nils-herrmann/clean_scirepeval huggingface dataset:
Blockchain platforms: A compendium

In recent years, cryptocurrencies gained popularity with Bitcoin. The main promising technology behind Bitcoin was ‘Blockchain’. Blockchain provided unique features like transactional privacy, system transparency, immutability of data, security with cryptography, etc. These features paved way for Blockchain in advancing many technologies like voting systems, IOT applications, supply chain management, banking, healthcare, insurance, etc. Blockchain development was boosted with the increasing demand of the technological update. Many blockchain platforms are available like hyperledger fabric, ethereum, corda, etc. We always end up with perplexity while choosing a platform for blockchain development. Through our survey, we provide a comparative analysis of all the hyperledger platforms, ethereum, corda to make a choice of the platform easily according to the requirement.
Graph kernels for chemoinformatics – a critical discussion

We analyze the use, advantages, and drawbacks of graph kernels in chemoin-formatics, including a comparison of kernel-based approaches with other methodology, as well as examples of applications. Kernel-based machine learning , now widely applied in chemoinformatics, delivers state-of-the-art performance  in tasks like classification and regression. Molecular graph kernels  are a recent development where kernels are defined directly on the molecular structure graph. This allows the adaptation of methods from graph theory to structure graphs and their direct use with kernel learning algorithms. The main advantage of kernel learning, the so-called “kernel trick”, allows for a systematic, computationally feasible, and often globally optimal search for non-linear patterns, as well as the direct use of non-numerical inputs such as strings and graphs. A drawback is that solutions are expressed indirectly in terms of similarity to training samples, and runtimes that are typically quadratic or cubic in the number of training samples. Graph kernels  are positive semidefinite functions defined directly on graphs. The most important types are based on random walks, subgraph patterns, optimal assignments, and graphlets. Molecular structure graphs have strong properties that can be exploited , e.g., they are undirected, have no self-loops and no multiple edges, are connected (except for salts), annotated, often planar in the graph-theoretic sense, and their vertex degree is bounded by a small constant. In many applications, they are small. Many graph kernels are generalpurpose, some are suitable for structure graphs, and a few have been explicitly designed for them. We present three exemplary applications of the iterative similarity optimal assignment kernel , which was designed for the comparison of small structure graphs: The discovery of novel agonists of the peroxisome proliferator-activated receptor g [6] (ligand-based virtual screening), the estimation of acid dissociation constants [7] (quantitative structure-property relationships), and molecular de novo design .
Fast Channel Tracking for Terahertz Beamspace Massive MIMO Systems

The recent concept of beamspace multiple input multiple output (MIMO) with discrete lens array can utilize beam selection to reduce the number of radio-frequency chains (RF) required in terahertz (THz) massive MIMO systems. However, to achieve the capacity-approaching performance, beam selection requires information on a beamspace channel of large size. This is difficult to obtain since the user mobility usually leads to the fast variation of THz beamspace channels, and the conventional real-time channel estimation schemes involve unaffordable pilot overhead. To solve this problem, in this paper, we propose the a priori aided (PA) channel tracking scheme. Specifically, by considering a practical user motion model, we first excavate a temporal variation law of the physical direction between the base station and each mobile user. Then, based on this law and the special sparse structure of THz beamspace channels, we propose to utilize the obtained beamspace channels in the previous time slots to predict the prior information of the beamspace channel in the following time slot without channel estimation. Finally, aided by the obtained prior information, the time-varying beamspace channels can be tracked with low pilot overhead. Simulation results verify that to achieve the same accuracy, the proposed PA channel tracking scheme requires much lower pilot overhead and signal-to-noise ratio (SNR) than the conventional schemes.
Augmented reality in healthcare education: an integrative review

The effective development of healthcare competencies poses great educational challenges. A possible approach to provide learning opportunities is the use of augmented reality (AR) where virtual learning experiences can be embedded in a real physical context. The aim of this study was to provide a comprehensive overview of the current state of the art in terms of user acceptance, the AR applications developed and the effect of AR on the development of competencies in healthcare.  We conducted an integrative review. Integrative reviews are the broadest type of research review methods allowing for the inclusion of various research designs to more fully understand a phenomenon of concern. Our review included multi-disciplinary research publications in English reported until 2012.  2529 research papers were found from ERIC, CINAHL, Medline, PubMed, Web of Science and Springer-link. Three qualitative, 20 quantitative and 2 mixed studies were included. Using a thematic analysis, we've described three aspects related to the research, technology and education. This study showed that AR was applied in a wide range of topics in healthcare education. Furthermore acceptance for AR as a learning technology was reported among the learners and its potential for improving different types of competencies.  AR is still considered as a novelty in the literature. Most of the studies reported early prototypes. Also the designed AR applications lacked an explicit pedagogical theoretical framework. Finally the learning strategies adopted were of the traditional style 'see one, do one and teach one' and do not integrate clinical competencies to ensure patients' safety.
Human-robot interaction in handing-over tasks

In many future joint-action scenarios, humans and robots will have to interact physically in order to successfully cooperate. Ideally, seamless human-robot interaction should not require training for the human, but should be intuitively simple. Nonetheless, seamless interaction and cooperation involve some degree of learning and adaptation. Here, we report on a simple case of physical human-robot interaction, a hand-over task. Even such a basic task as manually handing over an object from one agent to another requires that both partners agree upon certain basic prerequisites and boundary conditions. While some of them are negotiated explicitly, e.g. by verbal communication, others are determined indirectly and adaptively in the course of the cooperation. In the present study, we compared human-human hand-over interaction with the same task done by a robot and a human. To evaluate the importance of biological motion, the robot human interaction was tested with two different velocity profiles: a conventional trapezoidal velocity profile in joint coordinates and a minimum-jerk profile of the end-effector. Our results show a significantly shorter reaction time for minimum jerk profiles, which decreased over the first three hand-overs. The results of our comparison provide the background for implementing effective joint-action strategies in humanoid robot systems.
Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors

Many factors influence the adoption of cloud computing. Organizations must systematically evaluate these factors before deciding to adopt cloud-based solutions. To assess the determinants that influence the adoption of cloud computing, we develop a research model based on the innovation characteristics from the diffusion of innovation (DOI) theory and the technology-organization-environment (TOE) framework. Data collected from 369 firms in Portugal are used to test the related hypotheses. The study also investigates the determinants of cloud-computing adoption in the manufacturing and services sectors.  Corresponding author.
Mechanisms of neurotransmitter release by amphetamines: A review

Amphetamine and substituted amphetamines, including methamphetamine, methylphenidate (Ritalin), methylenedioxymethamphetamine (ecstasy), and the herbs khat and ephedra, encompass the only widely administered class of drugs that predominantly release neurotransmitter, in this case principally catecholamines, by a non-exocytic mechanism. These drugs play important medicinal and social roles in many cultures, exert profound effects on mental function and behavior, and can produce neurodegeneration and addiction. Numerous questions remain regarding the unusual molecular mechanisms by which these compounds induce catecholamine release. We review current issues on the two apparent primary mechanisms--the redistribution of catecholamines from synaptic vesicles to the cytosol, and induction of reverse transport of transmitter through plasma membrane uptake carriers--and on additional drug effects that affect extracellular catecholamine levels, including uptake inhibition, effects on exocytosis, neurotransmitter synthesis, and metabolism.
Machine learning and radiology

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.
LSbM-tree: Re-Enabling Buffer Caching in Data Management for Mixed Reads and Writes

LSM-tree has been widely used in data management production systems for write-intensive workloads. However, as read and write workloads co-exist under LSM-tree, data accesses can experience long latency and low throughput due to the interferences to buffer caching from the compaction, a major and frequent operation in LSM-tree. After a compaction, the existing data blocks are reorganized and written to other locations on disks. As a result, the related data blocks that have been loaded in the buffer cache are invalidated since their referencing addresses are changed, causing serious performance degradations. In order to re-enable high-speed buffer caching during intensive writes, we propose Log-Structured buffered-Merge tree (simplified as LSbM-tree) by adding a compaction buffer on disks, to minimize the cache invalidations on buffer cache caused by compactions. The compaction buffer efficiently and adaptively maintains the frequently visited data sets. In LSbM, strong locality objects can be effectively kept in the buffer cache with minimum or without harmful invalidations. With the help of a small on-disk compaction buffer, LSbM achieves a high query performance by enabling effective buffer caching, while retaining all the merits of LSM-tree for write-intensive data processing, and providing high bandwidth of disks for range queries. We have implemented LSbM based on LevelDB. We show that with a standard buffer cache and a hard disk, LSbM can achieve 2x performance improvement over LevelDB. We have also compared LSbM with other existing solutions to show its strong effectiveness.
Personality Measurement and Faking : An Integrative Framework Asl › Göncü Çankaya Üniversitesi

Personality testing is a popular method that used to be commonly employed in selection decisions in organizational settings. However, it is also a controversial practice according to a number researcher who claims that especially explicit measures of personality may be prone to the negative effects of faking and response distortion. The first aim of the present paper is to summarize Morgeson, Morgeson, Campion, Dipboye, Hollenbeck, Murphy and Schmitt’s paper that discussed the limitations of personality testing for performance ratings in relation to its basic conclusions about faking and response distortion. Secondly, the results of Rosse, Stecher, Miller and Levin’s study that investigated the effects of faking in personality testing on selection decisions will be discussed in detail. Finally, recent research findings related to implicit personality measures will be introduced along with the examples of the results related to the implications of those measures for response distortion in personality research and the suggestions for future research.
Loneliness and Facebook motives in adolescence: a longitudinal inquiry into directionality of effect.

The increasing popularity of Facebook among adolescents has stimulated research to investigate the relationship between Facebook use and loneliness, which is particularly prevalent in adolescence. The aim of the present study was to improve our understanding of the relationship between Facebook use and loneliness. Specifically, we examined how Facebook motives and two relationship-specific forms of adolescent loneliness are associated longitudinally. Cross-lagged analysis based on data from 256 adolescents (64% girls, M(age) = 15.88 years) revealed that peer-related loneliness was related over time to using Facebook for social skills compensation, reducing feelings of loneliness, and having interpersonal contact. Facebook use for making new friends reduced peer-related loneliness over time, whereas Facebook use for social skills compensation increased peer-related loneliness over time. Hence, depending on adolescents' Facebook motives, either the displacement or the stimulation hypothesis is supported. Implications and suggestions for future research are discussed.
System Infrastructure for Mobile-Cloud Convergence

The convergence of mobile computing and cloud computing enables new mobile applications that are both resource-intensive and interactive. For these applications, end-to-end network bandwidth and latency matter greatly when cloud resources are used to augment the computational power and battery life of a mobile device. This dissertation designs and implements a new architectural element called a cloudlet, that arises from the convergence of mobile computing and cloud computing. Cloudlets represent the middle tier of a 3-tier hierarchy, mobile device — cloudlet — cloud, to achieve the right balance between cloud consolidation and network responsiveness. We first present quantitative evidence that shows cloud location can affect the performance of mobile applications and cloud consolidation. We then describe an architectural solution using cloudlets that are a seamless extension of todays cloud computing infrastructure. Finally, we define minimal functionalities that cloudlets must offer above/beyond standard cloud computing, and address corresponding technical challenges.
Emerging Points of the Supraorbital and Supratrochlear Nerves in the Supraorbital Margin With Reference to the Lacrimal Caruncle: Implications for Regional Nerve Block in Upper Eyelid and Dermatologic Surgery.

Blocking the supraorbital nerve (SON) and supratrochlear nerve (STN) by injecting anesthetic distal to the surgical site has the advantage in upper eyelid surgery that avoids obscuring the surgical landmarks and compromising the levator function.



To identify the emerging points of the SON and STN in the supraorbital margin with reference to the lacrimal caruncle.



Forty-nine orbits from 27 embalmed Korean cadavers were dissected. The lacrimal caruncle and facial midline were used as landmarks. The emerging points of the SON and STN in the supraorbital margin were determined.



The emerging points of the SON and STN were, respectively, located at 3.0 mm lateral and 3.3 mm medial to the vertical line through the apex of the lacrimal caruncle along the supraorbital margin. The horizontal distances from the facial midline to the emerging points of the SON and STN were 22.8 and 15.2 mm, respectively.



The optimum sites for achieving SON and STN block are, respectively, located approximately 3 mm lateral and 3 mm medial to the vertical line through the apex of lacrimal caruncle along the supraorbital margin. This knowledge will help the surgeon achieve an easy and accurate approach for regional nerve block.
Application Note AN-4147 Design Guidelines for RCD Snubber of Flyback Converters

This article presents some design guidelines for the RCD snubber of flyback converters. When the MOSFET turns off, a high-voltage spike occurs on the drain pin because of a resonance between the leakage inductor (Llk) of the main transformer and the output capacitor (COSS) of the MOSFET. The excessive voltage on the drain pin may lead to an avalanche breakdown and eventually damage the MOSFET. Therefore, it is necessary to add an additional circuit to clamp the voltage.
A hybrid parallel Delaunay image-to-mesh conversion algorithm scalable on distributed-memory clusters

In this paper, we present a scalable three dimensional hybrid MPI+Threads parallel Delaunay image-to-mesh conversion algorithm. A nested master-worker communication model for parallel mesh generation is implemented which simultaneously explores process-level parallelization and thread-level parallelization: inter-node communication using MPI and inter-core communication inside one node using threads. In order to overlap the communication (task request and data movement) and computation (parallel mesh refinement), the inter-node MPI communication and intra-node local mesh refinement is separated. The master thread that initializes the MPI environment is in charge of the inter-node MPI communication while the worker threads of each process are only responsible for the local mesh refinement within the node. We conducted a set of experiments to test the performance of the algorithm on Turing, a distributed memory cluster at Old Dominion University High Performance Computing Center and observed that the granularity of coarse level data decomposition, which affects the coarse level concurrency, has a significant influence on the performance of the algorithm. With the proper value of granularity, the algorithm expresses impressive performance potential and is scalable to 30 distributed memory compute nodes with 20 cores each (the maximum number of nodes available for us in the experiments).  Peer-review under responsibility of organizing committee of the 25th International Meshing Roundtable (IMR25).
Structured language modeling

This paper presents an attempt at using the syntactic structure in natural language for improved language models for speech recognition. The structured language model merges techniques in automatic parsing and language modeling using an original probabilistic parameterization of a shift-reduce parser. A maximum likelihood re-estimation procedure belonging to the class of expectation-maximization algorithms is employed for training the model. Experiments on theWall Street JournalandSwitchboardcorpora show improvement in both perplexity and word error rate—word lattice rescoring—over the standard 3-gram language model.
Story Generation from Sequence of Independent Short Descriptions

Existing Natural Language Generation (nlg) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity. Eective solutions or, at least evaluations of modern nlg paradigms for such creative tasks have been elusive, unfortunately. is paper introduces and addresses the task of coherent story generation from independent descriptions, describing a scene or an event. Towards this, we explore along two popular text-generation paradigms – (1) Statistical Machine Translation (smt), posing story generation as a translation problem and (2) Deep Learning, posing story generation as a sequence to sequence learning problem. In SMT, we chose two popular methods such as phrase based SMT (pb-SMT) and syntax based SMT (syntax-SMT) to ‘translate’ the incoherent input text into stories. We then implement a deep recurrent neural network (rnn) architecture that encodes sequence of variable length input descriptions to corresponding latent representations and decodes them to produce well formed comprehensive story like summaries. e ecacy of the suggested approaches is demonstrated on a publicly available dataset with the help of popular machine translation and summarization evaluation metrics. We believe, a system like ours has dierent interesting applicationsfor example, creating news articles from phrases of event information.
Schizotypy versus openness and intelligence as predictors of creativity

Schizophrenia-spectrum risk alleles may persist in the population, despite their reproductive costs in individuals with schizophrenia, through the possible creativity benefits of mild schizotypy in non-psychotic relatives. To assess this creativity-benefit model, we measured creativity (using 6 verbal and 8 drawing tasks), schizotypy, Big Five personality traits, and general intelligence in 225 University of New Mexico students. Multiple regression analyses showed that openness and intelligence, but not schizotypy, predicted reliable observer ratings of verbal and drawing creativity. Thus, the 'madness-creativity' link seems mediated by the personality trait of openness, and standard creativity-benefit models seem unlikely to explain schizophrenia's evolutionary persistence.
DR. CHECKER: A Soundy Analysis for Linux Kernel Drivers

While kernel drivers have long been know to poses huge security risks, due to their privileged access and lower code quality, bug-finding tools for drivers are still greatly lacking both in quantity and effectiveness. This is because the pointer-heavy code in these drivers present some of the hardest challenges to static analysis, and their tight coupling with the hardware make dynamic analysis infeasible in most cases. In this work, we present DR. CHECKER, a soundy (i.e., mostly sound) bug-finding tool for Linux kernel drivers that is based on well-known program analysis techniques. We are able to overcome many of the inherent limitations of static analysis by scoping our analysis to only the most bug-prone parts of the kernel (i.e., the drivers), and by only sacrificing soundness in very few cases to ensure that our technique is both scalable and precise. DR. CHECKER is a fully-automated static analysis tool capable of performing general bug finding using both pointer and taint analyses that are flow-sensitive, context-sensitive, and fieldsensitive on kernel drivers. To demonstrate the scalability and efficacy of DR. CHECKER, we analyzed the drivers of nine production Linux kernels (3.1 million LOC), where it correctly identified 158 critical zero-day bugs with an overall precision of 78%.