, allowing for more accurate identification of radar types based on multiple views of emitter data along each emitter trajectory. Such a What-and-Where fusion strategy is motivated by a similar subdivision of labor in the brain. The fuzzy ARTMAP neural network is used to classify streams of pulses according to radar type using their functional parameters. Simulation results obtained with a radar pulse data set indicate that fuzzy ARTMAP compares favorably to several other approaches when performance is measured in terms of accuracy and computational complexity. Incorporation into fuzzy ARTMAP of negative match tracking (from ARTMAP-IC) facilitated convergence during training with this data set. Other modifications improved classification of data that include missing input pattern components and missing training classes. Fuzzy ARTMAP was combined with a bank of Kalman filters to group pulses transmitted from different emitters based on their position-specific parameters, and with a module to accumulate evidence from fuzzy ARTMAP responses corresponding to the track defined for each emitter. Simulation results demonstrate that the system provides a high level of performance on complex, incomplete and overlapping radar data.
Disentangling Variational Autoencoders for Image Classification

In this paper, I investigate the use of a disentangled VAE for downstream image classification tasks. I train a disentangled VAE in an unsupervised manner, and use the learned encoder as a feature extractor on top of which a linear classifier is learned. The models are trained and evaluated on the MNIST handwritten digits dataset. Experiments compared the disentangled VAE with both a standard (entangled) VAE and a vanilla supervised model. Results show that the disentangled VAE significantly outperforms the other two models when the proportion of labelled data is artificially reduced, while it loses this advantage when the amount of labelled data increases, and instead matches the performance of the other models. These results suggest that the disentangled VAE may be useful in situations where labelled data is scarce but unlabelled data is abundant.
Constrained Novelty Search: A Study on Game Content Generation

Novelty search is a recent algorithm geared toward exploring search spaces without regard to objectives. When the presence of constraints divides a search space into feasible space and infeasible space, interesting implications arise regarding how novelty search explores such spaces. This paper elaborates on the problem of constrained novelty search and proposes two novelty search algorithms which search within both the feasible and the infeasible space. Inspired by the FI-2pop genetic algorithm, both algorithms maintain and evolve two separate populations, one with feasible and one with infeasible individuals, while each population can use its own selection method. The proposed algorithms are applied to the problem of generating diverse but playable game levels, which is representative of the larger problem of procedural game content generation. Results show that the two-population constrained novelty search methods can create, under certain conditions, larger and more diverse sets of feasible game levels than current methods of novelty search, whether constrained or unconstrained. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. Additionally, the proposed enhancement of offspring boosting is shown to enhance performance in all cases of two-population novelty search.
A 32 nm High-k Metal Gate SRAM With Adaptive Dynamic Stability Enhancement for Low-Voltage Operation

SRAM bitcell design margin continues to shrink due to random and systematic process variation in scaled technologies and conventional SRAM faces a challenge in realizing the power and density benefits of technology scaling. Smart and adaptive assist circuits can improve design margins while satisfying SRAM power and performance requirements in scaled technologies. This paper introduces an adaptive, dynamic SRAM word-line under-drive (ADWLUD) scheme that uses a bitcell-based sensor to dynamically optimize the strength of WLUD for each die. The ADWLUD sensor enables 130 mV reduction in SRAM Vccmin while increasing frequency yield by 9% over conventional SRAM without WLUD. The sensor area overhead is limited to 0.02% and power overhead is 2% for a 3.4 Mb SRAM array.
Analysis and Comparison of CPU Scheduling Algorithms

Scheduling is a fundamental operating system function, since almost all computer resources are scheduled before use. The CPU is one of the primary computer resources. Central Processing Unit (CPU) scheduling plays an important role by switching the CPU among various processes. A processor is the important resource in computer; the operating system can make the computer more productive. The purpose of the operating system is that to allow the process as many as possible running at all the time in order to make best use of CPU. The high efficient CPU scheduler depends on design of the high quality scheduling algorithms which suits the scheduling goals. In this paper, we reviewed various fundamental CPU scheduling algorithms for a single CPU and shows which algorithm is best for the particular situation.  burst time, CPU scheduling, operating system, Scheduling Algorithm, turnaround time, waiting time.
Use Dynamic Code Loading ( DCL ) ? Yes Provenance / entity Identification Dynamic Analysis App Execution Engine

Android has provided dynamic code loading (DCL) since API level one. DCL allows an app developer to load additional code at runtime. DCL raises numerous challenges with regards to security and accountability analysis of apps. While previous studies have investigated DCL on Android, in this paper we formulate and answer three critical questions that are missing from previous studies: (1) Where does the loaded code come from (remotely fetched or locally packaged), and who is the responsible entity to invoke its functionality? (2) In what ways is DCL utilized to harden mobile apps, specifically, application obfuscation? (3) What are the security risks and implications that can be found from DCL in off-the-shelf apps? We design and implement DYDROID, a system which uses both dynamic and static analysis to analyze dynamically loaded code. Dynamic analysis is used to automatically exercise apps, capture DCL behavior, and intercept the loaded code. Static analysis is used to investigate malicious behavior and privacy leakage in that dynamically loaded code. We have used DYDROID to analyze over 46K apps with little manual intervention, allowing us to conduct a large-scale measurement to investigate five aspects of DCL, such as source identification, malware detection, vulnerability analysis, obfuscation analysis, and privacy tracking analysis. We have several interesting findings. (1) 27 apps are found to violate the content policy of Google Play by executing code downloaded from remote servers. (2) We determine the distribution, pros/cons, and implications of several common obfuscation methods, including DEX encryption/loading. (3) DCL’s stealthiness enables it to be a channel to deploy malware, and we find 87 apps loading malicious binaries which are not detected by existing antivirus tools. (4) We found 14 apps that are vulnerable to code injection attacks due to dynamically loading code which is writable by other apps. (5) DCL is mainly used by third-party SDKs, meaning that app developers may not know what sort of sensitive functionality is injected into their apps.
Fuzzy Logic Based Maximum Power Point Tracking Designed for 10 kW Solar Photovoltaic System with Different Membership Functions

The electric power supplied by a photovoltaic power generation systems depends on the solar irradiation and temperature. The PV system can supply the maximum power to the load at a particular operating point which is generally called as maximum power point (MPP), at which the entire PV system operates with maximum efficiency and produces its maximum power. Hence, a Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point. The proposed MPPT controller is designed for 10kW solar PV system installed at Cape Institute of Technology. This paper presents the fuzzy logic based MPPT algorithm. However, instead of one type of membership function, different structures of fuzzy membership functions are used in the FLC design. The proposed controller is combined with the system and the results are obtained for each membership functions in Matlab/Simulink environment. Simulation results are decided that which membership function is more suitable for this system.
Design of Power-Rail ESD Clamp Circuit With Ultra-Low Standby Leakage Current in Nanoscale

An ultra-low-leakage power-rail ESD clamp circuit, composed of the SCR device and new ESD detection circuit, has been proposed with consideration of gate current to reduce the standby leakage current. By controlling the gate current of the devices in the ESD detection circuit under a specified bias condition, the whole power-rail ESD clamp circuit can achieve an ultra-low standby leakage current. The new proposed circuit has been fabricated in a 1 V 65 nm CMOS process for experimental verification. The new proposed power-rail ESD clamp circuit can achieve 7 kV HBM and 325 V MM ESD levels while consuming only a standby leakage current of 96 nA at 1 V bias in room temperature and occupying an active area of only 49 m 21 m.
Streaming Connected Component Computation for Trillion Voxel Images

We describe a highly scalable algorithm for computing connected components in large binary 3D grids. We employ a streaming version of the union-find algorithm that—as it processes the voxel grid row-by-row and layer-by-layer—only maintains the component structure along a single layer of active rows. We can process voxel grids whose sizes are three orders of magnitude larger than what the best previously reported method is capable of and still use less than half the memory. For example, given a random binary grid of 10,000×10,000×10,000 (= one trillion) voxels our method can count the number of components and report the volume and surface area of each using no more than 350 MB of main memory.
A survey on wearable health monitoring systems

Wearable health monitoring systems are being used as a way of cost reduction, and extension of medical care outside medical facilities. But, despite considerable time and work of the research in the field, important challenges, such as power consumption or security issues, still are a major concern. In this paper we present a survey in the field, extended to systems not wearable, to give a broad perspective of the research in this area, and find some of the most relevant challenges that this technology presents nowadays.
Digital games-based learning for children

Digital game play is becoming increasingly prevalent. Its participant-players number in the millions and its revenues are in billions of dollars. As they grow in popularity, digital games are also growing in complexity, depth and sophistication. This paper presents reasons why games and game play matter to the future of education. Drawing upon these works, the potential for instruction in digital games is recognised. Previous works in the area were also analysed with respect to their theoretical findings. We then propose a framework for digital Game-based Learning approach for adoption in education setting.
Scalable high-performance architecture for convolutional ternary neural networks on FPGA

Thanks to their excellent performances on typical artificial intelligence problems, deep neural networks have drawn a lot of interest lately. However, this comes at the cost of large computational needs and high power consumption. Benefiting from high precision at acceptable hardware cost on these difficult problems is a challenge. To address it, we advocate the use of ternary neural networks (TNN) that, when properly trained, can reach results close to the state of the art using floatingpoint arithmetic. We present a highly versatile FPGA friendly architecture for TNN in which we can vary both the number of bits of the input data and the level of parallelism at synthesis time, allowing to trade throughput for hardware resources and power consumption. To demonstrate the efficiency of our proposal, we implement high-complexity convolutional neural networks on the Xilinx Virtex-7 VC709 FPGA board. While reaching a better accuracy than comparable designs, we can target either high throughput or low power. We measure a throughput up to 27 000 fps at ≈7W or up to 8.36 TMAC/s at ≈13 W.