nils-herrmann/clean_scirepeval huggingface dataset:
Discrete Bayesian Network Classifiers: A Survey

We have had to wait over 30 years since the naive Bayes model was first introduced in 1960 for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks, these classifiers have many strengths, like model interpretability, accommodation to complex data and classification problem settings, existence of efficient algorithms for learning and classification tasks, and successful applicability in real-world problems. In this article, we survey the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Bayes, seminaive Bayes, one-dependence Bayesian classifiers, k-dependence Bayesian classifiers, Bayesian network-augmented naive Bayes, Markov blanket-based Bayesian classifier, unrestricted Bayesian classifiers, and Bayesian multinets. Issues of feature subset selection and generative and discriminative structure and parameter learning are also covered.
Hindsight policy gradients

A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals. In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enable sample efficient learning. However, reinforcement learning agents have only recently been endowed with such capacity for hindsight. In this paper, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms. Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency.
Critical Success Factors (CSFs) of ERP in Higher Education Institutions

Recently, ERP implementation has been transformed from the business side into educational side. The failure implementation of ERP in higher education is referred to many factors. In addition to the increment of the level of risk that invoked on the study of ERP, the study of critical success factors has great effects on making system fail or success in implementation. Despite of efforts that had been done to enhance the implementation of ERP; it still suffers from failure, especially in higher education. The present study investigates that ERP system will succeed if we employ a set of factors such as clear vision and objectives, top management support and commitment, clear business process, information flow and organizational structure, budget size and cost, integrated department and solve the problem of human resources management, project management, training and education, careful change management and effective communication and connection in ERP higher education system. According to the preliminary results of this study, these factors help in successful implementation of ERP in higher education.  ERP, CSFs, Higher education, Successful ERP implementation, institutions.
Camera Model Identification Based on the Heteroscedastic Noise Model

The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the heteroscedastic noise model, which more accurately describes a natural raw image. This model is characterized by only two parameters, which are considered as unique fingerprint to identify camera models. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the likelihood ratio test (LRT) is presented and its performances are theoretically established. For a practical use, two generalized LRTs are designed to deal with unknown model parameters so that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural raw images highlight the relevance of the proposed approach.
A Low-Power Bidirectional Telemetry Device With a Near-Field Charging Feature for a Cardiac Microstimulator

In this paper, wireless telemetry using the near-field coupling technique with round-wire coils for an implanted cardiac microstimulator is presented. The proposed system possesses an external powering amplifier and an internal bidirectional microstimulator. The energy of the microstimulator is provided by a rectifier that can efficiently charge a rechargeable device. A fully integrated regulator and a charge pump circuit are included to generate a stable, low-voltage, and high-potential supply voltage, respectively. A miniature digital processor includes a phase-shift-keying (PSK) demodulator to decode the transmission data and a self-protective system controller to operate the entire system. To acquire the cardiac signal, a low-voltage and low-power monitoring analog front end (MAFE) performs immediate threshold detection and data conversion. In addition, the pacing circuit, which consists of a pulse generator (PG) and its digital-to-analog (D/A) controller, is responsible for stimulating heart tissue. The chip was fabricated by Taiwan Semiconductor Manufacturing Company (TSMC) with 0.35-μm complementary metal-oxide semiconductor technology to perform the monitoring and pacing functions with inductively powered communication. Using a model with lead and heart tissue on measurement, a -5-V pulse at a stimulating frequency of 60 beats per minute (bpm) is delivered while only consuming 31.5 μW of power.
Advanced illumination techniques for GPU volume raycasting

Volume raycasting techniques are important for both visual arts and visualization. They allow an efficient generation of visual effects and the visualization of scientific data obtained by tomography or numerical simulation. Thanks to their flexibility, experts agree that GPU-based raycasting is the state-of-the art technique for interactive volume rendering. It will most likely replace existing slice-based techniques in the near future. Volume rendering techniques are also effective for the direct rendering of implicit surfaces used for soft body animation and constructive solid geometry.
 The lecture starts off with an in-depth introduction to the concepts behind GPU-based ray-casting to provide a common base for the following parts. The focus of this course is on advanced illumination techniques which approximate the physically-based light transport more convincingly. Such techniques include interactive implementation of soft and hard shadows, ambient occlusion and simple Monte-Carlo based approaches to global illumination including translucency and scattering.
 With the proposed techniques, users are able to interactively create convincing images from volumetric data whose visual quality goes far beyond traditional approaches. The optical properties in participating media are defined using the phase function. Many approximations to the physically based light transport applied for rendering natural phenomena such as clouds or smoke assume a rather homogenous phase function model. For rendering volumetric scans on the other hand different phase function models are required to account for both surface-like structures and fuzzy boundaries in the data. Using volume rendering techniques, artists who create medical visualization for science magazines may now work on tomographic scans directly, without the necessity to fall back to creating polygonal models of anatomical structures.
Filtering of Hall-Sensor Signals for Improved Operation of Brushless DC Motors

Brushless dc motors controlled by Hall-effect sensors are used in variety of applications, wherein the Hall sensors should be placed 120 electrical degrees apart. This is difficult to achieve in practice especially in low-precision motors, which leads to unsymmetrical operation of the inverter/motor phases. To mitigate this phenomenon, an approach of filtering the Hall-sensor signals has been recently proposed. This letter extends the previous work and presents a very efficient digital implementation of such filters that can be easily included into various brushless dc motor-drive systems for restoring their operation in steady state and transients.
A study of low level vibrations as a power source for wireless sensor nodes

Advances in low power VLSI design, along with the potentially low duty cycle of wireless sensor nodes open up the possibility of powering small wireless computing devices from scavenged ambient power. A broad review of potential power scavenging technologies and conventional energy sources is first presented. Low-level vibrations occurring in common household and office environments as a potential power source are studied in depth. The goal of this paper is not to suggest that the conversion of vibrations is the best or most versatile method to scavenge ambient power, but to study its potential as a viable power source for applications where vibrations are present. Different conversion mechanisms are investigated and evaluated leading to specific optimized designs for both capacitive MicroElectroMechancial Systems (MEMS) and piezoelectric converters. Simulations show that the potential power density from piezoelectric conversion is significantly higher. Experiments using an off-the-shelf PZT piezoelectric bimorph verify the accuracy of the models for piezoelectric converters. A power density of 70 mW/cm has been demonstrated with the PZT bimorph. Simulations show that an optimized design would be capable of 250 mW/cm from a vibration source with an acceleration amplitude of 2.5 m/s at 120 Hz. q 2002 Elsevier Science B.V.. All rights reserved.
Suspended Diamond-Shaped Nanowire With Four {111} Facets for High-Performance Ge Gate-All-Around FETs

A feasible pathway to scale germanium (Ge) FETs in future technology nodes has been proposed using the tunable diamond-shaped Ge nanowire (NW). The Ge NW was obtained through a simple top-down dry etching and blanket Ge epitaxy techniques readily available in mass production. The different etching selectivity of surface orientations for Cl2 and HBr was employed for the three-step isotropic/anisotropic/isotropic dry etching. The ratio of Cl2 and HBr, mask width, and Ge recess depth were crucial for forming the nearly defect-free suspended Ge channel through effective removal of dislocations near the Si/Ge interface. This technique could also be applied for forming diamond-shaped Si NWs. The suspended diamond-shaped NW gate-all-around NWFETs feature excellent electrostatics, the favorable {111} surfaces along the (110) direction with high carrier mobility, and the nearly defect-free Ge channel. The pFET with a high ION/IOFF ratio of 6 × 107 and promising nFET performance have been demonstrated successfully.
Visual Search at Alibaba

This paper introduces the large scale visual search algorithm and system infrastructure at Alibaba. The following challenges are discussed under the E-commercial circumstance at Alibaba (a) how to handle heterogeneous image data and bridge the gap between real-shot images from user query and the online images. (b) how to deal with large scale indexing for massive updating data. (c) how to train deep models for effective feature representation without huge human annotations. (d) how to improve the user engagement by considering the quality of the content. We take advantage of large image collection of Alibaba and state-of-the-art deep learning techniques to perform visual search at scale. We present solutions and implementation details to overcome those problems and also share our learnings from building such a large scale commercial visual search engine. Specifically, model and search-based fusion approach is introduced to effectively predict categories. Also, we propose a deep CNN model for joint detection and feature learning by mining user click behavior. The binary index engine is designed to scale up indexing without compromising recall and precision. Finally, we apply all the stages into an end-to-end system architecture, which can simultaneously achieve highly efficient and scalable performance adapting to real-shot images. Extensive experiments demonstrate the advancement of each module in our system. We hope visual search at Alibaba becomes more widely incorporated into today's commercial applications.
The Persuasive Effect of Source Credibility : Tests of Cognitive Response

In experimental investigations of the persuasive effect of source credibility, it has been frequently demonstrated that highly trustworthy and expert spokespeople induce a greater positive attitude toward the position they advocate than do communicators with less credibility (cf. Stemthal, Phillips, and Dholakia in press). This finding can be explained in terms of cognitive response (cf. Greenwald 1968, 1970; Petty, Ostrom, and Brock 1978). According to this formulation, a message recipient's initial opinion is an important determinant of influence. In response to a persuasive appeal, individuals rehearse their issuerelevant thoughts, as well as those presented to them. Message rejection occurs when people opposed to the communicator's advocacy review counterarguments to assertions made to the message. If a highly credible source inhibits counterarguing. whereas a less credible source does not, cognitive response predicts the superior persuasive power of a highly credible communicator. Consistent with this interpretation. Cook (1969) reported less counterargumentation in response to a competent source than to an incompetent source. Despite the substantial number of studies indicating that a highly credible source is more persuasive than a low credibility source, this finding is less than univocal.
Object-based analysis of CT images for automatic detection and segmentation of hypodense liver lesions

Hypodense liver lesions are commonly detected in CT, so their segmentation and characterization are essential for diagnosis and treatment. Methods for automatic detection and segmentation of liver lesions were developed to support this task. The detection algorithm uses an object-based image analysis approach, allowing for effectively integrating domain knowledge and reasoning processes into the detection logic. The method is intended to succeed in cases typically difficult for computer-aided detection systems, especially low contrast of hypodense lesions relative to healthy tissue. The detection stage is followed by a dedicated segmentation algorithm needed to synthesize 3D segmentations for all true-positive findings. The automated method provides an overall detection rate of 77.8% with a precision of 0.53 and performs better than other related methods. The final lesion segmentation delivers appropriate quality in 89% of the detected cases, as evaluated by two radiologists. A new automated liver lesion detection algorithm employs the strengths of an object-based image analysis approach. The combination of automated detection and segmentation provides promising results with potential to improve diagnostic liver lesion evaluation.
Effects of current and voltage harmonics on distribution transformer losses

The drive to improve energy efficiency and reduce electrical loading has resulted in energy efficient lighting such as compact fluorescent lamps (CFLs) replacing conventional incandescent lamps. However, the presence of such non-linear loads has brought about the injection of voltage and current harmonics into electrical networks. As transformers are the interface between the supply and the non-linear loads, the investigation of their effects on transformer losses is of great importance. These harmonics can cause excessive loss and abnormal temperature rise in the transformers, thus reducing their operational life span. This paper investigates the impact of current and voltage harmonics of the loads on a single-phase 25kVA distribution transformer. Harmonic spectra of a range of non-linear loads including CFL, LED tube, PC and fluorescent lamp are obtained. A single-phase inverter is used for harmonic generation to simulate power supply harmonics injected into the transformer. Open circuit and short circuit tests are conducted on the transformer under the effect of harmonics, and the impacts on core loss are analyzed.
Exploring semi-supervised and active learning for activity recognition

In recent years research on human activity recognition using wearable sensors has enabled to achieve impressive results on real-world data. However, the most successful activity recognition algorithms require substantial amounts of labeled training data. The generation of this data is not only tedious and error prone but also limits the applicability and scalability of today's approaches. This paper explores and systematically analyzes two different techniques to significantly reduce the required amount of labeled training data. The first technique is based on semi-supervised learning and uses self-training and co-training. The second technique is inspired by active learning. In this approach the system actively asks which data the user should label. With both techniques, the required amount of training data can be reduced significantly while obtaining similar and sometimes even better performance than standard supervised techniques. The experiments are conducted using one of the largest and richest currently available datasets.
GASPP: A GPU-Accelerated Stateful Packet Processing Framework

Graphics processing units (GPUs) are a powerful platform for building high-speed network traffic processing applications using low-cost hardware. Existing systems tap the massively parallel architecture of GPUs to speed up certain computationally intensive tasks, such as cryptographic operations and pattern matching. However, they still suffer from significant overheads due to criticalpath operations that are still being carried out on the CPU, and redundant inter-device data transfers. In this paper we present GASPP, a programmable network traffic processing framework tailored to modern graphics processors. GASPP integrates optimized GPUbased implementations of a broad range of operations commonly used in network traffic processing applications, including the first purely GPU-based implementation of network flow tracking and TCP stream reassembly. GASPP also employs novel mechanisms for tackling control flow irregularities across SIMT threads, and sharing memory context between the network interface and the GPU. Our evaluation shows that GASPP can achieve multi-gigabit traffic forwarding rates even for computationally intensive and complex network operations such as stateful traffic classification, intrusion detection, and packet encryption. Especially when consolidating multiple network applications on the same device, GASPP achieves up to 16.2× speedup compared to standalone GPU-based implementations of the same applications.
Liquid Crystal Polymer (LCP): The ultimate solution for low-cost RF flexible electronics and antennas

In this paper, solutions for developing low cost electronics for antenna transceivers that take advantage of the stable electrical properties of the organic substrate liquid crystal polymer (LCP) has been presented. Three important ingredients in RF wireless transceivers namely embedded passives, a dual band filter and a RFid antenna have been designed and fabricated on LCP. Test results of all 3 of the structures show good agreement between the simulated and measured results over their respective bandwidths, demonstrating stable performance of the LCP substrate.
New approaches to storing and manipulating multi-dimensional sparse arrays

In this paper, we introduce some storage schemes for multi-dimensional sparse arrays (MDSAs) that handle the sparsity of the array with two primary goals; reducing the storage overhead and maintaining efficient data element access. Four schemes are proposed. These are: i.) The PATRICIA trie compressed storage method (PTCS) which uses PATRICIA trie to store the valid non-zero array elements; ii.)The extended compressed row storage (xCRS) which extends CRS method for sparse matrix storage to sparse arrays of higher dimensions and achieves the best data element access efficiency of all the methods; iii.) The bit encoded xCRS (BxCRS) which optimizes the storage utilization of xCRS by applying data compression methods with run length encoding, while maintaining its data access efficiency; and iv.) a hybrid approach that provides a desired balance between the storage utilization and data manipulation efficiency by combining xCRS and the Bit Encoded Sparse Storage (BESS). These storage schemes were evaluated and compared on three basic array operations; constructing the storage scheme, accessing a random element and retrieving a sub-array, using a set of synthetic sparse multi-dimensional arrays.
Burst Mode Elimination in High-Power $LLC$ Resonant Battery Charger for Electric Vehicles

In order to recover and fully charge batteries in electric vehicles, smart battery chargers should not only work under different loading conditions and output voltage regulations (close to zero to 1.5 times the nominal output voltage), but also provide a ripple-free charging current for battery packs and a noise-free environment for the battery management system (BMS). In this paper, an advanced LLC design procedure is investigated to provide advantageous extreme regulation and eliminate detrimental burst mode operation. A modified, special LLC tank driven by both variable frequency and phase shift proves to be a successful solution to achieve all the regulation requirements for battery charging (from recovery, bulk, equalization, to finish). The proposed solution can eliminate the negative impact of burst mode noises on the BMS, provide a ripple-free charging current for batteries in different states of charge, reduce the switching frequency variation, and facilitate the EMI filter and magnetic components designs procedure. In order to fully consider the characteristics of the full bridge LLC resonant converter, especially the output voltage regulation range and soft transitions of the MOSFETs in the fixed frequency phase shift mode, a new set of analytical equations is obtained for the LLC resonant converter with consideration of separated primary and secondary leakage inductances of the high frequency transformer. Based on the proposed strategy and analytical equations, multivariate statistical design methodology is employed to design and optimize a 120 VDC, 3-kW battery charger. The experimental results exhibit the excellent performance of the resulting converter, which has a peak efficiency of 96.5% with extreme regulation capability.