nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge Graph Embedding for Link Prediction and Triplet Classification

The link prediction (LP) and triplet classification (TC) are important tasks in the field of knowledge graph mining. However, the traditional link prediction methods of social networks cannot directly apply to knowledge graph data which contains multiple relations. In this paper, we apply the knowledge graph embedding method to solve the specific tasks with Chinese knowledge base Zhishi.me. The proposed method has been successfully used in the evaluation task of CCKS2016. Hopefully, it can achieve excellent performance.
Deep predictive policy training using reinforcement learning

Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small subnetwork with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.
Sensor Based PUF IoT Authentication Model for a Smart Home with Private Blockchain

With ubiquitous adoption of connected sensors, actuators and smart devices are finding inroads into daily life. Internet of Things (IoT) authentication is rapidly transforming from classical cryptographic user-centric knowledge based approaches to device signature based automated methodologies to corroborate identity between claimant and a verifier. Physical Unclonable Function (PUF) based IoT authentication mechanisms are gaining widespread interest as users are required to access IoT devices in real time while also expecting execution of sensitive (even physical) IoT actions immediately. This paper, delineates combination of BlockChain and Sensor based PUF authentication mechanism for solving real-time but non-repudiable access to IoT devices in a Smart Home by utilizing a mining less consensus mechanism for the provision of immutable assurance to users' and IoT devices' transactions i.e. commands, status alerts, actions etc.
Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares

Array processing is widely used in sensing applications for estimating the locations and waveforms of the sources in a given field. In the absence of a large number of snapshots, which is the case in numerous practical applications, such as underwater array processing, it becomes challenging to estimate the source parameters accurately. This paper presents a nonparametric and hyperparameter, free-weighted, least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES) in array processing. IAA-APES can work well with few snapshots (even one), uncorrelated, partially correlated, and coherent sources, and arbitrary array geometries. IAA-APES is extended to give sparse results via a model-order selection tool, the Bayesian information criterion (BIC). Moreover, it is shown that further improvements in resolution and accuracy can be achieved by applying the parametric relaxation-based cyclic approach (RELAX) to refine the IAA-APES&BIC estimates if desired. IAA-APES can also be applied to active sensing applications, including single-input single-output (SISO) radar/sonar range-Doppler imaging and multi-input single-output (MISO) channel estimation for communications. Simulation results are presented to evaluate the performance of IAA-APES for all of these applications, and IAA-APES is shown to outperform a number of existing approaches.
Organoids as an in vitro model of human development and disease

The in vitro organoid model is a major technological breakthrough that has already been established as an essential tool in many basic biology and clinical applications. This near-physiological 3D model facilitates an accurate study of a range of in vivo biological processes including tissue renewal, stem cell/niche functions and tissue responses to drugs, mutation or damage. In this Review, we discuss the current achievements, challenges and potential applications of this technique.
ioCane: A Smart-Phone and Sensor-Augmented Mobility Aid for the Blind

We present the design, implementation, and early results of ioCane, a mobility aid for blind cane users that uses detachable cane-mounted ultrasonic sensors connected to a circuit board to send contextual data wirelessly to an Android phone application. The system uses the built-in mobile phone modalities of vibrations and chimes to alert the user to object height and proximity. We believe this plug-and-play solution for visually impaired users has the potential to enhance user mobility and object avoidance with a minimal learning curve. A pilot study testing the performance of the ioCane with blind cane users showed a 47.3% improvement in obstacle avoidance. To our knowledge, the ioCane is the first sensor-based mobility assistance system to integrate natively with a mobile phone without any modifications to the phone or the system, as well as the first system of its kind to be evaluated by actual visually-impaired cane users.
Indoor Localization Using Camera Phones

Indoor localization has long been a goal of pervasive computing research. In this paper, we explore the possibility of determining user's location based on the camera images received from a smart phone. In our system, the smart phone is worn by the user as a pendant and images are periodically captured and transmitted over GPRS to a web server. The web server returns the location of the user by comparing the received images with images stored in a database. We tested our system inside the Computer Science department building. Preliminary results show that user's location can be determined correctly with more than 80% probability of success. As opposed to earlier solutions for indoor localization, this approach does not have any infrastructure requirements. The only cost is that of building an image database.
Hexahedral Meshing With Varying Element Sizes

Hexahedral (or Hex-) meshes are preferred in a number of scientific and engineering simulations and analyses due to their desired numerical properties. Recent state-of-the-art techniques can generate high quality hex-meshes. However, they typically produce hex-meshes with uniform element sizes and thus may fail to preserve small scale features on the boundary surface. In this work, we present a new framework that enables users to generate hex-meshes with varying element sizes so that small features will be filled with smaller and denser elements, while the transition from smaller elements to larger ones is smooth, compared to the octree-based approach. This is achieved by first detecting regions of interest (ROI) of small scale features. These ROIs are then magnified using the as-rigid-as-possible (ARAP) deformation with either an automatically determined or a user-specified scale factor. A hex-mesh is then generated from the deformed mesh using existing approaches that produce hex-meshes with uniform-sized elements. This initial hex-mesh is then mapped back to the original volume before magnification to adjust the element sizes in those ROIs. We have applied this framework to a variety of man-made and natural models to demonstrate its effectiveness.
Soft Tissue Modeling Techniques in Surgery Simulation

Modeling and simulating the live tissues is a very complex and challenging process as it requires a huge amount of hardware power and advanced algorithms to be run on real time. There are number of articles, books and research papers which describe the properties of the different anatomy structures. This tissue modeling quality differs ranging from surgery types: neurosurgery, heart surgery, abdominal surgery, plastic surgery, minimal invasive surgery etc. Deformation accuracy and the computation time are the two significant constraints in soft tissue modeling for surgery simulation. Thus it has identified in the surgery domain the required surgery types can be categorized under three major heading which are surgery panning, Surgery training and scientific analyzing. Here with this paper it is intend to compare and contrast the soft tissue modeling techniques and categorized them according to the surgery types. Keywords-component; Soft tissue modelling, Surgery types, Accuracy, Efficiency
Impact of timing and frequency offsets on multicarrier waveform candidates for 5G

This paper presents a study of the candidate waveforms for 5G when they are subject to timing and carrier frequency offset. These waveforms are: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC), circular filter bank multicarrier (C-FBMC), and linear filter bank multicarrier (FBMC). We are particularly interested in multiple access interference (MAI) when a number of users transmit their signals to a base station in an asynchronous or a quasi-synchronous manner. We identify the source of MAI in these waveforms and present some numerical analysis that confirm our findings. The goal of this study is to answer the following question, “Which one of the 5G candidate waveforms has more relaxed synchronization requirements?”.
NLSR: named-data link state routing protocol

This paper presents the design of the Named-data Link State Routing protocol (NLSR), a routing protocol for Named Data Networking (NDN). Since NDN uses names to identify and retrieve data, NLSR propagates reachability to name prefixes instead of IP prefixes. Moreover, NLSR differs from IP-based link-state routing protocols in two fundamental ways. First, NLSR uses Interest/Data packets to disseminate routing updates, directly benefiting from NDN's data authenticity. Second, NLSR produces a list of ranked forwarding options for each name prefix to facilitate NDN's adaptive forwarding strategies. In this paper we discuss NLSR's main design choices on (1) a hierarchical naming scheme for routers, keys, and routing updates, (2) a hierarchical trust model for routing within a single administrative domain, (3) a hop-by-hop synchronization protocol to replace the traditional network-wide flooding for routing update dissemination, and (4) a simple way to rank multiple forwarding options. Compared with IP-based link state routing, NLSR offers more efficient update dissemination, built-in update authentication, and native support of multipath forwarding.
Design of Distributed Voting Systems

Countries like Estonia, Norway or Australia developed elec tronic voting systems, which could be used to realize parliamentary elections with the he lp of personal computers and the Internet. These systems are completely different in the ir design and their way to solve the same problem. In this thesis, we analyze some of the largest real-world sys tem , describe their building blocks and their general design to focus on possible problem s in these electronic voting systems. Furthermore, we present a template for an e-voting system, w hich we designed to try to fulfill the preliminaries and requirements of a secure elect ronic voting system. We use the experiences and the building blocks of existing systems to combine them to another more secure system. Afterwards, we compare our concept with real-world systems to evaluate the fulfillments of the requirements. Conclusivel y, we discuss the occurring problems when designing a secure system. Peer-to-peer networks provide many advantages, like decen tralization, which might be applicable to electronic voting systems. Therefore, we taklook on the distributed database called blockchain1 and the usage in a peer-to-peer voting system. Our contribution to this topic is a modification of the proofof-stake, which enables the usage of common devices, like smartphones or tablets, for th e blockchain verification and inclusion of new ballots to the chain. This proof does not nee d much computing power and has a lower carbon footprint than the proof-of-work in th e Bitcoin protocol. 1The blockchain is a distributed database, which was first int roduced with the Bitcoin protocol.
Learning Dense Correspondence via 3D-Guided Cycle Consistency

Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or impossible to obtain? This paper tackles one such problem: establishing dense visual correspondence across different object instances. For this task, although we do not know what the ground-truth is, we know it should be consistent across instances of that category. We exploit this consistency as a supervisory signal to train a convolutional neural network to predict cross-instance correspondences between pairs of images depicting objects of the same category. For each pair of training images we find an appropriate 3D CAD model and render two synthetic views to link in with the pair, establishing a correspondence flow 4-cycle. We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and real-to-synthetic correspondences that are cycle-consistent with the ground-truth. At test time, no CAD models are required. We demonstrate that our end-to-end trained ConvNet supervised by cycle-consistency outperforms state-of-the-art pairwise matching methods in correspondence-related tasks.
Resilient overlay networks

A Resilient Overlay Network (RON) is an architecture that allows distributed Internet applications to detect and recover from path outages and periods of degraded performance within several seconds, improving over today's wide-area routing protocols that take at least several minutes to recover. A RON is an application-layer overlay on top of the existing Internet routing substrate. The RON nodes monitor the functioning and quality of the Internet paths among themselves, and use this information to decide whether to route packets directly over the Internet or by way of other RON nodes, optimizing application-specific routing metrics.Results from two sets of measurements of a working RON deployed at sites scattered across the Internet demonstrate the benefits of our architecture. For instance, over a 64-hour sampling period in March 2001 across a twelve-node RON, there were 32 significant outages, each lasting over thirty minutes, over the 132 measured paths. RON's routing mechanism was able to detect, recover, and route around all of them, in less than twenty seconds on average, showing that its methods for fault detection and recovery work well at discovering alternate paths in the Internet. Furthermore, RON was able to improve the loss rate, latency, or throughput perceived by data transfers; for example, about 5% of the transfers doubled their TCP throughput and 5% of our transfers saw their loss probability reduced by 0.05. We found that forwarding packets via at most one intermediate RON node is sufficient to overcome faults and improve performance in most cases. These improvements, particularly in the area of fault detection and recovery, demonstrate the benefits of moving some of the control over routing into the hands of end-systems.
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation

The `1-regularized maximum likelihood estimation problem has recently become a topic of great interest within the machine learning, statistics, and optimization communities as a method for producing sparse inverse covariance estimators. In this paper, a proximal gradient method (G-ISTA) for performing `1-regularized covariance matrix estimation is presented. Although numerous algorithms have been proposed for solving this problem, this simple proximal gradient method is found to have attractive theoretical and numerical properties. G-ISTA has a linear rate of convergence, resulting in an O(log ε) iteration complexity to reach a tolerance of ε. This paper gives eigenvalue bounds for the G-ISTA iterates, providing a closed-form linear convergence rate. The rate is shown to be closely related to the condition number of the optimal point. Numerical convergence results and timing comparisons for the proposed method are presented. G-ISTA is shown to perform very well, especially when the optimal point is well-conditioned.
Named Entity Recognition : Applications , Approaches and Challenges

Named Entity Recognition and classification is the task of identifying the text of special meaning and classifying into some predetermined categories. These categories may range from person, location, organization to dates, quantities, numeric expressions etc. Named entity recognition has been an important research area since 1996. Still it is a required field to be studied due to its important use in various natural language applications. In this article, we present a comprehensive survey of named entity recognition techniques, issues and challenges and application domains. The performance of some well-known rule-based and machine learning based named entity algorithms has been analyzed. This research work will help the researchers to select the most suitable named entity recognition technique in aspecific application and will also serve as a guide to identify the areas that need attention from the researchcommunity.
Deep learning based human action recognition: A survey

Human action recognition has attracted much attentions because of its great potential applications. With the rapid development of computer performance and Internet, the methods of human action recognition based on deep learning become mainstream and develop at a breathless pace. This paper will give a novel reasonable taxonomy and a review of deep learning human action recognition methods based on color videos, skeleton sequences and depth maps. In addition, some datasets and effective tricks in action recognition deep learning methods will be introduced, and also the development trend is discussed.
Computer security in the real world

Most computers today are insecure because security is costly in terms of user inconvenience and foregone features, and people are unwilling to pay the price. Real-world security depends more on punishment than on locks, but it's hard to even find network attackers, much less punish them. The basic elements of security are authentication, authorization, and auditing: the gold standard. The idea of one principal speaking for another is the key to doing these uniformly across the Internet.
Usability evaluation methods for the web: A systematic mapping study

In recent years, many Usability Evaluation Methods (UEMs) have been employed to evaluate Web applications. However, many of these applications still do not meet most customers’ usability expectations and many companies have folded as a result of not considering Web usability issues. No studies currently exist with regard to either the use of usability evaluation methods for the Web or the benefits they bring.  The objective of this paper is to summarize the current knowledge that is available as regards the Usability Evaluation Methods (UEMs) that have been employed to evaluate Web applications over the last 14 years.  A systematic mapping study was performed to assess the UEMs that have been used by researchers to evaluate Web applications and their relation to the Web development process. Systematic mapping studies are useful for categorizing and summarizing the existing information concerning a research question in an unbiased manner.  The results show that around 39% of the papers reviewed reported the use of evaluation methods that had been specifically crafted for the Web. The results also show that the type of method most widely used was that of User Testing. The results identify several research gaps, such as the fact that around 90% of the studies applied evaluations during the implementation phase of the Web application development, which is the most costly phase in which to perform changes. A list of the UEMs that were found is also provided in order to guide novice usability practitioners.  From an initial set of 2703 papers, a total of 206 research papers were selected for the mapping study. The results obtained allowed us to reach conclusions concerning the state-ofthe-art of UEMs for evaluating Web applications. This allowed us to identify several research gaps, which subsequently provided us with a framework in which new research activities can be more appropriately positioned, and from which useful information for novice usability practitioners can be extracted.
Failure Analysis of SGI XFS File System

Commodity file systems expect a fail stop disk. But todays disks fail in unexpected ways. Disks exhibit latent sector errors, silent corruption and transient failures to name a few. In this paper we study the behavior of SGI XFS to such errors. File systems play a key role in handling most data and hence the failure handling policy of the file system plays a major role in ensuring the integrity of the data. XFS is a highly scalable journaling file system developed by SGI. We analyze the failure handling policy of XFS file system. We fail reads and writes of various blocks that originate from the file system and analyze the file system behavior. Some such blocks include super block, journal header block, commit block, data block, index blocks. We classify our errors according to the extended IRON Taxonomy. We see that XFS is vulnerable to these failures and does not possess a uniform failure handling policy. Further the file system at times silently corrupts data. We also see that XFS has very little internal redundancy even for critical structures such as the super block and B+Tree root. Finally we see that XFS, like most commodity file systems, fails to recover from these failures putting the data at risk.