nils-herrmann/clean_scirepeval huggingface dataset:
Advances in the Engineering of the Gene Editing Enzymes and the Genomes: Understanding and Handling the Off-Target Effects of CRISPR/Cas9.

In the past decades, significant progresses have been achieved in genetic engineering of nucleases. Among the genetically engineered nucleases, zinc finger nucleases, transcription activator-like (TAL) effector nucleases, and CRIPSPR/Cas9 system form a new field of gene editing. The gene editing efficiency or targeting effect and the off-target effect are the two major determinant factors in evaluating the usefulness of a new enzyme. Engineering strategies in improving these gene editing enzymes, particularly in minimizing their off-target effects, are the focus of this paper. Examples of using these genetically engineered enzymes in genome modification are discussed in order to better understand the requirement of engineering efforts in obtaining more powerful and useful gene editing enzymes. In addition, the identification of naturally existed anti-Cas proteins has been employed in minimizing off-target effects. Considering the future application in human gene therapy, optimization of these well recognized gene editing enzymes and exploration of more novel enzymes are both required. Before people find an ideal gene editing system having virtually no off-target effect, technologies used to screen and identify off-target effects are of importance in clinical trials employing gene therapy.
The Feasibility of Canine Rabies Elimination in Africa: Dispelling Doubts with Data

Canine rabies causes many thousands of human deaths every year in Africa, and continues to increase throughout much of the continent.



This paper identifies four common reasons given for the lack of effective canine rabies control in Africa: (a) a low priority given for disease control as a result of lack of awareness of the rabies burden; (b) epidemiological constraints such as uncertainties about the required levels of vaccination coverage and the possibility of sustained cycles of infection in wildlife; (c) operational constraints including accessibility of dogs for vaccination and insufficient knowledge of dog population sizes for planning of vaccination campaigns; and (d) limited resources for implementation of rabies surveillance and control. We address each of these issues in turn, presenting data from field studies and modelling approaches used in Tanzania, including burden of disease evaluations, detailed epidemiological studies, operational data from vaccination campaigns in different demographic and ecological settings, and economic analyses of the cost-effectiveness of dog vaccination for human rabies prevention.



We conclude that there are no insurmountable problems to canine rabies control in most of Africa; that elimination of canine rabies is epidemiologically and practically feasible through mass vaccination of domestic dogs; and that domestic dog vaccination provides a cost-effective approach to the prevention and elimination of human rabies deaths.
Ant Colony Extended: Experiments on the Travelling Salesman Problem

Ant Colony Extended (ACE) is a novel algorithm belonging to the general Ant Colony Optimisation (ACO) framework. Two specific features of ACE are: The division of tasks between two kinds of ants, namely patrollers and foragers, and the implementation of a regulation policy to control the number of each kind of ant during the searching process. This paper explores the performance of ACE in the context of the Travelling Salesman Problem (TSP), a classical combinatorial optimisation problem. The results are compared with the results of two well known ACO algorithms: ACS and MMAS.
Intramuscular ketamine for pediatric sedation in the emergency department: safety profile in 1,022 cases.

To determine the safety of intramuscular ketamine when administered by emergency physicians for pediatric procedures in accordance with a defined protocol.



We assembled a consecutive case series of children aged 15 years or younger who were given ketamine in the emergency departments of a university medical center and an affiliated county hospital over a 9-year period. A protocol for ketamine use (4 mg/kg, intramuscularly) was followed. Treating physicians were instructed to complete data forms recording complications and adequacy of sedation concurrent with patient care. Subsequent chart review was used to determine indications, adjunctive drugs, time to discharge, and adverse reactions for all patients.



Intramuscular ketamine was administered 1,022 times, mainly for laceration repair and fracture reduction. Physicians completed data forms for 431 of treated children (42%). Transient airway complications occurred in 1.4%: airway malalignment (n = 7), laryngospasm (n = 4), apnea (n = 2), and respiratory depression (n = 1). All were quickly identified and treated without intubation or sequelae. Emesis occurred in 6.7%, without evidence of aspiration. Mild recovery agitation occurred in 17.6%, moderate to severe agitation in 1.6%. No child required hospitalization for complications caused by ketamine. Ketamine produced acceptable sedation in 98% of patients. The median time from injection to emergency department discharge was 110 minutes for children given a single dose of ketamine.



Intramuscular ketamine may be administered safely by emergency physicians to facilitate pediatric procedures in accordance with a defined protocol and with appropriate monitoring. Ketamine is highly effective, has a wide margin of safety, does not require intravenous access, and uniquely preserves protective airway reflexes.
Truthful Online Auction for Cloud Instance Subletting

Despite that IaaS users are busy scaling up/out their cloud instances to meet the ever-increasing demands, the dynamics of their demands, as well as the coarse-grained billing options offered by leading cloud providers, have led to substantial instance underutilization in both temporal and spatial domains. This paper theoretically examines an instance subletting service, where underutilized instances are leased to others within user-specified periods. Serving as a secondary market that complements the existing instance market of IaaS providers,we specifically identify the theoretical challenges in instance subletting services, and design an online auction mechanism tomake allocation and pricing decisions for the instances to besublet. Our mechanism guarantees truthfulness and individualrationality with the best possible competitive ratio. Extensivetrace-driven simulations show that our proposed mechanismachieves significant performance gains in both cost and socialwelfare.
The design of a DeviceNet - SPI converter module based on the STM32 MCU

According to the characteristics of the STM32 technology and DeviceNet fieldbus, this paper designs a DeviceNet - SPI converter module. The converter module solves the problem of conversion between the DeviceNet fieldbus and the SPI protocol, making the equipment with SPI interface have the communication function of DeviceNet fieldbus through the converter module, at the same time making up for the shortcomings of SPI protocol with no response mechanism. The converter module is introduced the design in detail from the hardware to software and the building of experimental platform to test the design of the system in this paper. Besides, the paper contains testing and verifying the feasibility and reliability of the converter module through the analysis of the results.
3D culture models of Alzheimer’s disease: a road map to a “cure-in-a-dish”

Alzheimer’s disease (AD) transgenic mice have been used as a standard AD model for basic mechanistic studies and drug discovery. These mouse models showed symbolic AD pathologies including β-amyloid (Aβ) plaques, gliosis and memory deficits but failed to fully recapitulate AD pathogenic cascades including robust phospho tau (p-tau) accumulation, clear neurofibrillary tangles (NFTs) and neurodegeneration, solely driven by familial AD (FAD) mutation(s). Recent advances in human stem cell and three-dimensional (3D) culture technologies made it possible to generate novel 3D neural cell culture models that recapitulate AD pathologies including robust Aβ deposition and Aβ-driven NFT-like tau pathology. These new 3D human cell culture models of AD hold a promise for a novel platform that can be used for mechanism studies in human brain-like environment and high-throughput drug screening (HTS). In this review, we will summarize the current progress in recapitulating AD pathogenic cascades in human neural cell culture models using AD patient-derived induced pluripotent stem cells (iPSCs) or genetically modified human stem cell lines. We will also explain how new 3D culture technologies were applied to accelerate Aβ and p-tau pathologies in human neural cell cultures, as compared the standard two-dimensional (2D) culture conditions. Finally, we will discuss a potential impact of the human 3D human neural cell culture models on the AD drug-development process. These revolutionary 3D culture models of AD will contribute to accelerate the discovery of novel AD drugs.
DEEP: Design-Oriented Evaluation of Perceived Usability

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A Generative Model for category text generation

The neural network model has been the fulcrum of the so-called AI revolution. Although very powerful for pattern-recognition tasks, however, the model has two main drawbacks: it tends to overfit when the training dataset is small, and it is unable to accurately capture category information when the class number is large. In this paper, we combine reinforcement learning, generative adversarial networks, and recurrent neural networks to build a new model, termed category sentence generative adversarial network (CS-GAN). Not only the proposed model is able to generate category sentences that enlarge the original dataset, but also it helps improve its generalization capability during supervised training. We evaluate the performance of CS-GAN for the task of sentiment analysis. Quantitative evaluation exhibits the accuracy improvement in polarity detection on a small dataset with high category information.
A UHF ultrabroadband vivaldi-type direction finding antenna

A modern state of the art direction finder system consists of several direction finding (DF) antennas and the direction finder electronics. In order to achieve most accurate results for all azimuth directions the direction finder antenna system is often built using a circular antenna array. The need for light weight mobile DF systems leads to smaller DF antenna designs using less frequency bands to cover the whole frequency range. In this paper, an ultrabroadband DF antenna array covering the whole UHF frequency range from 0.3 GHz to 3GHz is introduced. Unlike the common antenna designs using dipols [1, 2] the new design consists of ten vivaldi-type antenna elements to cover the UHF frequency range fully.
TO FEE-BASED ONLINE SERVICES : WHAT MAKES CONSUMER PAY FOR ONLINE CONTENT ?

Increasingly, Web-based content or service sites are turning to a subscription-based business model. This article examines factors that influence potential consumers’ opinions about such paid services. A survey was conducted to examine the impact of several factors on consumers’ willingness to access subscription-based Web content. The results indicated that respondents’ intentions are significantly influenced by these factors. Specifically, their willingness to pay for online content or services is positively related to their perception of convenience, essentiality, added-value, and service quality, and to their usage rate of a given service. In addition, their willingness to pay is negatively related to their perceived unfairness in a subscription-based online service model. Managerial implications based on our findings are discussed.
Performance analysis of data security algorithms used in the railway traffic control systems

First of all, the railway traffic control process should ensure the safety. One of the current research areas is to ensure the security of data in the distributed rail traffic control systems using wireless networks. Emerging security threats are the result of, among others, an unknown number of users who may want to access the network, and an unknown number and type of equipment that can be connected to the network. It can cause potential threats resulting from unknown format of data and hacker attacks. In order to counteract these threats, it is necessary to apply safety functions. These functions include the use of data integrity code and encryption methods. Additionally, due to character of railway traffic control systems, it is necessary to keep time determinism while sending telegrams. Exceeding the maximum execution time of a cryptographic algorithm and creating too large blocks of data constitute two critical factors that should be taken into account while developing the system for data transmission. This could result in the inability to transmit data at a given throughput of the transmission channel (bandwidth) at a certain time. The paper presents analysis of delays resulting from the realization of safety functions: such as to prepare the data for transfer and their later decoding. Following block encryption algorithms have been analyzed: Blowfish, Twofish, DES, 3DES, AES-128, AES-192 and AES-256 for modes: ECB, CBC, PCBC, CFB, OFB, CTR and data integrity codes: MD-5, SHA-1, SHA-224, SHA-256, SHA-384, SHA-512, SHA-512/224 and SHA-512/256. The obtained results can be very helpful in the development of new rail traffic control systems in which wireless data transmission is planned.
Improved Gradient-Based Optimization Over Discrete Distributions

In many applications we seek to maximize an expectation with respect to a distribution over discrete variables. Estimating gradients of such objectives with respect to the distribution parameters is a challenging problem. We analyze existing solutions including finite-difference (FD) estimators and continuous relaxation (CR) estimators in terms of bias and variance. We show that the commonly used Gumbel-Softmax estimator is biased and propose a simple method to reduce it. We also derive a simpler piece-wise linear continuous relaxation that also possesses reduced bias. We demonstrate empirically that reduced bias leads to a better performance in variational inference and on binary optimization tasks.
Advances in antenna designs for UHF RFID tags mountable on conductive items

The design of antennas for metal-mountable radio-frequency identification tags is driven by a unique set of challenges: cheap, small, low-profile, and conformal structures need to provide reliable operation when tags are mounted on conductive platforms of various shapes and sizes. During the past decade, a tremendous amount of research has been dedicated to meeting these stringent requirements. Currently, the tag-reading ranges of several meters are achieved with flexible-label types of tags. Moreover, a whole spectrum of tag-size performance ratios has been demonstrated through a variety of innovative antenna-design approaches. This article reviews and summarizes the progress made in antennas for metal-mountable tags, and presents future prospects.
Active Tactile Transfer Learning for Object Discrimination in an Unstructured Environment Using Multimodal Robotic Skin

In this paper, we propose a probabilistic active tactile transfer learning (ATTL) method to enable robotic systems to exploit their prior tactile knowledge while discriminating among objects via their physical properties (surface texture, stiffness, and thermal conductivity). Using the proposed method, the robot autonomously selects and exploits its most relevant prior tactile knowledge to efficiently learn about new unknown objects with a few training samples or even one. The experimental results show that using our proposed method, the robot successfully discriminated among new objects with 72% discrimination accuracy using only one training sample (on-shot-tactile-learning). Furthermore, the results demonstrate that our method is robust against transferring irrelevant prior tactile knowledge (negative tactile knowledge transfer).
TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems

With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems. At present, most methods for generating adversarial samples require training parameters of known models and are only applicable to image data. To address the challenge, we propose a testing framework for learning-based IoT traffic detection systems, TLTD. By introducing genetic algorithms and some technical improvements, TLTD can generate adversarial samples for IoT traffic detection systems and can perform a black-box test on the systems.
Calibration of low cost MEMS inertial Measurement Unit for an FPGA-based navigation system

According to a small, lightweight, low-cost high performance inertial Measurement Units(IMU), an effective calibration method is implemented to evaluate the performance of Micro-Electro-Mechanical Systems(MEMS) sensors suffering from various errors to get acceptable navigation results. A prototype development board based on FPGA, dual core processor's configuration for INS/GPS integrated navigation system is designed for experimental testing. The significant error sources of IMU such as bias, scale factor, and misalignment are estimated in virtue of static tests, rate tests, thermal tests. Moreover, an effective intelligent calibration method combining with Kalman Filter is proposed to estimate parameters and compensate errors. The proposed approach has been developed and its efficiency is demonstrated by various experimental scenarios with real MEMS data.
CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing

Cloud computing is the future generation of computational services delivered over the Internet. As cloud infrastructure expands, resource management in such a large heterogeneous and distributed environment is a challenging task. In a cloud environment, uncertainty and dispersion of resources encounters problems of allocation of resources. Unfortunately, existing resource management techniques, frameworks and mechanisms are insufficient to handle these environments, applications and resource behaviors. To provide an efficient performance and to execute workloads, there is a need of quality of service (QoS) based autonomic resource management approach which manages resources automatically and provides reliable, secure and cost efficient cloud services. In this paper, we present an intelligent QoS-aware autonomic resource management approach named as CHOPPER (Configuring, Healing, Optimizing and Protecting Policy for Efficient Resource management). CHOPPER offers self-configuration of applications and resources, self-healing by handling sudden failures, self-protection against security attacks and self-optimization for maximum resource utilization. We have evaluated the performance of the proposed approach in a real cloud environment and the experimental results show that the proposed approach performs better in terms of cost, execution time, SLA violation, resource contention and also provides security against attacks.
NEIGHBORHOOD AND NETWORK SEGREGATION : ETHNIC HOMOPHILY IN A ` SILENTLY SEPARATE

We examine the relationship between geography and ethnic homophily in Estonia, a linguistically divided country. Analyzing the physical locations and cellular communications of tens of thousands of individuals, we document a strong relationship between the ethnic concentration of an individual's geographic neighborhood and the ethnic composition of the people with whom he interacts. The empirical evidence is consistent with a theoretical model in which individuals prefer to form ties with others living close by and of the same ethnicity. Exploiting variation in the data caused by migrants and quasi-exogenous settlement patterns, we nd suggestive evidence that the ethnic composition of geographic neighborhoods has a causal in uence on the ethnic structure of social networks.
Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks

We present a new neural sequence-tosequence model for extractive summarization called SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive summaries comprising a salient subset of input sentences, often also contain important key words. Guided by this principle, we design SWAP-NET that models the interaction of key words and salient sentences using a new twolevel pointer network based architecture. SWAP-NET identifies both salient sentences and key words in an input document, and then combines them to form the extractive summary. Experiments on large scale benchmark corpora demonstrate the efficacy of SWAP-NET that outperforms state-of-the-art extractive summarizers.
Impedance Control: An Approach to Manipulation

Manipulation fundamentally requires a manipulator to be mechanically coupled to the object being manipulated. A consideration of the physical constraints imposed by dynamic interaction shows that control of a vector quantity such as position or force is inadequate and that control of the manipulator impedance is also necessary. Techniques for control of manipulator behaviour are presented which result in a unified approach to kinematically constrained motion, dynamic interaction, target acquisition and obstacle avoidance.
Sierpinski gasket fractal antenna with defected ground structure (DGS)

This paper presents the design and fabrication of Sierpinski gasket fractal antenna with defected ground structure (DGS) with center frequency at 5.8 GHz. A slot was used as a DGS. The antenna was designed and simulated using Computer Simulation Technology (CST) software and fabricated on FR-4 board with a substrate thickness of 1.6 mm and dielectric constant, er of 5.0 and dielectric loss tangent 0.025. Measurement of the parameters of the antenna was carried by using a Vector Network Analyzer. The result shows a good agreement between simulation and measurement and a compact size antenna was realized.