nils-herrmann/clean_scirepeval huggingface dataset:
First-person hyper-lapse videos

We present a method for converting first-person videos, for example, captured with a helmet camera during activities such as rock climbing or bicycling, into hyper-lapse videos, i.e., time-lapse videos with a smoothly moving camera. At high speed-up rates, simple frame sub-sampling coupled with existing video stabilization methods does not work, because the erratic camera shake present in first-person videos is amplified by the speed-up. Our algorithm first reconstructs the 3D input camera path as well as dense, per-frame proxy geometries. We then optimize a novel camera path for the output video that passes near the input cameras while ensuring that the virtual camera looks in directions that can be rendered well from the input. Finally, we generate the novel smoothed, time-lapse video by rendering, stitching, and blending appropriately selected source frames for each output frame. We present a number of results for challenging videos that cannot be processed using traditional techniques.
Low profile dipole antenna over compact AMC surface

In this paper, an alternative technique is proposed to miniaturize the Artificial Magnetic Conductors (AMC) unit cell. Novel unit cell achieves 60% surface size reduction at the same operating frequency compared to conventional mushroom-like unit cell. An AMC surface of 5×5 cells is designed under a dipole antenna with a distance of 0.05λ0, where λ0 denotes the free space wavelength at 0.9 GHz, for gain enhancement and radiation control. Both the dipole antenna and the AMC surface are fabricated and measured to demonstrate the potential of this new structure. At 0.92 GHz, a peak realized gain of 4.3 dBi of the antenna is achieved. The front-to-back ratio is under −15 dB and the maximum cross-polarization level, in the main beam, is less than −25 dB for both E and H plane. The simulated and the measured results are in good agreement.
Decentralized control: An overview

The paper reviews the past and present results in the area of decentralized control of large-scale complex systems. An emphasis is laid on decentralization, decomposition, and robustness. These methodologies serve as effective tools to overcome specific difficulties arising in largescale complex systems such as high dimensionality, information structure constraints, uncertainty, and delays. Several prospective topics for future research are introduced in this contents. The overview is focused on recent decomposition approaches in interconnected dynamic systems due to their potential in providing the extension of decentralized control into networked control systems.
Wavelet Network Model and Its Application to the Prediction of Hydrology

Based on the multi-time scale and the nonlinear characteristics of the observed time series, a new hybrid model between wavelet analysis and artificial neural network (ANN): wavelet network model, has been suggested. The present model absorbs some merits of wavelet transform and artificial neural network. Case studies, the short and long term prediction of hydrological time series, have been researched. The comparison results revealed that the suggested model could increase the forecasted accuracy and prolong the length time of prediction. The wavelet network model is satisfied.
Public Displays of Connection

Participants in social network sites create self-descriptive profiles that include their links to other members, creating a visible network of connections — the ostensible purpose of these sites is to use this network to make friends, dates, and business connections. In this paper we explore the social implications of the public display of one’s social network. Why do people display their social connections in everyday life, and why do they do so in these networking sites? What do people learn about another’s identity through the signal of network display? How does this display facilitate connections, and how does it change the costs and benefits of making and brokering such connections compared to traditional means? The paper includes several design recommendations for future networking sites.
Religious meaning and subjective well-being in late life.

The purpose of this study is to examine the relationship between religious meaning and subjective well-being. A major emphasis is placed on assessing race differences in the relationship between these constructs.



Interviews were conducted with a nationwide sample of older White and older Black adults. Survey items were administered to assess a sense of meaning in life that is derived specifically from religion. Subjective well-being was measured with indices of life satisfaction, self-esteem, and optimism.



The findings suggest that older adults who derive a sense of meaning in life from religion tend to have higher levels of life satisfaction, self-esteem, and optimism. The data further reveal that older Black adults are more likely to find meaning in religion than older White adults. In addition, the relationships among religious meaning, life satisfaction, self-esteem, and optimism tend to be stronger for older African Americans persons than older White persons.



Researchers have argued for some time that religion may be an important source of resilience for older Black adults, but it is not clear how these beneficial effects arise. The data from this study suggest that religious meaning may be an important factor.
Congenitally missing maxillary lateral incisors : Restorative replacement

A As dentists, we are often faced with the question: to close or not to close? Since maxillary lateral incisors are often congenitally missing, replacement of these teeth raises several important treatment planning concerns. Therefore, it is beneficial to use an interdisciplinary treatment approach to obtain the most predictable outcome. There are multiple treatment options for the replacement of congenitally missing lateral incisors, including canine substitution, single-tooth implants, and tooth-supported restorations. Drs Zachrisson, Rosa, and Toreskog beautifully illustrated the benefits of canine substitution by presenting the advantages and disadvantages of space closure as well as the functional and esthetic limitations that accompany this treatment choice. Like our European colleagues, our team agrees with the underlying principle to develop treatment plans that are both conservative and functional while maintaining excellent esthetics. However, the esthetic and functional success of canine substitution ultimately depends on variables such as malocclusion, crowding, profile, crown shape and color, and smiling lip level. Also, the amount of crown reduction that is often required to appropriately position these teeth esthetically and functionally in 3 planes of space can be excessive. Therefore, the optimal canine substitution patient is one who has small canines with crowns that match the shade of the central incisors. Ideally, he or she should also have a nice profile, a Class II dental relationship, and no crowding in the mandibular arch. However, many patients do not meet these criteria, and multiple veneers often need to be placed on the anterior teeth to overcome the
Understanding Social Engineering Based Scams

This short chapter focuses on targeting. Targeting increases the yield of attacks, i.e., the response rate. Targeting also reduces the efficacy of spam filters and related technologies, and as such, vastly improves the profits scammers reap. We overview how to estimate the yield of attacks, and how to identify scams that are likely to become more common. 1.1 Yields and Targeting Targeting a scam is known to dramatically increase the yield of the attack. Industry estimates of the yield of non-targeted phishing attacks fall in the range of a few percentage points, whereas targeted phishing attacks are believed to be an order of magnitude more effective. For example, in a 2011 report , Cisco details the economics of mass phishing attacks and targeted phishing attacks, as shown in Table 1.1. The table demonstrates how scammers benefit from targeting their victims. Here, the block rate is the likelihood with which the message gets blocked, e.g., by spam filters. The open rate is the probability that a recipient reads a delivered message. The click-through rate measures the probability that a user who reads the message clicks on a hyperlink in the message, and the conversion rate measures the likelihood that a recipient who clicks through falls for the attack by providing the information the attacker wants. As shown in Table 1.1, the probability that the recipient actually reads the message (“Open rate”) is more than twenty times greater with targeted attacks. Even more importantly, the fraction of users who both read the message and click a hyperlink within it (“Click-through rate”) is ten times higher with targeted attacks. The table suggests a tenfold increase in the profit of a campaign as a result of targeting, explaining why we see an increasing number of targeted attacks today. However, the block rates suggested in the table are probably inaccurate. The difference between non-targeted and targeted attack block rates is likely to be much greater because the estimates in the table do not take into consideration the benefits that targeting scammers reap from circumventing spam filters. Remember that the © Springer Science+Business Media New York 2016 M. Jakobsson (ed.), Understanding Social Engineering Based Scams, DOI 10.1007/978-1-4939-6457-4_1 1 2 1 Scams and Targeting Table 1.1 The economics of mass phishing vs. targeted phishing attacks Campaign type: Mass phishing attack Targeted phishing attack Messages in campaign 1,000,000 1,000 Block rate 99 % 99 %
Introducing Efficient Parallelism into Approximate String Matching and a New Serial Algorithm

Consider the stnng matching problem, where differences between characters of the pattern and characters of the text are allowed. Each difference is due to either a mismatch between a character of the text and a character of the pattern or a superfluous character in the text or a superfluous character in the pattern. Given a text of length n, a pattern of length m and an integer k, we present parallel and serial algorithms for finding all occurrences of the pattern in the text with at most k differences. The first part of the parallel algorithm consists of analysis of the pattern and takes 0 (log m ) time using m 2 processors. The rest of the algorithm consists of handling the text. The text han1. The research of this author was supported by NSF grants NSF-DCR-8318874 and NSF-DCR-8413359 and ONR grant
Learning to Sample

Processing large point clouds is a challenging task. Therefore, the data is often sampled to a size that can be processed more easily. The question is how to sample the data? A popular sampling technique is Farthest Point Sampling (FPS). However, FPS is agnostic to a downstream application (classification, retrieval, etc.). The underlying assumption seems to be that minimizing the farthest point distance, as done by FPS, is a good proxy to other objective functions. We show that it is better to learn how to sample. To do that, we propose a deep network to simplify 3D point clouds. The network, termed S-NET, takes a point cloud and produces a smaller point cloud that is optimized for a particular task. The simplified point cloud is not guaranteed to be a subset of the original point cloud. Therefore, we match it to a subset of the original points in a post-processing step. We contrast our approach with FPS by experimenting on two standard data sets and show significantly better results for a variety of applications. Our code is publicly available1.
Development and test of a dual-stator, spoke-type ferrite permanent magnet motor with high torque performance for direct-drive applications

The minimization or elimination of the use of rare earth magnets in electric motors has been recently recognized as a challenging problem due to the increased price and limited supply of rare earth magnetic materials. Before the emergence of new cost-effective magnetic materials with high properties, the development of rare-earth-free motors using ferrite magnets with improved motor topologies is appreciated. In the previous research, a dual-stator, spoke-type ferrite permanent magnet (PM) motor with the phase-group concentrated-coil (PGCC) windings has been reported to have high torque performance, including high torque density and low torque pulsations. However, the previous research mainly investigated the motor performance of the conceptual topologies by the aid of finite element method (FEM). In this paper, the dual-stator, spoke-type ferrite PM motor, designed based on the specifications of the referenced washer motor from LG electronics, is analyzed by considering the practical topology for manufactured prototype. To enhance torque density, the rotor-PM overhang structure, considering the length of stator end windings, is determined for the proposed motor. The effects of the misaligned angles between the outer and inner stators on motor performance are analyzed in detail. The contribution of the proposed motor with rotor-PM overhang structure is highlighted by comparing to the performance of the referenced washer motor at the washing speed case, utilizing a 3-D FEM. Finally, the performance of the proposed motor is verified by experimental test, which indicates the test results and simulation results have good agreement.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference – sometimes prohibitively so in the case of very large data sets and large models. Several authors have also charged that NMT systems lack robustness, particularly when input sentences contain rare words. These issues have hindered NMT’s use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google’s Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using residual connections as well as attention connections from the decoder network to the encoder. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units (“wordpieces”) for both input and output. This method provides a good balance between the flexibility of “character”-delimited models and the efficiency of “word”-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. To directly optimize the translation BLEU scores, we consider refining the models by using reinforcement learning, but we found that the improvement in the BLEU scores did not reflect in the human evaluation. On the WMT’14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google’s phrase-based production system.
Acute diffuse and total alopecia: A new subtype of alopecia areata with a favorable prognosis.

Alopecia areata (AA) appears in several clinical forms, all having different clinical courses and different prognoses. Acute diffuse and total alopecia (ADTA) has been reported to have a short clinical course ranging from acute hair loss to total baldness, followed by rapid recovery.



To determine the clinical course and prognosis of ADTA through precise clinical observations.



Thirty Korean patients who showed ADTA of the scalp within an average of 10 weeks after the onset of hair loss were studied.



Most patients were women who were older than 20 years of age. The histopathology of the lesion revealed infiltration of mononuclear cells around the hair follicles and prominent pigment incontinence. The patients experienced hair regrowth within about 6 months, without regard to the method of treatment.



The duration of follow-up after remission ranged from 3 to 49 months, with a mean of 24 months.



These cases can be categorized as having "acute diffuse and total alopecia," a new subtype of AA that is associated with a favorable prognosis and rapid and spontaneous recovery even without treatment.
Attributed Network Embedding via Subspace Discovery

Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding–attri2vec–that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically Daokun Zhang Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Australia E-mail: Daokun.Zhang@student.uts.edu.au Jie Yin Discipline of Business Analytics, The University of Sydney, Australia E-mail: jie.yin@sydney.edu.au Xingquan Zhu Department of CEECS, Florida Atlantic University, USA E-mail: xqzhu@cse.fau.edu Chengqi Zhang Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Australia E-mail: Chengqi.Zhang@uts.edu.au ar X iv :1 90 1. 04 09 5v 1 [ cs .S I] 1 4 Ja n 20 19 2 Daokun Zhang et al. validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at https://github.com/daokunzhang/attri2vec.
Tangible authoring of 3D virtual scenes in dynamic augmented reality environment

This paper proposes tangible interfaces and interactions for authoring 3D virtual and immersive scenes easily and intuitively in tangible augmented reality (AR) environment. It provides tangible interfaces for manipulating virtual objects in a natural and intuitive manner and supports adaptive and accurate vision-based tracking in AR environments. In particular, RFID is used to directly integrate physical objects with virtual objects and to systematically support the tangible query of the relation between physical objects and virtual ones, which can provide more intuitive tangibility and a new way of virtual object manipulation. Moreover, the proposed approach offers an easy and intuitive switching mechanism between tangible environment and virtual environment. This paper also proposes a context-adaptive marker tracking method which can remove an inconsistent problem while embedding virtual objects into physical ones in tangible AR environments. The context-adaptive tracking method not only adjusts the locations of invisible markers by interpolating the locations of existing reference markers and those of previous ones, but also removes a jumping effect of movable virtual objects when their references are changed from one marker to another. Several case studies for generating tangible virtual scenes and comparison with previous work are given to show the effectiveness and novelty of the proposed approach.
BlendCAC: A BLockchain-ENabled Decentralized Capability-based Access Control for IoTs

The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide smart services with or without human intervention. While leveraging the large-scale IoT-based applications like Smart Gird or Smart Cities, IoTs also incur more concerns on privacy and security. Among the top security challenges that IoTs face, access authorization is critical in resource sharing and information protection. One of the weaknesses in today’s access control (AC) is the centralized authorization server, which can be the performance bottleneck or the single point of failure. In this paper, BlendCAC, a blockchain-enabled decentralized capability-based AC is proposed for the security of IoTs. The BlendCAC aims at an effective access control processes to devices, services and information in large scale IoT systems. Based on the blockchain network, a capability delegation mechanism is suggested for access permission propagation. A robust identitybased capability token management strategy is proposed, which takes advantage of smart contract for registering, propagation and revocation of the access authorization. In the proposed BlendCAC scheme, IoT devices are their own master to control their resources instead of being supervised by a centralized authority. Implemented and tested on a Raspberry Pi device and on a local private blockchain network, our experimental results demonstrate the feasibility of the proposed BlendCAC approach to offer a decentralized, scalable, lightweight and fine-grained AC solution to IoT systems.
Routing perturbation for enhanced security in split manufacturing

Split manufacturing can mitigate security vulnerabilities at untrusted foundries by exposing only partial designs. Even so, attackers can make educated guess according to design conventions and thereby recover entire chip designs. In this work, a routing perturbation-based defense method is proposed such that such attacks become very difficult while wirelength/timing overhead is restricted to be very small. Experimental results on benchmark circuits confirm the effectiveness of the proposed techniques. The new techniques also significantly outperform the latest previous work.
Smart walkers!: enhancing the mobility of the elderly

The idea of Smart Walkers is to equip customary rolling walkers with sensors in order to assist users, caregivers and clinicians. The integral part of the Smart Walkers is an autonomous agent which monitors the activity of the user, assesses his physical conditions, and detects potential risks of falls. In this paper, we study methods which enable the agent to recognize the user activity from the sensor measurements. The proposed methods use Conditional Random Fields with features based on discriminant rules. A special case are features which, in order to distinguish between two activities, compare the sensor measurements to thresholds learned by a linear classifier. Experiments with real user data show that the methods achieve a good accuracy; the best results are obtained using “smooth” thresholds based on sigmoid functions.