nils-herrmann/clean_scirepeval huggingface dataset:
A survey on energy efficient techniques in wireless sensor networks

The myriad of potential applications supported by wireless sensor networks (WSNs) has generated much interest from the research community. Various applications range from small size low industrial monitoring to large scale energy constrained environmental monitoring. In all cases, an operational network is required to fulfill the application missions. In addition, energy consumption of nodes is a great challenge in order to maximize network lifetime. Unlike other networks, it can be hazardous, very expensive or even impossible to charge or replace exhausted batteries due to the hostile nature of environment. Researchers are invited to design energy efficient protocols while achieving the desired network operations. This paper focuses on different techniques to reduce the consumption of the limited energy budget of sensor nodes. After having identified the reasons of energy waste in WSNs, we classify energy efficient techniques into five classes, namely data reduction, control reduction, energy efficient routing, duty cycling and topology control. We then detail each of them, presenting subdivisions and giving many examples. We conclude by a recapitulative table.
Heart rate variability and autonomic activity at rest and during exercise in various physiological conditions

The rhythmic components of heart rate variability (HRV) can be separated and quantitatively assessed by means of power spectral analysis. The powers of high frequency (HF) and low frequency (LF) components of HRV have been shown to estimate cardiac vagal and sympathetic activities. The reliability of these spectral indices, as well as that of LF/HF ratio as a marker of autonomic interaction at rest and during exercise, is briefly reviewed. Modifications in autonomic activities induced by different physiological conditions, e.g. hypoxia exposure, training, and water immersion, have been found in HRV power spectra at rest. The changes in HF and LF powers and in LF/HF ratio observed during exercise have been shown not to reflect the decrease in vagal activity and the activation of sympathetic system occurring at increasing loads. HF peak was recognised in power spectra in the entire range of relative intensity, being responsible for the most part of HR variability at maximal load. LF power did not change during low intensity exercise and decreased to negligible values at medium–high intensity, where sympathetic activity was enhanced. There was no influence from factors such as fitness level, age, hypoxia, and blood distribution. In contrast, a dramatic effect of body position has been suggested by the observation that LF power increased at medium–high intensities when exercising in the supine position. The increased respiratory activity due to exercise would be responsible of HF modulation of HR via a direct mechanical effect. The changes in LF power observed at medium–high intensity might be the expression of the modifications in arterial pressure control mechanisms occurring with exercise. The finding of opposite trends for LF rhythm in supine and sitting exercises suggests that different readjustments might have occurred in relation to different muscular inputs in the two positions.
Fog computing: A cloud to the ground support for smart things and machine-to-machine networks

Cloud services to smart things face latency and intermittent connectivity issues. Fog devices are positioned between cloud and smart devices. Their high speed Internet connection to the cloud, and physical proximity to users, enable real time applications and location based services, and mobility support. Cisco promoted fog computing concept in the areas of smart grid, connected vehicles and wireless sensor and actuator networks. This survey article expands this concept to the decentralized smart building control, recognizes cloudlets as special case of fog computing, and relates it to the software defined networks (SDN) scenarios. Our literature review identifies a handful number of articles. Cooperative data scheduling and adaptive traffic light problems in SDN based vehicular networks, and demand response management in macro station and micro-grid based smart grids are discussed. Security, privacy and trust issues, control information overhead and network control policies do not seem to be studied so far within the fog computing concept.
A new ENT microsurgery robot: Error analysis and implementation

This paper reports the error specifications and error analysis for a new cooperatively controlled ENT microsurgery robot. The new robot is designed for three specific ENT surgeries, endonasal skull base surgery, transoral laryngeal surgery, and cochlear implant surgery. The design requirements for the robot resolution, accuracy, stiffness, and repeatability for these surgery types are discussed and analyzed, and the required robot parameters are calculated. The mechanical design of the robot is then analyzed and shown to fulfill these requirements in the case where it is in its home configuration. The analysis is then extended to cover the robot's whole workspace.
Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.
Bayesian Machine Learning: EEG\/MEG signal processing measurements

Electroencephalography (EEG) and magnetoencephalography (MEG) are the most common noninvasive brain-imaging techniques for monitoring electrical brain activity and inferring brain function. The central goal of EEG/MEG analysis is to extract informative brain spatiotemporal?spectral patterns or to infer functional connectivity between different brain areas, which is directly useful for neuroscience or clinical investigations. Due to its potentially complex nature [such as nonstationarity, high dimensionality, subject variability, and low signal-to-noise ratio (SNR)], EEG/MEG signal processing poses some great challenges for researchers. These challenges can be addressed in a principled manner via Bayesian machine learning (BML). BML is an emerging field that integrates Bayesian statistics, variational methods, and machine-learning techniques to solve various problems from regression, prediction, outlier detection, feature extraction, and classification. BML has recently gained increasing attention and widespread successes in signal processing and big-data analytics, such as in source reconstruction, compressed sensing, and information fusion. To review recent advances and to foster new research ideas, we provide a tutorial on several important emerging BML research topics in EEG/MEG signal processing and present representative examples in EEG/MEG applications.
Reliable Multiple-choice Iterative Algorithm for Crowdsourcing Systems

The appearance of web-based crowdsourcing systems gives a promising solution to exploiting the wisdom of crowds efficiently in a short time with a relatively low budget. Despite their efficiency, crowdsourcing systems have an inherent problem in that responses from workers can be unreliable since workers are low-paid and have low responsibility. Although simple majority voting can be a solution, various research studies have sought to aggregate noisy responses to obtain greater reliability in results through effective techniques such as Expectation-Maximization (EM) based algorithms. While EM-based algorithms get the limelight in crowdsourcing systems due to their useful inference techniques, Karger et al. made a significant breakthrough by proposing a novel iterative algorithm based on the idea of low-rank matrix approximations and the message passing technique. They showed that the performance of their iterative algorithm is order-optimal, which outperforms majority voting and EM-based algorithms. However, their algorithm is not always applicable in practice since it can only be applied to binary-choice questions. Recently, they devised an inference algorithm for multi-class labeling, which splits each task into a bunch of binary-choice questions and exploits their existing algorithm. However, it has difficulty in combining into real crowdsourcing systems since it overexploits redundancy in that each split question should be queried in multiple times to obtain reliable results.
 In this paper, we design an iterative algorithm to infer true answers for multiple-choice questions, which can be directly applied to real crowdsourcing systems. Our algorithm can also be applicable to short-answer questions as well. We analyze the performance of our algorithm, and prove that the error bound decays exponentially. Through extensive experiments, we verify that our algorithm outperforms majority voting and EM-based algorithm in accuracy.
The diffusion of political memes in social media: keynote abstract

This talk presents ongoing work on the study of information diffusion in social media, focusing in particular on political communication in the Twitter microblogging network. Social media platforms play an important role in shaping political discourse in the US and around the world. The truthy.indiana.edu infrastructure allows us to mine and visualize a large stream of social media data related to political themes. The analyses in this keynote address polarization and cross-ideological communication, and partisan asymmetries in the online political activities of social media users. Machine learning efforts can successfully leverage the structure of meme diffusion networks to detect orchestrated astroturf attacks that simulate grassroots campaigns, and to predict the political affiliation of active users. The retweet network segregates individuals into two distinct, homogenous communities of left- and right-leaning users. The mention network does not exhibit this kind of segregation, instead forming a communication bridge across which information flows between these two partisan communities. We propose a mechanism of action to explain these divergent topologies and provide statistical evidence in support of this hypothesis. Related to political communication are questions about the birth of online social movements. Social media data provides an opportunity to look for signatures that capture these seminal events. Finally, I will introduce a model of the competition for attention in social media. A dynamic of information diffusion emerges from this process, where a few ideas go viral while most do not. I will show that the relative popularity of different topics, the diversity of information to which we are exposed, and the fading of our collective interests for specific memes, can all be explained as deriving from a combination between the competition for limited attention and the structure of social networks. Surprisingly, one can reproduce the massive heterogeneity in the popularity and persistence of ideas without the need to assume different intrinsic values among those ideas.
RFID Light Bulb: Enabling Ubiquitous Deployment of Interactive RFID Systems

Radio-Frequency Identification (RFID) technology has the potential to provide inexpensive, wireless, battery-free connectivity and interactivity for objects that are traditionally not instrumented. However, these systems have not seen widespread deployment outside warehouses and supply chains, owing to the complexity of installing bulky RFID readers, antennas, and their supporting power and network infrastructure.
 In this work, we leverage advances in semiconductor optics, RF antenna design and system integration to create a hybrid RFID reader and smart LED lamp, in the form factor of a standard light bulb. This makes deploying RFID readers literally as easy as screwing in a light bulb. We explore the home-scale RFID interactions enabled by these smart bulbs, including infrastructure monitoring, localization and guided navigation, and standalone lighting effects.
AxNN: Energy-efficient neuromorphic systems using approximate computing

Neuromorphic algorithms, which are comprised of highly complex, large-scale networks of artificial neurons, are increasingly used for a variety of recognition, classification, search and vision tasks. However, their computational and energy requirements can be quite high, and hence their energy-efficient implementation is of great interest.
 We propose a new approach to design energy-efficient hardware implementations of large-scale neural networks (NNs) using approximate computing. Our work is motivated by the observations that (i) NNs are used in applications where less-than-perfect results are acceptable, and often inevitable, and (ii) they are highly resilient to inexactness in many (but not all) of their constituent computations. We make two key contributions. First, we propose a method to transform any given NN into an Approximate Neural Network (AxNN). This is performed by (i) adapting the backpropagation technique, which is commonly used to train these networks, to quantify the impact of approximating each neuron to the overall network quality (e.g., classification accuracy), and (ii) selectively approximating those neurons that impact network quality the least. Further, we make the key observation that training is a naturally error-healing process that can be used to mitigate the impact of approximations to neurons. Therefore, we incrementally retrain the network with the approximations in-place, reclaiming a significant portion of the quality ceded by approximations. As a second contribution, we propose a programmable and quality-configurable neuromorphic processing engine (qcNPE), which utilizes arrays of specialized processing elements that execute neuron computations with dynamically configurable accuracies and can be used to execute AxNNs from diverse applications. We evaluated the proposed approach by constructing AXNNs for 6 recognition applications (ranging in complexity from 12-47,818 neurons and 160-3,155,968 connections) and executing them on two different platforms--qcNPE implementation containing 272 processing elements in 45nm technology and a commodity Intel Xeon server. Our results demonstrate 1.14X-1.92X energy benefits for virtually no loss (< 0.5%) in output quality, and even higher improvements (upto 2.3X) when some loss (upto 7.5%) in output quality is acceptable.
An MEG-based brain–computer interface (BCI)

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.
Magnesium and ischemic heart disease: a review of epidemiological, experimental, and clinical evidences.

Magnesium (Mg) plays an essential role in a wide range of fundamental cellular reactions in patients with ischemic heart disease. It has been well known that Mg plays a pivotal role in control of cardiac excitability, neuromuscular transmission, vasomotor tone, and blood pressure, among other functions. Especially, many epidemiological, experimental, and clinical studies support a pathological role for Mg in the etiology and development of major coronary risk factors as diabetes mellitus, hypertension, and hyperlipidemia as well as ischemic heart disease. Furthermore, the therapeutic value of Mg in the management of coronary risk factors and ischemic heart disease has been clarified. Dietary Mg supplementation should be considered as a preventive element in atherosclerosis and ischemic heart disease.
Ultra-wideband low noise amplifier with shunt resistive feedback in 0.18µm CMOS process

A CMOS low noise amplifier (LNA) for ultra-wideband (UWB) systems is presented. The proposed LNA achieve wide operating bandwidth for 3–10.6 GHz by using resistive shunt feedback topology. Two stage amplifiers and an inter stage circuit are designed to achieve wider gain bandwidth. The shunt resistive feedback are employed in input and output stage to provide wideband input matching with low noise figure (NF). This work is designed and fabricated in TSMC 0.18µm CMOS process. The proposed UWB LNA achieves a measured flat gain 15 dB and has a noise figure of 4 dB over the entire band while consuming 21.5 mW of power. The measured third order intercept point IIP3 is 2.5 dBm.
Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming

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An efficient parallel neural network-based multi-instance learning algorithm

Multiple instance learning (MIL) has been studied actively in recent years. However, it is facing a computational challenge due to the large scale of data volume. Parallel computing is a good way of overcoming the computational challenge. In this paper, we propose a new MIL method based on a MIL back-propagation neural network (MIBP), which is an extension of the standard back-propagation neural network (BPNN) that uses labeled bags of instances as training data. We use parallel computing to speed up the learning process. The proposed method finds a concept point t in the feature space which is close to instances from positive bags and far from instances in negative bags. The description of our method is as follows: First, train MIBP with positive and negative bags. Second, extract t from the trained MIBP. This is achieved by, for each positive bag, presenting all the instances to the trained MIBP and selecting the one with maximal output value. The t is then obtained by averaging all the extracted instances. Finally, a sensitivity analysis of the trained MIBP is performed to obtain feature relevance/weighting information. Parallel computing is performed during the training of the MIBP. We conduct experiments to measure the performance of the obtained t when used for classification purposes and evaluate the parallel computing method. The experimental results on the MUSK data set show that our method has better classification performance and is more computationally efficient than other well-established MIL methods.
Time series classification with ensembles of elastic distance measures

Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series classification (TSC) problems. These include variants of dynamic time warping (DTW), such as weighted and derivative DTW, and edit distance-based measures, including longest common subsequence, edit distance with real penalty, time warp with edit, and move–split–merge. These measures have the common characteristic that they operate in the time domain and compensate for potential localised misalignment through some elastic adjustment. Our aim is to experimentally test two hypotheses related to these distance measures. Firstly, we test whether there is any significant difference in accuracy for TSC problems between nearest neighbour classifiers using these distance measures. Secondly, we test whether combining these elastic distance measures through simple ensemble schemes gives significantly better accuracy. We test these hypotheses by carrying out one of the largest experimental studies ever conducted into time series classification. Our first key finding is that there is no significant difference between the elastic distance measures in terms of classification accuracy on our data sets. Our second finding, and the major contribution of this work, is to define an ensemble classifier that significantly outperforms the individual classifiers. We also demonstrate that the ensemble is more accurate than approaches not based in the time domain. Nearly all TSC papers in the data mining literature cite DTW (with warping window set through cross validation) as the benchmark for comparison. We believe that our ensemble is the first ever classifier to significantly outperform DTW and as such raises the bar for future work in this area.
A balanced psychology and a full life.

Psychology since World War II has been largely devoted to repairing weakness and understanding suffering. Towards that end, we have made considerable gains. We have a classification of mental illness that allows international collaboration, and through this collaboration we have developed effective psychotherapeutic or pharmacological treatments for 14 major mental disorders. However, while building a strong science and practice of treating mental illness, we largely forgot about everyday well-being. Is the absence of mental illness and suffering sufficient to let individuals and communities flourish? Were all disabling conditions to disappear, what would make life worth living? Those committed to a science of positive psychology can draw on the effective research methods developed to understand and treat mental illness. Results from a new randomized, placebo-controlled study demonstrate that people are happier and less depressed three months after completing exercises targeting positive emotion. The ultimate goal of positive psychology is to make people happier by understanding and building positive emotion, gratification and meaning. Towards this end, we must supplement what we know about treating illness and repairing damage with knowledge about nurturing well-being in individuals and communities.