nils-herrmann/clean_scirepeval huggingface dataset:
Vision-Based Classification of Skin Cancer using Deep Learning

This study proposes the use of deep learning algorithms to detect the presence of skin cancer, specifically melanoma, from images of skin lesions taken by a standard camera. Skin cancer is the most prevalent form of cancer in the US where 3.3 million people get treated each year. The 5-year survival rate of melanoma is 98% when detected and treated early yet over 10,000 people are lost each year due mostly to late-stage diagnoses . Thus, there is a need to make melanoma screening and diagnoses methods cheaper, quicker, simpler, and more accessible. This study aims to produce an inexpensive and fast computer-vision based machine learning tool that can be used by doctors and patients to track and classify suspicious skin lesions as benign or malignant with adequate accuracy using only a cell phone camera. The data set was trained on 3 separate learning models with increasingly improved classification accuracy. The 3 models included logistic regression, a deep neural network, and a fine-tuned, pre-trained, VGG-16 Convolutional Neural Network (CNN) . Preliminary results show the developed algorithm’s ability to segment moles from images with 70% accuracy and classify skin lesions as melanoma with 78% balanced accuracy using a fine-tuned VGG-16 CNN.
Rule-Based Neural Networks for Classification and Probability Estimation

In this paper we propose a network architecture that combines a rule-based approach with that of the neural network paradigm. Our primary motivation for this is to ensure that the knowledge embodied in the network is explicitly encoded in the form of understandable rules. This enables the network's decision to be understood, and provides an audit trail of how that decision was arrived at. We utilize an information theoretic approach to learning a model of the domain knowledge from examples. This model takes the form of a set of probabilistic conjunctive rules between discrete input evidence variables and output class variables. These rules are then mapped onto the weights and nodes of a feedforward neural network resulting in a directly specified architecture. The network acts as parallel Bayesian classifier, but more importantly, can also output posterior probability estimates of the class variables. Empirical tests on a number of data sets show that the rule-based classifier performs comparably with standard neural network classifiers, while possessing unique advantages in terms of knowledge representation and probability estimation.
Diagnosis of diabetic retinopathy using machine learning classification algorithm

Diabetic Retinopathy is human eye disease which causes damage to retina of eye and it may eventually lead to complete blindness. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. Many physical tests like visual acuity test, pupil dilation, optical coherence tomography can be used to detect diabetic retinopathy but are time consuming and affects patients as well. This review paper focuses on decision about the presence of disease by applying ensemble of machine learning classifying algorithms on features extracted from output of different retinal image processing algorithms, like diameter of optic disk, lesion specific (microaneurysms, exudates), image level (pre-screening, AM/FM, quality assessment). Decision making for predicting the presence of diabetic retinopathy was performed using alternating decision tree, adaBoost, Naive Bayes, Random Forest and SVM.
A Novel Unit Power Factor Rectifier Based on Three-phase Digital PLL

A novel three-phase phase-locked loop solution is proposed based on D-Q transformation aiming at the AC-DC rectifier with high efficiency and high power factor. The phase-locked loop is implemented digitally using the Xilinx blockset integrated with Matlab/Simulink. The three-phase digital phase-locked loop (TDPLL) is elaborately designed with the parameters defined in detail. The AC-DC converter (rectifier) model with the TDPLL is built and simulated in the high-speed VHS-ADC simulation platform from Canada. The simulation and test results show the TDPLL is locked right after the different three-phase voltage disturbances and very suitable for control of the rectifier with high parallelism through space vector pulse width modulation (SVPWM).
Globally Optimal Crowdsourcing Quality Management

We study crowdsourcing quality management, that is, given worker responses to a set of tasks, our goal is to jointly estimate the true answers for the tasks, as well as the quality of the workers. Prior work on this problem relies primarily on applying ExpectationMaximization (EM) on the underlying maximum likelihood problem to estimate true answers as well as worker quality. Unfortunately, EM only provides a locally optimal solution rather than a globally optimal one. Other solutions to the problem (that do not leverage EM) fail to provide global optimality guarantees as well. In this paper, we focus on filtering, where tasks require the evaluation of a yes/no predicate, and rating, where tasks elicit integer scores from a finite domain. We design algorithms for finding the global optimal estimates of correct task answers and worker quality for the underlying maximum likelihood problem, and characterize the complexity of these algorithms. Our algorithms conceptually consider all mappings from tasks to true answers (typically a very large number), leveraging two key ideas to reduce, by several orders of magnitude, the number of mappings under consideration, while preserving optimality. We also demonstrate that these algorithms often find more accurate estimates than EM-based algorithms. This paper makes an important contribution towards understanding the inherent complexity of globally optimal crowdsourcing quality management.
Education and the Brain : A Bridge Too Far

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General Stability of Stepwise Waveform of an Adiabatic Charge Recycling Circuit With Any Circuit Topology

The stability of a stepwise waveform of an adiabatic charge recycling circuit with tank capacitors is investigated. We propose a new tank capacitor circuit with two-string capacitor arrays. We experimentally confirmed the stability of the five-step waveform from the tank capacitors. The voltage changes of tank capacitors in the experiment are consistent with the simulation. The power consumption is reduced to one-fifth due to the five-step waveform. We analyze the stability using matrix theory. The results prove that the step waveform is stable for any circuit topology. Moreover, we consider the effects of conductors fixed at a certain voltage and floating conductors and confirm the system is stable using matrix theory.
Gadge me if you can: secure and efficient ad-hoc instruction-level randomization for x86 and ARM

Code reuse attacks such as return-oriented programming are one of the most powerful threats to contemporary software. ASLR was introduced to impede these attacks by dispersing shared libraries and the executable in memory. However, in practice its entropy is rather low and, more importantly, the leakage of a single address reveals the position of a whole library in memory. The recent mitigation literature followed the route of randomization, applied it at different stages such as source code or the executable binary. However, the code segments still stay in one block. In contrast to previous work, our randomization solution, called Xifer, (1) disperses all code (executable and libraries) across the whole address space, (2) re-randomizes the address space for each run, (3) is compatible to code signing, and (4) does neither require offline static analysis nor source-code. Our prototype implementation supports the Linux ELF file format and covers both mainstream processor architectures x86 and ARM. Our evaluation demonstrates that Xifer performs efficiently at load- and during run-time (1.2% overhead).
Cosmetics as a Feature of the Extended Human Phenotype: Modulation of the Perception of Biologically Important Facial Signals

Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first glance and at longer inspection.
A Review of Productivity Factors and Strategies on Software Development

Since the late seventies, efforts to catalog factors that influences productivity, as well as actions to improve it, has been a huge concern for both academy and software development industry. Despite numerous studies, software organizations still do not know which the most significant factors are and what to do with it. Several studies present the factors in a very superficial way, some others address only the related factors or there are those that describe only a single factor. Actions to deal with the factors are spread and frequently were not mapped. Through a literature review, this paper presents a consolidated view of the main factors that have affected productivity over the years, and the strategies to deal with these factors nowadays. This research aims to support software development industry on the selection of their strategies to improve productivity by maximizing the positive factors and minimizing or avoiding the impact of the negative ones.
Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set

ISSN 2250 – 1061 | In machine learning, classification refers to a step by step procedure for designating a given piece of input data into any one of the given categories. There are many classification problem occurs and need to be solved. Different types are classification algorithms like tree-based, rule-based, etc are widely used. This work studies the effectiveness of Rule-Based classifiers for classification by taking a sample data set from UCI machine learning repository using the open source machine learning tool. A comparison of different rulebased classifiers used in Data Mining and a practical guideline for selecting the most suited algorithm for a classification is presented and some empirical criteria for describing and evaluating the classifiers are given.
Passive repeater for removal of blind spot in NLOS path for 5G fixed wireless access (FWA) system

This paper presents an approach to improve the link budget in a non-line-of-sight (NLOS) path between a base station (BS) and a customer premises equipment (CPE) for 5G fixed wireless access (FWA) system. The oblique incidence of transmitting power into a receiver is one of most challenging scenarios for the 5G mm-wave system with highly directive antennas. In this work, we propose application of an additional antenna structure attached near window such as a passive repeater array which can reflect an incident power into a specific angle. With the passive repeater, the path loss is measured in the real environment assuming a feasible challenging scenario where the incident angle is near 90°. The measurement shows that the passive repeater array enables to improve the link budget by 40 dB, compared to the case without any additional reflector structure.
MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) pose novel challenges compared with traditional networks. To answer such challenges a new communication paradigm, data-centric communication, is emerging. One form of data-centric communication is the publish/subscribe messaging system. Compared with other data-centric variants, publish/subscribe systems are common and wide-spread in distributed computing. Thus, extending publish/subscribe systems intoWSNs will simplify the integration of sensor applications with other distributed applications. This paper describes MQTT-S , an extension of the open publish/subscribe protocol message queuing telemetry transport (MQTT)  to WSNs. MQTT-S is designed in such a way that it can be run on low-end and battery-operated sensor/actuator devices and operate over bandwidth-constraint WSNs such as ZigBee-based networks. Various protocol design points are discussed and compared. MQTT-S has been implemented and is currently being tested on the IBM wireless sensor networking testbed . Implementation aspects, open challenges and future work are also presented.
Linear SLAM: A linear solution to the feature-based and pose graph SLAM based on submap joining

This paper presents a strategy for large-scale SLAM through solving a sequence of linear least squares problems. The algorithm is based on submap joining where submaps are built using any existing SLAM technique. It is demonstrated that if submaps coordinate frames are judiciously selected, the least squares objective function for joining two submaps becomes a quadratic function of the state vector. Therefore, a linear solution to large-scale SLAM that requires joining a number of local submaps either sequentially or in a more efficient Divide and Conquer manner, can be obtained. The proposed Linear SLAM technique is applicable to both feature-based and pose graph SLAM, in two and three dimensions, and does not require any assumption on the character of the covariance matrices or an initial guess of the state vector. Although this algorithm is an approximation to the optimal full nonlinear least squares SLAM, simulations and experiments using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization started from an accurate initial value. The C/C++ and MATLAB source codes for the proposed algorithm are available on OpenSLAM.
Cyclops: Miniature Robotic Reconnaissance System

Miniature mobile robots are desirable for military applications because of their ability to function covertly in highly constrained environments. However, work on such tiny robotic systems has been hampered by the difficulty of packaging locomotion, sensing, computing and power subsystems into diminutive spaces. This paper presents the design and initial prototype of a miniature mobile robot designed to carry out surveillance and reconnaissance missions in an urban environment. This initial prototype is spherical in shape, with a diameter of 5.5 inches, and weighs only 4.5 pounds. A novel inertial steering system, along with the robot’s robust control and communication systems, make it useful for covert video surveillance and reconnaissance in tightly constrained spaces.
Playful bottle: a mobile social persuasion system to motivate healthy water intake

This study of mobile persuasion system explores the use of a mobile phone, when attached to an everyday object used by an everyday behavior, becomes a tool to sense and influence that behavior. This mobile persuasion system, called Playful Bottle system, makes use of a mobile phone attached to an everyday drinking mug and motivates office workers to drink healthy quantities of water. A camera and accelerometer sensors in the phone are used to build a vision/motion-based water intake tracker to detect the amount and regularity of water consumed by the user. Additionally, the phone includes hydration games in which natural drinking actions are used as game input. Two hydration games are developed: a single-user TreeGame with automated computer reminders and a multi-user ForestGame with computer-mediated social reminders from members of the group playing the game. Results from 7-week user study with 16 test subjects suggest that both hydration games are effective for encouraging adequate and regular water intake by users. Additionally, results of this study suggest that adding social reminders to the hydration game is more effective than system reminders alone.
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features

Recurrent neural network (RNN) and long short-term memory (LSTM) have achieved great success in processing sequential multimedia data and yielded the state-of-the-art results in speech recognition, digital signal processing, video processing, and text data analysis. In this paper, we propose a novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network. First, deep features are extracted from every sixth frame of the videos, which helps reduce the redundancy and complexity. Next, the sequential information among frame features is learnt using DB-LSTM network, where multiple layers are stacked together in both forward pass and backward pass of DB-LSTM to increase its depth. The proposed method is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval. Experimental results show significant improvements in action recognition using the proposed method on three benchmark data sets including UCF-101, YouTube 11 Actions, and HMDB51 compared with the state-of-the-art action recognition methods.
SLANT+: A Nonlinear Model for Opinion Dynamics in Social Networks

Online Social Networks (OSNs) have emerged as a global media for forming and shaping opinions on a broad spectrum of topics like politics, e-commerce, sports, etc. So, research on understanding and predicting opinion dynamics in OSNs, especially using a tractable linear model, has abound in literature. However, these linear models are too simple to uncover the actual complex dynamics of opinion flow in social networks. In this paper, we propose SLANT+, a novel nonlinear generative model for opinion dynamics, by extending our earlier linear opinion model SLANT [7]. To design this model, we rely on a network-guided recurrent neural network architecture which learns a proper temporal representation of the messages as well as the underlying network. Furthermore, we probe various signals from the real life datasets and offer a conceptually interpretable nonlinear function that not only provides concrete clues of the opinion exchange process, but also captures the coupled dynamics of message timings and opinion flow. As a result, with five real-life datasets crawled from Twitter, our proposal gives significant accuracy boost over six state-of-the-art baselines.
The Ontology Extraction & Maintenance Framework Text-To-Onto

Ontologies play an increasingly important role in Knowledge Management. One of the main problems associated with ontologies is that they need to be constructed and maintained. Manual construction of larger ontologies is usually not feasible within companies because of the effort and costs required. Therefore, a semi-automatic approach to ontology construction and maintenance is what everybody is wishing for. The paper presents a framework for semi-automatically learning ontologies from domainspecific texts by applying machine learning techniques. The TEXT-TO-ONTO framework integrates manual engineering facilities to follow a balanced cooperative modelling paradigm.
Identifying First-Person Camera Wearers in Third-Person Videos

We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in scenarios in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene. To do this, we need to establish person-level correspondences across first-and third-person videos, which is challenging because the camera wearer is not visible from his/her own egocentric video, preventing the use of direct feature matching. In this paper, we propose a new semi-Siamese Convolutional Neural Network architecture to address this novel challenge. We formulate the problem as learning a joint embedding space for first-and third-person videos that considers both spatial-and motion-domain cues. A new triplet loss function is designed to minimize the distance between correct first-and third-person matches while maximizing the distance between incorrect ones. This end-to-end approach performs significantly better than several baselines, in part by learning the first-and third-person features optimized for matching jointly with the distance measure itself.
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU ) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.