nils-herrmann/clean_scirepeval huggingface dataset:
Shakeout: A New Regularized Deep Neural Network Training Scheme

Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. The invention of effective training techniques largely contributes to this success. The so-called "Dropout" training scheme is one of the most powerful tool to reduce over-fitting. From the statistic point of view, Dropout works by implicitly imposing an L2 regularizer on the weights. In this paper, we present a new training scheme: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, our method randomly chooses to enhance or inverse the contributions of each unit to the next layer. We show that our scheme leads to a combination of L1 regularization and L2 regularization imposed on the weights, which has been proved effective by the Elastic Net models in practice. We have empirically evaluated the Shakeout scheme and demonstrated that sparse network weights are obtained via Shakeout training. Our classification experiments on real-life image datasets MNIST and CIFAR10 show that Shakeout deals with over-fitting effectively.
Robust Multi-pose Facial Expression Recognition

Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training set by rendering 3D face scans from the BU-4DFE dynamic facial expression database  at 49 different viewpoints. We extract Local Binary Pattern (LBP) descriptors and further utilize multiple instance learning to mitigate the influence of inaccurate alignment in this challenging task. Experimental results demonstrate the power and validate the effectiveness of the proposed multi-pose facial expression recognition framework.
Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D

The k-Means clustering problem on n points is NP-Hard for any dimension d ≥ 2, however, for the 1D case there exists exact polynomial time algorithms. Previous literature reported an O(kn) time dynamic programming algorithm that uses O(kn) space. It turns out that the problem has been considered under a different name more than twenty years ago. We present all the existing work that had been overlooked and compare the various solutions theoretically. Moreover, we show how to reduce the space usage for some of them, as well as generalize them to data structures that can quickly report an optimal k-Means clustering for any k. Finally we also generalize all the algorithms to work for the absolute distance and to work for any Bregman Divergence. We complement our theoretical contributions by experiments that compare the practical performance of the various algorithms.
Mobile robot navigation based on lidar

A wheeled mobile robot based on Arduino was described in this article. Hardware selection, Simultaneous Localization and Mapping (SLAM), Path Planning and the principle of the algorithm applied in this process are introduced in detail. The algorithm used in ROS is further verified by experiments. In order to verify the feasibility of two different 2D SLAM algorithms, we use the wheeled robot built in the lab to do the experiment in the same indoor environment and compare their results.
Bioleaching mechanism of Co and Li from spent lithium-ion battery by the mixed culture of acidophilic sulfur-oxidizing and iron-oxidizing bacteria.

The bioleaching mechanism of Co and Li from spent lithium-ion batteries by mixed culture of sulfur-oxidizing and iron-oxidizing bacteria was investigated. It was found that the highest release of Li occurred at the lowest pH of 1.54 with elemental sulfur as an energy source, the lowest occurred at the highest pH of 1.69 with FeS(2). In contrast, the highest release of Co occurred at higher pH and varied ORP with S + FeS(2), the lowest occurred at almost unchanged ORP with S. It is suggested that acid dissolution is the main mechanism for Li bioleaching independent of energy matters types, however, apart from acid dissolution, Fe(2+) catalyzed reduction takes part in the bioleaching process as well. Co(2+) was released by acid dissolution after insoluble Co(3+) was reduced into soluble Co(2+) by Fe(2+) in both FeS(2) and FeS(2) + S systems. The proposed bioleaching mechanism mentioned above was confirmed by the further results obtained from the experiments of bioprocess-stimulated chemical leaching and from the changes in structure and component of bioleaching residues characterized by XPS, SEM and EDX.
Evolution of Architectural Floor Plans

Layout planning is a process of sizing and placing rooms (e.g. in a house) while attempting to optimize various criteria. Often there are conflicting criteria such as construction cost, minimizing the distance between related activities, and meeting the area requirements for these activities. The process of layout planning has mostly been done by hand, with a handful of attempts to automate the process. This thesis explores some of these past attempts and describes several new techniques for automating the layout planning process using evolutionary computation. These techniques are inspired by the existing methods, while adding some of their own innovations. Additional experiments are done to test the possibility of allowing polygonal exteriors with rectilinear interior walls. Several multi-objective approaches are used to evaluate and compare fitness. The evolutionary representation and requirements specification used provide great flexibility in problem scope and depth and is worthy of considering in future layout and design attempts. The system outlined in this thesis is capable of evolving a variety of floor plans conforming to functional and geometric specifications. Many of the resulting plans look reasonable even when compared to a professional floor plan. Additionally polygonal and multi-floor buildings were also generated.
Discrete Content-aware Matrix Factorization

Precisely recommending relevant items from massive candidates to a large number of users is an indispensable yet computationally expensive task in many online platforms (e.g., Amazon.com and Netflix.com). A promising way is to project users and items into a Hamming space and then recommend items via Hamming distance. However, previous studies didn't address the cold-start challenges and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Discrete Content-aware Matrix Factorization (DCMF) model, 1) to derive compact yet informative binary codes at the presence of user/item content information; 2) to support the classification task based on a local upper bound of logit loss; 3) to introduce an interaction regularization for dealing with the sparsity issue. We further develop an efficient discrete optimization algorithm for parameter learning. Based on extensive experiments on three real-world datasets, we show that DCFM outperforms the state-of-the-arts on both regression and classification tasks.
Popularity versus similarity in growing networks

The principle that ‘popularity is attractive’ underlies preferential attachment, which is a common explanation for the emergence of scaling in growing networks. If new connections are made preferentially to more popular nodes, then the resulting distribution of the number of connections possessed by nodes follows power laws, as observed in many real networks. Preferential attachment has been directly validated for some real networks (including the Internet), and can be a consequence of different underlying processes based on node fitness, ranking, optimization, random walks or duplication. Here we show that popularity is just one dimension of attractiveness; another dimension is similarity. We develop a framework in which new connections optimize certain trade-offs between popularity and similarity, instead of simply preferring popular nodes. The framework has a geometric interpretation in which popularity preference emerges from local optimization. As opposed to preferential attachment, our optimization framework accurately describes the large-scale evolution of technological (the Internet), social (trust relationships between people) and biological (Escherichia coli metabolic) networks, predicting the probability of new links with high precision. The framework that we have developed can thus be used for predicting new links in evolving networks, and provides a different perspective on preferential attachment as an emergent phenomenon.
A case study of OSPF behavior in a large enterprise network

Open Shortest Path First (OSPF) is widely deployed in IP networks to manage intra-domain routing. OSPF is a link-state protocol, in which routers reliably flood "Link State Advertisements" (LSAs), enabling each to build a consistent, global view of the routing topology. Reliable performance hinges on routing stability, yet the behavior of large operational OSPF networks is not well understood. In this paper, we provide a case study on the eharacteristics and dynamics of LSA traffic for a large enterprise network. This network consists of several hundred routers, distributed in tens of OSPF areas, and connected by LANs and private lines. For this network, we focus on LSA traffic and analyze: (a) the class of LSAs triggered by OSPF's soft-state refresh, (b) the class of LSAs triggered by events that change the status of the network, and (c) a class of "duplicate" LSAs received due to redundancy in OSPF's reliable LSA flooding mechanism. We derive the baseline rate of refresh-triggered LSAs automatically from network configuration information. We also investigate finer time scale statistical properties of this traffic, including burstiness, periodicity, and synchronization. We discuss root causes of event-triggered and duplicate LSA traffic, as well as steps identified to reduce this traffic (e.g., localizing a failing router or changing the OSPF configuration).
Bayesian Neural Networks for Internet Traffic Classification

Internet traffic identification is an important tool for network management. It allows operators to better predict future traffic matrices and demands, security personnel to detect anomalous behavior, and researchers to develop more realistic traffic models. We present here a traffic classifier that can achieve a high accuracy across a range of application types without any source or destination host-address or port information. We use supervised machine learning based on a Bayesian trained neural network. Though our technique uses training data with categories derived from packet content, training and testing were done using features derived from packet streams consisting of one or more packet headers. By providing classification without access to the contents of packets, our technique offers wider application than methods that require full packet/payloads for classification. This is a powerful advantage, using samples of classified traffic to permit the categorization of traffic based only upon commonly available information
Adaptive Selective Verification

We consider Denial of Service (DoS) attacks within the province of a shared channel model in which attack rates may be large but are bounded and client request rates vary within fixed bounds. In this setting it is shown that the clients can respond effectively to an attack by using bandwidth as a payment scheme and time-out windows to adaptively boost request rates. The server will be able to process client requests with high probability while pruning out most of the attack by selective random sampling. Our protocol, which we call Adaptive Selective Verification (ASV) is shown to be efficient in terms of bandwidth consumption using both a theoretical model and network simulations. It differs from previously-investigated adaptive mechanisms for bandwidth-based payment by requiring very limited state on the server.
Causation, Action and Counterfactuals

The central aim of many empirical studies in the physical, behavioral, social, and biological sciences is the elucidation of cause-effect relationships among variables. It is through causeeffect relationships that we obtain a sense of a "deep understanding" of a given phenomenon, and it is through such relationships that we obtain a sense of being "in control," namely, that we are able to shape the course of events by deliberate actions or policies. It is for these two reasons, understanding and control, that causal thinking is so pervasive, popping up in everything from everyday activities to high-level decision-making: For example, every car owner wonders why an engine won't start; a cigarette smoker would like to know, given his/her specific characteristics, to what degree his/her health would be affected by refraining from further smoking; a policy maker would like to know to what degree anti-smoking advertising would reduce costs of health care; and so on. Although a plethora of data has been collected on cars and on smoking and health, the appropriate methodology for extracting answers to such questions from the data has been fiercely debated, partly because some fundamental questions of causality have not been given fully satisfactory answers. The two fundamental questions of causality are:
All learning is Local: Multi-agent Learning in Global Reward Games

In large multiagent games, partial observability , coordination, and credit assignmentpersistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world fr om a single agent’s limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and effectively learn a near-optimal policy in a wide variety of settings.A sequenceof increasinglycomplex empirical tests verifies the efficacy of this technique.
Simba: Efficient In-Memory Spatial Analytics

Large spatial data becomes ubiquitous. As a result, it is critical to provide fast, scalable, and high-throughput spatial queries and analytics for numerous applications in location-based services (LBS). Traditional spatial databases and spatial analytics systems are disk-based and optimized for IO efficiency. But increasingly, data are stored and processed in memory to achieve low latency, and CPU time becomes the new bottleneck. We present the Simba (Spatial In-Memory Big data Analytics) system that offers scalable and efficient in-memory spatial query processing and analytics for big spatial data. Simba is based on Spark and runs over a cluster of commodity machines. In particular, Simba extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and the DataFrame API. It introduces indexes over RDDs in order to work with big spatial data and complex spatial operations. Lastly, Simba implements an effective query optimizer, which leverages its indexes and novel spatial-aware optimizations, to achieve both low latency and high throughput. Extensive experiments over large data sets demonstrate Simba's superior performance compared against other spatial analytics system.
Learning Distributed Representations of Sentences from Unlabelled Data

Unsupervised methods for learning distributed representations of words are ubiquitous in today’s NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-bilinear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance.
ATR-Vis: Visual and Interactive Information Retrieval for Parliamentary Discussions in Twitter

The worldwide adoption of Twitter turned it into one of the most popular platforms for content analysis as it serves as a gauge of the public’s feeling and opinion on a variety of topics. This is particularly true of political discussions and lawmakers’ actions and initiatives. Yet, one common but unrealistic assumption is that the data of interest for analysis is readily available in a comprehensive and accurate form. Data need to be retrieved, but due to the brevity and noisy nature of Twitter content, it is difficult to formulate user queries that match relevant posts that use different terminology without introducing a considerable volume of unwanted content. This problem is aggravated when the analysis must contemplate multiple and related topics of interest, for which comments are being concurrently posted.  The method proposes a set of active retrieval strategies to involve an analyst in such a way that a major improvement in retrieval coverage and precision is attained with minimal user effort. ATR-Vis enables non-technical users to benefit from the aforementioned active learning strategies by providing visual aids to facilitate the requested supervision. This supports the exploration of the space of potentially relevant tweets, and affords a better understanding of the retrieval results. We evaluate our approach in scenarios in which the task is to retrieve tweets related to multiple parliamentary debates within a specific time span. We collected two Twitter datasets, one associated with debates in the Canadian House of Commons during a particular week in May 2014, and another associated with debates in the Brazilian Federal Senate during a selected week in May 2015. The two use cases illustrate the effectiveness of ATR-Vis for the retrieval of relevant tweets, while quantitative results show that our approach achieves high retrieval quality with a modest amount of supervision. Finally, we evaluated our tool with three external users who perform searching in social media as part of their professional work.
Community detection algorithms: a comparative analysis.

Uncovering the community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplified structure, which is very uncommon in real systems. Here we test several methods against a recently introduced class of benchmark graphs, with heterogeneous distributions of degree and community size. The methods are also tested against the benchmark by Girvan and Newman [ (2002)] and on random graphs. As a result of our analysis, three recent algorithms introduced by Rosvall and Bergstrom [ (2007);  (2008)], Blondel [ Theory Exp. (2008), P10008], and Ronhovde and Nussinov [ (2009)] have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.
Hidden Markov model-based speech emotion recognition

In this contribution we introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared throughout the paper. Within the first method a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased temporal complexity applying continuous hidden Markov models considering several states using low-level instantaneous features instead of global statistics. The paper addresses the design of working recognition engines and results achieved with respect to the alluded alternatives. A speech corpus consisting of acted and spontaneous emotion samples in German and English language is described in detail. Both engines have been tested and trained using this equivalent speech corpus. Results in recognition of seven discrete emotions exceeded 86% recognition rate. As a basis of comparison the similar judgment of human deciders classifying the same corpus at 79.8% recognition rate was analyzed.
Improved tools for biological sequence comparison.

We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.
Motor-Current-Based Estimation of Cartesian Contact Forces and Torques for Robotic Manipulators and Its Application to Force Control

We present a Kalman filter-based approach for estimating external forces and torques relying on a dynamic model of a serial-chain robotic manipulator where only motor signals (currents, joint angles, and joint speeds) are measurable. The method does not require any additional sensing compared to standard robot control systems. The approach exploits redundancy in 7DOF arms, but also applies to traditional 6DOF manipulators. Automatic filter calibration routines are presented minimizing the number of parameters that must be tuned in order to successfully apply the proposed scheme and to optimize estimation quality. The approach is verified by measurement data gathered from an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm. Furthermore, measurement results are presented employing force and torque estimates in a compliance control scheme, verifying that the estimation quality achieved is improved compared to existing approaches and is sufficient to employ the estimates in force-controlled applications.Note to Practitioners—More and more robotic applications involve contact with at least partially unknown environments. As a consequence, they require control approaches that go beyond the traditional position control. In particular, information about contact forces and torques has to be taken into account. However, integrating additional sensing equipment to obtain the required force/torque information is often technically challenging and expensive. Cartesian contact force and torque estimation allows obtaining force/torque information solely from available sensors. The estimation technique can be regarded as a virtual sensor, and hence this brief deals with a key technology enabling force controlled robotic applications such as assembly, grinding, and deburring without the need for expensive additional sensing.