nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Personal Identification and Verification Using Fingerprints Associated with Multimode Biometrics Approach

This paper provides a background to fingerprint recognition, describes the biometric use of fingerprints, biometric standards and related security issues .and also discusses several Biometric scan technologies: finger-scan, facials can and retinal-scan. As accurate automatic personal identification is critical in a wide range of application domain such as ID cards, electronic commerce and automated banking and several other information repositories . Biometrics , which refers to automatic identification of persons based on his/her physiological or behavioral characteristics ,in inherently more reliable and more capable in differentiating between an authorized person and fraudulent imposter than traditional methods such as passwords and pin numbers. Automatic fingerprinting and other biometric aspects like face, voice, iris , etc are more reliable and secure and ways for identification and verification of person to claim the access to the information system. We have explored the fingerprint technique and also mentioned some other techniques which when accompanied with the fingerprint provides more powerful security to the system by granting identification to correct and verified access to the person by extracting some physical features from these physiological parts of the body to attain respective security traits (minutiae).
What¿s in a Like? Attitudes and behaviors around receiving Likes on Facebook

What social value do Likes on Facebook hold? This research examines peopleâs attitudes and behaviors related to receiving one-click feedback in social media. Likes and other kinds of lightweight affirmation serve as social cues of acceptance and maintain interpersonal relationships, but may mean different things to different people. Through surveys and de-identified, aggregated behavioral Facebook data, we find that in general, people care more about who Likes their posts than how many Likes they receive, desiring feedback most from close friends, romantic partners, and family members other than their parents. While most people do not feel strongly that receiving “enough” Likes is important, roughly two-thirds of posters regularly receive more than “enough.” We also note a “Like paradox,” a phenomenon in which peopleâs friends receive more Likes because their friends have more friends to provide those Likes. Individuals with lower levels of self-esteem and higher levels of self-monitoring are more likely to think that Likes are important and to feel bad if they do not receive “enough” Likes. The results inform product design and our understanding of how lightweight interactions shape our experiences online.
GroudSim: An Event-Based Simulation Framework for Computational Grids and Clouds

We present GroudSim, a Grid and Cloud simulation toolkit for scientific applications based on a scalable simulation-independent discrete-event core. GroudSim provides a comprehensive set of features for complex simulation scenarios from simple job executions on leased computing resources to calculation of costs, and background load on resources. Simulations can be parameterised and are easily extendable by probability distribution packages for failures which normally occur in complex environments. Experimental results demonstrate the improved scalability of GroudSim compared to a related process-based approach.
ViennaRNA Package 2.0

Secondary structure forms an important intermediate level of description of nucleic acids that encapsulates the dominating part of the folding energy, is often well conserved in evolution, and is routinely used as a basis to explain experimental findings. Based on carefully measured thermodynamic parameters, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties. The ViennaRNA Package has been a widely used compilation of RNA secondary structure related computer programs for nearly two decades. Major changes in the structure of the standard energy model, the Turner 2004 parameters, the pervasive use of multi-core CPUs, and an increasing number of algorithmic variants prompted a major technical overhaul of both the underlying RNAlib and the interactive user programs. New features include an expanded repertoire of tools to assess RNA-RNA interactions and restricted ensembles of structures, additional output information such as centroid structures and maximum expected accuracy structures derived from base pairing probabilities, or z-scores for locally stable secondary structures, and support for input in fasta format. Updates were implemented without compromising the computational efficiency of the core algorithms and ensuring compatibility with earlier versions. The ViennaRNA Package 2.0, supporting concurrent computations via OpenMP, can be downloaded from http://www.tbi.univie.ac.at/RNA .
Agile Load Transportation : Safe and Efficient Load Manipulation with Aerial Robots

In the past few decades, unmanned aerial vehicles (UAVs) have become promising mobile platforms capable of navigating semiautonomously or autonomously in uncertain environments. The level of autonomy and the flexible technology of these flying robots have rapidly evolved, making it possible to coordinate teams of UAVs in a wide spectrum of tasks. These applications include search and rescue missions; disaster relief operations, such as forest fires ; and environmental monitoring and surveillance. In some of these tasks, UAVs work in coordination with other robots, as in robot-assisted inspection at sea . Recently, radio-controlled UAVs carrying radiation sensors and video cameras were used to monitor, diagnose, and evaluate the situation at Japans Fukushima Daiichi nuclear plant facility .
The CMU METAL Farsi NLP Approach

While many high-quality tools are available for analyzing major languages such as English, equivalent freely-available tools for important but lower-resourced languages such as Farsi are more difficult to acquire and integrate into a useful NLP front end. We report here on an accurate and efficient Farsi analysis front end that we have assembled, which may be useful to others who wish to work with written Farsi. The pre-existing components and resources that we incorporated include the Carnegie Mellon TurboParser and TurboTagger (Martins et al., 2010) trained on the Dadegan Treebank (Rasooli et al., 2013), the Uppsala Farsi text normalizer PrePer (Seraji, 2013), the Uppsala Farsi tokenizer (Seraji et al., 2012a), and Jon Dehdari’s PerStem (Jadidinejad et al., 2010). This set of tools (combined with additional normalization and tokenization modules that we have developed and made available) achieves a dependency parsing labeled attachment score of 89.49%, unlabeled attachment score of 92.19%, and label accuracy score of 91.38% on a held-out parsing test data set. All of the components and resources used are freely available. In addition to describing the components and resources, we also explain the rationale for our choices.
GreenDroid: Automated Diagnosis of Energy Inefficiency for Smartphone Applications

Smartphone applications' energy efficiency is vital, but many Android applications suffer from serious energy inefficiency problems. Locating these problems is labor-intensive and automated diagnosis is highly desirable. However, a key challenge is the lack of a decidable criterion that facilitates automated judgment of such energy problems. Our work aims to address this challenge. We conducted an in-depth study of 173 open-source and 229 commercial Android applications, and observed two common causes of energy problems: missing deactivation of sensors or wake locks, and cost-ineffective use of sensory data. With these findings, wepropose an automated approach to diagnosing energy problems in Android applications. Our approach explores an application's state space by systematically executing the application using Java PathFinder (JPF). It monitors sensor and wake lock operations to detect missing deactivation of sensors and wake locks. It also tracks the transformation and usage of sensory data and judges whether they are effectively utilized by the application using our state-sensitive data utilization metric. In this way, our approach can generate detailed reports with actionable information to assist developers in validating detected energy problems. We built our approach as a tool, GreenDroid, on top of JPF. Technically, we addressed the challenges of generating user interaction events and scheduling event handlers in extending JPF for analyzing Android applications. We evaluated GreenDroid using 13 real-world popular Android applications. GreenDroid completed energy efficiency diagnosis for these applications in a few minutes. It successfully located real energy problems in these applications, and additionally found new unreported energy problems that were later confirmed by developers.
Ontology evolution: a process-centric survey

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.
A multidimensional approach for detecting irony in Twitter

Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social, the problem of irony detection will become even more pressing. We describe here a set of textual features for recognizing irony at a linguistic level, especially in short texts created via social media such as Twitter postings or ‘‘tweets’’. Our experiments concern four freely available data sets that were retrieved from Twitter using content words (e.g. ‘‘Toyota’’) and user-generated tags (e.g. ‘‘#irony’’). We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making.
A Rule-Based Classification Algorithm for Uncertain Data

Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, outdated sources and sampling errors. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, we propose a new rule-based classification and prediction algorithm called uRule for classifying uncertain data. This algorithm introduces new measures for generating, pruning and optimizing rules. These new measures are computed considering uncertain data interval and probability distribution function. Based on the new measures, the optimal splitting attribute and splitting value can be identified and used for classification and prediction. The proposed uRule algorithm can process uncertainty in both numerical and categorical data. Our experimental results show that uRule has excellent performance even when data is highly uncertain.
Pano2CAD: Room Layout from a Single Panorama Image

This paper presents a method of estimating the geometry of a room and the 3D pose of objects from a single 360 panorama image. Assuming ManhattanWorld geometry, we formulate the task as an inference problem in which we estimate positions and orientations of walls and objects. The method combines surface normal estimation, 2D object detection and 3D object pose estimation. Quantitative results are presented on a dataset of synthetically generated 3D rooms containing objects, as well as on a subset of handlabeled images from the public SUN360 dataset.
Study of IC aging on ring oscillator physical unclonable functions

Silicon Physical Unclonable Functions (PUF) are novel circuits that exploit the random variations that exist in CMOS manufacturing process to generate chip-unique random bits. As their applications are security based, it is highly desired to have PUF responses that are very stable against the reliability issues that exist in Integrated Circuits (IC). One of the major sources of unreliability in the technology nodes 90nm and below is device aging. Aging is primarily due to phenomena like Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI). In this work, we study the effect of device aging on the stability of Ring Oscillator PUFs for different PUF circuit-level choices and operating conditions. We observe that most of the PUF's aging instability happens early in its lifetime. Due to the typical differential nature of PUF structures, stability does not change significantly with age. Further, a high correlation has been observed between instability that is caused due to aging and instability that is caused due to temperature. In various RO-PUF setups and operating conditions, we observe that around 4% of the PUF bits are prone to instability due to aging.
Dual-Path Convolutional Image-Text Embedding

This paper considers the task of matching images and sentences. The challenge consists in discriminatively embedding the two modalities onto a shared visual-textual space. Existing work in this field largely uses Recurrent Neural Networks (RNN) for text feature learning and employs off-the-shelf Convolutional Neural Networks (CNN) for image feature extraction. Our system, in comparison, differs in two key aspects. Firstly, we build a convolutional network amenable for fine-tuning the visual and textual representations, where the entire network only contains four components, i.e., convolution layer, pooling layer, rectified linear unit function (ReLU), and batch normalisation. Endto-end learning allows the system to directly learn from the data and fully utilise the supervisions. Secondly, we propose instance loss according to viewing each multimodal data pair as a class. This works with a large margin objective to learn the inter-modal correspondence between images and their textual descriptions. Experiments on two generic retrieval datasets (Flickr30k and MSCOCO) demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language person retrieval, we improve the state of the art by a large margin. Code is available at https://github.com/layumi/ Image-Text-Embedding
Optimizing N-dimensional, winograd-based convolution for manycore CPUs

Recent work on Winograd-based convolution allows for a great reduction of computational complexity, but existing implementations are limited to 2D data and a single kernel size of 3 by 3. They can achieve only slightly better, and often worse performance than better optimized, direct convolution implementations. We propose and implement an algorithm for N-dimensional Winograd-based convolution that allows arbitrary kernel sizes and is optimized for manycore CPUs. Our algorithm achieves high hardware utilization through a series of optimizations. Our experiments show that on modern ConvNets, our optimized implementation, is on average more than 3 x, and sometimes 8 x faster than other state-of-the-art CPU implementations on an Intel Xeon Phi manycore processors. Moreover, our implementation on the Xeon Phi achieves competitive performance for 2D ConvNets and superior performance for 3D ConvNets, compared with the best GPU implementations.
Decentralized Access Control with Anonymous Authentication of Data Stored in Clouds

We propose a new decentralized access control scheme for secure data storage in clouds that supports anonymous authentication. In the proposed scheme, the cloud verifies the authenticity of the series without knowing the user's identity before storing data. Our scheme also has the added feature of access control in which only valid users are able to decrypt the stored information. The scheme prevents replay attacks and supports creation, modification, and reading data stored in the cloud. We also address user revocation. Moreover, our authentication and access control scheme is decentralized and robust, unlike other access control schemes designed for clouds which are centralized. The communication, computation, and storage overheads are comparable to centralized approaches.
A Recipe for Soft Fluidic Elastomer Robots

This work provides approaches to designing and fabricating soft fluidic elastomer robots. That is, three viable actuator morphologies composed entirely from soft silicone rubber are explored, and these morphologies are differentiated by their internal channel structure, namely, ribbed, cylindrical, and pleated. Additionally, three distinct casting-based fabrication processes are explored: lamination-based casting, retractable-pin-based casting, and lost-wax-based casting. Furthermore, two ways of fabricating a multiple DOF robot are explored: casting the complete robot as a whole and casting single degree of freedom (DOF) segments with subsequent concatenation. We experimentally validate each soft actuator morphology and fabrication process by creating multiple physical soft robot prototypes.
A Blockchain Based New Secure Multi-Layer Network Model for Internet of Things

This paper presents a multi-layer secure IoT network model based on blockchain technology. The model reduces the difficulty of actual deployment of the blockchain technology by dividing the Internet of Things into a multi-level de-centric network and adopting the technology of block chain technology at all levels of the network, with the high security and credibility assurance of the blockchain technology retaining. It provides a wide-area networking solution of Internet of Things.
Trajectory planning for stair climbing in the leg-wheel hybrid mobile robot quattroped

The algorithm of trajectory planning and four leg coordination for quasi-static stair climbing in a quadruped robot is reported. The development is based on the geometrical interactions between the robot legs and the stair, starting from single-leg analysis, followed by two-leg collaboration, and then four-leg coordination. A brief study on stability of the robot is included. In addition, the first-step and last-step algorithm which deals with the transient between the flat ground and the stair is reported as well. Finally, simulation and experimental validation are performed to evaluate the performance of the algorithm.
Medium Access Control protocols for ad hoc wireless networks: A survey

Studies of ad hoc wireless networks are a relatively new field gaining more popularity for various new applications. In these networks, the Medium Access Control (MAC) protocols are responsible for coordinating the access from active nodes. These protocols are of significant importance since the wireless communication channel is inherently prone to errors and unique problems such as the hidden-terminal problem, the exposedterminal problem, and signal fading effects. Although a lot of research has been conducted on MAC protocols, the various issues involved have mostly been presented in isolation of each other. We therefore make an attempt to present a comprehensive survey of major schemes, integrating various related issues and challenges with a view to providing a big-picture outlook to this vast area. We present a classification of MAC protocols and their brief description, based on their operating principles and underlying features. In conclusion, we present a brief summary of key ideas and a general direction for future work.
Text Categorization

Text categorization (also known as text classification, or topic spotting) is the task of automatically sorting a set of documents into categories from a predefined set. This task has several applications, including automated indexing of scientific articles according to predefined thesauri of technical terms, filing patents into patent directories, selective dissemination of information to information consumers, automated population of hierarchical catalogues of Web resources, spam filtering, identification of document genre, authorship attribution, survey coding, and even automated essay grading. Automated text classification is attractive because it frees organizations from the need of manually organizing document bases, which can be too expensive, or simply infeasible given the time constraints of the application or the number of documents involved. The accuracy of modern text classification systems rivals that of trained human professionals, thanks to a combination of information retrieval (IR) technology and machine learning (ML) technology. This will outline the fundamental traits of the technologies involved, of the applications that can feasibly be tackled through text classification, and of the tools and resources that are available to the researcher and developer wishing to take up these technologies for deploying real-world applications.
Probabilistic Models for Learning a Semantic Parser Lexicon

We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new application domain and the quality of the learned lexicon significantly affects both the accuracy and efficiency of the final semantic parser. Existing work on lexicon learning has focused on heuristic methods that lack convergence guarantees and require significant human input in the form of lexicon templates or annotated logical forms. In contrast, our probabilistic models are trained directly from question/answer pairs using EM and our simplest model has a concave objective that guarantees convergence to a global optimum. An experimental evaluation on a set of 4th grade science questions demonstrates that our models improve semantic parser accuracy (35-70% error reduction) and efficiency (4-25x more sentences per second) relative to prior work despite using less human input. Our models also obtain competitive results on GEO880 without any datasetspecific engineering.
Psychological sex differences. Origins through sexual selection.

Men and women clearly differ in some psychological domains. A. H. Eagly (1995) shows that these differences are not artifactual or unstable. Ideally, the next scientific step is to develop a cogent explanatory framework for understanding why the sexes differ in some psychological domains and not in others and for generating accurate predictions about sex differences as yet undiscovered. This article offers a brief outline of an explanatory framework for psychological sex differences--one that is anchored in the new theoretical paradigm of evolutionary psychology. Men and women differ, in this view, in domains in which they have faced different adaptive problems over human evolutionary history. In all other domains, the sexes are predicted to be psychologically similar. Evolutionary psychology jettisons the false dichotomy between biology and environment and provides a powerful metatheory of why sex differences exist, where they exist, and in what contexts they are expressed (D. M. Buss, 1995).