nils-herrmann/clean_scirepeval huggingface dataset:
Structural Aging: The Facial Recurve Concept

Cutaneous facial aging is responsible for the increasingly wrinkled and blotchy appearance of the skin, whereas aging of the facial structures is attributed primarily to gravity. This article purports to show, however, that the primary etiology of structural facial aging relates instead to repeated contractions of certain facial mimetic muscles, the age marker fascicules, whereas gravity only secondarily abets an aging process begun by these muscle contractions. Magnetic resonance imaging (MRI) has allowed us to study the contrasts in the contour of the facial mimetic muscles and their associated deep and superficial fat pads in patients of different ages. The MRI model shows that the facial mimetic muscles in youth have a curvilinear contour presenting an anterior surface convexity. This curve reflects an underlying fat pad lying deep to these muscles, which acts as an effective mechanical sliding plane. The muscle’s anterior surface convexity constitutes the key evidence supporting the authors’ new aging theory. It is this youthful convexity that dictates a specific characteristic to the muscle contractions conveyed outwardly as youthful facial expression, a specificity of both direction and amplitude of facial mimetic movement. With age, the facial mimetic muscles (specifically, the age marker fascicules), as seen on MRI, gradually straighten and shorten. The authors relate this radiologic end point to multiple repeated muscle contractions over years that both expel underlying deep fat from beneath the muscle plane and increase the muscle resting tone. Hence, over time, structural aging becomes more evident as the facial appearance becomes more rigid.
Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’

Computation underlies the organization of cells into higher-order structures, for example during development or the spatial association of bacteria in a biofilm. Each cell performs a simple computational operation, but when combined with cell–cell communication, intricate patterns emerge. Here we study this process by combining a simple genetic circuit with quorum sensing to produce more complex computations in space. We construct a simple NOR logic gate in Escherichia coli by arranging two tandem promoters that function as inputs to drive the transcription of a repressor. The repressor inactivates a promoter that serves as the output. Individual colonies of E. coli carry the same NOR gate, but the inputs and outputs are wired to different orthogonal quorum-sensing ‘sender’ and ‘receiver’ devices. The quorum molecules form the wires between gates. By arranging the colonies in different spatial configurations, all possible two-input gates are produced, including the difficult XOR and EQUALS functions. The response is strong and robust, with 5- to >300-fold changes between the ‘on’ and ‘off’ states. This work helps elucidate the design rules by which simple logic can be harnessed to produce diverse and complex calculations by rewiring communication between cells.
High-Order Terminal Sliding-Mode Observer for Parameter Estimation of a Permanent-Magnet Synchronous Motor

This paper proposes a terminal sliding-mode (TSM) observer for estimating the immeasurable mechanical parameters of permanent-magnet synchronous motors (PMSMs) used for complex mechanical systems. The observer can track the system states in finite time with high steady-state precision. A TSM control strategy is designed to guarantee the global finite-time stability of the observer and, meanwhile, to estimate the mechanical parameters of the PMSM. A novel second-order sliding-mode algorithm is designed to soften the switching control signal of the observer. The effect of the equivalent low-pass filter can be properly controlled in the algorithm based on requirements. The smooth signal of the TSM observer is directly used for the parameter estimation. The experimental results in a practical CNC machine tool are provided to demonstrate the effectiveness of the proposed method.
The centrality of pivotal points in the evolution of scientific networks

In this paper, we describe the development of CiteSpace as an integrated environment for identifying and tracking thematic trends in scientific literature. The goal is to simplify the process of finding not only highly cited clusters of scientific articles, but also pivotal points and trails that are likely to characterize fundamental transitions of a knowledge domain as a whole. The trails of an advancing research field are captured through a sequence of snapshots of its intellectual structure over time in the form of Pathfinder networks. These networks are subsequently merged with a localized pruning algorithm. Pivotal points in the merged network are algorithmically identified and visualized using the betweenness centrality metric. An example of finding clinical evidence associated with reducing risks of heart diseases is included to illustrate how CiteSpace could be used. The contribution of the work is its integration of various change detection algorithms and interactive visualization capabilities to simply users' tasks.
JFTL: A flash translation layer based on a journal remapping for flash memory

In flash memory-based storage, a Flash Translation Layer (FTL) manages the mapping between the logical addresses of a file system and the physical addresses of the flash memory. When a journaling file system is set up on the FTL, the consistency of the file system is guaranteed by duplications of the same file system changes in both the journal region of the file system and the home locations of the changes. However, these duplications inevitably degrade the performance of the file system. In this article we present an efficient FTL, called JFTL, based on a journal remapping technique. The FTL uses an address mapping method to write all the data to a new region in a process known as an out-of-place update. Because of this process, the existing data in flash memory is not overwritten by such an update. By using this characteristic of the FTL, the JFTL remaps addresses of the logged file system changes to addresses of the home locations of the changes, instead of writing the changes once more to flash memory. Thus, the JFTL efficiently eliminates redundant data in the flash memory as well as preserving the consistency of the journaling file system. Our experiments confirm that, when associated with a writeback or ordered mode of a conventional EXT3 file system, the JFTL enhances the performance of EXT3 by up to 20%. Furthermore, when the JFTL operates with a journaled mode of EXT3, there is almost a twofold performance gain in many cases. Moreover, the recovery performance of the JFTL is much better than that of the FTL.
Eye-tracking patterns in schizophrenia.

A significant number of schizophrenic patients show patterns of smooth pursuit eye-tracking patterns that differ strikingly from the generally smooth eye-tracking seen in normals and in nonschizophrenic patients. These deviations are probably referable not only to motivational or attentional factors, but also to oculomotor involvement that may have a critical relevance for perceptual dysfunction in schizophrenia.
Securing Sensor to Cloud Ecosystem using Internet of Things (IoT) Security Framework

The Internet of things (IoT) refers to every object, which is connected over a network with the ability to transfer data. Users perceive this interaction and connection as useful in their daily life. However any improperly designed and configured technology will exposed to security threats. Therefore an ecosystem for IoT should be designed with security embedded in each layer of its ecosystem. This paper will discussed the security threats to IoT and then proposed an IoT Security Framework to mitigate it. Then IoT Security Framework will be used to develop a Secure IoT Sensor to Cloud Ecosystem.
The Psychology of Curiosity : A Review and Reinterpretation

Research on curiosity has undergone 2 waves of intense activity. The 1st, in the 1960s, focused mainly on curiosity's psychological underpinnings. The 2nd, in the 1970s and 1980s, was characterized by attempts to measure curiosity and assess its dimensionality. This article reviews these contributions with a concentration on the 1st wave. It is argued that theoretical accounts of curiosity proposed during the 1st period fell short in 2 areas: They did not offer an adequate explanation for why people voluntarily seek out curiosity, and they failed to delineate situational determinants of curiosity. Furthermore, these accounts did not draw attention to, and thus did not explain, certain salient characteristics of curiosity: its intensity, transience, association with impulsivity, and tendency to disappoint when satisfied. A new account of curiosity is offered that attempts to address these shortcomings. The new account interprets curiosity as a form of cognitively induced deprivation that arises from the perception of a gap in knowledge or understanding.
Harvesting Implicit Group Attitudes and Beliefs From a Demonstration Web Site

Respondents at an Internet site completed over 600,000 tasks between October 1998 and April 2000 measuring attitudes toward and stereotypes of social groups. Their responses demonstrated, on average, implicit preference for White over Black and young over old and stereotypic associations linking male terms with science and career and female terms with liberal arts and family. The main purpose was to provide a demonstration site at which respondents could experience their implicit attitudes and stereotypes toward social groups. Nevertheless, the data collected are rich in information regarding the operation of attitudes and stereotypes, most notably the strength of implicit attitudes, the association and dissociation between implicit and explicit attitudes, and the effects of group membership on attitudes and stereotypes.
Electronic Banking Channels and Task-Channel Fit

The increase in electronically mediated self-servic e technologies in the banking industry has impacted on the way banks service consumers. Despit e a large body of research on electronic banking channels, no study has been undertaken to e xplor the fit between electronic banking channels and banking tasks. Nor has there been rese a ch into how the ‘task-channel fit’ and other factors impact on consumers’ intention to use elect ronic banking channels. This paper proposes a theoretical model addressing these gaps. An explora tory study was first conducted, investigating industry experts’ perceptions towards the concept o f ‘task-channel fit’ and its relationship to other electronic banking channel variables. The findings demonstrated that the concept was perceived as being highly relevant by bank managers. A resear ch model was then developed drawing on the existing literature. To evaluate the research mode l quantitatively, a survey will be developed and validated, administered to a sample of consumers, a nd the resulting data used to test both measurement and structural aspects of the research model.
A survey on attack detection on cloud using supervised learning techniques

Cloud computing has brought a revolution in the field of computing. Apart from the merits of cloud computing, it possesses several demerits also. There are several types of attacks on cloud. All the attacks are focused on a particular layer of cloud architecture. Cloud computing architecture composed of three layers Infrastructure as a service (IaaS), platform as a service (PaaS) and Application as a service (AaaS). If Iaas is vulnerable then all above layers can't be secure. The principal concern of security in Iaas is Virtualization. There are several attacks on virtualization in IaaS layer like attack on VM image sharing. VM isolation violation, insecure VM migration and VM escape. In this paper all such attacks are studied and the solutions are also discussed.
Distributed Optimization Algorithms for Wide-Area Oscillation Monitoring in Power Systems Using Interregional PMU-PDC Architectures

In this paper, we present a set of distributed algorithms for estimating the electro-mechanical oscillation modes of large power system networks using synchrophasors. With the number of phasor measurement units (PMUs) in the North American grid scaling up to the thousands, system operators are gradually inclining toward distributed cyber-physical architectures for executing wide-area monitoring and control operations. Traditional centralized approaches, in fact, are anticipated to become untenable soon due to various factors such as data volume, security, communication overhead, and failure to adhere to real-time deadlines. To address this challenge, we propose three different communication and computational architectures by which estimators located at the control centers of various utility companies can run local optimization algorithms using local PMU data, and thereafter communicate with other estimators to reach a global solution. Both synchronous and asynchronous communications are considered. Each architecture integrates a centralized Prony-based algorithm with several variants of alternating direction method of multipliers (ADMM). We discuss the relative advantages and bottlenecks of each architecture using simulations of IEEE 68-bus and IEEE 145-bus power system, as well as an Exo-GENI-based software defined network.
Proposal and Evaluation of Serendipitous Recommendation Method Using General Unexpectedness

Recommender systems support users in selecting items and services in an information-rich environment. Although recommender systems have been improved in terms of accuracy, such systems are still insufficient in terms of novelty and serendipity, giving unsatisfactory results to users. Two methods of “serendipitous recommendation” are therefore proposed. However, a method for recommending serendipitous items accurately to users does not yet exist, because what kinds of items are serendipitous is not clearly defined. Accordingly, a human preference model of serendipitous items based on actual data concerning a user’s impression collected by questionnaires was devised. Two serendipitous recommendation methods based on the model were devised and evaluated according to a user’s actual impression. The evaluation results show that one of these recommendation methods, the one using general unexpectedness independent of user profiles, can recommend the serendipitous items accurately.
Correcting robot mistakes in real time using EEG signals

Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink Robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction.
Collective Classification via Discriminative Matrix Factorization on Sparsely Labeled Networks

We address the problem of classifying sparsely labeled networks, where labeled nodes in the network are extremely scarce. Existing algorithms, such as collective classification, have been shown to be effective for jointly deriving labels of related nodes, by exploiting class label dependencies among neighboring nodes. However, when the underlying network is sparsely labeled, most nodes have too few or even no connections to labeled nodes. This makes it very difficult to leverage supervised knowledge from labeled nodes to accurately estimate label dependencies, thereby largely degrading the classification accuracy. In this paper, we propose a novel discriminative matrix factorization (DMF) based algorithm that effectively learns a latent network representation by exploiting topological paths between labeled and unlabeled nodes, in addition to nodes' content information. The main idea is to use matrix factorization to obtain a compact representation of the network that fully encodes nodes' content information and network structure, and unleash discriminative power inferred from labeled nodes to directly benefit collective classification. To achieve this, we formulate a new matrix factorization objective function that integrates network representation learning with an empirical loss minimization for classifying node labels. An efficient optimization algorithm based on conjugate gradient methods is proposed to solve the new objective function. Experimental results on real-world networks show that DMF yields superior performance gain over the state-of-the-art baselines on sparsely labeled networks.
Representing and reasoning about preferences in requirements engineering

The priorities that stakeholders associate with requirements may vary from stakeholder to stakeholder and from one situation to the next. Differing priorities, in turn, imply different design decisions for the system to be. While elicitation of requirement priorities is a well-studied activity, modeling and reasoning with prioritization has not enjoyed equal attention. In this paper, we address this problem by extending a state-of-the-art goal modeling notation to support the representation of preference (“nice-to-have”) requirements. In our extension, preference goals are distinguished from mandatory ones. Then, quantitative prioritizations of the former are constructed and used as criteria for evaluating alternative ways to achieve the latter. To generate solutions, an existing preference-based planner is utilized to efficiently search for alternatives that best satisfy a given set of mandatory and preferred requirements. With such a planning tool, analysts can acquire a better understanding of the impact of high-level stakeholder preferences on low-level design decisions.
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation

An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the former teaches the summarization model how to look for salient questioning-worthy details, and the latter teaches the model how to rewrite a summary which is a directed-logical subset of the input document. We also propose novel multitask architectures with high-level (semantic) layer-specific sharing across multiple encoder and decoder layers of the three tasks, as well as soft-sharing mechanisms (and show performance ablations and analysis examples of each contribution). Overall, we achieve statistically significant improvements over the state-ofthe-art on both the CNN/DailyMail and Gigaword datasets, as well as on the DUC2002 transfer setup. We also present several quantitative and qualitative analysis studies of our model’s learned saliency and entailment skills.
Empowering the Internet of Things with Software Defined Networking [ WHITE PAPER ]

The flexibility and general programmability offered by the Software Defined Networking (SDN) technology has supposed a disruption in the evolution of the network. It offers enormous benefits to network control and opens new ways of communication by defining powerful but simple switching elements (forwarders) that can use any single field of a packet or message to determine the outgoing port to which it will be forwarded. Such benefits can be applied to the Internet of Things (IoT) and thus resolve some of the main challenges it exposes, such as the ability to let devices connected to heterogeneous networks to communicate each other. In the present document we describe a general model to integrate SDN and IoT so that heterogeneous communications are achieved. However, it exposes other (simpler) challenges must be resolved, evaluated, and validated against current and future solutions before the design of the integrated approach can be finished.
Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013

In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.
The enhancement of gate-induced-drain-leakage (GIDL) current in short-channel SOI MOSFET and its application in measuring lateral bipolar current gain beta

An off-state leakage current unique for short-channel SOI MOSFETs is reported. This off-state leakage is the amplification of gate-induced-drain-leakage current by the lateral bipolar transistor in an SOI device due to the floating body. The leakage current can be enhanced by as much as 100 times for 1/4 mu m SOI devices. This can pose severe constraints in future 0.1 mu m SOI device design. A novel technique was developed based on this mechanism to measure the lateral bipolar transistor current gain beta of SOI devices without using a body contact.<<ETX>>
Zadoff–Chu Sequence Design for Random Access Initial Uplink Synchronization in LTE-Like Systems

A Zadoff–Chu (ZC) sequence is uncorrelated with a non-zero cyclically shifted version of itself. However, this alone is insufficient to mitigate inter-code interference in LTE initial uplink synchronization. The performance of the state-of-the-art algorithms vary widely depending on the specific ZC sequences employed. We develop a systematic procedure to choose the ZC sequences that yield the optimum performance. It turns out that the procedure for ZC code selection in LTE standard is suboptimal when the carrier frequency offset is not small.