nils-herrmann/clean_scirepeval huggingface dataset:
A fast-transient LDO based on buffered flipped voltage follower

In this work, the analysis of the flipped voltage follower (FVF) based single-transistor-control (STC) LDO is given. Two evolved versions of FVF, cascaded FVF (CAFVF) and level shifted FVF (LSFVF), are studied. Then, a buffered FVF (BFVF) for LDO application is proposed, combining the virtues of both CAFVF and LSFVF structures. It alleviates the minimum loading requirement of FVF and the simulation result shows that it has faster transient response and better load regulation.
Experiment on the Automatic Detection of Function Clones in a Software System Using Metrics

This paper presents a technique to automatically identify duplicate and near duplicate functions in a large software system. The identification technique is based on metrics extracted fi’om the source code using the tool Datrix? This clone identificafion technique uses 21 function metrics grouped into four points of comparison. Each point of comparison is used to compare functions and determine their cloning level. An ordinal scale of eight cloning levels is defined. The levels range from an exact copy to distinct functions. The metrics, the thresholds and the process used are fully described. The results of applying the clone detection technique to two telecommunication monitoring systems tofaling one million lines of source code are provided as examples. The information provided by this study is useful in monitoring the maintainability of large software systems.
Experimental evaluation of functional splits for 5G cloud-RANs

Centralized RAN processing has been identified as one of the major enablers for 5G mobile network access. By moving the baseband units (BBU) to the Cloud, multiple instances can be instantiated on the fly, serving several Remote Radio Head (RRH) units. The goal is to satisfy the existing demand of particular geographical areas, whereas drastically reducing the overall CAPEX and OPEX costs of the mobile operators. In this work, we present an experimental study of real Cloud-RAN deployments, with respect to different functional splits. We use as a reference architecture the 3GPP LTE stack, and argue about the functional split applicability in contemporary networks. We evaluate Layer 2 functional splits, that can be used for the convergence of multiple heterogeneous wireless technologies in an all-in-one unit. By deploying our approach in a real testbed setup, we extract the backhaul network transfer requirements for the different splits and present our experimental findings, compared with the respective simulation results.
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data

In this paper, we present an overview of generalized expectation criteria (GE), a simple, robust, scalable method for semi-supervised training using weakly -labeled data. GE fits model parameters by favoring models that match certain expectation constrai nts, such as marginal label distributions, on the unlabeled data. This paper shows how to apply generali z d expectation criteria to two classes of parametric models: maximum entropy models and condition al random fields. Experimental results demonstrate accuracy improvements over supervise d training and a number of other stateof-the-art semi-supervised learning methods for these mod els.
Zero Voltage Vector Sampling Method for PMSM Three-Phase Current Reconstruction Using Single Current Sensor

For the purpose of reducing cost and volume, techniques of reconstructing three-phase currents through a single current sensor have been reported for permanent magnet synchronous motor vector control system. In existing studies, the reconstruction precision is largely affected by the dead zones in space vector PWM plane, which requires additional efforts to compensate the dead zones either by modifying pulse width modulation (PWM) modulation strategy or by phase-shifting of PWM signal. In this paper, a novel zero voltage vector sampling method (ZVVSM) is proposed, which can move the current reconstruction dead zones in low modulation region and sector boundary regions toward the outline of the space vector hexagon without modifying PWM signal. By arranging the single current sensor at a novel position, the proposed method is able to sample current in two zero voltage vectors (ZVV). ZVVSM avoids the complicated algorithms as well as the increase of the switching times, so that it is beneficial to the PMSM drive performance. The proposed method is validated by both simulation and experiments.
Adaptive flow orientation-based feature extraction in fingerprint images

-A reliable method for extracting structural features from fingerprint images is presented. Viewing fingerprint images as a textured image, an orientation flow field is computed. The rest of the stages in the algorithm use the flow field to design adaptive filters for the input image. To accurately locate ridges, a waveform projection-based ridge segmentation algorithm is used. The ridge skeleton image is obtained and smoothed using morphological operators to detect the features. A large number of spurious features from the detected set of minutiae is deleted by a postprocessing stage. The performance of the proposed algorithm has been evaluated by computing a "goodness index" (GI) which compares the results of automatic extraction with manually extracted ground truth. The significance of the observed GI values is determined by comparing the index for a set of fingerprints against the GI values obtained under a baseline distribution. The detected features are observed to be reliable and accurate. Fingerprints Feature extraction Texture Flow orientation Minutiae Segmentation Skeleton
3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning

The performance and efficiency of distributed machine learning (ML) depends significantly on how long it takes for nodes to exchange state changes. Overly-aggressive attempts to reduce communication often sacrifice final model accuracy and necessitate additional ML techniques to compensate for this loss, limiting their generality. Some attempts to reduce communication incur high computation overhead, which makes their performance benefits visible only over slow networks. We present 3LC, a lossy compression scheme for state change traffic that strikes balance between multiple goals: traffic reduction, accuracy, computation overhead, and generality. It combines three new techniques—3-value quantization with sparsity multiplication, quartic encoding, and zero-run encoding—to leverage strengths of quantization and sparsification techniques and avoid their drawbacks. It achieves a data compression ratio of up to 39–107×, almost the same test accuracy of trained models, and high compression speed. Distributed ML frameworks can employ 3LC without modifications to existing ML algorithms. Our experiments show that 3LC reduces wall-clock training time of ResNet-110–based image classifiers for CIFAR-10 on a 10-GPU cluster by up to 16–23× compared to TensorFlow’s baseline design.
The effect of music type on emotion regulation : An emotional-Stroop experiment

Emotion regulation, the process of changing one’s emotion or one’s response to emotion, is necessary for efficiency when performing cognitive tasks, and is often measured using a Stroop-like task that provides conflict between emotional and factual information. Researchers have found that listening to music increases performance on cognitive tasks. For the present research it was hypothesized that listening to music samples that evoke different arousal and valence levels would affect participants’ emotion regulation skills.  While completing an emotional-Stroop task, 38 Georgia State University undergraduates listened to three-minute excerpts of film scores known to evoke a particular mood and arousal state.  A repeated measures ANOVA revealed a significant difference between the types of music and an interaction between music type and word context.  These results provide evidence that different types of music may place different demands on attention, differentially disrupting Stroop performance, at least for the color-naming of nonacademic words. However, the absence of valence effects in these results suggest that the findings likely reflect arousal rather than mood induction. THE EFFECT OF MUSIC TYPE ON EMOTION REGULATION 3
Performance comparison of NSGA-II and NSGA-III on various many-objective test problems

Recently NSGA-III has been frequently used for performance comparison of newly proposed evolutionary many-objective optimization algorithms. That is, NSGA-III has been used as a benchmark algorithm for evolutionary many-objective optimization. However, unfortunately, its source code is not available from the authors of the NSGA-III paper. This leads to an undesirable situation where a different implementation is used in a different study. Moreover, comparison is usually performed on DTLZ and WFG test problems. As a result, the performance of NSGA-III on a wide variety of many-objective test problems is still unclear whereas it has been frequently used for performance comparison in the literature. In this paper, we evaluate the performance of NSGA-III in comparison with NSGA-II on four totally different types of many-objective test problems with 3-10 objectives: DTLZ1-4 problems, their maximization variants, distance minimization problems, and knapsack problems. We use two different implementations of NSGA-II and NSGA-III. We show through computational experiments that NSGA-III does not always outperform NSGA-II even for ten-objective problems. That is, their comparison results depend not only on the number of objectives but also on the type of test problems. The choice of test problems has a larger effect on their comparison results than the number of objectives in our computational experiments. We also demonstrate that totally different results are obtained from different implementations of NSGA-III for some test problems.
A Nonlinear-Disturbance-Observer-Based DC-Bus Voltage Control for a Hybrid AC/DC Microgrid

DC-bus voltage control is an important task in the operation of a dc or a hybrid ac/dc microgrid system. To improve the dc-bus voltage control dynamics, traditional approaches attempt to measure and feedforward the load or source power in the dc-bus control scheme. However, in a microgrid system with distributed dc sources and loads, the traditional feedforward-based methods need remote measurement with communications. In this paper, a nonlinear disturbance observer (NDO) based dc-bus voltage control is proposed, which does not need the remote measurement and enables the important “plug-and-play” feature. Based on this observer, a novel dc-bus voltage control scheme is developed to suppress the transient fluctuations of dc-bus voltage and improve the power quality in such a microgrid system. Details on the design of the observer, the dc-bus controller and the pulsewidth-modulation (PWM) dead-time compensation are provided in this paper. The effects of possible dc-bus capacitance variation are also considered. The performance of the proposed control strategy has been successfully verified in a 30 kVA hybrid microgrid including ac/dc buses, battery energy storage system, and photovoltaic (PV) power generation system.
Improved Answer Selection with Pre-Trained Word Embeddings

is paper evaluates existing and newly proposed answer selection methods based on pre-trained word embeddings. Word embeddings are highly effective in various natural language processing tasks and their integration into traditional information retrieval (IR) systems allows for the capture of semantic relatedness between questions and answers. Empirical results on three publicly available data sets show significant gains over traditional term frequency based approaches in both supervised and unsupervised seings. We show that combining these word embedding features with traditional learning-to-rank techniques can achieve similar performance to state-of-the-art neural networks trained for the answer selection
CMOS Amperometric Instrumentation and Packaging for Biosensor Array Applications

An integrated CMOS amperometric instrument with on-chip electrodes and packaging for biosensor arrays is presented. The mixed-signal integrated circuit supports a variety of electrochemical measurement techniques including linear sweep, constant potential, cyclic and pulse voltammetry. Implemented in 0.5 μm CMOS, the 3 × mm2 chip dissipates 22.5 mW for a 200 kHz clock. The highly programmable chip provides a wide range of user-controlled stimulus rate and amplitude settings with a maximum scan range of 2 V and scan rates between 1 mV/sec and 400 V/sec. The amperometric readout circuit provides ±500 fA linear resolution and supports inputs up to ±47 μA. A 2 × 2 gold electrode array was fabricated on the surface of the CMOS instrumentation chip. An all-parylene packaging scheme was developed for compatibility with liquid test environments as well as a harsh piranha electrode cleaning process. The chip was tested using cyclic voltammetry of different concentrations of potassium ferricyanide at 100 mV/s and 200 mV/s, and results were identical to measurements using commercial instruments.
Gain Improvement of Rectangular Dielectric Resonator Antenna by Engraving Grooves on Its Side Walls

A new technique for increasing the boresight gain of a rectangular dielectric resonator antenna (DRA) operating at its fundamental radiating <inline-formula><tex-math notation="LaTeX">$TE_{111}^y$</tex-math></inline-formula> mode is introduced. The idea is to increase the radiations from the side walls of the DRA compared to that of its top wall by engraving grooves on the side walls. A model based on the array theory is developed to explain the high-gain nature of the antenna. The measured results demonstrate that the proposed antenna achieves an impedance bandwidth of 21% over a band of 3.24–4 GHz, with a maximum gain of 9.6 dB. This is significantly higher with respect to available data in the literature.
Singular point detection based on orientation filed regularization and poincaré index in fingerprint images

Detection of singular points (SPs) in fingerprint images is an important task in fingerprint recognition. In this paper, we propose a novel technique for SPs detection using orientation field regularization and the Poincaré Index (PI) technique. The squared orientation field is first extracted from a fingerprint image. In order to distinguish the local orientation patterns of genuine SPs from that of spurious SPs, a novel technique based on the Discrete Hodge Helmholtz Decomposition (DHHD) is proposed to reconstruct a regular orientation field of the fingerprint. Based on the regular orientation field, the PI technique is then applied to extract the SPs. Experimental results on the public fingerprint database FVC2002 show that, the proposed technique is rather accurate and robust in identifying SPs.
PPChecker: Plagiarism Pattern Checker in Document Copy Detection

Nowadays, most of documents are produced in digital format, in which they can be easily accessed and copied. Document copy detection is a very important tool for protecting the author’s copyright. We present PPChecker, a document copy detection system based on plagiarism pattern checking. PPChecker calculates the amount of data copied from the original document to the query document, based on linguistically-motivated plagiarism patterns. Experiments performed on CISI document collection show that PPChecker produces better decision information for document copy detection than existing systems.
A new single phase to three phase converter with active input current shaping for low cost AC motor drives

A single-phase-to-three-phase converter for a low-cost AC motor drive is proposed. The converter employs only six switches and incorporates a front-end half-bridge active rectifier structure that provides the DC link with an active input current shaping feature, which results in sinusoidal input current at close to unity power factor. The front-end rectifier in the converter permits bidirectional power flow and provides for excellent regulation against fluctuations in source voltage, facilitating regenerative braking of the AC motor drive. A control strategy that maintains a near-unity power factor over the full operating range and is easy to implement is described. Suitable design guides for the selection of filter components are presented. Simulation and experimental results that verify the developed theoretical models are also presented.<<ETX>>
Ethical Considerations in Artificial Intelligence Courses

The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course.
Visual saliency detection based on Bayesian model

Image saliency detection is very useful in many computer vision tasks while it still remains a challenging problem. In this paper, we propose a new computational saliency detection model which is implemented with a coarse to fine strategy under the Bayesian framework. First, saliency points are applied to get a coarse location of the saliency region. And then, based on the rough region, we compute a prior map for the Bayesian model to achieve the final saliency map. Experimental results on a public available dataset show the effectiveness of the proposed prior map and the strength of our saliency map compared with several previous method.
Enemy of the State: A State-Aware Black-Box Web Vulnerability Scanner

Black-box web vulnerability scanners are a popular choice for finding security vulnerabilities in web applications in an automated fashion. These tools operate in a point-and-shoot manner, testing any web application— regardless of the server-side language—for common security vulnerabilities. Unfortunately, black-box tools suffer from a number of limitations, particularly when interacting with complex applications that have multiple actions that can change the application’s state. If a vulnerability analysis tool does not take into account changes in the web application’s state, it might overlook vulnerabilities or completely miss entire portions of the web application. We propose a novel way of inferring the web application’s internal state machine from the outside—that is, by navigating through the web application, observing differences in output, and incrementally producing a model representing the web application’s state. We utilize the inferred state machine to drive a blackbox web application vulnerability scanner. Our scanner traverses a web application’s state machine to find and fuzz user-input vectors and discover security flaws. We implemented our technique in a prototype crawler and linked it to the fuzzing component from an open-source web vulnerability scanner. We show that our state-aware black-box web vulnerability scanner is able to not only exercise more code of the web application, but also discover vulnerabilities that other vulnerability scanners miss.
weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming

Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable and accurate weed detection to minimize damage to surrounding plants. In this letter, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder–decoder cascaded convolutional neural network, SegNet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the normalized difference vegetation index as a distinguishable feature for automatic ground truth generation. We train six models with different numbers of input channels and condition (fine tune) it to achieve  $\sim$0.8 F1-score and 0.78 area under the curve classification metrics. For the model deployment, an embedded Graphics Processing Unit (GPU) system (Jetson TX2) is tested for MAV integration. Dataset used in this letter is released to support the community and future work.
File Encryption and Decryption Using Secure RSA

In this paper we have introduced secure RSA for secure file transmission. There are many cases where we need secure file transmission for example in banking transactions, eshopping etc [4]. In this paper we present modified RSA algorithm for secure file transmission. RSA algorithm is asymmetric key cryptography also called Public Key cryptography. Two keys are generated in RSA, one key is used for encryption &other key which is only known to authenticated receiver can decrypt message. No other key can decrypt the message. Every communicating party needs just a key pair for communicating with any number of other communicating parties. Once someone obtains a key pair, he /she can communicate with anyone else. RSA is a well known public key cryptography algorithm and was one of the first great advances in public key cryptography. Even if it is efficient algorithm it is vulnerable to attackers. With the help of all brute force attacks hacker can obtain private key.Many improvements has been done to improve RSA like BATCH RSA, MultiPrime RSA, MultiPower RSA, Rebalanced RSA, RPrime RSA etc. As craze of internet is increasing exponentially, it is used for email, chatting, transferring data and files from one end to other. It needs to be a secure communication among the two parties . This paper focuses on file transfer using Secure RSA, which eliminates some loopholes of RSA that might prevent a hacker from stealing and misuse of data. This paper also presents comparison between RSA file transfer and Secure RSA file
3D Human Face Recognition Using Point Signature

In this paper, we present a novel face recognition algorithm based on the Point Signature a representation for free-form surfaces. We treat face recognition problem as a non-rigid object recognition problem. The rigid parts of the face of one person are extracted after registering the range data sets of faces having different facial expressions. These rigid parts are used to create a model library for efficient indexing. For a test face, models are indexed from the library and the most appropriate models are ranked according to their similarity with the test face. Verification of each model proceeds according to their ranking. In this way, the correct model face can be quickly and efficiently identified. Experimental results with range data involving six human subjects, each with four different facial expressions, have demonstrated the validity and effectiveness of our algorith-