nils-herrmann/clean_scirepeval huggingface dataset:
A pre-clinical framework for neural control of a therapeutic upper-limb exoskeleton

In this paper, we summarize a novel approach to robotic rehabilitation that capitalizes on the benefits of patient intent and real-time assessment of impairment. Specifically, an upper-limb, physical human-robot interface (the MAHI EXO-II robotic exoskeleton) is augmented with a non-invasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy `active' and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. Robotic measures of motor impairment are derived from real-time sensor data from the MAHI EXO-II and the BMI. These measures can be validated through correlation with widely used clinical measures and used to drive patient-specific therapy sessions adapted to the capabilities of the individual, with the MAHI EXO-II providing assistance or challenging the participant as appropriate to maximize rehabilitation outcomes. This approach to robotic rehabilitation takes a step towards the seamless integration of BMIs and intelligent exoskeletons to create systems that can monitor and interface with brain activity and movement. Such systems will enable more focused study of various issues in development of devices and rehabilitation strategies, including interpretation of measurement data from a variety of sources, exploration of hypotheses regarding large scale brain function during robotic rehabilitation, and optimization of device design and training programs for restoring upper limb function after stroke.
Increasing accuracy of traffic light color detection and recognition using machine learning

Traffic light detection is an important system because it can alert driver on upcoming traffic light so that he/she can anticipate a head of time. In this paper we described our work on detecting traffic light color using machine learning approach. Using HSV color representation, our approach is to extract features based on an area of X×X pixels. Traffic light color model is then created by applying a learning algorithm on a set of examples of features representing pixels of traffic and non-traffic light colors. The learned model is then used to classify whether an area of pixels contains traffic light color or not. Evaluation of this approach reveals that it significantly improves the detection performance over the one based on value-range color segmentation technique.
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usually lacks the language complexity of human interlocutors and the biases in its design may tend to degrade the agent. To address these issues, we present Deep Dyna-Q, which to our knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning. We incorporate into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience. During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience. The effectiveness of our approach is demonstrated on a movie-ticket booking task in both simulated and human-in-theloop settings1.
Urban Metabolism : A review of recent literature on the subject

Urban areas are the primary habitat for a majority of the global population. The development of cities not only entails a fundamental change in human settlement patterns but also a dramatic transformation of the physical environment. Thus, urban areas and their development are at the centre of all discussions on sustainability and/or sustainable development. This review essay introduces the notion of Urban Metabolism (UM), a term that provides a conceptual framework to study how a city functions, and hence, a way to address the sustainability issue of a city. Due to the significance and scope of the subject, the notion of UM is interpreted and thus approached differently across diverse disciplines from both the natural and social science fields. In order to comprehend the commonalities and controversies between them, the present review also briefly introduces the historical roots of the term. This review reveals the increasing significance of a rich and rapidly evolving field of research on the metabolism of urban areas.
A 4 DOF exoskeleton robot with a novel shoulder joint mechanism

At present, medical experts and researchers turn their attention towards using robotic devices to facilitate human limb rehabilitation. An exoskeleton is such a robotic device, which is used to perform rehabilitation, motion assistance and power augmentation tasks. For effective operation, it is supposed to follow the structure and the motion of the natural human limb. This paper propose a robotic rehabilitation exoskeleton with novel shoulder joint actuation mechanism with a moving center of glenohumeral (CGH) joint. The proposed exoskeleton has four active degrees of freedom (DOFs), namely; shoulder flexion/extension, abduction/adduction, pronation/supination (external/internal rotation), and elbow flexion/extension. In addition to those motions mentioned above, three passive DOFs had been introduced to the shoulder joint mechanism in order to provide allowance for the scapular motion of the shoulder. The novel mechanism allows the movement of CGH — joint in two planes; namely frontal plane during shoulder abduction/adduction and transverse plane during flexion/extension. The displacement of the CGH — joint axis was measured experimentally. These results are then incorporated into the novel mechanism, which takes into account the natural movement characteristics of the human shoulder joint. It is intended to reduce excessive stress on patient's upper limb while carrying out rehabilitation exercises.
Autism and epistemology IV: Does autism need a theory of mind?

In their article, "Does the autistic child have a 'theory of mind'?," Baron-Cohen et al. [1985] proposed a novel paradigm to explain social impairment in children diagnosed as autistic (AD). Much research has been undertaken since their article went to print. The purpose of this commentary is to gauge whether Theory of Mind (ToM)-or lack thereof-is a valid model for explaining abnormal social behavior in children with AD. ToM is defined as "the ability to impute mental states to oneself and to others" and "the ability to make inferences about what other people believe to be the case." The source for their model was provided by an article published earlier by Premack and Woodruff, "Does the chimpanzee have a theory of mind?" Later research in chimpanzees did not support a ToM in primates. From the outset, ToM as a neurocognitive model of autism has had many shortcomings-methodological, logical, and empirical. Other ToM assumptions, for example, its universality in all children in all cultures and socioeconomic conditions, are not supported by data. The age at which a ToM emerges, or events that presage a ToM, are too often not corroborated. Recent studies of mirror neurons, their location and interconnections in brain, their relationship to social behavior and language, and the effect of lesions there on speech, language and social behavior, strongly suggests that a neurobiological as opposed to neurocognitive model of autism is a more parsimonious explanation for the social and behavioral phenotypes observed in autism.
The project workplace for organizational learning development

This paper conceptually examines how and why projects and project teams may be conceived as highly generative episodic individual and team learning places that can serve as vehicles or agents to promote organizational learning. It draws on and dissects a broad and relevant literature concerning situated learning, organizational learning, learning spaces and project management. The arguments presented signal a movement towards a project workplace becoming more organizationally acknowledged and supported as a learning intense entity wherein, learning is a more conspicuous, deliberate and systematic social activity by project participants. This paper challenges conventional and limited organizational perceptions about project teams and their practices and discloses their extended value contributions to organizational learning development.
Synaptic, transcriptional, and chromatin genes disrupted in autism

The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability–transcription coupling, as well as histone-modifying enzymes and chromatin remodellers—most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.
Comparison of Risk Assessment for a Nuclear Power Plant Construction Project Based on Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process

Recently, plant construction throughout the world, including nuclear power plant construction, has grown significantly. The scale of Korea’s nuclear power plant construction in particular, has increased gradually since it won a contract for a nuclear power plant construction project in the United Arab Emirates in 2009. However, time and monetary resources have been lost in some nuclear power plant construction sites due to lack of risk management ability. The need to prevent losses at nuclear power plant construction sites has become more urgent because it demands professional skills and large-scale resources. Therefore, in this study, the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) were applied in order to make comparisons between decision-making methods, to assess the potential risks at nuclear power plant construction sites. To suggest the appropriate choice between two decision-making methods, a survey was carried out. From the results, the importance and the priority of 24 risk factors, classified by process, cost, safety, and quality, were analyzed. The FAHP was identified as a suitable method for risk assessment of nuclear power plant construction, compared with risk assessment using the AHP. These risk factors will be able to serve as baseline data for risk management in nuclear power plant construction projects.
Biotechnological and in situ food production of polyols by lactic acid bacteria

Polyols such as mannitol, erythritol, sorbitol, and xylitol are naturally found in fruits and vegetables and are produced by certain bacteria, fungi, yeasts, and algae. These sugar alcohols are widely used in food and pharmaceutical industries and in medicine because of their interesting physicochemical properties. In the food industry, polyols are employed as natural sweeteners applicable in light and diabetic food products. In the last decade, biotechnological production of polyols by lactic acid bacteria (LAB) has been investigated as an alternative to their current industrial production. While heterofermentative LAB may naturally produce mannitol and erythritol under certain culture conditions, sorbitol and xylitol have been only synthesized through metabolic engineering processes. This review deals with the spontaneous formation of mannitol and erythritol in fermented foods and their biotechnological production by heterofermentative LAB and briefly presented the metabolic engineering processes applied for polyol formation.
Global cancer transitions according to the Human Development Index (2008-2030): a population-based study.

Cancer is set to become a major cause of morbidity and mortality in the coming decades in every region of the world. We aimed to assess the changing patterns of cancer according to varying levels of human development.



We used four levels (low, medium, high, and very high) of the Human Development Index (HDI), a composite indicator of life expectancy, education, and gross domestic product per head, to highlight cancer-specific patterns in 2008 (on the basis of GLOBOCAN estimates) and trends 1988-2002 (on the basis of the series in Cancer Incidence in Five Continents), and to produce future burden scenario for 2030 according to projected demographic changes alone and trends-based changes for selected cancer sites.



In the highest HDI regions in 2008, cancers of the female breast, lung, colorectum, and prostate accounted for half the overall cancer burden, whereas in medium HDI regions, cancers of the oesophagus, stomach, and liver were also common, and together these seven cancers comprised 62% of the total cancer burden in medium to very high HDI areas. In low HDI regions, cervical cancer was more common than both breast cancer and liver cancer. Nine different cancers were the most commonly diagnosed in men across 184 countries, with cancers of the prostate, lung, and liver being the most common. Breast and cervical cancers were the most common in women. In medium HDI and high HDI settings, decreases in cervical and stomach cancer incidence seem to be offset by increases in the incidence of cancers of the female breast, prostate, and colorectum. If the cancer-specific and sex-specific trends estimated in this study continue, we predict an increase in the incidence of all-cancer cases from 12·7 million new cases in 2008 to 22·2 million by 2030.



Our findings suggest that rapid societal and economic transition in many countries means that any reductions in infection-related cancers are offset by an increasing number of new cases that are more associated with reproductive, dietary, and hormonal factors. Targeted interventions can lead to a decrease in the projected increases in cancer burden through effective primary prevention strategies, alongside the implementation of vaccination, early detection, and effective treatment programmes.
Exploiting Neural Embeddings for Social Media Data Analysis

In this paper, we describe our microblog realtime filtering system developed and submitted for the Text Retrieval Conference (TREC 2015) microblog track. We submitted six runs for two tasks related to real-time filtering by using various Information Retrieval (IR), and Machine Learning (ML) techniques to analyze the Twitter sample live stream and match relevant tweets corresponding to specific user interest profiles. Evaluation results demonstrate the effectiveness of our approach as we achieved 3 of the top 7 best scores among automatic submissions across all participants and obtained the best (or close to best) scores in more than 25% of the evaluated topics for the real-time mobile push notification task.
Rapid Manufacture of Novel Variable Impedance Robots

Variable stiffness and variable damping can play an important role in robot movement, particularly for legged robots such as bipedal walkers. Variable impedance also introduces new control problems, since there are more degrees of freedom to control, and the resulting robot has more complex dynamics. In this paper, we introduce novel design and fabrication methodologies that are capable of producing cost effective hardware prototypes suitable for investigating the efficacy of impedance modulation. We present two variable impedance bipedal platforms produced using a combination of waterjet cutting and 3D printing, and a novel fused deposition modeling (FDM) 3D printing based method for producing hybrid plastic/metal parts. We evaluate walking trajectories at different speeds and stiffness levels.
Computer methods for 3D motion tracking in real-time

This thesis is devoted to marker-less 3D human motion tracking in calibrated and synchronized multicamera systems. Pose estimation is based on a 3D model, which is transformed into the image plane and then rendered. Owing to elaborated techniques the tracking of the full body has been achieved in real-time via dynamic optimization or dynamic Bayesian filtering. The objective function of a particle swarm optimization algorithm and the observation model of a particle filter are based on matching between the rendered 3D models in the required poses and image features representing the extracted person. In such an approach the main part of the computational overload is associated with the rendering of 3D models in hypothetical poses as well as determination of value of objective function. Effective methods for rendering of 3D models in real-time with support of OpenGL as well as parallel methods for determining the objective function on the GPU were developed. The elaborated solutions permit 3D tracking of full body motion in real-time.
The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning.

3D-printed anatomical models play an important role in medical and research settings. The recent successes of 3D anatomical models in healthcare have led many institutions to adopt the technology. However, there remain several issues that must be addressed before it can become more wide-spread. Of importance are the problems of cost and time of manufacturing. Machine learning (ML) could be utilized to solve these issues by streamlining the 3D modeling process through rapid medical image segmentation and improved patient selection and image acquisition. The current challenges, potential solutions, and future directions for ML and 3D anatomical modeling in healthcare are discussed.  This review covers research articles in the field of machine learning as related to 3D anatomical modeling. Topics discussed include automated image segmentation, cost reduction, and related time constraints.  We have the necessary computational tools to tackle the problems discussed. The difficulty now lies in our ability to collect sufficient data.
On Stabilization of Cart-Inverted Pendulum System : An Experimental Study

The Cart-Inverted Pendulum System (CIPS) is a classical benchmark control problem. Its dynamics resembles with that of many real world systems of interest like missile launchers, pendubots, human walking and segways and many more. The control of this system is challenging as it is highly unstable, highly non-linear, non-minimum phase system and underactuated. Further, the physical constraints on the track position control voltage etc. also pose complexity in its control design. The thesis begins with the description of the CIPS together with hardware setup used for research, its dynamics in state space and transfer function models. In the past, a lot of research work has been directed to develop control strategies for CIPS. But, very little work has been done to validate the developed design through experiments. Also robustness margins of the developed methods have not been analysed. Thus, there lies an ample opportunity to develop controllers and study the cart-inverted pendulum controlled system in real-time. The objective of this present work is to stabilize the unstable CIPS within the different physical constraints such as in track length and control voltage. Also, simultaneously ensure good robustness. A systematic iterative method for the state feedback design by choosing weighting matrices key to the Linear Quadratic Regulator (LQR) design is presented. But, this yields oscillations in cart position. The Two-Loop-PID controller yields good robustness, and superior cart responses. A sub-optimal LQR based state feedback subjected to H∞ constraints through Linear Matrix Inequalities (LMIs) is solved and it is observed from the obtained results that a good stabilization result is achieved. Non-linear cart friction is identified using an exponential cart friction and is modeled as a plant matrix uncertainty. It has been observed that modeling the cart friction as above has led to improved cart response. Subsequently an integral sliding mode controller has been designed for the CIPS. From the obtained simulation and experiments it is seen that the ISM yields good robustness towards the output channel gain perturbations. The efficacies of the developed techniques are tested both in simulation and experimentation. It has been also observed that the Two-Loop PID Controller yields overall satisfactory response in terms of superior cart position and robustness. In the event of sensor fault the ISM yields best performance out of all the techniques.
Improved Butler Matrix configuration for smart beamforming operations

In this work the authors propose a Butler Matrix (BM) based beamforming network (BFN) that feeds a linear antenna array. A BM 8×8 is integrated with a specific Switching Network (SN) and a group of Switched-Line Phase Shifters (SLPS), in order to properly drive the excitation of the antenna elements. The resulting configuration provides enhanced beamforming flexibility compared to a Switched Beam System (SBS) fed by a typical BM. Thus, a smart beamforming structure can be designed that combines the simplicity of a SBS with some of the advantages of an Adaptive Array System (AAS).
Waveform Coding for Passive Multiplexing: Application to Microwave Imaging

This paper proposes a novel passive technique for the collection of microwave images. A compact component is developed that passively codes and sums the waves received by an antenna array to which it is connected, and produces a unique signal that contains all of the scene information. This technique of passive multiplexing simplifies the microwave reception chains for radar and beamforming systems (whose complexity and cost highly increase with the number of antennas) and does not require any active elements to achieve beamsteering. The preservation of the waveforms is ensured using orthogonal codes supplied by the propagation through the component's uncorrelated channels. Here we show a multiplexing technique in the physical layer that, besides being compact and passive, is compatible with all ultrawideband antennas, enabling its implementation in various fields.
A Java implementation of paillier homomorphic encryption scheme

This paper discusses how to implement Paillier homomorphic encryption (HE) scheme in Java as an API. We first analyze existing Pailler HE libraries and discuss their limitations. We then design a comparatively accomplished and efficient Pailler HE Java library. As a proof of concept, we applied our Pailler HE library in an electronic voting system that allows the voting server to sum up the candidates' votes in the encrypted form with voters remain anonymous. Our library records an average of only 2766ms for each vote placement through HTTP POST request.
Simulating strategy and dexterity for puzzle games

We examine the impact of strategy and dexterity on video games in which a player must use strategy to decide between multiple moves and must use dexterity to correctly execute those moves. We run simulation experiments on variants of two popular, interactive puzzle games: Tetris, which exhibits dexterity in the form of speed-accuracy time pressure, and Puzzle Bobble, which requires precise aiming. By modeling dexterity and strategy as separate components, we quantify the effect of each type of difficulty using normalized mean score and artificial intelligence agents that make human-like errors. We show how these techniques can model and visualize dexterity and strategy requirements as well as the effect of scoring systems on expressive range.