nils-herrmann/clean_scirepeval huggingface dataset:
Digital Image Enhancement and Noise Filtering by Use of Local Statistics

Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.
A 0.5-V bulk-input fully differential operational transconductance amplifier

We present a fully differential two-stage Miller op-amp operating from a 0.5 V power supply. The input signal is applied to the bulk nodes of the input devices. A prototype was designed in a standard 0.18 /spl mu/m CMOS process using standard 0.5 V V/sub T/ devices. It has a measured 52 dB DC gain, a 2.5 MHz gain-bandwidth and consumes 110 /spl mu/W.
Design of charge pump circuit with consideration of gate-oxide reliability in low-voltage CMOS processes

A new charge pump circuit with consideration of gateoxide reliability is designed with two pumping branches in this paper. The charge transfer switches in the new proposed circuit can be completely turned on and turned off, so its pumping efficiency is higher than that of the traditional designs. Moreover, the maximum gate-source and gate-drain voltages of all devices in the proposed charge pump circuit do not exceed the normal operating power supply voltage (VDD). Two test chips have been implemented in a 0.35m 3.3-V CMOS process to verify the new proposed charge pump circuit. The measured output voltage of the new proposed four-stage charge pump circuit with each pumping capacitor of 2 pF to drive the capacitive output load is around 8.8 V under 3.3-V power supply (VDD = 3 3 V), which is limited by the junction breakdown voltage of the parasitic pn-junction in the given process. The new proposed circuit is suitable for applications in low-voltage CMOS processes because of its high pumping efficiency and no overstress across the gate oxide of devices.
Learning hierarchical control structures for multiple tasks and changing environments

While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a di cult task. The bene t of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques.
A Broadband Dual-Polarized Planar Antenna for 2G/3G/LTE Base Stations

A broadband dual-polarized planar antenna is proposed for 2G/3G/LTE base stations. The dual-polarized antenna is composed of two perpendicularly crossed bow-tie dipoles. Each bow-tie dipole is excited by a microstrip stub that is directly fed by a coaxial line, making the dual-polarized antenna full planar. Due to the coupling between two crossed bow-tie dipoles, a broad bandwidth is achieved. It is shown that the dual-polarized antenna has a bandwidth of 45% (1.7-2.7 GHz) for return loss >15 dB with an isolation of higher than 30 dB between two polarization input ports. The dual-polarized antenna has a half-power beam width (HPBW) of around 65 ° and an average gain of 8.5 dBi for slant ±45° polarizations. An 8-element dual-polarized planar antenna array is developed for base station applications. A bandwidth of 56% (1.63-2.9 GHz) is obtained for the antenna array. The antenna gain of the array is about 16 dBi and the HPBW is 65±8° for each polarization.
Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution

Adapting game content to a particular player’s needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficulty to keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase or decrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimize the player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.
A wall climbing robot for tank inspection. An autonomous prototype

This paper describes a solution to a mobile climbing robot on magnetic wheels, designed for inspecting exterior oil tank surfaces made of metal sheets. A mechanical design has been developed which presents a practical solution without an umbilical cord. The inspection system has been developed based on client/server architecture. The robot runs a client application and a remote PC executes the server functions. They will both allow any necessary inspections to be performed simultaneously by more than one robot. A sensorial system and a data fusion strategy to estimate the absolute robot position is proposed to allow the robot to navigate autonomously. The graphical monitoring of the robot position in the remote PC (server application) provides the operator with the possibility of controlling the robot, even in situations in which the operator visibility of an area tank is very low or inexistent. Previous experiments have demonstrated the mechanical system's robustness. These experiments consist of robot trajectory measurements and the comparison to a motion kinematic model.
Multi-view image and ToF sensor fusion for dense 3D reconstruction

Multi-view stereo methods frequently fail to properly reconstruct 3D scene geometry if visible texture is sparse or the scene exhibits difficult self-occlusions. Time-of-Flight (ToF) depth sensors can provide 3D information regardless of texture but with only limited resolution and accuracy. To find an optimal reconstruction, we propose an integrated multi-view sensor fusion approach that combines information from multiple color cameras and multiple ToF depth sensors. First, multi-view ToF sensor measurements are combined to obtain a coarse but complete model. Then, the initial model is refined by means of a probabilistic multi-view fusion framework, optimizing over an energy function that aggregates ToF depth sensor information with multi-view stereo and silhouette constraints. We obtain high quality dense and detailed 3D models of scenes challenging for stereo alone, while simultaneously reducing complex noise of ToF sensors.
An auditory oddball (P300) spelling system for brain-computer interfaces.

This study was designed to develop and test an auditory event-related potential (ERP) based spelling system for a brain-computer interface (BCI) and to compare user's performance between the auditory and visual modality. The spelling system, where letters in a matrix were coded with acoustically presented numbers, was tested on a group of healthy volunteers. The results were compared with a visual spelling system. Nine of the 13 participants presented with the auditory ERP spelling system scored above a predefined criterion level control for communication. Compared to the visual spelling system, users' performance was lower and the peak latencies of the auditorily evoked ERPs were delayed. It was concluded that auditorily evoked ERPs from the majority of the users could be reliably classified. High accuracies were achieved in these users, rendering item selection with a BCI based on auditory stimulation feasible for communication.
A Prospective, Open-Label Study of Hyaluronic Acid-Based Filler With Lidocaine (VYC-15L) Treatment for the Correction of Infraorbital Skin Depressions.

Infraorbital skin depressions are one of the most troublesome facial areas for aesthetically aware patients.



Evaluate effectiveness and safety of Juvéderm Volbella with Lidocaine (VYC-15L; Allergan plc, Dublin, Ireland) for correction of bilateral infraorbital depressions.



In this 12-month, prospective, uncontrolled, open-label study, subjects aged ≥18 years with infraorbital depressions rated ≥1 on the Allergan Infra-oRbital Scale (AIRS) received injections of VYC-15L with optional touch-up treatment on Day 14. The primary efficacy measure was ≥1 AIRS grade improvement from baseline at month 1.



Of 80 subjects initially treated with VYC-15L, 75 (94%) completed the study. All injections were intentionally deep, most using multiple microbolus technique. At 1 month, 99.3% of eyes achieved ≥1 AIRS grade improvement. The responder rate (subjects with ≥1 AIRS grade improvement in both eyes) was 99% at month 1, 92% at month 6, and 54% at month 12. Most injection site reactions (e.g., bruising, redness, irregularities/bumps) were mild and resolved by day 14. Late-onset mild to moderate edema was observed in 11% of eyes at month 6% and 4% of eyes at month 12.



VYC-15L is effective and safe for the treatment of infraorbital depressions, with effectiveness lasting up to 12 months.
A Definition of Peer-to-Peer Networking for the Classification of Peer-to-Peer Architectures and Applications

The main contribution of the poster, which is shortly outlined in the following, is to offer a definition for Peerto-Peer networking and to make the differences to common so called Client/Server-architectures clear. With this definition we are able to classify currently existing networking concepts in the Internet either as Pure Peer-to-Peer, or "Hybrid" Peer-to-Peer or Client/Server architecture.
Multiclass Imbalance Problems: Analysis and Potential Solutions

Class imbalance problems have drawn growing interest recently because of their classification difficulty caused by the imbalanced class distributions. In particular, many ensemble methods have been proposed to deal with such imbalance. However, most efforts so far are only focused on two-class imbalance problems. There are unsolved issues in multiclass imbalance problems, which exist in real-world applications. This paper studies the challenges posed by the multiclass imbalance problems and investigates the generalization ability of some ensemble solutions, including our recently proposed algorithm AdaBoost.NC, with the aim of handling multiclass and imbalance effectively and directly. We first study the impact of multiminority and multimajority on the performance of two basic resampling techniques. They both present strong negative effects. “Multimajority” tends to be more harmful to the generalization performance. Motivated by the results, we then apply AdaBoost.NC to several real-world multiclass imbalance tasks and compare it to other popular ensemble methods. AdaBoost.NC is shown to be better at recognizing minority class examples and balancing the performance among classes in terms of G-mean without using any class decomposition.
Authentication of FPGA Bitstreams: Why and How

Encryption of volatile FPGA bitstreams provides confidentiality to the design but does not ensure its authenticity. This paper motivates the need for adding authentication to the configuration process by providing application examples where this functionality is useful. An examination of possible solutions is followed by suggesting a practical one in consideration of the FPGA’s configuration environment constraints. The solution presented here involves two symmetric-key encryption cores running in parallel to provide both authentication and confidentiality while sharing resources for efficient implementation.
IoT based smart emergency response system for fire hazards

The Internet of Things pertains to connecting currently unconnected things and people. It is the new era in transforming the existed systems to amend the cost effective quality of services for the society. To support Smart city vision, Urban IoT design plans exploit added value services for citizens as well as administration of the city with the most advanced communication technologies. To make emergency response real time, IoT enhances the way first responders and provides emergency managers with the necessary up-to-date information and communication to make use those assets. IoT mitigates many of the challenges to emergency response including present problems like a weak communication network and information lag. In this paper it is proposed that an emergency response system for fire hazards is designed by using IoT standardized structure. To implement this proposed scheme a low-cost Espressif wi-fi module ESP-32, Flame detection sensor, Smoke detection sensor (MQ-5), Flammable gas detection sensor and one GPS module are used. The sensors detects the hazard and alerts the local emergency rescue organizations like fire departments and police by sending the hazard location to the cloud-service through which all are connected. The overall network utilizes a light weighted data oriented publish-subscribe message protocol MQTT services for fast and reliable communication. Thus, an intelligent integrated system is designed with the help of IoT.
Synonym Discovery for Structured Entities on Heterogeneous Graphs

With the increasing use of entities in serving people's daily information needs, recognizing synonyms---different ways people refer to the same entity---has become a crucial task for many entity-leveraging applications. Previous works often take a "literal" view of the entity, i.e., its string name. In this work, we propose adopting a "structured" view of each entity by considering not only its string name, but also other important structured attributes. Unlike existing query log-based methods, we delve deeper to explore sub-queries, and exploit tailed synonyms and tailed web pages for harvesting more synonyms. A general, heterogeneous graph-based data model which encodes our problem insights is designed by capturing three key concepts (synonym candidate, web page and keyword) and different types of interactions between them. We cast the synonym discovery problem into a graph-based ranking problem and demonstrate the existence of a closed-form optimal solution for outputting entity synonym scores. Experiments on several real-life domains demonstrate the effectiveness of our proposed method.
Selecting Coherent and Relevant Plots in Large Scatterplot Matrices

The scatterplot matrix (SPLOM) is a well-established technique to visually explore high-dimensional data sets. It is characterized by the number of scatterplots (plots) of which it consists of. Unfortunately, this number quadratically grows with the number of the data set’s dimensions. Thus, a SPLOM scales very poorly. Consequently, the usefulness of SPLOMs is restricted to a small number of dimensions. For this, several approaches already exist to explore such “small” SPLOMs. Those approaches address the scalability problem just indirectly and without solving it. Therefore, we introduce a new greedy approach to manage “large” SPLOMs with more than hundred dimensions. We establish a combined visualization and interaction scheme that produces intuitively interpretable SPLOMs by combining known quality measures, a pre-process reordering, and a perception-based abstraction. With this scheme, the user can interactively find large amounts of relevant plots in large SPLOMs.
Empirical vulnerability analysis of automated smart contracts security testing on blockchains

e emerging blockchain technology supports decentralized computing paradigm shi and is a rapidly approaching phenomenon. While blockchain is thought primarily as the basis of Bitcoin, its application has grown far beyond cryptocurrencies due to the introduction of smart contracts. Smart contracts are self-enforcing pieces of soware, which reside and run over a hosting blockchain. Using blockchain-based smart contracts for secure and transparent management to govern interactions (authentication, connection, and transaction) in Internet-enabled environments, mostly IoT, is a niche area of research and practice. However, writing trustworthy and safe smart contracts can be tremendously challenging because of the complicated semantics of underlying domain-specic languages and its testability. ere have been high-prole incidents that indicate blockchain smart contracts could contain various code-security vulnerabilities, instigating nancial harms. When it involves security of smart contracts, developers embracing the ability to write the contracts should be capable of testing their code, for diagnosing security vulnerabilities, before deploying them to the immutable environments on blockchains. However, there are only a handful of security testing tools for smart contracts. is implies that the existing research on automatic smart contracts security testing is not adequate and remains in a very stage of infancy. With a specic goal to more readily realize the application of blockchain smart contracts in security and privacy, we should rst understand their vulnerabilities before widespread implementation. Accordingly, the goal of this paper is to carry out a far-reaching experimental assessment of current static smart contracts security testing tools, for the most widely used blockchain, the Ethereum and its domain-specic programming language, Solidity, to provide the rst body of knowledge for creating more secure blockchainbased soware. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
Using the opinion leaders in social networks to improve the cold start challenge in recommender systems

The increasing volume of information about goods and services has been growing confusion for online buyers in cyberspace and this problem still continues. One of the most important ways to deal with the information overload is using a system called recommender system. The task of a recommender system is to offer the most appropriate and the nearest product to the user's demands and needs. In this system, one of the main problems is the cold start challenge. This problem occurs when a new user logs on and because there is no sufficient information available in the system from the user, the system won't be able to provide appropriate recommendation and the system error will rise. In this paper, we propose to use a new measurement called opinion leaders to alleviate this problem. Opinion leader is a person that his opinion has an impact on the target user. As a result, in the case of a new user logging in and the user — item's matrix sparseness, we can use the opinion of opinion leaders to offer the appropriate recommendation for new users and thereby increase the accuracy of the recommender system. The results of several conducted tests showed that opinion leaders combined with recommender systems will effectively reduce the recommendation errors.
Differential Privacy as a Mutual Information Constraint

Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between ε-differential privacy and (ε,δ)-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.
Multi-task learning of dish detection and calorie estimation

In recent years, a rise in healthy eating has led to various food management applications, which have image recognition to automatically record meals. However, most image recognition functions in existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network, which has improved accuracies of various kinds of image recognition tasks, such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates food calories while simultaneously detecting dishes by multi-task learning of food calorie estimation and food dish detection with a single CNN. It is expected to achieve high speed and save memory by simultaneous estimation in a single network. Currently, there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, so in this work, we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos with bounding-boxes attached and single-dish food photos with food calories. Our results show that our multi-task method achieved higher speed and a smaller network size than a sequential model of food detection and food calorie estimation.
A Spatio-temporal Role-Based Access Control Model

With the growing advancement of pervasive computing technologies, we are moving towards an era where spatio-temporal information will be necessary for access control. The use of such information can be used for enhancing the security of an application, and it can also be exploited to launch attacks. For critical applications, a formal model for spatio-temporal-based access control is needed that increases the security of the application and ensures that the location information cannot be exploited to cause harm. In this paper, we propose a spatio-temporal access control model, based on the Role-Based Access Control (RBAC) model, that is suitable for pervasive computing applications. We show the association of each component of RBAC with spatio-temporal information. We formalize the model by enumerating the constraints. This model can be used for applications where spatial and temporal information of a subject and an object must be taken into account before granting or denying access.