nils-herrmann/clean_scirepeval huggingface dataset:
Resource optimization in fog enabled IoT deployments

Internet of Things (IoT) devices are typically deployed in resource (energy, computational capacity) constrained environments. Connecting such devices to the cloud is not practical due to variable network behavior as well as high latency overheads. Fog computing refers to a scalable, distributed computing architecture which moves computational tasks closer to Edge devices or smart gateways. As an example of mobile IoT scenarios, in robotic deployments, computationally intensive tasks such as run time mapping may be performed on peer robots or smart gateways. Most of these computational tasks involve running optimization algorithms inside compute nodes at run time and taking rapid decisions based on results. In this paper, we incorporate optimization libraries within the Robot Operating System (ROS) deployed on robotic sensor-actuators. Using the ROS based simulation environment Gazebo, we demonstrate case-study scenarios for runtime optimization. The use of optimized distributed computations are shown to provide significant improvement in latency and battery saving for large computational loads. The possibility to perform run time optimization opens up a wide range of use-cases in mobile IoT deployments.
Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective

There is great interest in building intrinsic motivation into artificial systems using the reinforcement learning framework. Yet, what intrinsic motivation may mean computationally, and how it may differ from extrinsic motivation, remains a murky and controversial subject. In this paper, we adopt an evolutionary perspective and define a new optimal reward framework that captures the pressure to design good primary reward functions that lead to evolutionary success across environments. The results of two computational experiments show that optimal primary reward signals may yield both emergent intrinsic and extrinsic motivation. The evolutionary perspective and the associated optimal reward framework thus lead to the conclusion that there are no hard and fast features distinguishing intrinsic and extrinsic reward computationally. Rather, the directness of the relationship between rewarding behavior and evolutionary success varies along a continuum.
SMART HEALTH CARE SYSTEM IMPLEMENTED USING IoT

The convincing development in Internet of Things (IoT) enables the solutions to spur the advent of novel and fascinating applications. The main aim is to integrate IoT aware architecture to enhance smart healthcare systems for automatic environmental monitoring of hospital and patient health. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complimentary, technologies, specifically RFID, WSN and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. RADIO frequency identification technologies have been increasingly used in various applications, such as inventory control, and object tracking. An RFID system typically consist of one or several readers and numerous tags. Each tag has a unique ID. The proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward.
Nonlinear programming without a penalty function

In this paper the solution of nonlinear programming problems by a Sequential Quadratic Programming (SQP) trust-region algorithm is considered. The aim of the present work is to promote global convergence without the need to use a penalty function. Instead, a new concept of a “filter” is introduced which allows a step to be accepted if it reduces either the objective function or the constraint violation function. Numerical tests on a wide range of test problems are very encouraging and the new algorithm compares favourably with LANCELOT and an implementation of Sl1QP.
Low-Power CMOS Rectifier Design for RFID Applications

We investigate theoretical and practical aspects of the design of far-field RF power extraction systems consisting of antennas, impedance matching networks and rectifiers. Fundamental physical relationships that link the operating bandwidth and range are related to technology dependent quantities like threshold voltage and parasitic capacitances. This allows us to design efficient planar antennas, coupled resonator impedance matching networks and low-power rectifiers in standard CMOS technologies (0.5-mum and 0.18-mum) and accurately predict their performance. Experimental results from a prototype power extraction system that operates around 950 MHz and integrates these components together are presented. Our measured RF power-up threshold (in 0.18-mum, at 1 muW load) was 6 muWplusmn10%, closely matching the predicted value of 5.2 muW.
BOLD Features to Detect Texture-less Objects

Object detection in images withstanding significant clutter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and distinctive representation of groups of neighboring line segments aggregated over limited spatial supports and invariant to rotation, translation and scale changes. Peculiarly, our proposal allows for leveraging on the inherent strengths of descriptor-based approaches, i.e. robustness to occlusion and clutter and scalability with respect to the size of the model library, also when dealing with scarcely textured objects.
Zero Oscillation and Irradiance Slope Tracking for Photovoltaic MPPT

Maximum power point tracking (MPPT) strategies in photovoltaic (PV) systems ensure efficient utilization of PV arrays. Among different strategies, the perturb and observe (P&O) algorithm has gained wide popularity due to its intuitive nature and simple implementation. However, such simplicity in P&O introduces two inherent issues, namely, an artificial perturbation that creates losses in steady-state operation and a limited ability to track transients in changing environmental conditions. This paper develops and discusses in detail an MPPT algorithm with zero oscillation and slope tracking to address those technical challenges. The strategy combines three techniques to improve steady-state behavior and transient operation: 1) idle operation on the maximum power point (MPP); 2) identification of the irradiance change through a natural perturbation; and 3) a simple multilevel adaptive tracking step. Two key elements, which form the foundation of the proposed solution, are investigated: 1) the suppression of the artificial perturb at the MPP; and 2) the indirect identification of irradiance change through a current-monitoring algorithm, which acts as a natural perturbation. The zero-oscillation adaptive step P&O strategy builds on these mechanisms to identify relevant information and to produce efficiency gains. As a result, the combined techniques achieve superior overall performance while maintaining simplicity of implementation. Simulations and experimental results are provided to validate the proposed strategy, and to illustrate its behavior in steady and transient operations.
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework

Despite the importance of sparsity in many big data applications, there are few existing methods for efficient distributed optimization of sparsely-regularized objectives. In this paper, we present a communication-efficient framework for L1-regularized optimization in distributed environments. By taking a nontraditional view of classical objectives as part of a more general primal-dual setting, we obtain a new class of methods that can be efficiently distributed and is applicable to common L1-regularized regression and classification objectives, such as Lasso, sparse logistic regression, and elastic net regression. We provide convergence guarantees for this framework and demonstrate strong empirical performance as compared to other stateof-the-art methods on several real-world distributed datasets.
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine

Straightforward application of Deep Belief Nets (DBNs) to acoustic modeling produces a rich distributed representation of speech data that is useful for recognition and yields impressive results on the speaker-independent TIMIT phone recognition task. However, the first-layer Gaussian-Bernoulli Restricted Boltzmann Machine (GRBM) has an important limitation, shared with mixtures of diagonalcovariance Gaussians: GRBMs treat different components of the acoustic input vector as conditionally independent given the hidden state. The mean-covariance restricted Boltzmann machine (mcRBM), first introduced for modeling natural images, is a much more representationally efficient and powerful way of modeling the covariance structure of speech data. Every configuration of the precision units of the mcRBM specifies a different precision matrix for the conditional distribution over the acoustic space. In this work, we use the mcRBM to learn features of speech data that serve as input into a standard DBN. The mcRBM features combined with DBNs allow us to achieve a phone error rate of 20.5%, which is superior to all published results on speaker-independent TIMIT to date.
Ultra-Fast Shapelets for Time Series Classification

Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. Since the discovery of time series shapelets is costly in terms of time, the applicability on long or multivariate time series is difficult. In this work we propose Ultra-Fast Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast Shapelets yield the same prediction quality as current state-of-theart shapelet-based time series classifiers that carefully select the shapelets by being by up to three orders of magnitudes. Since this method allows a ultra-fast shapelet discovery, using shapelets for long multivariate time series classification becomes feasible. A method for using shapelets for multivariate time series is proposed and Ultra-Fast Shapelets is proven to be successful in comparison to state-of-the-art multivariate time series classifiers on 15 multivariate time series datasets from various domains. Finally, time series derivatives that have proven to be useful for other time series classifiers are investigated for the shapelet-based classifiers. It is shown that they have a positive impact and that they are easy to integrate with a simple preprocessing step, without the need of adapting the shapelet discovery algorithm.
International Happiness : A New View on the Measure of Performance by

Executive Overview This paper describes the findings from a new, and intrinsically interdisciplinary, literature on happiness and well-being. The paper focuses on international evidence. We report the patterns in modern data, discuss what has been persuasively established and what has not, and suggest paths for future research. Looking ahead, our instinct is that this social science research avenue will gradually merge with a related literature—from the medical, epidemiological, and biological sciences—on biomarkers and health. Nevertheless, we expect that intellectual convergence to happen slowly.
What Security Questions Do Developers Ask? A Large-Scale Study of Stack Overflow Posts

Security has always been a popular and critical topic. With the rapid development of information technology, it is always attracting people’s attention. However, since security has a long history, it covers a wide range of topics which change a lot, from classic cryptography to recently popular mobile security. There is a need to investigate security-related topics and trends, which can be a guide for security researchers, security educators and security practitioners. To address the above-mentioned need, in this paper, we conduct a large-scale study on security-related questions on Stack Overflow. Stack Overflow is a popular on-line question and answer site for software developers to communicate, collaborate, and share information with one another. There are many different topics among the numerous questions posted on Stack Overflow and security-related questions occupy a large proportion and have an important and significant position. We first use two heuristics to extract from the dataset the questions that are related to security based on the tags of the posts. And then we use an advanced topic model, Latent Dirichlet Allocation (LDA) tuned using Genetic Algorithm (GA), to cluster different security-related questions based on their texts. After obtaining the different topics of security-related questions, we use their metadata to make various analyses. We summarize all the topics into five main categories, and investigate the popularity and difficulty of different topics as well. Based on the results of our study, we conclude several implications for researchers, educators and practitioners.
Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q-learning" and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.
An optimized 3D-stacked memory architecture by exploiting excessive, high-density TSV bandwidth

Memory bandwidth has become a major performance bottleneck as more and more cores are integrated onto a single die, demanding more and more data from the system memory. Several prior studies have demonstrated that this memory bandwidth problem can be addressed by employing a 3D-stacked memory architecture, which provides a wide, high frequency memory-bus interface. Although previous 3D proposals already provide as much bandwidth as a traditional L2 cache can consume, the dense through-silicon-vias (TSVs) of 3D chip stacks can provide still more bandwidth. In this paper, we contest that we need to re-architect our memory hierarchy, including the L2 cache and DRAM interface, so that it can take full advantage of this massive bandwidth. Our technique, SMART-3D, is a new 3D-stacked memory architecture with a vertical L2 fetch/write-back network using a large array of TSVs. Simply stated, we leverage the TSV bandwidth to hide latency behind very large data transfers. We analyze the design trade-offs for the DRAM arrays, careful enough to avoid compromising the DRAM density because of TSV placement. Moreover, we propose an efficient mechanism to manage the false sharing problem when implementing SMART-3D in a multi-socket system. For single-threaded memory-intensive applications, the SMART-3D architecture achieves speedups from 1.53 to 2.14 over planar designs and from 1.27 to 1.72 over prior 3D designs. We achieve similar speedups for multi-program and multi-threaded workloads on multi-core and multi-socket processors. Furthermore, SMART-3D can even lower the energy consumption in the L2 cache and 3D DRAM for it reduces the total number of row buffer misses.
Just noticeable defocus blur detection and estimation

We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images. This type of blur is common during photo taking. Although it is not strong, the slight edge blurriness contains informative clues related to depth. We found existing blur descriptors based on local information cannot distinguish this type of small blur reliably from unblurred structures. We propose a simple yet effective blur feature via sparse representation and image decomposition. It directly establishes correspondence between sparse edge representation and blur strength estimation. Extensive experiments manifest the generality and robustness of this feature.
Defending false data injection attack on smart grid network using adaptive CUSUM test

In modern smart grid networks, the traditional power grid is enabled by the technological advances in sensing, measurement, and control devices with two-way communications between the suppliers and customers. The smart grid integration helps the power grid networks to be smarter, but it also increases the risk of adversaries because of the currently obsoleted cyber-infrastructure. Adversaries can easily paralyzes the power facility by misleading the energy management system with injecting false data. In this paper, we proposes a defense strategy to the malicious data injection attack for smart grid state estimation at the control center. The proposed “adaptive CUSUM algorithm”, is recursive in nature, and each recursion comprises two inter-leaved stages: Stage 1 introduces the linear unknown parameter solver technique, and Stage 2 applies the multi-thread CUSUM algorithm for quickest change detection. The proposed scheme is able to determine the possible existence of adversary at the control center as quickly as possible without violating the given constraints such as a certain level of detection accuracy and false alarm. The performance of the proposed algorithm is evaluated by both mathematic analysis and numerical simulation.
A Game-Theoretic Approach to Energy Trading in the Smart Grid

Electric storage units constitute a key element in the emerging smart grid system. In this paper, the interactions and energy trading decisions of a number of geographically distributed storage units are studied using a novel framework based on game theory. In particular, a noncooperative game is formulated between storage units, such as plug-in hybrid electric vehicles, or an array of batteries that are trading their stored energy. Here, each storage unit's owner can decide on the maximum amount of energy to sell in a local market so as to maximize a utility that reflects the tradeoff between the revenues from energy trading and the accompanying costs. Then in this energy exchange market between the storage units and the smart grid elements, the price at which energy is traded is determined via an auction mechanism. The game is shown to admit at least one Nash equilibrium and a novel algorithm that is guaranteed to reach such an equilibrium point is proposed. Simulation results show that the proposed approach yields significant performance improvements, in terms of the average utility per storage unit, reaching up to 130.2% compared to a conventional greedy approach.
Presto: distributed machine learning and graph processing with sparse matrices

It is cumbersome to write machine learning and graph algorithms in data-parallel models such as MapReduce and Dryad. We observe that these algorithms are based on matrix computations and, hence, are inefficient to implement with the restrictive programming and communication interface of such frameworks.
 In this paper we show that array-based languages such as R [3] are suitable for implementing complex algorithms and can outperform current data parallel solutions. Since R is single-threaded and does not scale to large datasets, we have built Presto, a distributed system that extends R and addresses many of its limitations. Presto efficiently shares sparse structured data, can leverage multi-cores, and dynamically partitions data to mitigate load imbalance. Our results show the promise of this approach: many important machine learning and graph algorithms can be expressed in a single framework and are substantially faster than those in Hadoop and Spark.
Fiber-based wearable electronics: a review of materials, fabrication, devices, and applications.

Fiber-based structures are highly desirable for wearable electronics that are expected to be light-weight, long-lasting, flexible, and conformable. Many fibrous structures have been manufactured by well-established lost-effective textile processing technologies, normally at ambient conditions. The advancement of nanotechnology has made it feasible to build electronic devices directly on the surface or inside of single fibers, which have typical thickness of several to tens microns. However, imparting electronic functions to porous, highly deformable and three-dimensional fiber assemblies and maintaining them during wear represent great challenges from both views of fundamental understanding and practical implementation. This article attempts to critically review the current state-of-arts with respect to materials, fabrication techniques, and structural design of devices as well as applications of the fiber-based wearable electronic products. In addition, this review elaborates the performance requirements of the fiber-based wearable electronic products, especially regarding the correlation among materials, fiber/textile structures and electronic as well as mechanical functionalities of fiber-based electronic devices. Finally, discussions will be presented regarding to limitations of current materials, fabrication techniques, devices concerning manufacturability and performance as well as scientific understanding that must be improved prior to their wide adoption.
Stock Market Forecasting Using LASSO Linear Regression Model

Predicting stock exchange rates is receiving increasing attention and is a vital financial problem as it contributes to the development of effective strategies for stock exchange transactions. The forecasting of stock price movement in general is considered to be a thought-provoking and essential task for financial time series' exploration. In this paper, a Least Absolute Shrinkage and Selection Operator (LASSO) method based on a linear regression model is proposed as a novel method to predict financial market behavior. LASSO method is able to produce sparse solutions and performs very well when the numbers of features are less as compared to the number of observations. Experiments were performed with Goldman Sachs Group Inc. stock to determine the efficiency of the model. The results indicate that the proposed model outperforms the ridge linear regression model.