nils-herrmann/clean_scirepeval huggingface dataset:
Dual-band leaky-wave antenna based on a dual-layer frequency selective surface for bi-directional satcom-on-the-move in Ka-band

A 2D-periodic leaky-wave antenna for a Ka-band satcom-on-the-move ground user terminal is presented. The antenna panel operates at the 20 GHz downlink as well as the 30 GHz uplink bands with the respective circular polarizations, using a common radiation aperture and a common phase centre. The dual-band performance is achieved by a carefully designed stacked dual-layer frequency selective surface, with one layer operating at 20 GHz and being transparent at 30 GHz, and the second layer acting vice versa. The paper describes the design of the circularly polarized primary feed, the dual-layer structures, and the complete compact leaky-wave antenna panel. The measured radiation performance reveals realized-gain values above 22 dBi and efficiencies above 60%. The cross-polarization discrimination and sidelobe level are suitable to meet the power spectral requirements for satellite communications at Ka-band.
Systems to rate the strength of scientific evidence.

ii This document is in the public domain and may be used and reprinted without permission except those copyrighted materials noted for which further reproduction is prohibited without the specific permission of copyright holders. Preface The Agency for Healthcare Research and Quality (AHRQ), formerly the Agency for Health Care Policy and Research (AHCPR), through its Evidence-Based Practice Centers (EPCs), sponsors the development of evidence reports and technology assessments to assist public-and private-sector organizations in their efforts to improve the quality of health care in the United States. The reports and assessments provide organizations with comprehensive, science-based information on common, costly medical conditions and new health care technologies. The EPCs systematically review the relevant scientific literature on topics assigned to them by AHRQ and conduct additional analyses when appropriate prior to developing their reports and assessments. To bring the broadest range of experts into the development of evidence reports and health technology assessments, AHRQ encourages the EPCs to form partnerships and enter into collaborations with other medical and research organizations. The EPCs work with these partner organizations to ensure that the evidence reports and technology assessments they produce will become building blocks for health care quality improvement projects throughout the Nation. The reports undergo peer review prior to their release. AHRQ expects that the EPC evidence reports and technology assessments will inform individual health plans, providers, and purchasers as well as the health care system as a whole by providing important information to help improve health care quality. We welcome written comments on this evidence report. They may be sent to: Director, and Quality (AHRQ) (Task No. 7). We acknowledge the continuing support of Jacqueline Besteman, JD, MA, the AHRQ Task Order Officer for this project. The investigators deeply appreciate the considerable support, commitment, and contributions from Research Triangle Institute staff Sheila White and Loraine Monroe. In addition, we would like to extend our appreciation to the members of our Technical Expert Advisory Group (TEAG), who served as vital resources throughout our process. We owe our thanks as well to our external peer reviewers, who provided constructive feedback and insightful suggestions for improvement of our report. Peer reviewers were Alfred O. In addition, we would like to extend our thanks to the seven anonymous reviewers designated by AHRQ. Finally, we are indebted as well to several senior members of the faculty at the University of Objectives. Health care decisions are increasingly …
A comparative study of Artificial Bee Colony algorithm

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.
Energy efficient stochastic-based deep spiking neural networks for sparse datasets

With large deep neural networks (DNNs) necessary to solve complex and data-intensive problems, energy efficiency is a key bottleneck for effectively deploying DL in the real world. Deep spiking NNs have gained much research attention recently due to the interest in building biological neural networks and the availability of neuromorphic platforms, which can be orders of magnitude more energy efficient compared to CPUs and GPUs. Although spiking NNs have proven to be an efficient technique for solving many machine learning and computer vision problems, to the best of our knowledge, this is the first attempt to adapt spiking NNs to sparse datasets. In this paper, we study the behaviour of spiking NNs in handling NLP datasets and the sparsity in their data representation. Then, we propose a novel framework for spiking NN using the concept of stochastic computing. Specifically, instead of generating spike trains with firing rates proportional to the intensity of each value in the feature set separately, the whole feature set is treated as a distribution function and a stochastic spiking train that follow this distribution is generated. This framework reduces the connectivity between NN layers from O(N) to O(log N). Also, it encodes input data differently and make suitable to handle sparse datasets. Finally, the framework achieves high energy efficiency since it uses Integrate and Fire neurons same as conventional spiking NNs. The results show that our proposed stochastic-based SNN achieves nearly the same accuracy as the original DNN on MNIST dataset, and it has better performance than state-of-the-art SNN. Besides that stochastic-based SNN is energy efficient, where the fully connected DNN, the conventional SNN, and the data normalized SNN consume 38.24, 1.83, and 1.85-times more energy than the stochastic-based SNN, respectively. For sparse datasets, including IMDb and In-House clinical datasets, stochastic-based SNN achieves performance comparable to that of the conventional DNN. However, the conventional spiking NN has a significant decline in classification accuracy.
Human-Like Motion Generation and Control for Humanoid's Dual Arm Object Manipulation

The robot manipulation in human environment is a challenging issue because the human environment is complex, dynamic, unstructured, and difficult to perceive reliably. In order to implement promising robot applications in our daily lives, robots need to perform manipulation tasks within human environment. Particularly for a humanoid robot, the manipulability of the objects is essential to assist the humans in human environment. This paper presents a method for manipulating an object with both arms of a humanoid robot. We focus on the generation of human-like movements by using the human motion capture data. Then, the control method based on the virtual dynamics model is proposed to control both the motion and force under the uniform control system. This method empowers the robot to perform the object manipulation task including reaching, grasping, and moving an object in sequence. The proposed algorithm is implemented on a humanoid robot with independent joint controller at each motor; its performance is demonstrated by manipulating an object with both arms.
Change detection in streaming data in the era of big data: models and issues

Big Data is identified by its three Vs, namely velocity, volume, and variety. The area of data stream processing has long dealt with the former two Vs velocity and volume. Over a decade of intensive research, the community has provided many important research discoveries in the area. The third V of Big Data has been the result of social media and the large unstructured data it generates. Streaming techniques have also been proposed recently addressing this emerging need. However, a hidden factor can represent an important fourth V, that is variability or change. Our world is changing rapidly, and accounting to variability is a crucial success factor. This paper provides a survey of change detection techniques as applied to streaming data. The review is timely with the rise of Big Data technologies, and the need to have this important aspect highlighted and its techniques categorized and detailed.
Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge

Building knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into sub-problems such as mention and named entity recognition, entity linking and relation extraction. However, combining these components has shown to be under-constrained and often produces KBs with supersize entities and common-sense errors in relations (a person has multiple birthdates). The errors are difficult to resolve solely with IE tools but become obvious with world knowledge at the corpus level. By analyzing Freebase and a large text collection, we found that per-relation cardinality and the popularity of entities follow the power-law distribution favoring flat long tails with lowfrequency instances. We present a probabilistic joint inference algorithm to incorporate this world knowledge during KB construction. Our approach yields stateof-the-art performance on the TAC Cold Start task, and 42% and 19.4% relative improvements in F1 over our baseline on Cold Start hop-1 and all-hop queries respectively. ∗The third author is currently with The Affinity project. This work was done while she was at BBN. This work was supported by DARPA/I2O Contract No. FA8750-13-C-0008 under the DEFT program. The views, opinions, and/or findings contained in this article are those of the author and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. This document does not contain technology or technical data controlled under either the U.S. International Traffic in Arms Regulations or the U.S. Export Administration Regulations.
Prospects of encoding Java source code in XML

Currently, the only standard format for representing Java source code is plain text-based. This paper explores the prospects of using Extensible Markup Language (XML) for this purpose. XML enables the leverage of tools and standards more powerful than those available for plain-text formats, while retaining broad accessibility. The paper outlines the potential benefits of future XML grammars that would allow for improved code structure and querying possibilities; code extensions, construction, and formatting; and referencing parts of code. It also introduces the concept of grammar levels and argues for the inclusion of several grammar levels into a common framework. It discusses conversions between grammars and some practical grammar design issues. XML, Java, source code, parsing, code formatting
Secure Communication Method Based on Encryption and Steganography

Encryption is a traditional method of scrambling data beyond recognition. For years, encryption has been widely used in various areas and domains where secrecy and confidentiality were required. However, there are certain encryption techniques that are not allowed in some countries. In these cases, steganography may come as a solution for hiding data using an apparently inoffensive and innocent carrier. The purpose of steganography is to deliver secret information from a sender to a receiver by scrambling the communication channel not the information itself. This way, it is very unlikely for someone to identify and extract the secret message without knowing exactly how it was embedded into the carrier. Moreover, this task is more difficult since most steganographic methods rely on file or packet header redundancy, lossy multimedia algorithms or insignificant content alteration. All of these are very hard to detect. In this paper, a combination of encryption with image based steganography is presented in order to offer similar robustness as offered by advanced encryption algorithms such as RSA, AES or DES but with lower resource needs. The most original contribution of this paper consists in how a secret message is embedded into a image carrier generating 3 different but very similar images. These images are sent to the destination using different channels and then used to recover the original (secret) message.
Challenges for the Self-Safety in Autonomous Vehicles

The combination of multiple functions having different and complementary capabilities enables the emergence of Autonomous Vehicles. Their deployment is limited by the level of complexity they represent together with the challenges encountered in real environments with strong safety concerns. Thus a major concern prior to massive deployment is on how to ensure the safety of autonomous vehicles despite likely internal (e.g. malfunctions) and external (e.g., aggressive behaviors) disturbances they might undergo. This paper presents the challenges that undergoes the design and development of autonomous vehicles with respect to their functional architecture and adaptive behaviors from a safety perspective. For the purpose of the rationales, we define needs and requirements that lead to the formulation of an architectural framework. Our approach is based on paradigms and technologies from non-automotive domains to address non-functional system properties like safety, reliability and security. The notion of micro-services is also introduced for the self-safety of autonomous vehicles. These are part of the proposed framework that should facilitate the analysis, design, development and validation for the adequate composition and orchestration of services aimed to warrant the required non-functional properties, such as safety. In the present paper, we introduce the structural and behavioral adaptations of the framework to offer a holistic and scalable vision of the safety over the system.
Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

Camera viewpoint selection is an important aspect of visual grasp detection, especially in clutter where many occlusions are present. Where other approaches use a static camera position or fixed data collection routines, our MultiView Picking (MVP) controller uses an active perception approach to choose informative viewpoints based directly on a distribution of grasp pose estimates in real time, reducing uncertainty in the grasp poses caused by clutter and occlusions. In trials of grasping 20 objects from clutter, our MVP controller achieves 80% grasp success, outperforming a single-viewpoint grasp detector by 12%. We also show that our approach is both more accurate and more efficient than approaches which consider multiple fixed viewpoints.
Sarcasm detection of tweets: A comparative study

Sarcasm is a nuanced form of communication where the individual states opposite of what is implied. One of the major challenges of sarcasm detection is its ambiguous nature. There is no prescribed definition of sarcasm. Another major challenge is the growing size of the languages. Every day hundreds of new slang words are being created and used on these sites. Hence, the existing corpus of positive and negative sentiments may not prove to be accurate in detecting sarcasm. Also, the recent developments in online social networks allow its users to use varied kind of emoticons with the text. These emoticons may change the polarity of the text and make it sarcastic. Due to these difficulties and the inherently tricky nature of sarcasm it is generally ignored during social network analysis. As a result the results of such analysis are affected adversely. Thus, sarcasm detection poses to be one of the most critical problems which we need to overcome. Detection of sarcastic content is vital to various NLP based systems such as text summarization and sentiment analysis. In this paper we address the problem of sarcasm detection by leveraging the most common expression of sarcasm — “positive sentiment attached to a negative situation”. Our work uses two ensemble based approaches — voted ensemble classifier and random forest classifier. Unlike current approaches to sarcasm detection which rely on existing corpus of positive and negative sentiments for training the classifiers, we use a seeding algorithm to generate training corpus. The proposed model also uses a pragmatic classifier to detect emoticon based sarcasm.
Hourly rounding to improve nursing responsiveness: a systematic review.

The aims of this study were to synthesize the evidence concerning the effect of hourly rounding programs on patient satisfaction with nursing care and discuss implications for nurse administrators.



Patient satisfaction is a key metric that influences both hospital ratings and reimbursement. Studies have suggested that purposeful nursing rounds can improve patient satisfaction, but the evidence to date has not been systematically examined.



A systematic review of published literature and GRADE analysis of evidence regarding nursing rounds were conducted.



There is little consistency in how results of hourly rounds were measured, precluding quantitative analysis. There is moderate-strength evidence that hourly rounding programs improve patients' perception of nursing responsiveness. There is also moderate-strength evidence that these programs reduce patient falls and call light use.



Nurse administrators should consider implementing an hourly rounding program while controlled trials discern the most cost-effective approach.
VMHunt: A Verifiable Approach to Partially-Virtualized Binary Code Simplification

Code virtualization is a highly sophisticated obfuscation technique adopted by malware authors to stay under the radar. However, the increasing complexity of code virtualization also becomes a "double-edged sword" for practical application. Due to its performance limitations and compatibility problems, code virtualization is seldom used on an entire program. Rather, it is mainly used only to safeguard the key parts of code such as security checks and encryption keys. Many techniques have been proposed to reverse engineer the virtualized code, but they share some common limitations. They assume the scope of virtualized code is known in advance and mainly focus on the classic structure of code emulator. Also, few work verifies the correctness of their deobfuscation results. In this paper, with fewer assumptions on the type and scope of code virtualization, we present a verifiable method to address the challenge of partially-virtualized binary code simplification. Our key insight is that code virtualization is a kind of process-level virtual machine (VM), and the context switch patterns when entering and exiting the VM can be used to detect the VM boundaries. Based on the scope of VM boundary, we simplify the virtualized code. We first ignore all the instructions in a given virtualized snippet that do not affect the final result of that snippet. To better revert the data obfuscation effect that encodes a variable through bitwise operations, we then run a new symbolic execution called multiple granularity symbolic execution to further simplify the trace snippet. The generated concise symbolic formulas facilitate the correctness testing of our simplification results. We have implemented our idea as an open source tool, VMHunt, and evaluated it with real-world applications and malware. The encouraging experimental results demonstrate that VMHunt is a significant improvement over the state of the art.
A Smart Contract for Boardroom Voting with Maximum Voter Privacy

We present the first implementation of a decentralised and self-tallying internet voting protocol with maximum voter privacy using the Blockchain. The Open Vote Network is suitable for boardroom elections and is written as a smart contract for Ethereum. Unlike previously proposed Blockchain e-voting protocols, this is the first implementation that does not rely on any trusted authority to compute the tally or to protect the voter’s privacy. Instead, the Open Vote Network is a selftallying protocol, and each voter is in control of the privacy of their own vote such that it can only be breached by a full collusion involving all other voters. The execution of the protocol is enforced using the consensus mechanism that also secures the Ethereum blockchain. We tested the implementation on Ethereum’s official test network to demonstrate its feasibility. Also, we provide a financial and computational breakdown of its execution cost.
Cartesian Cubical Computational Type Theory: Constructive Reasoning with Paths and Equalities

We present a dependent type theory organized around a Cartesian notion of cubes (with faces, degeneracies, and diagonals), supporting both fibrant and non-fibrant types. The fibrant fragment validates Voevodsky’s univalence axiom and includes a circle type, while the non-fibrant fragment includes exact (strict) equality types satisfying equality reflection. Our type theory is defined by a semantics in cubical partial equivalence relations, and is the first two-level type theory to satisfy the canonicity property: all closed terms of boolean type evaluate to either true or false.
A Fast-Response Pseudo-PWM Buck Converter With PLL-Based Hysteresis Control

Hysteresis voltage-mode control is a simple and fast control scheme for switched-mode power converters. However, it is well-known that the switching frequency of a switched-mode power converter with hysteresis control depends on many factors such as loading current and delay of the controller which vary from time to time. It results in a wide noise spectrum and leads to difficulty in shielding electro-magnetic interference. In this work, a phase-lock loop (PLL) is utilized to control the hysteresis level of the comparator used in the controller, while not interfering with the intrinsic behavior of the hysteresis controller. Some design techniques are used to solve the integration problem and to improve the settling speed of the PLL. Moreover, different control modes are implemented. A buck converter with proposed control scheme is fabricated using a commercial 0.35-μ m CMOS technology. The chip area is 1900 μm × 2200 μm. The switching frequency is locked to 1 MHz, and the measured frequency deviation is within ±1%. The measured load transient response between 160 and 360 mA is 5 μ s only.
Improved SVPWM schemes for vienna rectifiers without current distortion

Vienna rectifier is one of the most popular topologies for three-phase PFC converter. Many control schemes were presented for Vienna rectifiers with unity power factor. Because of the small phase shift θ caused by the inductor or current detection delay, a zero-crossing detection error occurs, resulting in zero-crossing current spikes. This paper presents two SVPWM methods for three phase Vienna rectifiers without current distortion. Due to the current commutation characteristics in Vienna rectifiers, the available space voltage vectors are limited. When Vienna rectifiers work with the small phase shift 0, the limitation of the available space voltage vectors is changed. The proposed schemes choose suitable vectors according to the polarity of the input current, so that the Vienna rectifiers can work normally without current distortion. The feasibility of the proposed schemes is verified by simulation and experiment.