nils-herrmann/clean_scirepeval huggingface dataset:
Integrating Technical and Legal Concepts of Privacy

We provide an overview of legal and technical concepts of privacy protection. Data protection guarantees exist in European Union at a primary law level since 2009, when the Lisbon Treaty has become effective. The new regulation called general data protection regulation adopted on April 27, 2016 will enter into effect on May 25, 2018. Regarding data protection, the US follows a very different approach. The cornerstone of the technical framework of data protection is the concept of differential privacy for which the privacy’s provable guarantees hold even against worst-case scenarios when the access to arbitrary auxiliary information is provided. Research directions for integrating legal and technical concepts of privacy are discussed. Although both fields, legal and technical, approach privacy from different perspectives, we expect that these approaches will converge to a common framework. The privacy-preserving framework, therefore, should be a balance between: 1) ambiguous legal concepts and precise mathematical notions of privacy; 2) the desired privacy level; 3) the information loss, which is measured by data utility metrics; and 4) the complexity and the practical feasibility of the proposed technique. Key notions of fairness are incompatible with each other, and hence any privacy-preserving framework should also include the trade-offs between conditions imposed in the definitions of fairness. The complex assembly of legal and technical components implementing various privacy requirements in the privacy-preserving framework, should be realized within the privacy-by-design concept.
Unsupervised Text Normalization Using Distributed Representations of Words and Phrases

Text normalization techniques that use rule-based normalization or string similarity based on static dictionaries are typically unable to capture domain-specific abbreviations (custy, cx → customer) and shorthands (5ever, 7ever → forever) used in informal texts. In this work, we exploit the property that noisy and canonical forms of a particular word share similar context in a large noisy text collection (millions or billions of social media feeds from Twitter, Facebook, etc.). We learn distributed representations of words to capture the notion of contextual similarity and subsequently learn normalization lexicons from these representations in a completely unsupervised manner. We experiment with linear and non-linear distributed representations obtained from log-linear models and neural networks, respectively. We apply our framework for normalizing customer care notes and Twitter. We also extend our approach to learn phrase normalization lexicons (g2g → got to go) by training distributed representations over compound words. Our approach outperforms Microsoft Word, Aspell and a manually compiled urban dictionary from the Web and achieves state-of-the-art results on a publicly available Twitter dataset.
Visual FHIR: An Interactive Browser to Navigate HL7 FHIR Specification

The HL7 Fast Healthcare Interoperability Resources (FHIR) specification is emerging as a next generation standards framework for the exchange of electronic health records (EHR) data. The rich semantic representation and sophisticated structure definition of the FHIR requires a relatively deep learning curve to understand and utilize. The objective of our study is to design and develop a user-friendly interface for navigating and manipulating the FHIR specification. We prototyped a visualization platform for interactively exploring FHIR core resources and profiles. We evaluated the utility of the FHIR visualization platform using the evaluation metrics mainly focusing on its usability, interactive mechanisms and content expressiveness. We demonstrated that the visualization techniques are helpful for navigating the HL7 FHIR specification and aiding its profiling.
Fully parallel RRAM synaptic array for implementing binary neural network with (+1, −1) weights and (+1, 0) neurons

Binary Neural Networks (BNNs) have been recently proposed to improve the area-/energy-efficiency of the machine/deep learning hardware accelerators, which opens an opportunity to use the technologically more mature binary RRAM devices to effectively implement the binary synaptic weights. In addition, the binary neuron activation enables using the sense amplifier instead of the analog-to-digital converter to allow bitwise communication between layers of the neural networks. However, the sense amplifier has intrinsic offset that affects the threshold of binary neuron, thus it may degrade the classification accuracy. In this work, we analyze a fully parallel RRAM synaptic array architecture that implements the fully connected layers in a convolutional neural network with (+1, −1) weights and (+1, 0) neurons. The simulation results with TSMC 65 nm PDK show that the offset of current mode sense amplifier introduces a slight accuracy loss from ∼98.5% to ∼97.6% for MNIST dataset. Nevertheless, the proposed fully parallel BNN architecture (P-BNN) can achieve 137.35 TOPS/W energy efficiency for the inference, improved by ∼20X compared to the sequential BNN architecture (S-BNN) with row-by-row read-out scheme. Moreover, the proposed P-BNN architecture can save the chip area by ∼16% as it eliminates the area overhead of MAC peripheral units in the S-BNN architecture.
Safety in shipping: the human element.

There are numerous diverse papers that have addressed issues within maritime safety; to date there has been no comprehensive review of this literature to aggregate the causal factors within accidents in shipping and surmise current knowledge.



This paper reviewed the literature on safety in three key areas: common themes of accidents, the influence of human error, and interventions to make shipping safer. The review included 20 studies of seafaring across the following areas: fatigue, stress, health, situation awareness, teamwork, decision-making, communication, automation, and safety culture.



The review identifies the relative contributions of individual and organizational factors in shipping accidents, and also presents the methodological issues with previous research.



The paper concludes that monitoring and modifying the human factors issues presented in this paper could contribute to maritime safety performance.


IMPACT ON INDUSTRY
This review illustrates which human factors issues are prevalent in incidents therefore this gives shipping practitioners a focus for interventions.
Overcoming the Bell-Shaped Dose-Response of Cannabidiol by Using Cannabis Extract Enriched in Cannabidiol

Cannabidiol (CBD), a major constituent of Cannabis, has been shown to be a powerful anti‐in‐ flammatory and anti‐anxiety drug, without exerting a psychotropic effect. However, when given either intraperitoneally or orally as a purified product, a bell‐shaped dose‐response was observed, which limits its clinical use. In the present study, we have studied in mice the anti‐inflammatory and anti‐nociceptive activities of standardized plant extracts derived from the Cannabis sativa L., clone 202, which is highly enriched in CBD and hardly contains any psychoactive ingredients. In stark contrast to purified CBD, the clone 202 extract, when given either intraperitoneally or orally, provided a clear correlation between the anti‐inflammatory and anti‐nociceptive responses and the dose, with increasing responses upon increasing doses, which makes this plant medicine ideal for clinical uses. The clone 202 extract reduced zymosan‐induced paw swelling and pain in mice, and prevented TNFα production in vivo. It is likely that other components in the extract synergize with CBD to achieve the desired anti‐inflammatory action that may contribute to overcoming the bell‐shaped dose‐response of purified CBD. We therefore propose that Cannabis clone 202 (Avi‐ dekel) extract is superior over CBD for the treatment of inflammatory conditions.
Automatic Question Generation using Relative Pronouns and Adverbs

This paper presents a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. Our system is syntax-based, runs on dependency parse information of a single-sentence input, and achieves high accuracy in terms of syntactic correctness, semantic adequacy, fluency and uniqueness. One of the key advantages of our system, in comparison with other rulebased approaches, is that we nearly eliminate the chances of getting a wrong whword in the generated question, by fetching the requisite wh-word from the input sentence itself. Depending upon the input, we generate both factoid and descriptive type questions. To the best of our information, the exploitation of wh-pronouns and wh-adverbs to generate questions is novel in the Automatic Question Generation task.
Path planning algorithm development for autonomous vacuum cleaner robots

A vacuum cleaner robot, generally called a robovac, is an autonomous robot that is controlled by intelligent program. Autonomous vacuum cleaning robot will perform task like sweeping and vacuuming in a single pass. The DVR-1 vacuum cleaning robot consists of two DC motor operated wheels that allow 360 degree rotation, a castor wheel, side spinning brushes, a front bumper and a miniature vacuum pump. Sensors in the bumper are used for generating binary information of obstacle detection then they are processed by some controlling algorithms. These algorithms are used for path planning and navigation. The robot's bumper prevents them from bumping into walls and furniture by reversing or changing path accordingly.
Aspects of anthocyanin absorption, metabolism and pharmacokinetics in humans.

Interest in the health-promoting properties of berry anthocyanins is intensifying; however, findings are primarily based on in vitro characteristics, leaving mechanisms associated with absorption, metabolism and pharmacokinetics largely unexplored. The present review integrates the available anthocyanin literature with that of similar flavonoids or polyphenols in order to form hypotheses regarding absorption, metabolism and clearance in humans. Of the limited available literature regarding the absorption and clearance kinetics of anthocyanins, maximum plasma concentrations are reported anywhere between 1.4 and 592 nmol/l and occur at 0.5-4 h post-consumption (doses; 68-1300 mg). Average urinary excretion is reported between 0.03 and 4 % of the ingested dose, having elimination half-lives of 1.5-3 h. In addition, much is unknown regarding the metabolism of anthocyanins. The most commonly cited conjugation reactions involved in the metabolism of other flavonoids include glucuronidation, methylation and sulfation. It is reasonable to suspect that anthocyanins are metabolised in much the same manner; however, until recently, there was little evidence to suggest that anthocyanins were metabolised to any significant extent. New evidence now suggests that anthocyanins are absorbed and transported in human serum and urine primarily as metabolites, with recent studies documenting as much as 68-80 % of anthocyanins as metabolised derivatives in human urine. Further research is required to resolve mechanisms associated with the absorption, metabolism and clearance of anthocyanins in order to establish their true biological activities and health effects. The presented evidence will hopefully focus future research, refining study design and propagating a more complete understanding of anthocyanins' biological significance in humans.
Data Exfiltration From Internet of Things Devices: iOS Devices as Case Studies

Increasingly, big data (including sensitive and commercial-in-confidence data) is being accessible and stored on a range of Internet of Things (IoT) devices, such as our mobile devices. Therefore, any vulnerability in IoT devices, operating system or software can be exploited by cybercriminals seeking to exfiltrate our data. In this paper, we use iOS devices as case studies and highlight the potential for pairing mode in iOS devices (which allows the establishment of a trusted relationship between an iOS device and a personal computer) to be exploited for covert data exfiltration. In our three case studies, we demonstrate how an attacker could exfiltrate data from a paired iOS device by abusing a library and a command line tool distributed with iTunes. With the aim of avoiding similar attacks in the future, we present two recommendations.
Frequent Pattern Mining on Message Passing Multiprocessor Systems

Extraction of frequent patterns in transaction-oriented database is crucial to several data mining tasks such as association rule generation, time series analysis, classification, etc. Most of these mining tasks require multiple passes over the database and if the database size is large, which is usually the case, scalable high performance solutions involving multiple processors are required. This paper presents an efficient scalable parallel algorithm for mining frequent patterns on parallel shared nothing platforms. The proposed algorithm is based on one of the best known sequential techniques referred to as Frequent Pattern (FP) Growth algorithm. Unlike most of the earlier parallel approaches based on different variants of the Apriori Algorithm, the algorithm presented in this paper does not explicitly result in having entire counting data structure duplicated on each processor. Furthermore, the proposed algorithm introduces minimum communication (and hence synchronization) overheads by efficiently partitioning the list of frequent elements list over processors. The experimental results show scalable performance over different machine and problem sizes. The comparison of implementation results with existing parallel approaches show significant gains in the speedup. On an 8-processor machine, we report an average speedup of 6 for different problem sizes.
Real time automation of agricultural environment

The paper, “Real time automation of agricultural environment”, using PIC16F877A and GSM SIM300 modem is focused on automating the irrigation system for social welfare of Indian agricultural system. This system will be useful for monitoring the soil moisture condition of the farm as well as controlling the soil moisture by monitoring the level of water in the water source and accordingly switching the motor ON/OFF for irrigation purposes. The system proposes a soil moisture sensor at each place where the moisture has to be monitored. Once the moisture reaches a particular level, the system takes appropriate steps to regulate or even stop the water flow. The circuit also monitors the water in the water source so that if the water level becomes very low, it switches off the motor to prevent damage to the motor due to dry run. The system also consists of a GSM modem through which the farmer can easily be notified about the critical conditions occurring during irrigation process.
Comparative analysis of PWM/PFM controlled SMPS using Matlab Simulink

The design of PWM/PFM controller for SMPS is explained in this paper. An application concerning logic controller to manage is given. controller used here is Pulse frequency modulation (PFM)and pulse width modulation(PWM). In general for light load pulse width modulated buck converter operates with high efficiency since it has less switching losses, for heavy load operation pulse frequency modulation buck converter operates with less losses. Discontinuous conduction mode of pulse frequency modulated buck converter is considered in this paper for its concert in an application connecting a source with unstable voltage conditions with a load which requires constant voltage. Here, the pulses are apply to the semiconductor MOSFET control in step down converter is ended at some point and released at some point depending upon the load and threshold values of output voltage of the converter.
Feature line extraction from unorganized noisy point clouds using truncated Fourier series

The detection of feature lines is important for representing and understanding geometric features of 3D models. In this paper, we introduce a new and robust method for extracting feature lines from unorganized point clouds. We use a one-dimensional truncated Fourier series for detecting feature points. Each point and its neighbors are approximated along the principal directions by using the truncated Fourier series, and the curvature of the point is computed from the approximated curves. The Fourier coefficients are computed by Fast Fourier Transform (FFT). We apply low-pass filtering to remove noise and to compute the curvature of the point robustly. For extracting feature points from the detected potential feature points, the potential feature points are thinned using a curvature weighted Laplacian-like smoothing method. The feature lines are constructed through growing extracted points and then projected onto the original point cloud. The efficiency and robustness of our approach is illustrated by several experimental results.
Time-dependent visual adaptation for fast realistic image display

Human vision takes time to adapt to large changes in scene intensity, and these transient adjustments have a profound effect on visual appearance. This paper offers a new operator to include these appearance changes in animations or interactive real-time simulations, and to match a user's visual responses to those the user would experience in a real-world scene.
Large, abrupt changes in scene intensities can cause dramatic compression of visual responses, followed by a gradual recovery of normal vision. Asymmetric mechanisms govern these time-dependent adjustments, and offer adaptation to increased light that is much more rapid than adjustment to darkness. We derive a new tone reproduction operator that simulates these mechanisms. The operator accepts a stream of scene intensity frames and creates a stream of color display images.
All operator components are derived from published quantitative measurements from physiology, psychophysics, color science, and photography. ept intentionally simple to allow fast computation, the operator is meant for use with real-time walk-through renderings, high dynamic range video cameras, and other interactive applications. We demonstrate its performance on both synthetically generated and acquired “real-world” scenes with large dynamic variations of illumination and contrast.
A novel soil measuring wireless sensor network

Emerging technologies have made low-power and low-cost wireless sensor networks feasible. This paper presents a hierarchical wireless sensor network for measuring soil parameters such as temperature and humidity. Specifically, we designed sensor nodes that are placed completely underground and are used to collect soil measurements. These nodes use their radios to deliver the collected measurements to one of multiple relay nodes located above ground. In turn, relay nodes that are capable of long-range communications forward the data collected from the network's sensor nodes to a base node, which is connected to a workstation. The proposed hierarchical wireless sensor network uses a probabilistic communication protocol to achieve a very low duty cycle and hence a long life-time for soil monitoring applications.
On Optimizing VLC Networks for Downlink Multi-User Transmission: A Survey

The evolving explosion in high data rate services and applications will soon require the use of untapped, abundant unregulated spectrum of the visible light for communications to adequately meet the demands of the fifth-generation (5G) mobile technologies. Radio-frequency (RF) networks are proving to be scarce to cover the escalation in data rate services. Visible light communication (VLC) has emerged as a great potential solution, either in replacement of, or complement to, existing RF networks, to support the projected traffic demands. Despite of the prolific advantages of VLC networks, VLC faces many challenges that must be resolved in the near future to achieve a full standardization and to be integrated to future wireless systems. Here, we review the new, emerging research in the field of VLC networks and lay out the challenges, technological solutions, and future work predictions. Specifically, we first review the VLC channel capacity derivation, discuss the performance metrics and the associated variables; the optimization of VLC networks are also discussed, including resources and power allocation techniques, user-to-access point (AP) association and APs-toclustered-users-association, APs coordination techniques, nonorthogonal multiple access (NOMA) VLC networks, simultaneous energy harvesting and information transmission using the visible light, and the security issue in VLC networks. Finally, we propose several open research problems to optimize the various VLC networks by maximizing either the sum rate, fairness, energy efficiency, secrecy rate, or harvested energy.
User practice in password security: An empirical study of real-life passwords in the wild

Due to increasing security awareness of password from the public and little attention on the characteristics of real-life passwords, it is thus natural to understand the current state of characteristics of real-life passwords, and to explore how password characteristics change over time and how earlier password practice is understood in current context. In this work, we attempt to present an in-depth and comprehensive understanding of user practice in real-life passwords, and to see whether the previous observations can be confirmed or reversed, based on large-scale measurements rather than anecdotal experiences or user surveys. Specifically, we measure password characteristics on over 6 million passwords, in terms of password length, password composition, and password selection. We then make informed comparisons of the findings between our investigation and previously reported results. Our general findings include: (1) average password length is at least 12% longer than previous results, and 85% of our passwords have the length between 8 and 11 characters;
Social Media for Opioid Addiction Epidemiology: Automatic Detection of Opioid Addicts from Twitter and Case Studies

Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid abuse and addiction. The role of social media in biomedical knowledge mining has turned into increasingly significant in recent years. In this paper, we propose a novel framework named AutoDOA to automatically detect the opioid addicts from Twitter, which can potentially assist in sharpening our understanding toward the behavioral process of opioid abuse and addiction. In AutoDOA, to model the users and posted tweets as well as their rich relationships, a structured heterogeneous information network (HIN) is first constructed. Then meta-path based approach is used to formulate similarity measures over users and different similarities are aggregated using Laplacian scores. Based on HIN and the combined meta-path, to reduce the cost of acquiring labeled examples for supervised learning, a transductive classification model is built for automatic opioid addict detection. To the best of our knowledge, this is the first work to apply transductive classification in HIN into drug-addiction domain. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of our developed system AutoDOA in opioid addict detection by comparisons with other alternate methods. The results and case studies also demonstrate that knowledge from daily-life social media data mining could support a better practice of opioid addiction prevention and treatment.