nils-herrmann/clean_scirepeval huggingface dataset:
Pay Attention to Those Sets! Learning Quantification from Images

Major advances have recently been made in merging language and vision representations. But most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even preschool children) can abstract over raw data to perform certain types of higher-level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current language and vision strategies model such relations. We show that state-of-the-art attention mechanisms coupled with a traditional linguistic formalisation of quantifiers gives best performance on the task. Additionally, we provide insights on the role of 'gist' representations in quantification. A 'logical' strategy to tackle the task would be to first obtain a numerosity estimation for the two involved sets and then compare their cardinalities. We however argue that precisely identifying the composition of the sets is not only beyond current state-of-the-art models but perhaps even detrimental to a task that is most efficiently performed by refining the approximate numerosity estimator of the system.
Detection of false data injection attacks in smart-grid systems

Smart grids are essentially electric grids that use information and communication technology to provide reliable, efficient electricity transmission and distribution. Security and trust are of paramount importance. Among various emerging security issues, FDI attacks are one of the most substantial ones, which can significantly increase the cost of the energy distribution process. However, most current research focuses on countermeasures to FDIs for traditional power grids rather smart grid infrastructures. We propose an efficient and real-time scheme to detect FDI attacks in smart grids by exploiting spatial-temporal correlations between grid components. Through realistic simulations based on the US smart grid, we demonstrate that the proposed scheme provides an accurate and reliable solution.
Evaluation of Deep Models for Real-Time Small Object Detection

Real-time object detection is crucial for many applications. Approaches based on Deep Learning have achieved state-of-the-art performance on challenging datasets. Although several evaluations of the models have been conducted, there is no extensive evaluation with specific focuses on real-time small object detection. In this work, we present an in-depth evaluation of existing deep learning models in detecting small objects. We evaluate three state-of-the-art models including You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster R-CNN with related trade-off factors i.e. accuracy, execution time and resource constraints. Experiments were conducted on benchmark datasets and a newly generated dataset for small object detection. All analyses and findings are then presented.
Control Strategies for Soft Robotic Manipulators: A Survey.

With the rise of soft robotics technology and applications, there have been increasing interests in the development of controllers appropriate for their particular design. Being fundamentally different from traditional rigid robots, there is still not a unified framework for the design, analysis, and control of these high-dimensional robots. This review article attempts to provide an insight into various controllers developed for continuum/soft robots as a guideline for future applications in the soft robotics field. A comprehensive assessment of various control strategies and an insight into the future areas of research in this field are presented.
Differentially-Private "Draw and Discard" Machine Learning

In this work, we propose a novel framework for privacy-preserving client-distributed machine learning. It is motivated by the desire to achieve differential privacy guarantees in the local model of privacy in a way that satisfies all systems constraints using asynchronous client-server communication and provides attractive model learning properties. We call it “Draw and Discard” because it relies on random sampling of models for load distribution (scalability), which also provides additional server-side privacy protections and improved model quality through averaging. We present the mechanics of client and server components of “Draw and Discard” and demonstrate how the framework can be applied to learning Generalized Linear models. We then analyze the privacy guarantees provided by our approach against several types of adversaries and showcase experimental results that provide evidence for the framework’s viability in practical deployments. We believe our framework is the first deployed distributed machine learning approach that operates in the local privacy model.
Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making

The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby’s murder to train and test a supervised machine learning text classifier that distinguishes between hateful and/or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.
Porter ’ s Generic Competitive Strategies Ritika Tanwar

Generic Competitive Strategy: Basically, strategy is about two things: deciding where you want your business to go, and deciding how to get there. A more complete definition is based on competitive advantage, the object of most corporate strategy: “Competitive advantage grows out of value a firm is able to create for its buyers that exceeds the firm's cost of creating it. Value is what buyers are willing to pay, and superior value stems from offering lower prices than competitors for equivalent benefits or providing unique benefits that more than offset a higher price. There are two basic types of competitive advantage: cost leadership and differentiation.” Michael Porter Competitive strategies involve taking offensive or defensive actions to create a defendable position in the industry. Generic strategies can help the organization to cope with the five competitive forces in the industry and do better than other organization in the industry. Generic strategies include ‘overall cost leadership’, ‘differentiation’, and ‘focus’. Generally firms pursue only one of the above generic strategies. However some firms make an effort to pursue only one of the above generic strategies. However some firms make an effort to pursue more than one strategy at a time by bringing out a differentiated product at low cost. Though approaches like these are successful in short term, they are hardly sustainable in the long term. If firms try to maintain cost leadership as well as differentiation at the same time, they may fail to achieve either.
Does cyberbullying impact youth suicidal behaviors?

Even though several youth fatal suicides have been linked with school victimization, there is lack of evidence on whether cyberbullying victimization causes students to adopt suicidal behaviors. To investigate this issue, I use exogenous state-year variation in cyberbullying laws and information on high school students from the Youth Risk Behavioral Survey within a bivariate probit framework, and complement these estimates with matching techniques. I find that cyberbullying has a strong impact on all suicidal behaviors: it increases suicidal thoughts by 14.5 percentage points and suicide attempts by 8.7 percentage points. Even if the focus is on statewide fatal suicide rates, cyberbullying still leads to significant increases in suicide mortality, with these effects being stronger for men than for women. Since cyberbullying laws have an effect on limiting cyberbullying, investing in cyberbullying-preventing strategies can improve individual health by decreasing suicide attempts, and increase the aggregate health stock by decreasing suicide rates.
UAV PHOTOGRAMMETRY FOR MAPPING AND 3 D MODELING – CURRENT STATUS AND FUTURE PERSPECTIVES –

UAV platforms are nowadays a valuable source of data for inspection, surveillance, mapping and 3D modeling issues. New applications in the shortand close-range domain are introduced, being the UAVs a low-cost alternatives to the classical manned aerial photogrammetry. Rotary or fixed wing UAVs, capable of performing the photogrammetric data acquisition with amateur or SLR digital cameras, can fly in manual, semi-automated and autonomous modes. With a typical photogrammetric pipeline, 3D results like DSM/DTM, contour lines, textured 3D models, vector data, etc. can be produced, in a reasonable automated way. The paper reports the latest developments of UAV image processing methods for photogrammetric applications, mapping and 3D modeling issues. Automation is nowadays necessary and feasible at the image orientation, DSM generation and orthophoto production stages, while accurate feature extraction is still an interactive procedure. New perspectives are also addressed.  Example of scenes surveyed with a UAV system (Microdrone MD4-200) and photogrammetric results achieved from the acquired images: digital surface model, orthoimages and overlaid contours (archaeological area in Montalcino, Italy).
Gut-Microbiota and Mental Health : Current and Future Perspectives

Recently, there is a growing interest of research on the relationship of gut-microbiota and neurological disorders. Increasing number of findings suggests the broader role of gut-microbiota in the modulation of various physiological and pathological conditions and it is now well recognized that a bidirectional communication between brain and gut-microbiota is essential to maintain homeostasis. The gut-brain axis includes central nervous system (CNS), the neuroendocrine and neuroimmune systems, autonomic nervous system, enteric nervous system, and intestinal microbiota. Probiotics (i.e., live microorganisms similar to beneficial microorganisms found in the human gut) are reported to modulate a number of disorders including metabolic disorders, behavioral conditions and cognitive functions. This review covers the significance of gut-brain axis in relation to the overall mental well-being. Apart from the recent studies highlighting the importance of gut-brain axis, here we also reviewed the interaction of few herbal medicines with gut-brain axis. Animal studies have indicated that some herbs or their isolated constituents alter the normal gut flora and have prominent effect on behavioral condition such as anxiety depression and cognition. Thus alteration of gut-brain axis by traditional medicines will be a potential strategy for the management of comorbid CNS disorders and gastrointestinal problems.
Design of a under voltage lock out circuit with bandgap structure

According to the necessary function of Under Voltage Lock Out (UVLO) in DC-DC power management systems, an improved UVLO circuit is proposed. The circuit realizes stabilization of parameters such as threshold point voltage, hysteretic range of the comparator etc. without utilizing an extra bandgap reference voltage source for comparison. The UVLO circuit is implemented in CSMC 0.5µm BCD process. Hspice simulation results show that the UVLO circuit presents advantages such as simple topology, sensitive response, low temperature draft, low power consumption.
Preserving Local and Global Information for Network Embedding

Networks such as social networks, airplane networks, and citation networks are ubiquitous. The adjacency matrix is often adopted to represent a network, which is usually high dimensional and sparse. However, to apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while completely neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. Hence, it also has potential to advance network embedding. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model LOcal and Global information. Experimental results demonstrate the ability to preserve global information of the proposed framework. Further experiments are conducted to demonstrate the effectiveness of learned representations of the proposed framework.
Generational diversity: what nurse managers need to know.

This article presents a discussion of generational differences and their impact on the nursing workforce and how this impact affects the work environment.



The global nursing workforce represents four generations of nurses. This generational diversity frames attitudes, beliefs, work habits and expectations associated with the role of the nurse in the provision of care and in the way the nurse manages their day-to-day activities.



An electronic search of MEDLINE, PubMed and Cinahl databases was performed using the words generational diversity, nurse managers and workforce. The search was limited to 2000-2012.



Generational differences present challenges to contemporary nurse managers working in a healthcare environment which is complex and dynamic, in terms of managing nurses who think and behave in a different way because of disparate core personal and generational values, namely, the three Cs of communication, commitment and compensation.



An acceptance of generational diversity in the workplace allows a richer scope for practice as the experiences and knowledge of each generation in the nursing environment creates an environment of acceptance and harmony facilitating retention of nurses.



Acknowledgement of generational characteristics provides the nurse manager with strategies which focus on mentoring and motivation; communication, the increased use of technology and the ethics of nursing, to bridge the gap between generations of nurses and to increase nursing workforce cohesion.
Lattice Boltzmann Method for Fluid Simulations

In the last two decades, the Lattice Boltzmann method (LBM) has emerged as a promising tool for modelling the Navier-Stokes equations and simulating complex fluid flows. LBM is based on microscopic models and mesoscopic kinetic equations. In some perspective, it can be viewed as a finite difference method for solving the Boltzmann transport equation. Moreover the Navier-Stokes equations can be recovered by LBM with a proper choice of the collision operator. In Section 2 and 3, we first introduce this method and describe some commonly used boundary conditions. In Section 4, the validity of this method is confirmed by comparing the numerical solution to the exact solution of the steady plane Poiseuille flow and convergence of solution is established. Some interesting numerical simulations, including the lid-driven cavity flow, flow past a circular cylinder and the Rayleigh-Bénard convection for a range of Reynolds numbers, are carried out in Section 5, 6 and 7. In Section 8, we briefly highlight the procedure of recovering the Navier-Stokes equations from LBM. A summary is provided in Section 9.
HOW TO DRAW A GRAPH

W E use the definitions of (11). However, in deference to some recent attempts to unify the terminology of graph theory we replace the term 'circuit' by 'polygon', and 'degree' by 'valency'. A graph G is 3-connected (nodally 3-connected) if it is simple and non-separable and satisfies the following condition; if G is the union of two proper subgraphs H and K such that HnK consists solely of two vertices u and v, then one of H and K is a link-graph (arc-graph) with ends u and v. It should be noted that the union of two proper subgraphs H and K of G can be the whole of G only if each of H and K includes at least one edge or vertex not belonging to the other. In this paper we are concerned mainly with nodally 3-connected graphs, but a specialization to 3-connected graphs is made in § 12. In § 3 we discuss conditions for a nodally 3-connected graph to be planar, and in § 5 we discuss conditions for the existence of Kuratowski subgraphs of a given graph. In §§ 6-9 we show how to obtain a convex representation of a nodally 3-connected graph, without Kuratowski subgraphs, by solving a set of linear equations. Some extensions of these results to general graphs, with a proof of Kuratowski's theorem, are given in §§ 10-11. In § 12 we discuss the representation in the plane of a pair of dual graphs, and in § 13 we draw attention to some unsolved problems.
Construction of soil moisture and irrigation IoT monitoring system using Project Based Learning

This article describes a project developed using principles of Project Based Learning (PBL) and aims to use an embedded system for soil monitoring, thus measuring soil moisture and implementing automatic irrigation, as well as, the temperature and humidity of the environment. The configuration data for irrigation time control and the lower and upper limits of soil moisture percentage can be adjusted through a PHP page, where also the monitoring of the variation of soil moisture through a graph can be done in real time. The proposed project is divided into two steps using PBL. Phase one focuses on developing a circuit with sensors capable of doing measurement of ambient parameters and soil moisture, and phase two, on developing an irrigation system to control soil moisture, both using Internet of Things (IoT) concepts.
A k-core Decomposition Framework for Graph Clustering

Graph clustering or community detection constitutes an important task for investigating the internal structure of graphs, with a plethora of applications in several domains. Traditional techniques for graph clustering, such as spectral methods, typically suffer from high time and space complexity. In this article, we present CoreCluster, an efficient graph clustering framework based on the concept of graph degeneracy, that can be used along with any known graph clustering algorithm. Our approach capitalizes on processing the graph in an hierarchical manner provided by its core expansion sequence, an ordered partition of the graph into different levels according to the k-core decomposition. Such a partition provides an efficient way to process the graph in an incremental manner that preserves its clustering structure, while making the execution of the chosen clustering algorithm much faster due to the smaller size of the graph’s partitions onto which the algorithm operates. An experimental analysis on a multitude of real and synthetic data demonstrates that our approach can be applied to any clustering algorithm accelerating the clustering process, while the quality of the clustering structure is preserved or even improved.
Automatic Detection of User Abilities through the SmartAbility Framework

This paper presents a proposed smartphone application for the unique SmartAbility Framework that supports interaction with technology for people with reduced physical ability, through focusing on the actions that they can perform independently. The Framework is a culmination of knowledge obtained through previously conducted technology feasibility trials and controlled usability evaluations involving the user community. The Framework is an example of ability-based design that focuses on the abilities of users instead of their disabilities. The paper includes a summary of Versions 1 and 2 of the Framework, including the results of a two-phased validation approach, conducted at the UK Mobility Roadshow and via a focus group of domain experts. A holistic model developed by adapting the House of Quality (HoQ) matrix of the Quality Function Deployment (QFD) approach is also described. A systematic literature review of sensor technologies built into smart devices establishes the capabilities of sensors in the Android and iOS operating systems. The review defines a set of inclusion and exclusion criteria, as well as search terms used to elicit literature from online repositories. The key contribution is the mapping of ability-based sensor technologies onto the Framework, to enable the future implementation of a smartphone application. Through the exploitation of the SmartAbility application, the Framework will increase technology amongst people with reduced physical ability and provide a promotional tool for assistive technology manufacturers.
Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model

In this paper, automatic motion control is investigated for wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed into a fully actuated second-order subsystem Σa consisting of planar movement of vehicle forward motion and yaw angular motions, and a passive (nonactuated) first-order subsystem Σb of pendulum tilt motion. Due to the unknown dynamics of subsystem Σa and universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σa. Model reference approach has been used, whereas the reference model is optimized by finite time linear quadratic regulation technique. Inspired by human control strategy of inverted pendulum, the tilt angular motion in the passive subsystem Σb has been indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σa, such that the satisfactory tracking of set tilt angle can be guaranteed. Rigorous theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method.