nils-herrmann/clean_scirepeval huggingface dataset:
A Privacy-Preserving Framework for Personalized, Social Recommendations

We consider the problem of producing item recommendations that are personalized based on a user’s social network, while simultaneously preventing the disclosure of sensitive user-item preferences (e.g., product purchases, ad clicks, web browsing history, etc.). Our main contribution is a privacypreserving framework for a class of social recommendation algorithms that provides strong, formal privacy guarantees under the model of differential privacy. Existing mechanisms for achieving differential privacy lead to an unacceptable loss of utility when applied to the social recommendation problem. To address this, the proposed framework incorporates a clustering procedure that groups users according to the natural community structure of the social network and significantly reduces the amount of noise required to satisfy differential privacy. Although this reduction in noise comes at the cost of some approximation error, we show that the benefits of the former significantly outweigh the latter. We explore the privacy-utility trade-off for several different instantiations of the proposed framework on two real-world data sets and show that useful social recommendations can be produced without sacrificing privacy. We also experimentally compare the proposed framework with several existing differential privacy mechanisms and show that the proposed framework significantly outperforms all of them in this setting.
Microplastic pollution in the surface waters of the Laurentian Great Lakes.

Neuston samples were collected at 21 stations during an ~700 nautical mile (~1300 km) expedition in July 2012 in the Laurentian Great Lakes of the United States using a 333 μm mesh manta trawl and analyzed for plastic debris. Although the average abundance was approximately 43,000 microplastic particles/km², station 20, downstream from two major cities, contained over 466,000 particles/km², greater than all other stations combined. SEM analysis determined nearly 20% of particles less than 1 mm, which were initially identified as microplastic by visual observation, were aluminum silicate from coal ash. Many microplastic particles were multi-colored spheres, which were compared to, and are suspected to be, microbeads from consumer products containing microplastic particles of similar size, shape, texture and composition. The presence of microplastics and coal ash in these surface samples, which were most abundant where lake currents converge, are likely from nearby urban effluent and coal burning power plants.
Environmental and health hazard ranking and assessment of plastic polymers based on chemical composition.

Plastics constitute a large material group with a global annual production that has doubled in 15 years (245 million tonnes in 2008). Plastics are present everywhere in society and the environment, especially the marine environment, where large amounts of plastic waste accumulate. The knowledge of human and environmental hazards and risks from chemicals associated with the diversity of plastic products is very limited. Most chemicals used for producing plastic polymers are derived from non-renewable crude oil, and several are hazardous. These may be released during the production, use and disposal of the plastic product. In this study the environmental and health hazards of chemicals used in 55 thermoplastic and thermosetting polymers were identified and compiled. A hazard ranking model was developed for the hazard classes and categories in the EU classification and labelling (CLP) regulation which is based on the UN Globally Harmonized System. The polymers were ranked based on monomer hazard classifications, and initial assessments were made. The polymers that ranked as most hazardous are made of monomers classified as mutagenic and/or carcinogenic (category 1A or 1B). These belong to the polymer families of polyurethanes, polyacrylonitriles, polyvinyl chloride, epoxy resins, and styrenic copolymers. All have a large global annual production (1-37 million tonnes). A considerable number of polymers (31 out of 55) are made of monomers that belong to the two worst of the ranking model's five hazard levels, i.e. levels IV-V. The polymers that are made of level IV monomers and have a large global annual production (1-5 million tonnes) are phenol formaldehyde resins, unsaturated polyesters, polycarbonate, polymethyl methacrylate, and urea-formaldehyde resins. This study has identified hazardous substances used in polymer production for which the risks should be evaluated for decisions on the need for risk reduction measures, substitution, or even phase out.
Divide and conquer: neuroevolution for multiclass classification

Neuroevolution is a powerful and general technique for evolving the structure and weights of artificial neural networks. Though neuroevolutionary approaches such as NeuroEvolution of Augmenting Topologies (NEAT) have been successfully applied to various problems including classification, regression, and reinforcement learning problems, little work has explored application of these techniques to larger-scale multiclass classification problems. In this paper, NEAT is evaluated in several multiclass classification problems, and then extended via two ensemble approaches: One-vs-All and One-vs-One. These approaches decompose multiclass classification problems into a set of binary classification problems, in which each binary problem is solved by an instance of NEAT. These ensemble models exhibit reduced variance and increasingly superior accuracy as the number of classes increases. Additionally, higher accuracy is achieved early in training, even when artificially constrained for the sake of fair comparison with standard NEAT. However, because the approach can be trivially distributed, it can be applied quickly at large scale to solve real problems. In fact, these approaches are incorporated into DarwinTM, an enterprise automatic machine learning solution that also incorporates various other algorithmic enhancements to NEAT. The resulting complete system has proven robust to a wide variety of client datasets.
The big data challenges of connectomics

The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes. Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s with a the study of the small nervous system of a worm and has recently garnered general interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains. However, modern connectomics produces 'big data', unprecedented quantities of digital information at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions. Here we describe some of the key difficulties that may arise and provide suggestions for managing them.
The effect of preceptor role effectiveness on newly licensed registered nurses' perceived psychological empowerment and professional autonomy.

The first year turnover rate for newly licensed registered nurses is roughly 30% and increases to about 57% in the second year (Twibell et al., 2012). An effective preceptorship has been shown to better facilitate the first year transition (Hodges et al., 2008) and increase retention rates (Pine and Tart, 2007). The purpose of this study was to examine the relationships between newly licensed registered nurses' perceived preceptor role effectiveness, psychological empowerment and professional autonomy. A prospective, cross-sectional, descriptive research design was used. Sixty-nine newly licensed registered nurses were recruited and surveyed. Newly licensed registered nurses were found to have moderately high levels of perceived preceptor role effectiveness, psychological empowerment, and professional autonomy. Preceptor role effectiveness had significant, moderately, positive relationships with professional autonomy and psychological empowerment. There was also a significant relationship found between professional autonomy and psychological empowerment. Results show that preceptor role effectiveness is linked to increased professional autonomy and psychological empowerment. Therefore, effective preceptorships are necessary in easing the newly licensed registered nurse's transition to practice. Strategies to ensure effective preceptorships and enhance the NRLN's transition to practice are proposed.
Memory traces in dynamical systems.

To perform nontrivial, real-time computations on a sensory input stream, biological systems must retain a short-term memory trace of their recent inputs. It has been proposed that generic high-dimensional dynamical systems could retain a memory trace for past inputs in their current state. This raises important questions about the fundamental limits of such memory traces and the properties required of dynamical systems to achieve these limits. We address these issues by applying Fisher information theory to dynamical systems driven by time-dependent signals corrupted by noise. We introduce the Fisher Memory Curve (FMC) as a measure of the signal-to-noise ratio (SNR) embedded in the dynamical state relative to the input SNR. The integrated FMC indicates the total memory capacity. We apply this theory to linear neuronal networks and show that the capacity of networks with normal connectivity matrices is exactly 1 and that of any network of N neurons is, at most, N. A nonnormal network achieving this bound is subject to stringent design constraints: It must have a hidden feedforward architecture that superlinearly amplifies its input for a time of order N, and the input connectivity must optimally match this architecture. The memory capacity of networks subject to saturating nonlinearities is further limited, and cannot exceed square root N. This limit can be realized by feedforward structures with divergent fan out that distributes the signal across neurons, thereby avoiding saturation. We illustrate the generality of the theory by showing that memory in fluid systems can be sustained by transient nonnormal amplification due to convective instability or the onset of turbulence.
Multiple Access and Waveforms for 5G: IDMA and Universal Filtered Multi-Carrier

In this paper we investigate multiple access schemes and multi-carrier waveforms in the context of future 5th Generation (5G) wireless communication systems. We compare classical Frequency Division Multiple Access (FDMA) to Interleave-Division Multiple Access (IDMA) on top of two different multicarrier waveforms: Orthogonal Frequency Division Multiplexing (OFDM) and a new approach called Universal Filtered Multi-Carrier (UFMC). A relaxation of timing and frequency alignment requirements is taken into account for supporting applications like Machine Type Communications (MTC) and the Internet of Things (IoT). This paper contains a first uplink comparison scenario where traffic with Relaxed Synchronicity (RS) is embedded into synchronous traffic. Two main users of interest are either using IDMA or FDMA on top of either OFDM or UFMC modulation. Simulation results give first suitability indications for 5G for the combination of waveform and multiple access scheme. The numerical results reveal that IDMA brings in significant enhancement for low rate users, and UFMC introduces additional protection to high-rate users. Both schemes can be combined well.
DESIGN AND MEASUREMENT ERROR ANALYSIS OF A LOW-FRICTION , LIGHTWEIGHT LINEAR SERIES ELASTIC ACTUATOR

Series elastic actuators (SEAs) have many benefits for force controlled robotic applications. Placing an elastic member in series with a rigid actuator output enables more-stable force control and the potential for energy storage while sacrificing position control bandwidth. This paper presents the design and measurement error analysis of a low-friction, lightweight linear SEA used in the Shipboard Autonomous Fire Fighting Robot (SAFFiR). The SAFFiR SEA pairs a stand-alone linear actuator with a configurable compliant member. Unlike most electric linear actuators, this actuator does not use a linear guide, which reduces friction and weight. Unlike other SEAs which measure the force by measuring the spring deflection, a tension and compression load cell is integrated into the design for accurate force measurements. The configurable compliant member is a titanium cantilever with manually adjustable length. The final SEA weighs 0.82[kg] with a maximum force of 1,000[N]. The configurable compliant mechanism has in a spring constant range of 145-512[kN/m]. Having no linear guide and incorporating the load cell into the universal joint both introduce measurement errors. The length error across a parallel ankle joint is less than 0.015[mm] and the force measurement error is less than 0.25% of the actual force. Finally, several changes are suggested for the next iteration of the SEA to improve its usability on future robots.
Introduction to astroML: Machine learning for astrophysics

Astronomy and astrophysics are witnessing dramatic increases in data volume as detectors, telescopes and computers become ever more powerful. During the last decade, sky surveys across the electromagnetic spectrum have collected hundreds of terabytes of astronomical data for hundreds of millions of sources. Over the next decade, the data volume will enter the petabyte domain, and provide accurate measurements for billions of sources. Astronomy and physics students are not traditionally trained to handle such voluminous and complex data sets. In this paper we describe astroML; an initiative, based on python and scikit-learn, to develop a compendium of machine learning tools designed to address the statistical needs of the next generation of students and astronomical surveys. We introduce astroML and present a number of example applications that are enabled by this package.
Evidence for a basic level in a taxonomy of everyday action sounds

We searched for evidence that the auditory organization of categories of sounds produced by actions includes a privileged or “basic” level of description. The sound events consisted of single objects (or substances) undergoing simple actions. Performance on sound events was measured in two ways: sounds were directly verified as belonging to a category, or sounds were used to create lexical priming. The category verification experiment measured the accuracy and reaction time to brief excerpts of these sounds. The lexical priming experiment measured reaction time benefits and costs caused by the presentation of these sounds prior to a lexical decision. The level of description of a sound varied in how specifically it described the physical properties of the action producing the sound. Both identification and priming effects were superior when a label described the specific interaction causing the sound (e.g. trickling) in comparison to the following: (1) more general descriptions (e.g. pour, liquid: trickling is a specific manner of pouring liquid), (2) more detailed descriptions using adverbs to provide detail regarding the manner of the action (e.g. trickling evenly). These results are consistent with neuroimaging studies showing that auditory representations of sounds produced by actions familiar to the listener activate motor representations of the gestures involved in sound production.
dCompaction: Delayed Compaction for the LSM-Tree

(KV) stores have become a backbone of large-scale applications in today’s data centers. Write-optimized data structures like the Log-Structured Merge-tree (LSM-tree) and their variants are widely used in KV storage systems like BigTable and RocksDB. Conventional LSM-tree organizes KV items into multiple, successively larger components, and uses compaction to push KV items from one smaller component to another adjacent larger component until the KV items reach the largest component. Unfortunately, current compaction scheme incurs significant write amplification due to repeated KV item reads and writes, and then results in poor throughput. We propose a new compaction scheme, delayed compaction (dCompaction), that decreases write amplification. dCompaction postpones some compactions and gather them into the following compaction. In this way, it avoids KV item reads and writes during compaction, and consequently improves the throughput of LSM-tree based KV stores. We implement dCompaction on RocksDB, and conduct extensive experiments. Validation using YCSB framework shows that compared with RocksDB dCompaction has about 30% write performance improvements and also comparable read performance.
Towards dropout training for convolutional neural networks

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.
Variational Recurrent Neural Networks for Speech Separation

We present a new stochastic learning machine for speech separation based on the variational recurrent neural network (VRNN). This VRNN is constructed from the perspectives of generative stochastic network and variational auto-encoder. The idea is to faithfully characterize the randomness of hidden state of a recurrent neural network through variational learning. The neural parameters under this latent variable model are estimated by maximizing the variational lower bound of log marginal likelihood. An inference network driven by the variational distribution is trained from a set of mixed signals and the associated source targets. A novel supervised VRNN is developed for speech separation. The proposed VRNN provides a stochastic point of view which accommodates the uncertainty in hidden states and facilitates the analysis of model construction. The masking function is further employed in network outputs for speech separation. The benefit of using VRNN is demonstrated by the experiments on monaural speech separation.
A Self-Cloning Agents Based Model for High-Performance Mobile-Cloud Computing

The rise of the mobile-cloud computing paradigm in recent years has enabled mobile devices with processing power and battery life limitations to achieve complex tasks in real-time. While mobile-cloud computing is promising to overcome the limitations of mobile devices for real-time computing, the lack of frameworks compatible with standard technologies and techniques for dynamic performance estimation and program component relocation makes it harder to adopt mobile-cloud computing at large. Most of the available frameworks rely on strong assumptions such as the availability of a full clone of the application code and negligible execution time in the cloud. In this paper, we present a dynamic computation offloading model for mobile-cloud computing, based on autonomous agents. Our approach does not impose any requirements on the cloud platform other than providing isolated execution containers, and it alleviates the management burden of offloaded code by the mobile platform using stateful, autonomous application partitions. We also investigate the effects of different cloud runtime environment conditions on the performance of mobile-cloud computing, and present a simple and low-overhead dynamic make span estimation model integrated into autonomous agents to enhance them with self-performance evaluation in addition to self-cloning capabilities. The proposed performance profiling model is used in conjunction with a cloud resource optimization scheme to ensure optimal performance. Experiments with two mobile applications demonstrate the effectiveness of the proposed approach for high-performance mobile-cloud computing.
BlueID: A practical system for Bluetooth device identification

Despite the widespread use of Bluetooth technology, identity management of Bluetooth devices remains a significant challenge because the MAC address and name of Bluetooth device are easy to forge. In this paper, we present BlueID - a practical system that identifies Bluetooth devices by fingerprinting their clocks. Previous approaches to clock fingerprinting exclusively rely on the timestamps carried by packet headers, which can be easily spoofed by hacking the user-space device driver. In comparison, BlueID performs clock fingerprinting based on the temporal feature of Bluetooth frequency hopping, which is impossible to forge without a customized baseband. Due to the proprietary nature of chipset firmware that implements baseband on commodity Bluetooth devices, BlueID will significantly raise the bar of identity spoofing. Moreover, BlueID employs simple yet efficient techniques to detect and differentiate low power Bluetooth transmissions from a distance, making it suitable for mobile applications like energy efficient localization and tracking. BlueID is implemented on a low cost wireless development platform and extensively evaluated based on 56 commodity devices. We show that BlueID can detect Bluetooth radios from 100m away, and identify different devices with high accuracy, short delay, and low computational overhead. Although this paper focuses on Bluetooth, the design of BlueID is general and can be applied to other frequency hopping based wireless systems.
A Theory of Program Size Formally Identical to Information Theory

Hopefully members enjoyed the "dividend" mailing of the Proceedings of the Symposium on Theory of Computing. Since it went to all members, and a good number of them presumably have an interest in any errors, we will publish any errata sheets which we receive. Cook and Reckhow have sent errata for their paper , and they appear in this issue. Also included is a paper of somewhat greater length than we usually publish, but which is a followuD to an earlier article in an area that needs some encouragement. Generally, we like to keep contributions to five Da~es or less.