nils-herrmann/clean_scirepeval huggingface dataset:
Structure, Genetics and Worldwide Spread of New Delhi Metallo-β-lactamase (NDM): a threat to public health

The emergence of carbapenemase producing bacteria, especially New Delhi metallo-β-lactamase (NDM-1) and its variants, worldwide, has raised amajor public health concern. NDM-1 hydrolyzes a wide range of β-lactam antibiotics, including carbapenems, which are the last resort of antibiotics for the treatment of infections caused by resistant strain of bacteria. In this review, we have discussed bla NDM-1variants, its genetic analysis including type of specific mutation, origin of country and spread among several type of bacterial species. Wide members of enterobacteriaceae, most commonly Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, and gram-negative non-fermenters Pseudomonas spp. and Acinetobacter baumannii were found to carry these markers. Moreover, at least seventeen variants of bla NDM-type gene differing into one or two residues of amino acids at distinct positions have been reported so far among different species of bacteria from different countries. The genetic and structural studies of these variants are important to understand the mechanism of antibiotic hydrolysis as well as to design new molecules with inhibitory activity against antibiotics. This review provides a comprehensive view of structural differences among NDM-1 variants, which are a driving force behind their spread across the globe.
Securing Real-Time Microcontroller Systems through Customized Memory View Switching

Real-time microcontrollers have been widely adopted in cyber-physical systems that require both real-time and security guarantees. Unfortunately, security is sometimes traded for real-time performance in such systems. Notably, memory isolation, which is one of the most established security features in modern computer systems, is typically not available in many real-time microcontroller systems due to its negative impacts on performance and violation of real-time constraints. As such, the memory space of these systems has created an open, monolithic attack surface that attackers can target to subvert the entire systems. In this paper, we present MINION, a security architecture that intends to virtually partition the memory space and enforce memory access control of a real-time microcontroller. MINION can automatically identify the reachable memory regions of realtime processes through off-line static analysis on the system’s firmware and conduct run-time memory access control through hardware-based enforcement. Our evaluation results demonstrate that, by significantly reducing the memory space that each process can access, MINION can effectively protect a microcontroller from various attacks that were previously viable. In addition, unlike conventional memory isolation mechanisms that might incur substantial performance overhead, the lightweight design of MINION is able to maintain the real-time properties of the microcontroller.
Sportsman hernia: what can we do?

Sportsman (sports) hernia is a medially located bulge in the posterior wall of the inguinal canal that is common in football players. About 90% of cases occur in males. The injury is also found in the general population. The presenting symptom is chronic groin pain which develops during exercise, aggravated by sudden movements, accompanied by subtle physical examination findings and a medial inguinal bulge on ultrasound. Pain persists after a game, abates during a period of lay-off, but returns on the resumption of sport. Frequently, sports hernia is one component of a more extensive pattern of injury known as ‘groin disruption injury’ consisting of osteitis pubis, conjoint tendinopathy, adductor tendinopathy and obturator nerve entrapment. Certain risk factors have been identified, including reduced hip range of motion and poor muscle balance around the pelvis, limb length discrepancy and pelvic instability. The suggested aetiology of the injury is repetitive athletic loading of the symphysis pubis disc, leading to accelerated disc degeneration with consequent pelvic instability and vulnerability to micro-fracturing along the pubic osteochondral junction, periosteal stripping of the pubic ligaments and para-symphyseal tendon tears, causing tendon dysfunction. Diagnostic imaging includes an erect pelvic radiograph (X-ray) with flamingo stress views of the symphysis pubis, real-time ultrasound and, occasionally, computed tomography (CT) scanning and magnetic resonance imaging (MRI), but seldom contrast herniography. Other imaging tests occasionally performed can include nuclear bone scan, limb leg measurement and test injections of local anaesthetic/corticosteroid. The injury may be prevented by the detection and monitoring of players at risk and by correcting significant limb length inequality. Groin reconstruction operation consists of a Maloney darn hernia repair technique, repair of the conjoint tendon, transverse adductor tenotomy and obturator nerve release. Rehabilitation involves core stabilisation exercises and the maintenance of muscle control and strength around the pelvis. Using this regimen of groin reconstruction and post-operative rehabilitation, a player would be anticipated to return to their pre-injury level of activity approximately 3 months after surgery.
All-memristive neuromorphic computing with level-tuned neurons.

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.
Bridging Machine Learning and Cryptography in Defence Against Adversarial Attacks

In the last decade, deep learning algorithms have become very popular thanks to the achieved performance in many machine learning and computer vision tasks. However, most of the deep learning architectures are vulnerable to so called adversarial examples. This questions the security of deep neural networks (DNN) for many securityand trust-sensitive domains. The majority of the proposed existing adversarial attacks are based on the differentiability of the DNN cost function. Defence strategies are mostly based on machine learning and signal processing principles that either try to detect-reject or filter out the adversarial perturbations and completely neglect the classical cryptographic component in the defence. In this work, we propose a new defence mechanism based on the second Kerckhoffs’s cryptographic principle which states that the defence and classification algorithm are supposed to be known, but not the key. To be compliant with the assumption that the attacker does not have access to the secret key, we will primarily focus on a gray-box scenario and do not address a white-box one. More particularly, we assume that the attacker does not have direct access to the secret block, but (a) he completely knows the system architecture, (b) he has access to the data used for training and testing and (c) he can observe the output of the classifier for each given input. We show empirically that our system is efficient against most famous state-of-the-art attacks in black-box and gray-box scenarios.
Virtual labs in electrical engineering education - The VEMA environment

3D worlds provide excellent platforms to achieve performance results when designed for the needs of students, helping them to improve acquisition and allowing greater interaction with contents in a unique way. Virtual Labs are the future of education. Simulations, in which environmental measurements and data analysis can be simulated, are being created to combine real-world environments with information supplied by devices. Virtual Labs (VL) designed for educational purposes in Electrical Engineering provide tasks or problems within a virtual environment or context. Users can explore the environment and examine digital objects as they are designed to support inquiry-based learning and conceptual understanding. Online virtual labs simulate learning environments and are designed to be highly attractive. They can complement the traditional formal learning programs, providing the opportunity for an interaction across a range of learning experiences in 3D. Online learning such as virtual classrooms will become the major part of training education in the future. Virtual lab applications are an engaging and agreeable learning system. Educational virtual labs should not merely focus on the virtual environment; they are useful to help students learn to think creatively and understand the complexities of solving meaningful problems, but the support from the teacher is also important. This study presents a virtual scenario of an instrumentation and measurements laboratory. Students work with representations of electrical circuits focused on learning electrical concepts and circuit analysis, also studying and developing action procedures. The goal is to help them learn to think like engineers. While they are learning about circuit theory, they are also learning to apply the many skills of an engineer such as organizing information for writing technical reports. This work was developed to demonstrate how a desktop Virtual Reality (VR) prototype, "Virtual Electric Manual"VEMA, can be applied to an engineering unit and used to enhance security and resourcefulness in using electrical equipment. Several interactive scenes were developed to illustrate the idea using a measurements and instrumentation laboratory as virtual environment.
Scalable Test Problems for Evolutionary Multi-Objective Optimization

After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their efficacy in handling problems having more than two objectives. In this paper, we have suggested three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Pareto-optimal front, and introduction of controlled difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of the above features, they should be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and better understanding of the working principles of MOEAs.
Medical Cyber Physical Systems and Bigdata Platforms

Over the past few decades the capabilities of adapting new class of devices for health monitoring system have improved significantly.But the increase in usage of low cost sensors and various communication media for data transmission in health monitoring have lead to a major concern for current existing platforms i.e., inefficiency in processing massive amount of data in real time. To advance this field requires a new look at the computing framework and infrastructure. This paper describes our initial work for Bigdata processing framework for MCPS that combines the real world and cyber world aspects with dynamic provisioning and fully elastic system for decision making in health care application.
Fast Decoupled Power Flow for Unbalanced Radial Distribution Systems

This paper presents a novel power flow formulation and an effective solution method for general unbalanced radial distribution systems. Comprehensive models are considered including lines, switches, transformers, shunt capacitors, cogenerators, and several types of loads. A new problem formulation of three-phase distribution power flow equations taking into account the radial structure of the distribution network is presented. A distinguishing feature of the new problem formulation is that it significantly reduces the number of power flow equations, as compared with the conventional formulation. The numerical properties as well as the structural properties of distribution systems are exploited resulting in a fast decoupled solution algorithm. The proposed solution algorithm is evaluated on three-phase unbalanced 292-bus and 394-bus test systems with very promising results.
Flexible transparent conductive materials based on silver nanowire networks: a review.

The class of materials combining high electrical or thermal conductivity, optical transparency and flexibility is crucial for the development of many future electronic and optoelectronic devices. Silver nanowire networks show very promising results and represent a viable alternative to the commonly used, scarce and brittle indium tin oxide. The science and technology research of such networks are reviewed to provide a better understanding of the physical and chemical properties of this nanowire-based material while opening attractive new applications.
Investigation of Research impacts on the Zika Virus: An Approach Focusing on Social network Analysis and Altmetrics

Zika has drawn international attention in 2015, both for its epidemic proportion and its consequences. Scientific advances are made, but is the population aware? Allow the population to understand the scientific advances of a disease, achievements made and current risk situation is important to avoid panic, prejudice and violence. This work aims to study the impact of the main Zika researchers and the repercussion of their research on population. For this purpose, the main researchers were identified in co-authorship networks and, through altmetric indexes (alternative metrics), the repercussion of these researchers were analyzed in news and social media.
A Novel Wideband Slotline Antenna with Dual Resonances: Principle and Design Approach

A novel wideband slotline antenna is proposed using the multimode resonance concept. By symmetrically introducing two slot stubs along the slotline radiator near the nulls of electric-field distribution of the second odd-order mode, two radiation modes are excited in a single slotline resonator. With the help of the two stubs, the second odd-order mode gradually merges with its first counterpart and results into a wideband radiation with two resonances. Prototype antennas are then fabricated to experimentally validate the principle and design approach of the proposed slotline antenna. It is shown that the proposed slotline antenna's impedance bandwidth could be effectively increased to 32.7% while keeping an inherent narrow slot structure.
Anterior knee pain in the young athlete: diagnosis and treatment.

The underlying etiology of anterior knee pain has been extensively studied. Despite many possible causes, often times the diagnosis is elusive. The most common causes in the young athlete are osteosynchondroses, patellar peritendinitis and tendinosis, synovial impingement, malalignment, and patellar instability. Less common causes are osteochondritis dissecans and tumors. It is always important to rule out underlying hip pathology and infections. When a diagnosis cannot be established, the patient is usually labeled as having idiopathic anterior knee pain. A careful history and physical examination can point to the correct diagnosis in the majority of cases. For most of these conditions, treatment is typically nonoperative with surgery reserved for refractory pain for an established diagnosis.
Feature Selection Using Artificial Neural Networks

Machine learning is useful for building robust learning models, and it is based on a set of features that identify a state of an object. Unfortunately, some data sets may contain a large number of features making, in some cases, the learning process time consuming and the generalization capability of machine learning poor. To make a data set easy to learn and understand, it is typically recommended to remove the most irrelevant features from the set. However, choosing what data should be kept or eliminated may be performed by complex selection algorithms, and optimal feature selection may require an exhaustive search of all possible subsets of features which is computationally expensive. This paper proposes a simple method to perform feature selection using artificial neural networks. It is shown experimentally that genetic algorithms in combination with artificial neural networks can easily be used to extract those features that are required to produce a desired result. Experimental results show that very few hidden neurons are required for feature selection as artificial neural networks are only used to assess the quality of an individual, which is a chosen subset of features.
A Model-Based Approach to Systematic Review of Research Literature

A systematic approach to develop a literature review is attractive because it aims to achieve a repeatable, unbiased and evidence-based outcome. However the existing form of systematic review such as Systematic Literature Review (SLR) and Systematic Mapping Study (SMS) are known to be an effort, time, and intellectual intensive endeavour. To address these issues, this paper proposes a model-based approach to Systematic Review (SR) production. The approach uses a domain-specific language expressed as a meta-model to represent research literature, a meta-model to specify SR constructs in a uniform manner, and an associated development process all of which can benefit from computer-based support. The meta-models and process are validated using real-life case study. We claim that the use of meta-modeling and model synthesis lead to a reduction in time, effort and the current dependence on human expertise.
A novel fuzzy logic approach to contrast enhancement

Contrast enhancement is one of the most important issues of image processing, pattern recognition and computer vision. The commonly used techniques for contrast enhancement fall into two categories: (1) indirect methods of contrast enhancement and (2) direct methods of contrast enhancement. Indirect approaches mainly modify histogram by assigning new values to the original intensity levels. Histogram speci"cation and histogram equalization are two popular indirect contrast enhancement methods. However, histogram modi"cation technique only stretches the global distribution of the intensity. The basic idea of direct contrast enhancement methods is to establish a criterion of contrast measurement and to enhance the image by improving the contrast measure. The contrast can be measured globally and locally. It is more reasonable to de"ne a local contrast when an image contains textual information. Fuzzy logic has been found many applications in image processing, pattern recognition, etc. Fuzzy set theory is a useful tool for handling the uncertainty in the images associated with vagueness and/or imprecision. In this paper, we propose a novel adaptive direct fuzzy contrast enhancement method based on the fuzzy entropy principle and fuzzy set theory. We have conducted experiments on many images. The experimental results demonstrate that the proposed algorithm is very e!ective in contrast enhancement as well as in preventing over-enhancement. ( 2000 Pattern Recognition Society.
Risk-sensitive inverse reinforcement learning via semi- and non-parametric methods

The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive IRL in order to explicitly account for a human’s risk sensitivity. To this end, we propose a flexible class of models based on coherent risk measures, which allow us to capture an entire spectrum of risk preferences from risk-neutral to worst-case. We propose efficient non-parametric algorithms based on linear programming and semi-parametric algorithms based on maximum likelihood for inferring a human’s underlying risk measure and cost function for a rich class of static and dynamic decision-making settings. The resulting approach is demonstrated on a simulated driving game with ten human participants. Our method is able to infer and mimic a wide range of qualitatively different driving styles from highly risk-averse to risk-neutral in a data-efficient manner. Moreover, comparisons of the Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL framework more accurately captures observed participant behavior both qualitatively and quantitatively, especially in scenarios where catastrophic outcomes such as collisions can occur.
Meta-game Equilibrium for Multi-agent Reinforcement Learning

This paper proposes a multi-agent Q-learning algorithm called meta-game-Q learning that is developed from the meta-game equilibrium concept. Different from Nash equilibrium, meta-game equilibrium can achieve the optimal joint action game through deliberating its preference and predicting others’ policies in the general-sum game. A distributed negotiation algorithm is used to solve the meta-game equilibrium problem instead of using centralized linear programming algorithms. We use the repeated prisoner’s dilemma example to empirically demonstrate that the algorithm converges to meta-game equilibrium.
Linearly convergent stochastic heavy ball method for minimizing generalization error

In this work we establish the first linear convergence result for the stochastic heavy ball method. The method performs SGD steps with a fixed stepsize, amended by a heavy ball momentum term. In the analysis, we focus on minimizing the expected loss and not on finite-sum minimization, which is typically a much harder problem. While in the analysis we constrain ourselves to quadratic loss, the overall objective is not necessarily strongly convex.