nils-herrmann/clean_scirepeval huggingface dataset:
Medical leadership: why it's important, what is required, and how we develop it.

Good medical leadership is vital in delivering high-quality healthcare, and yet medical career progression has traditionally seen leadership lack credence in comparison with technical and academic ability. Individual standards have varied, leading to variations in the quality of medical leadership between different organisations and, on occasions, catastrophic lapses in the standard of care provided to patients. These high-profile events, plus increasing evidence linking clinical leadership to performance of units, has led recently to more focus on leadership development for all doctors, starting earlier and continuing throughout their careers. There is also an increased drive to see doctors take on more significant leadership roles throughout the healthcare system. The achievement of these aims will require doctors to develop strong personal and professional values, a range of non-technical skills that allow them to lead across professional boundaries, and an understanding of the increasingly complex environment in which 21st century healthcare is delivered. Developing these attributes will require dedicated resources and the sophisticated application of a variety of different learning methodologies such as mentoring, coaching, action learning and networking.
How to acquire a ‘ representational theory of mind

concepts are now thought to emerge early in development. Mental state concepts, such as BELIEVE, DESIRE, and PRETEND, are among the most abstract we possess. It is striking that these concepts are routinely acquired by all normally developing children before they attend school and are even acquired by children who are mentally retarded. The verbal labels associated with these concepts are never explicitly taught, yet are typically in use around the third birthday; by contrast, words for colors, a salient sensory property, very often are explicitly taught by parents, but are typically not learned any earlier and are often learned later. Mental state concepts provide a crucial challenge to our attempts to understand 1 Acknowledgments: I am grateful to the following friends and colleagues: Eric Margolis and Susan Carey for helpful discussions, and to Jerry Fodor, Shaun Nichols, and Brian Scholl for helpful discussions and detailed comments on an earlier draft. 2 I use small caps when referring to a concept as opposed to what the concept denotes (italicized). Normally, one could simply say that the concept is a psychological entity, while what it denotes is not, e.g., DOG refers to dogs. But in the case of mental state concepts what they denote are also psychological entities.
A Contextual Bandits Framework for Personalized Learning Action Selection

Recent developments in machine learning have the potential to revolutionize education by providing an optimized, personalized learning experience for each student. We study the problem of selecting the best personalized learning action that each student should take next given their learning history; possible actions could include reading a textbook section, watching a lecture video, interacting with a simulation or lab, solving a practice question, and so on. We first estimate each student’s knowledge profile from their binary-valued graded responses to questions in their previous assessments using the SPARFA framework. We then employ these knowledge profiles as contexts in the contextual (multi-armed) bandits framework to learn a policy that selects the personalized learning actions that maximize each student’s immediate success, i.e., their performance on their next assessment. We develop two algorithms for personalized learning action selection. While one is mainly of theoretical interest, we experimentally validate the other using a real-world educational dataset. Our experimental results demonstrate that our approach achieves superior or comparable performance as compared to existing algorithms in terms of maximizing the students’ immediate success.
Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.
A microgrid protection system with central protection unit and extensive communication

Microgrids have been proposed in order to handle the impacts of Distributed Generators (DGs) and make conventional grids suitable for large scale deployments of distributed generation. However, the introduction of microgrids brings some challenges. Protection of a microgrid and its entities is one of them. Due to the existence of generators at all levels of the distribution system and two distinct operating modes, i.e. Grid Connected and Islanded modes, the fault currents in a system vary substantially. Consequently, the traditional fixed current relay protection schemes need to be improved. This paper presents a conceptual design of a microgrid protection system which utilizes extensive communication to monitor the microgrid and update relay fault currents according to the variations in the system. The proposed system is designed so that it can respond to dynamic changes in the system such as connection/disconnection of DGs.
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning

One of the main obstacles to broad application of reinforcement learning methods is the parameter sensitivity of our core learning algorithms. In many large-scale applications, online computation and function approximation represent key strategies in scaling up reinforcement learning algorithms. In this setting, we have effective and reasonably well understood algorithms for adapting the learning-rate parameter, online during learning. Such meta-learning approaches can improve robustness of learning and enable specialization to current task, improving learning speed. For temporaldifference learning algorithms which we study here, there is yet another parameter, λ, that similarly impacts learning speed and stability in practice. Unfortunately, unlike the learning-rate parameter, λ parametrizes the objective function that temporal-difference methods optimize. Different choices of λ produce different fixed-point solutions, and thus adapting λ online and characterizing the optimization is substantially more complex than adapting the learningrate parameter. There are no meta-learning method for λ that can achieve (1) incremental updating, (2) compatibility with function approximation, and (3) maintain stability of learning under both on and off-policy sampling. In this paper we contribute a novel objective function for optimizing λ as a function of state rather than time. We derive a new incremental, linear complexity λ-adaption algorithm that does not require offline batch updating or access to a model of the world, and present a suite of experiments illustrating the practicality of our new algorithm in three different settings. Taken together, our contributions represent a concrete step towards black-box application of temporaldifference learning methods in real world problems.
Knowledge Processes and Ontologies

Technology for knowledge management has so far focused on the management of knowledge containers. We present an approach that is oriented towards managing knowledge contents instead by identifying knowledge items at various levels of formality. This is done by providing various types of meta data that are tied to ontologies for conceptual interlinkage. Knowledge items are embedded into knowledge processes, which are supported by a suite of ontology-based tools. In order to handle this sort of rich knowledge process, we introduce a meta process that puts special emphasis on constructing and maintaining the ontology when introducing knowledge management systems. In order to elucidate our approach, we describe a case study about the building of CHAR, the Corporate History AnalyzeR.
Emerging Security Threats and Countermeasures in IoT

IoT (Internet of Things) diversifies the future Internet, and has drawn much attention. As more and more gadgets (i.e. Things) connected to the Internet, the huge amount of data exchanged has reached an unprecedented level. As sensitive and private information exchanged between things, privacy becomes a major concern. Among many important issues, scalability, transparency, and reliability are considered as new challenges that differentiate IoT from the conventional Internet. In this paper, we enumerate the IoT communication scenarios and investigate the threats to the large-scale, unreliable, pervasive computing environment. To cope with these new challenges, the conventional security architecture will be revisited. In particular, various authentication schemes will be evaluated to ensure the confidentiality and integrity of the exchanged data.
Swap Bribery

In voting theory, bribery is a form of manipulative behavior in which an external actor (the briber) offers to pay the voters to change their votes in order to get her preferred candidate elected. We investigate a model of bribery where the price of each vote depends on the amount of change that the voter is asked to implement. Specifically, in our model the briber can change a voter’s preference list by paying for a sequence of swaps of consecutive candidates. Each swap may have a different price; the price of a bribery is the sum of the prices of all swaps that it involves. We prove complexity results for this model, which we call swap bribery, for a broad class of election systems, including variants of approval and k-approval, Borda, Copeland, and maximin.
Cloud-Assisted Safety Message Dissemination in VANET–Cellular Heterogeneous Wireless Network

In vehicular ad hoc networks (VANETs), efficient message dissemination is critical to road safety and traffic efficiency. Since many VANET-based schemes suffer from high transmission delay and data redundancy, the integrated VANET–cellular heterogeneous network has been proposed recently and attracted significant attention. However, most existing studies focus on selecting suitable gateways to deliver safety message from the source vehicle to a remote server, whereas rapid safety message dissemination from the remote server to a targeted area has not been well studied. In this paper, we propose a framework for rapid message dissemination that combines the advantages of diverse communication and cloud computing technologies. Specifically, we propose a novel Cloud-assisted Message Downlink dissemination Scheme (CMDS), with which the safety messages in the cloud server are first delivered to the suitable mobile gateways on relevant roads with the help of cloud computing (where gateways are buses with both cellular and VANET interfaces), and then being disseminated among neighboring vehicles via vehicle-to-vehicle (V2V) communication. To evaluate the proposed scheme, we mathematically analyze its performance and conduct extensive simulation experiments. Numerical results confirm the efficiency of CMDS in various urban scenarios.
Why Total Quality Management Programs Do Not Persist: The Role of Management Quality and Implications for Leading a TQM Transformation

Top-down total quality management (TQM) programs often fail to create deep and sustained change in organizations. They become a fad soon replaced by another fad. Failure to institutionalize TQM can be attributed to a gap between top management’s rhetoric about their intentions for TQM and the reality of implementation in various subunits of the organization. The gap varies from subunit to subunit due to the quality of management in each. By quality of management is meant the capacity of senior team to (1) develop commitment to the new TQM direction and behave and make decisions that are consistent with it, (2) develop the cross-functional mechanisms, leadership skills, and team culture needed for TQM implementation, and (3) create a climate of open dialogues about progress in the TQM transformation that will enable learning and further change. The TQM transformations will persist only if top management requires and ultimately institutionalizes an honest organizational-wide conversation that surfaces valid data about the quality of management in each subunit of the firm and leads to changes in management quality or replacement of managers.  Leadership and Organizational Learning, Organizational Change, Strategy Implementation, and Total Quality Management.
Gait Analysis for Human Identification

Human gait is an attractive modality for recognizing people at a distance. In this paper we adopt an appearance-basedapproach to the problem of gait recognition. The width of the outer contour of the binarized silhouette of a walking person is chosen as the basic image feature. Different gait features are extracted from the width vector such as the dowsampled, smoothed width vectors, the velocity profile etc. and sequences of such temporally ordered feature vectors are used for representing a person’s gait. We use the dynamic time-warping (DTW) approach for matching so that non-linear time normalization may be used to deal with the naturally-occuring changes in walking speed. The performance of the proposed method is tested using different gait databases.
Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System

A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks ( p-value <;0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.
Security Impact of High Resolution Smartphone Cameras

Nearly every modern mobile device includes two cameras. With advances in technology the resolution of these sensors has constantly increased. While this development provides great convenience for users, for example with video-telephony or as dedicated camera replacement, the security implications of including high resolution cameras on such devices has yet to be considered in greater detail. With this paper we demonstrate that an attacker may abuse the cameras in modern smartphones to extract valuable information from a victim. First, we consider exploiting a front-facing camera to capture a user’s keystrokes. By observing facial reflections, it is possible to capture user input with the camera. Subsequently, individual keystrokes can be extracted from the images acquired with the camera. Furthermore, we demonstrate that these cameras can be used by an attacker to extract and forge the fingerprints of a victim. This enables an attacker to perform a wide range of malicious actions, including authentication bypass on modern biometric systems and falsely implicating a person by planting fingerprints in a crime scene. Finally, we introduce several mitigation strategies for the identified threats.
Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings

Commercial buildings contribute to 19% of the primary energy consumption in the US, with HVAC systems accounting for 39.6% of this usage. To reduce HVAC energy use, prior studies have proposed using wireless occupancy sensors or even cameras for occupancy based actuation showing energy savings of up to 42%. However, most of these solutions require these sensors and the associated network to be designed, deployed, tested and maintained within existing buildings which is significantly costly.
 We present Sentinel, a system that leverages existing WiFi infrastructure in commercial buildings along with smartphones with WiFi connectivity carried by building occupants to provide fine-grained occupancy based HVAC actuation. We have implemented Sentinel on top of RESTful web services, and demonstrate that it is scalable and compatible with legacy building management. We show that Sentinel accurately determines the occupancy in office spaces 86% of the time, with 6.2% false negative errors. We high-light the reasons for the inaccuracies, mostly attributed to aggressive power management by smartphones. Finally, we actuate 23% of the HVAC zones within a commercial building using Sentinel for one day and measure HVAC electrical energy savings of 17.8%.
CLASSIFICATION OF DISEASED PLANT LEAVES USING NEURAL NETWORK ALGORITHMS

Agriculture is the mother of all cultures. It played a vital role in the development of human civilization. But plant leaf diseases can damage the crops there may be economic losses in crops. Without knowing about the diseases affected in the plant, the farmers are using excessive pesticides for the plant disease treatment. To overcome this, the detected spot diseases in leaves are classified based on the diseased leaf types using various neural network algorithms. By this approach one can detect the diseased leaf variety and thus can take necessary steps in time to minimize the loss of production. The proposed methodology uses to classify the diseased plant leaves using Feed Forward Neural Network (FFNN), Learning Vector Quantization (LVQ) and Radial Basis Function Networks (RBF) by processing the set of shape and texture features from the affected leaf image. The simulation results show the effectiveness of the proposed scheme. With the help of this work, a machine learning based system can be formed for the improvement of the crop quality in the Indian Economy.
Chrysophycean cysts: indicators of eutrophication in the recent sediments of Frains Lake, Michigan, U.S.A.

The recent sediments of Frains Lake, Michigan contain a rich and well preserved association of chrysophycean cysts. Forty one forms are revealed by scanning electron microscopy (SEM) and light microscopy (LM). Taxonomic descriptions. and SEM micrographs are provided for the dominant forms. The three dominant taxa throughout the sediments, Cysta minima, C. modica and C. subbavaricum, do not show significant shifts in proportional abundance associated with European settlement and the onset of cultural eutrophication. However, certain subdominant taxa do show clear trends. Density counts indicate a dramatic decline in cyst concentration (by volume and by dry mass) and a sharp increase in absolute accumulation (net annual influx) following settlement. The Frains Lake profile of chrysophycean cysts is compared to sequences of other North American and European temperate lakes. The utility of chrysophycean cysts as paleoenvironmental indicators is considered on the basis of these results.
Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy

Learning with imbalanced data is one of the recent challenges in machine learning. Various solutions have been proposed in order to find a treatment for this problem, such as modifying methods or the application of a preprocessing stage. Within the preprocessing focused on balancing data, two tendencies exist: reduce the set of examples (undersampling) or replicate minority class examples (oversampling). Undersampling with imbalanced datasets could be considered as a prototype selection procedure with the purpose of balancing datasets to achieve a high classification rate, avoiding the bias toward majority class examples. Evolutionary algorithms have been used for classical prototype selection showing good results, where the fitness function is associated to the classification and reduction rates. In this paper, we propose a set of methods called evolutionary undersampling that take into consideration the nature of the problem and use different fitness functions for getting a good trade-off between balance of distribution of classes and performance. The study includes a taxonomy of the approaches and an overall comparison among our models and state of the art undersampling methods. The results have been contrasted by using nonparametric statistical procedures and show that evolutionary undersampling outperforms the nonevolutionary models when the degree of imbalance is increased.
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals

Recently, reinforcement learning has been successfully applied to the logical game of Go, various Atari games, and even a 3D game, Labyrinth, though it continues to have problems in sparse reward settings. It is difficult to explore, but also difficult to exploit, a small number of successes when learning policy. To solve this issue, the subgoal and option framework have been proposed. However, discovering subgoals online is too expensive to be used to learn options in large state spaces. We propose Micro-objective learning (MOL) to solve this problem. The main idea is to estimate how important a state is while training and to give an additional reward proportional to its importance. We evaluated our algorithm in two Atari games: Montezuma’s Revenge and Seaquest. With three experiments to each game, MOL significantly improved the baseline scores. Especially in Montezuma’s Revenge, MOL achieved two times better results than the previous state-of-the-art model.