nils-herrmann/clean_scirepeval huggingface dataset:
A study of count-based exploration and bonus for reinforcement learning

In order to better balance exploration and exploitation and solve the problem of sparse reward in reinforcement learning, this paper focus on changing the traditional exploration strategy, such as ε-greedy strategy, by introducing the count for state-action visitation into the Boltzmann distribution method as a new behavioral exploration strategy, and adding count-based exploration bonus to guide the agent to exploration, proposes a method of count-based exploration and bonus for reinforcement learning. Selecting Sarsa(λ) learning algorithm as the basis, the effectiveness of the count-based Sarsa(λ) learning algorithm is verified by carrying out comparative experiments of the Q learning algorithm based on the ε-greedy strategy, the Sarsa(λ) learning algorithm based on the ε-greedy strategy, and the count-based Sarsa(λ) learning algorithm in the tank combat simulation problem. Due to its simplicity, it provides an easy yet powerful baseline for solving MDPs that require informed exploration.
Augmented Reality: A class of displays on the reality-virtuality continuum

In this paper we discuss Augmented Reality (AR) displays in a general sense, within the context of a Reality-Virtuality (RV) continuum, encompassing a large class of "Mixed Reality" (MR) displays, which also includes Augmented Virtuality (AV). MR displays are defined by means of seven examples of existing display concepts in which real objects and virtual objects are juxtaposed. Essential factors which distinguish different Mixed Reality display systems from each other are presented, first by means of a table in which the nature of the underlying scene, how it is viewed, and the observer's reference to it are compared, and then by means of a three dimensional taxonomic framework, comprising: Extent of World Knowledge (EWK), Reproduction Fidelity (RF) and Extent of Presence Metaphor (EPM). A principal objective of the taxonomy is to clarify terminology issues and to provide a framework for classifying research across different disciplines.
Single-stage Flyback converter for constant current output LED driver with power factor correction

This paper presents a LED driver circuit based on single-stage Flyback converter with power factor correction. Voltage following control and Nonlinear carrier control are applied to constant current mode Flyback converter which is combined with constant current driver technique. By designing the outer loop to stabilize the output voltage and the inner loop to shape input current waveform, that makes the system achieve unity power factor. The circuit doesn't need an expensive multiplier as classical multiplier control. To verify the performance, the proposed circuit was confirmed by simulation and experiment. From the results, the proposed system shows high power factor with 0.96 and constant current output.
Quasi-hyperbolic momentum and Adam for deep learning

Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover. Finally, we propose a QH variant of Adam called QHAdam, and we empirically demonstrate that our algorithms lead to significantly improved training in a variety of settings, including a new state-of-theart result on WMT16 EN-DE. We hope that these empirical results, combined with the conceptual and practical simplicity of QHM and QHAdam, will spur interest from both practitioners and researchers. PyTorch code is immediately available.
An Analysis of Latch Comparator Offset Due to Load Capacitor Mismatch

This brief analyzes the effect of load capacitor mismatch on the offset of a regenerative latch comparator. Two analytical models are presented and compared with HSpice simulations. Our results indicate that in a typical 0.18-mum CMOS latch, a capacitive imbalance of only 1 fF can lead to offsets of several tens of millivolts
Quality Metrics for Object-Oriented Environments

Object-oriented design and development are popular concepts in today's software development environment. They are often heralded as the silver bullet for solving software problems. While in reality there is no silver bullet, object-oriented development has proved its value for systems that must be maintained and modified. Object-oriented software development requires a different approach from more traditional functional decomposition and data flow development methods. This includes the software metrics used to evaluate object-oriented software.
Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis

Infant motion analysis enables early detection of neurodevelopmental disorders like cerebral palsy (CP). Diagnosis, however, is challenging, requiring expert human judgement. An automated solution would be beneficial but requires the accurate capture of 3D fullbody movements. To that end, we develop a non-intrusive, low-cost, lightweight acquisition system that captures the shape and motion of infants. Going beyond work on modeling adult body shape, we learn a 3D Skinned Multi-Infant Linear body model (SMIL†) from noisy, lowquality, and incomplete RGB-D data. We demonstrate the capture of shape and motion with 37 infants in a clinical environment. Quantitative experiments show that SMIL faithfully represents the data and properly factorizes the shape and pose of the infants. With a case study based on general movement assessment (GMA), we demonstrate that SMIL captures enough information to allow medical assessment. SMIL provides a new tool and a step towards a fully automatic system for GMA.
Cybercrime: Vandalizing the Information Society

Cybercrime has received significant coverage in recent years, with the media, law enforcers, and governments all working to bring the issue to our attention. This paper begins by presenting an overview of the problem, considering the scope and scale of reported incidents. From this, a series of common attack types are considered (focusing upon website defacement, denial of service and malware), with specific emphasis upon the potential for these to be automated and mounted by novices. Leading on from this, the problem of policing cybercrime is considered, with attention to the need for suitable legislation, and appropriate resourcing of law enforcers. It is concluded that that cybercrime is an inevitable downside of the information society, and that organizations and individuals consequently have a stake in ensuring their own protection.
Cloud-dew architecture : realizing the potential of distributed database systems in unreliable networks

Distributed database systems, which continue to inspire new architectures and new applications, have great potential in the modern computing world. In this paper, we show that the newly-proposed cloud-dew architecture realizes the potential of distributed database systems in the unreliable network environment, and provides the possibility of websurfing without an Internet connection. Distributed database systems are generic and versatile; the proper applications of distributed database systems and their features will be beneficial to users and service providers.
A smart parking system to minimize searching time, fuel consumption and CO2 emission

This paper proposes a Smart Parking System (SPS) based on Internet of Thing (IoT) and Wireless Sensors Network (WSN). It is assumed that Global Positioning System (GPS) and wireless communication system is placed in the vehicle. GPS gets the position of vehicle and wireless communication send it to server. A smartphone uses for user to remotely view the parking, status of spots and book a slot manually. The proposed system decreases the searching time of free slot by giving route to free slot. As the fuel consumption and carbon dioxide (CO2) emission decreases by decreasing the distance and wise versa because there is direct relationship between them. It is shown in simulations result at the end of the paper that the objectives of the paper to minimize searching time, fuel consumption and CO2 emission achieved.
The McGill pain questionnaire: from description to measurement.

On the language of pain. By Ronald Melzack, Warren S. Torgerson. Anesthesiology 1971; 34:50-9. Reprinted with permission. The purpose of this study was to develop new approaches to the problem of describing and measuring pain in human subjects. Words used to describe pain were brought together and categorized, and an attempt was made to scale them on a common intensity dimension. The data show that: 1) there are many words in the English language to describe the varieties of pain experience; 2) there is a high level of agreement that the words fall into classes and subclasses that represent particular dimensions or properties of pain experience; 3) substantial portions of the words have approximately the same relative positions on a common intensity scale for people who have widely divergent backgrounds. The word lists provide a basis for a questionnaire to study the effects of anesthetic and analgesic agents on the experience of pain.
MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity

We present MultiGranCNN, a general deep learning architecture for matching text chunks. MultiGranCNN supports multigranular comparability of representations: shorter sequences in one chunk can be directly compared to longer sequences in the other chunk. MultiGranCNN also contains a flexible and modularized match feature component that is easily adaptable to different types of chunk matching. We demonstrate stateof-the-art performance of MultiGranCNN on clause coherence and paraphrase identification tasks.
Refactoring Inspection Support for Manual Refactoring Edits

Refactoring is commonly performed manually, supported by regression testing, which serves as a safety net to provide confidence on the edits performed. However, inadequate test suites may prevent developers from initiating or performing refactorings. We propose RefDistiller, a static analysis approach to support the inspection of manual refactorings. It combines two techniques. First, it applies predefined templates to identify potential missed edits during manual refactoring. Second, it leverages an automated refactoring engine to identify extra edits that might be incorrect. RefDistiller also helps determine the root cause of detected anomalies. In our evaluation, RefDistiller identifies 97 percent of seeded anomalies, of which 24 percent are not detected by generated test suites. Compared to running existing regression test suites, it detects 22 times more anomalies, with 94 percent precision on average. In a study with 15 professional developers, the participants inspected problematic refactorings with RefDistiller versus testing only. With RefDistiller, participants located 90 percent of the seeded anomalies, while they located only 13 percent with testing. The results show RefDistiller  can help check the correctness of manual refactorings.
The External Nasal Valve.

The external nasal valve is a complex entity comprised of multiple structures and tissue types. As such, there is no single operation that can address all problems of the external valve. This article reviews the relevant anatomy, pathologic conditions, and treatments for external nasal valve dysfunction, including a detailed review of the nasal muscles and their contribution to external nasal valve patency. Surgical and nonsurgical options for treatment and the evidence supporting the importance of proper external nasal valve function on quality-of-life measures are discussed.
Mental Health in Lesbian, Gay, Bisexual, and Transgender (LGBT) Youth.

Today's lesbian, gay, bisexual, and transgender (LGBT) youth come out at younger ages, and public support for LGBT issues has dramatically increased, so why do LGBT youth continue to be at high risk for compromised mental health? We provide an overview of the contemporary context for LGBT youth, followed by a review of current science on LGBT youth mental health. Research in the past decade has identified risk and protective factors for mental health, which point to promising directions for prevention, intervention, and treatment. Legal and policy successes have set the stage for advances in programs and practices that may foster LGBT youth mental health. Implications for clinical care are discussed, and important areas for new research and practice are identified.
Next-generation DNA sequencing

DNA sequence represents a single format onto which a broad range of biological phenomena can be projected for high-throughput data collection. Over the past three years, massively parallel DNA sequencing platforms have become widely available, reducing the cost of DNA sequencing by over two orders of magnitude, and democratizing the field by putting the sequencing capacity of a major genome center in the hands of individual investigators. These new technologies are rapidly evolving, and near-term challenges include the development of robust protocols for generating sequencing libraries, building effective new approaches to data-analysis, and often a rethinking of experimental design. Next-generation DNA sequencing has the potential to dramatically accelerate biological and biomedical research, by enabling the comprehensive analysis of genomes, transcriptomes and interactomes to become inexpensive, routine and widespread, rather than requiring significant production-scale efforts.
Hate is in the air ! But where ? Introducing an algorithm to detect hate speech in digital microenvironments

With the objective of facilitating and reducing analysis tasks undergone by law enforcement agencies and service providers, and using a sample of digital messages (i.e., tweets) sent via Twitter following the June 2017 London Bridge terror attack (N = 200,880), the present study introduces a new algorithm designed to detect hate speech messages in cyberspace. Unlike traditional designs based on semantic and syntactic approaches, the algorithm hereby imple‐ mented feeds solely on metadata, achieving high level of precision. Through the application of the machine learning classification technique Random Forests, our analysis indicates that metadata associated with the interaction and structure of tweets are especially relevant to identify the content they contain. However, metadata of Twitter accounts are less useful in the classification process. Collectively, findings from the current study allow us to demonstrate how digital microenvironment patterns defined by metadata can be used to create a computer algorithm capable of detecting online hate speech. The application of the algorithm and the direction of future research in this area are discussed.
Culture and Identity-Protective Cognition : Explaining the White Male Effect in Risk Perception

Why do white men fear various risks less than women and minorities? Known as the “white-male effect,” this pattern is well documented but poorly understood. This article proposes a new explanation: identityprotective cognition. Putting work on the cultural theory of risk together with work on motivated cognition in social psychology suggests that individuals selectively credit and dismiss asserted dangers in a manner supportive of their cultural identities. This dynamic, it is hypothesized, drives the white-male effect, which reflects the risk skepticism that hierarchical and individualistic white males display when activities integral to their cultural identities are challenged as harmful. The article presents the results of an 1,800-person study that confirmed that cultural worldviews interact with the impact of gender and race on risk perception in patterns that suggest cultural-identity-protective cognition. It also discusses the implications of these findings for risk regulation and communication.
Design of one-lead ECG data logger

Cardiovascular diseases are a serious problem in the world. Early diagnosis of heart disease leads to minimization of deaths. Electrocardiography (ECG) is often the only indicator of pathological heart process. The analysis of electrocardiogram patterns and cardiac frequency is used for detecting arrhythmias and discovering the nature of disease. A Holter monitor is used for continuously monitoring various electrical activity of the cardiovascular system for at least 24 hours. Arrhythmias are not always present during Holter monitoring. In many cases patients encounter heart problems (pain, sudden change of heart beat) but no longer have an ECG device and/or equipment for measurement. These drawbacks can be overcome using an ECG logger. This paper describes the design of one-lead ECG data logger. The designed ECG logger is a small and compact device without any cables - it is a miniature, hand-held device with fixed electrodes for fingers. Measured data are stored on built-in microSD memory card and then transferred to PC via USB connection.
Semantic Segmentation using Adversarial Networks

Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets.
Swarm-Based Spatial Sorting

To present an algorithm for spatially sorting objects into an annular structure.  A swarm-based model that requires only stochastic agent behaviour coupled with a pheromone-inspired “attraction-repulsion” mechanism.  The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature.  Experimental evidence supports previous theoretical arguments about the nature and mechanism of spatial sorting by insects.  The algorithm may find applications in distributed robotics.  The model offers a powerful minimal algorithmic framework, and also sheds further light on the nature of attraction-repulsion algorithms and underlying natural processes.
Avoiding Traffic Jam Using Ant Colony Optimization - A Novel Approach

Ant colony optimization (ACO) is a meta-heuristic based on colony of artificial ants which work cooperatively, building solutions by moving on the problem graph and by communicating through artificial pheromone trails mimicking real ants. One of the active research directions is the application of ACO algorithms to solve dynamic shortest path problems. Solving traffic jams is one such problem where the cost i.e. time to travel increases during rush hours resulting in tremendous strain on daily commuters and chaos. This paper describes a new approach-DSATJ (Dynamic System for Avoiding Traffic Jam) which aims at choosing an alternative optimum path to avoid traffic jam and then resuming that same path again when the traffic is regulated. The approach is inspired by variants of ACO algorithms. Traffic jam is detected through pheromone values on edges which are updated according to goodness of solution on the optimal tours only. Randomness is introduced in the probability function to ensure maximum exploration by ants. Experiments were carried out with the partial road map of North-West region of Delhi, India, to observe the performance of our approach.
Towards 3-level hybrid security model for Android Operating Systems

The extensive use and rapid growth in popularity of Android phones have attracted the malware developer's attention. Due to this reason, malware attacks on Android devices are increasing every year. In this research, firstly we have investigated existing anti-malware techniques and identified their limitations. Secondly, we provided the description of a novel 3-level hybrid malware detection model for Android operating systems, which is an open-ended project and is currently under development. It is designed to ensure accurate detection of malware through the combination of i) Static & Dynamic Analysis; ii) Local & Remote Host; and iii) Machine Learning Intelligence. Through experimental results, it is shown that the 3-level hybrid malware detection model can achieve 98.5% detection rate, which is higher in comparison to the detection rate of Drebin.
Ka-band high-gain mesh deployable reflector antenna enabling the first radar in a CubeSat: RainCube

This paper presents a novel mesh deployable Ka-band antenna design folding in a 1.5U (10×10×15cm3) stowage volume suitable for 6U (10×20×30cm3) class CubeSats. Detailed results, considering all aspects of the deployable mesh reflector antenna, show that 42.6 dBi gain and 52% aperture efficiency is achievable at 35.75GHz. The mechanical deployment mechanism is also described as it constraints and drive the RF choices. The mesh prototype antenna has been fabricated and measurement results show excellent agreement with simulations.
SDN On-The-Go (OTG) physical testbed

An emerging field of research, Software Defined Networks (SDN) promises to change the landscape of traditional network topology and management. Researchers and early adopters alike need adequate SDN testing facilities for their experiments but their options are limited. Industry is responding slowly with embedded support for SDN in their enterprise grade network hardware but it is cost prohibitive for many test environments with a single SDN switch costing thousands of dollars. There are a few emerging community SDN test networks that are fantastic for testing large topologies with production grade traffic but there is a cost associated with membership and some controlled experiments are difficult. A free and indispensible alternative to a dedicated hardware SDN is to use network emulation tools. These software tools are widely used and invaluable to SDN research. They provide an amazingly precise representation of physical network nodes and behavior but are inherently limited by their aggregation with other virtual devices on the same compute node. Some of our research requires a higher precision than software emulation can provide. Our solution is to build a low cost, portable, standalone SDN testbed. Called SDN On-The-Go (OTG), it is a complete, self-contained testbed that consists of four dedicated ZodiacFX SDN switches, four RaspberryPi3 hosts, a dedicated Kangaroo+ controller with 4GB RAM and a couple of routers to form the network isolation. The testbed supports many configurations for pseudo real-world SDN experiments that produce reliable and repeatable results. It can be used as a standalone research tool or as part of a larger network with production quality traffic. SDN OTG is designed to be used as a portable teaching device, moved from classroom to classroom or taken home for private research. We achieved our repeatability factor of an order of magnitude greater than emulation based testing. Our SDN OTG physical testbed weighs only twenty pounds, costs about a thousand US dollars, provides repeatable, precise time sensitive data and can be setup as a fully functional SDN testbed in a matter of minutes.