nils-herrmann/clean_scirepeval huggingface dataset:
Joint Object and Part Segmentation Using Deep Learned Potentials

Segmenting semantic objects from images and parsing them into their respective semantic parts are fundamental steps towards detailed object understanding in computer vision. In this paper, we propose a joint solution that tackles semantic object and part segmentation simultaneously, in which higher object-level context is provided to guide part segmentation, and more detailed part-level localization is utilized to refine object segmentation. Specifically, we first introduce the concept of semantic compositional parts (SCP) in which similar semantic parts are grouped and shared among different objects. A two-stream fully convolutional network (FCN) is then trained to provide the SCP and object potentials at each pixel. At the same time, a compact set of segments can also be obtained from the SCP predictions of the network. Given the potentials and the generated segments, in order to explore long-range context, we finally construct an efficient fully connected conditional random field (FCRF) to jointly predict the final object and part labels. Extensive evaluation on three different datasets shows that our approach can mutually enhance the performance of object and part segmentation, and outperforms the current state-of-the-art on both tasks.
A novel actuation concept for a multi rotor UAV

This paper introduces a new strategy to improve the performance and reliability of multi-rotor vehicles. The strategy uses a dual axes tilting propeller in order to generate control actions. The mechanisms of actuation are: gyroscopic torques, thrust vectoring and differential thrusting. A realisation of the new concept is detailed in this paper. The model of the actuators is also described, including the rigid body equations of the propellers and the motors and servomotors dynamic response. A conventional control system is presented implementing a PD and a pseudoinverse control allocator to stabilise the vehicle. The tests show that the vehicle is faster than a conventional counterpart and that it can resist the failure of two rotors. The results suggest that higher inertia propellers can lead to further substantial improvements in performance.
CMOS Image Sensors With Multi-Bucket Pixels for Computational Photography

This paper presents new image sensors with multi- bucket pixels that enable time-multiplexed exposure, an alter- native imaging approach. This approach deals nicely with scene motion, and greatly improves high dynamic range imaging, structured light illumination, motion corrected photography, etc. To implement an in-pixel memory or a bucket, the new image sensors incorporate the virtual phase CCD concept into a standard 4-transistor CMOS imager pixel. This design allows us to create a multi-bucket pixel which is compact, scalable, and supports true correlated double sampling to cancel kTC noise. Two image sensors with dual and quad-bucket pixels have been designed and fabricated. The dual-bucket sensor consists of a 640H × 576V array of 5.0 μm pixel in 0.11 μm CMOS technology while the quad-bucket sensor comprises 640H × 512V array of 5.6 μm pixel in 0.13 μm CMOS technology. Some computational photography applications were implemented using the two sensors to demonstrate their values in eliminating artifacts that currently plague computational photography.
Friction observer and compensation for control of robots with joint torque measurement

In this paper we introduce a friction observer for robots with joint torque sensing (in particular for the DLR medical robot) in order to increase the positioning accuracy and the performance of torque control. The observer output corresponds to the low-pass filtered friction torque. It is used for friction compensation in conjunction with a MIMO controller designed for flexible joint arms. A passivity analysis is done for this friction compensation, allowing a Lyapunov based convergence analysis in the context of the nonlinear robot dynamics. For the complete controlled system, global asymptotic stability can be shown. Experimental results validate the practical efficiency of the approach.
Using Wavefront Tracing for the Visualization and Optimization of Progressive Lenses

Progressive addition lenses are a relatively new approach t o compensate for defects of the human visual system. While traditional spectacles use rotationally symmetric l enses, progressive lenses require the specification of free form surfaces. This poses difficult problems for the optimal design and its visual evaluation. This paper presents two new techniques for the visualizatio n of ptical systems and the optimization of progressive lenses. Both are based on the same wavefront tracingapproach to accurately evaluate the refraction properties of complex optical systems. We use the results of wavefront tracing for continuously refocusing the eye during rendering. Together with distribution ray tracing, this yields high-quality images that accurately simulate the visual quality of an optical system. The design of progressive lenses is difficult due to t he trade-off between the desired properties of the lens and unavoidable optical errors, such as astigmatism an d distortions. We use wavefront tracing to derive an accurate error functional describing the desired properti s and the optical error across a lens. Minimizing this error yields optimal free-form lens surfaces. While the basic approach is much more general, in this paper, w describe its application to the particular problem of designing and evaluating progressive lenses and demonst rate the benefits of the new approach with several example images.
Distributed Video Coding

Distributed coding is a new paradigm for video compression, based on Slepian and Wolf's and Wyner and Ziv's information-theoretic results from the 1970s. This paper reviews the recent development of practical distributed video coding schemes. Wyner-Ziv coding, i.e., lossy compression with receiver side information, enables low-complexity video encoding where the bulk of the computation is shifted to the decoder. Since the interframe dependence of the video sequence is exploited only at the decoder, an intraframe encoder can be combined with an interframe decoder. The rate-distortion performance is superior to conventional intraframe coding, but there is still a gap relative to conventional motion-compensated interframe coding. Wyner-Ziv coding is naturally robust against transmission errors and can be used for joint source-channel coding. A Wyner-Ziv MPEG encoder that protects the video waveform rather than the compressed bit stream achieves graceful degradation under deteriorating channel conditions without a layered signal representation.
The Three Generations of Field-Level Networks—Evolution and Compatibility Issues

Field-level networks have been one of the keys to modern automation systems. Be it in factory, process, or building automation, networks allow for horizontal and vertical integration of distributed devices and functions. This paper reviews the evolution of field-level networks comprising fieldbus systems, industrial Ethernet, and recent industrial wireless networks. The main focus is on demonstrating the continuity in the development of the three generations that ensured backward compatibility at the expense of radical innovation. Given the wide set of modern communication technologies, this paper then discusses how architectures for future automation networks might look. Particular emphasis is put on hybrid architectures for combined wired/wireless networks. A generic concept for integration of multiple wireless segments will be presented that supports seamless roaming for mobile nodes in industrial environment.
Forensics and mitochondrial DNA: applications, debates, and foundations.

Debate on the validity and reliability of scientific methods often arises in the courtroom. When the government (i.e., the prosecution) is the proponent of evidence, the defense is obliged to challenge its admissibility. Regardless, those who seek to use DNA typing methodologies to analyze forensic biological evidence have a responsibility to understand the technology and its applications so a proper foundation(s) for its use can be laid. Mitochondrial DNA (mtDNA), an extranuclear genome, has certain features that make it desirable for forensics, namely, high copy number, lack of recombination, and matrilineal inheritance. mtDNA typing has become routine in forensic biology and is used to analyze old bones, teeth, hair shafts, and other biological samples where nuclear DNA content is low. To evaluate results obtained by sequencing the two hypervariable regions of the control region of the human mtDNA genome, one must consider the genetically related issues of nomenclature, reference population databases, heteroplasmy, paternal leakage, recombination, and, of course, interpretation of results. We describe the approaches, the impact some issues may have on interpretation of mtDNA analyses, and some issues raised in the courtroom.
A Wavelet-Based Anytime Algorithm for K-Means Clustering of Time Series

The emergence of the field of data mining in the last decade has sparked an increasing interest in clustering of time series. Although there has been much research on clustering in general, most classic machine learning and data mining algorithms do not work well for time series due to their unique structure. In particular, the high dimensionality, very high feature correlation, and the (typically) large amount of noise that characterize time series data present a difficult challenge. In this work we address these challenges by introducing a novel anytime version of k-Means clustering algorithm for time series. The algorithm works by leveraging off the multi-resolution property of wavelets. In particular, an initial clustering is performed with a very coarse resolution representation of the data. The results obtained from this “quick and dirty” clustering are used to initialize a clustering at a slightly finer level of approximation. This process is repeated until the clustering results stabilize or until the “approximation” is the raw data. In addition to casting k-Means as an anytime algorithm, our approach has two other very unintuitive properties. The quality of the clustering is often better than the batch algorithm, and even if the algorithm is run to completion, the time taken is typically much less than the time taken by the original algorithm. We explain, and empirically demonstrate these surprising and desirable properties with comprehensive experiments on several publicly available real data sets.
SMART POOL : Practical Decentralized Pooled Mining

Many blockchain-based cryptocurrencies such as Bitcoin and Ethereum use Nakamoto consensus protocol to reach agreement on the blockchain state between a network of participant nodes. The Nakamoto consensus protocol probabilistically selects a leader via a mining process which rewards network participants (or miners) to solve computational puzzles. Finding solutions for such puzzles requires an enormous amount of computation. Thus, miners often aggregate resources into pools and share rewards amongst all pool members via pooled mining protocol. Pooled mining helps reduce the variance of miners’ payoffs significantly and is widely adopted in popular cryptocurrencies. For example, as of this writing, more than 95% of mining power in Bitcoin emanates from 10 mining pools. Although pooled mining benefits miners, it severely degrades decentralization, since a centralized pool manager administers the pooling protocol. Furthermore, pooled mining increases the transaction censorship significantly since pool managers decide which transactions are included in blocks. Due to this widely recognized threat, the Bitcoin community has proposed an alternative called P2Pool which decentralizes the operations of the pool manager. However, P2Pool is inefficient, increases the variance of miners’ rewards, requires much more computation and bandwidth from miners, and has not gained wide adoption. In this work, we propose a new protocol design for a decentralized mining pool. Our protocol called SMARTPOOL shows how one can leverage smart contracts, which are autonomous agents themselves running on decentralized blockchains, to decentralize cryptocurrency mining. SMARTPOOL guarantees high security, low reward’s variance for miners and is cost-efficient. We implemented a prototype of SMARTPOOL as an Ethereum smart contract working as a decentralized mining pool for Bitcoin. We have deployed it on the Ethereum testnet and our experiments confirm that SMARTPOOL is efficient and ready for practical use.
Singularity avoidance for nonholonomic, omnidirectional wheeled mobile platforms with variable footprint

One characteristic attribute of mobile platforms equipped with a set of independent steering wheels is their omnidirectionality and the ability to realize complex translational and rotational trajectories. An accurate coordination of steering angle and spinning rate of each wheel is necessary for a consistent motion. Since the orientations of the wheels must align to the Instantaneous Center of Rotation (ICR), the current location and velocity of this specific point is essential for describing the state of the platform. However, singular configurations of the controlled system exist depending on the ICR, leading to unfeasible control inputs, i.e., infinite steering rates. Within this work we address and analyze this problem in general. Furthermore, we propose a solution for mobile platforms with variable footprint. An existing controller based on dynamic feedback linearization is augmented by a new potential field-based algorithm for singularity avoidance which uses the tunable leg lengths as an additional control input to minimize deviations from the nominal motion trajectory. Simulations and experimental results on the mobile platform of DLR's humanoid manipulator Justin support our approach.
Human behaviour recognition based on trajectory analysis using neural networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.
Social search in "Small-World" experiments

The "algorithmic small-world hypothesis" states that not only are pairs of individuals in a large social network connected by short paths, but that ordinary individuals can find these paths. Although theoretically plausible, empirical evidence for the hypothesis is limited, as most chains in "small-world" experiments fail to complete, thereby biasing estimates of "true" chain lengths. Using data from two recent small-world experiments, comprising a total of 162,328 message chains, and directed at one of 30 "targets" spread across 19 countries, we model heterogeneity in chain attrition rates as a function of individual attributes. We then introduce a rigorous way of estimating true chain lengths that is provably unbiased, and can account for empirically-observed variation in attrition rates. Our findings provide mixed support for the algorithmic hypothesis. On the one hand, it appears that roughly half of all chains can be completed in 6-7 steps--thus supporting the "six degrees of separation" assertion--but on the other hand, estimates of the mean are much longer, suggesting that for at least some of the population, the world is not "small" in the algorithmic sense. We conclude that search distances in social networks are fundamentally different from topological distances, for which the mean and median of the shortest path lengths between nodes tend to be similar.
Reflectarray Antenna at Terahertz Using Graphene

The use of graphene for fixed-beam reflectarray antennas at Terahertz (THz) is proposed. Graphene's unique electronic band structure leads to a complex surface conductivity at THz frequencies, which allows the propagation of very slow plasmonic modes. This leads to a drastic reduction of the electrical size of the array unit cell and thereby good array performance. The proposed reflectarray has been designed at 1.3 THz and comprises more than 25000 elements of size about λ0/16. The array reflective unit cell is analyzed using a full vectorial approach, taking into account the variation of the angle of incidence and assuming local periodicity. Good performance is obtained in terms of bandwidth, cross-polar, and grating lobes suppression, proving the feasibility of graphene-based reflectarrays and other similar spatially fed structures at Terahertz frequencies. This result is also a first important step toward reconfigurable THz reflectarrays using graphene electric field effect.
THU_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM

Predicting valence-arousal ratings for words and phrases is very useful for constructing affective resources for dimensional sentiment analysis. Since the existing valence-arousal resources of Chinese are mainly in word-level and there is a lack of phrase-level ones, the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) task aims to predict the valencearousal ratings for Chinese affective words and phrases automatically. In this task, we propose an approach using a densely connected LSTM network and word features to identify dimensional sentiment on valence and arousal for words and phrases jointly. We use word embedding as major feature and choose part of speech (POS) and word clusters as additional features to train the dense LSTM network. The evaluation results of our submissions (1st and 2nd in average performance) validate the effectiveness of our system to predict valence and arousal dimensions for Chinese words and phrases.
Assessing binary classifiers using only positive and unlabeled data

Assessing the performance of a learned model is a crucial part of machine learning. Most evaluation metrics can only be computed with labeled data. Unfortunately, in many domains we have many more unlabeled than labeled examples. Furthermore, in some domains only positive and unlabeled examples are available, in which case most standard metrics cannot be computed at all. In this paper, we propose an approach that is able to estimate several widely used metrics including ROC and PR curves using only positive and unlabeled data. We provide theoretical bounds on the quality of our estimates. Empirically, we demonstrate that even given only a small number of positive examples and unlabeled data, we are able to reliable estimate both ROC and PR curves.
A Machine Learning Approach to Android Malware Detection

With the recent emergence of mobile platforms capable of executing increasingly complex software and the rising ubiquity of using mobile platforms in sensitive applications such as banking, there is a rising danger associated with malware targeted at mobile devices. The problem of detecting such malware presents unique challenges due to the limited resources avalible and limited privileges granted to the user, but also presents unique opportunity in the required metadata attached to each application. In this article, we present a machine learning-based system for the detection of malware on Android devices. Our system extracts a number of features and trains a One-Class Support Vector Machine in an offline (off-device) manner, in order to leverage the higher computing power of a server or cluster of servers.
Cartesian impedance control of redundant manipulators for human-robot co-manipulation

This paper addresses the problem of controlling a robot arm executing a cooperative task with a human who guides the robot through direct physical interaction. This problem is tackled by allowing the end effector to comply according to an impedance control law defined in the Cartesian space. While, in principle, the robot's dynamics can be fully compensated and any impedance behaviour can be imposed by the control, the stability of the coupled human-robot system is not guaranteed for any value of the impedance parameters. Moreover, if the robot is kinematically or functionally redundant, the redundant degrees of freedom play an important role. The idea proposed here is to use redundancy to ensure a decoupled apparent inertia at the end effector. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we show that inertial decoupling enables a more flexible choice of the impedance parameters and improves the performance during manual guidance.
Recursive Unsolvability of Post ' s Problem of " Tag " and other Topics in Theory of Turing Machines

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Classical hardness of learning with errors

We show that the Learning with Errors (LWE) problem is classically at least as hard as standard worst-case lattice problems. Previously this was only known under quantum reductions.
 Our techniques capture the tradeoff between the dimension and the modulus of LWE instances, leading to a much better understanding of the landscape of the problem. The proof is inspired by techniques from several recent cryptographic constructions, most notably fully homomorphic encryption schemes.
An empirical evaluation of models of text document similarity

Modeling the semantic similarity between text documents presents a significant theoretical challenge for cognitive science, with ready-made applications in information handling and decision support systems dealing with text. While a number of candidate models exist, they have generally not been assessed in terms of their ability to emulate human judgments of similarity. To address this problem, we conducted an experiment that collected repeated similarity measures for each pair of documents in a small corpus of short news documents. An analysis of human performance showed inter-rater correlations of about 0.6. We then considered the ability of existing models—using wordbased, n-gram and Latent Semantic Analysis (LSA) approaches—to model these human judgments. The best performed LSA model produced correlations of about 0.6, consistent with human performance, while the best performed word-based and n-gram models achieved correlations closer to 0.5. Many of the remaining models showed almost no correlation with human performance. Based on our results, we provide some discussion of the key strengths and weaknesses of the models we examined.