nils-herrmann/clean_scirepeval huggingface dataset:
Deep Clustering using Auto-Clustering Output Layer

In this paper, we propose a novel method to enrich the representation provided to the output layer of feedforward neural networks in the form of an auto-clustering output layer (ACOL) which enables the network to naturally create sub-clusters under the provided main class labels. In addition, a novel regularization term is introduced which allows ACOL to encourage the neural network to reveal its own explicit clustering objective. While the underlying process of finding the subclasses is completely unsupervised, semi-supervised learning is also possible based on the provided classification objective. The results show that ACOL can achieve a 99.2% clustering accuracy for the semi-supervised case when partial class labels are presented and a 96% accuracy for the unsupervised clustering case. These findings represent a paradigm shift especially when it comes to harnessing the power of deep networks for primary and secondary clustering applications in large datasets.
A complete solution to asymmetric S-curve motion profile: Theory & experiments

In this paper, we propose the asymmetric S-curve motion profile that enables easy manipulation of jerks during the arrival time in order to effectively reduce the residual vibration. To derive the complete closed form solution to the asymmetric S-curve, a design parameter the so-called jerk ratio is introduced to scale down the jerks during the deceleration period so that the velocity profile is in an asymmetric S-curve. Thanks to the jerk ratio, the motion parameters are remarkably simplified in analytic forms for short, medium and long distances. The effectiveness of the proposed approach is validated by experiments with the AC motor control system. As the jerk ratio increases, the residual vibration decreases, but, the motion profile is lengthened as expected by simulations.
Design, fabrication and control of soft robots

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.
Incorporating query-specific feedback into learning-to-rank models

Relevance feedback has been shown to improve retrieval for a broad range of retrieval models. It is the most common way of adapting a retrieval model for a specific query. In this work, we expand this common way by focusing on an approach that enables us to do query-specific modification of a retrieval model for learning-to-rank problems. Our approach is based on using feedback documents in two ways: 1) to improve the retrieval model directly and 2) to identify a subset of training queries that are more predictive than others. Experiments with the Gov2 collection show that this approach can obtain statistically significant improvements over two baselines; learning-to-rank (SVM-rank) with no feedback and learning-to-rank with standard relevance feedback.
Improving Packet Delivery Performance of Publish/Subscribe Protocols in Wireless Sensor Networks

MQTT-S and CoAP are two protocols able to use the publish/subscribe model in Wireless Sensor Networks (WSNs). The high scalability provided by the publish/subscribe model may incur a high packet loss and therefore requires an efficient reliability mechanism to cope with this situation. The reliability mechanism of MQTT-S and CoAP employs a method which defines a fixed value for the retransmission timeout (RTO). This article argues that this method is not efficient for deploying publish/subscribe in WSN, because it may be unable to recover a packet, therefore resulting in a lower packet delivery ratio (PDR) at the subscriber nodes. This article proposes and evaluates an adaptive RTO method, which consists in using a Smooth Round-trip Time and multiplying it by a constant parameter (K). Thanks to this method, the reliability mechanism of MQTT-S and CoAP would be able to react properly to packet loss and would also be lightweight in terms of energy, memory and computing for sensor nodes where these resources are critical. We present a detailed evaluation of the effects of the K value on the calculation of the adaptive RTO method. We also establish the setting for obtaining the highest PDR on the subscriber nodes for single-hop and multi-hop scenarios. The results for single-hop scenario show that use of the appropriate K value for the adaptive RTO method increases the PDR up to 76% for MQTT-S and up to 38% for CoAP when compared with the use of fixed RTO method for both protocols, respectively. Meanwhile the same comparison for multi-hop scenario, the adaptive RTO method increases the PDR up to 36% for MQTT-S and up to 14% for CoAP.
Repetitive extreme-acceleration (14-g) spatial jumping with Salto-1P

In this work we present a new robotic system, Salto-1P, for exploring extreme jumping locomotion. Salto-1P weighs 0.098 kg, and has an active leg length of 14.4 cm. The robot is able to perform a standing vertical leap of 1.25 m, continuously hop to heights over 1 m, and jump over 2 m horizontally. Salto-1P uses aerodynamic thrusters and an inertial tail to control its attitude in the air. A linearized Raibert step controller was sufficient to enable unconstrained in-place hopping and forwards-backwards locomotion with external position feedback. We present studies of extreme jumping locomotion in which the robot spends just 7.7% of its time on the ground, experiencing accelerations of 14 times earth gravity in its stance phase. An experimentally collected dataset of 772 observed jumps was used to establish the range of achievable horizontal and vertical impulses for Salto-1P.
Posterior probability support vector Machines for unbalanced data

This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification problems is formulated and a new concept of support vectors established. Furthermore, a soft PPSVM with an interpretable parameter /spl nu/ is obtained which is similar to the /spl nu/-SVM developed by Scho/spl uml/lkopf et al., and an empirical method for determining the posterior probability is proposed as a new approach to determine /spl nu/. The main advantage of an PPSVM classifier lies in that fact that it is closer to the Bayes optimal without knowing the distributions. To validate the proposed method, two synthetic classification examples are used to illustrate the logical correctness of PPSVMs and their relationship to regular SVMs and Bayesian methods. Several other classification experiments are conducted to demonstrate that the performance of PPSVMs is better than regular SVMs in some cases. Compared with fuzzy support vector machines (FSVMs), the proposed PPSVM is a natural and an analytical extension of regular SVMs based on the statistical learning theory.
A preliminary radicalisation framework based on social engineering techniques

The use of online forums and social media sites by extremists for recruiting and radicalising individuals has been covered extensively by researchers. Meanwhile, the social engineering techniques utilised by these extremists to lure marginalised individuals into radicalisation has been neglected. In this article, the social engineering aspects of online radicalisation will be explored. Specifically, the five Principles of Persuasion in Social Engineering (PPSE) will be mapped onto the online radicalisation methods employed by extremists online. Analysing these tactics will aid in gaining a deeper understanding of the process of indoctrination and of the psychology of both the attacker and the target of such attacks. This understanding has enabled the development of a preliminary radicalisation framework based on the social traits of a target that may be exploited during an attack.
A framework for computational thinking across the curriculum

We describe a framework for implementing computational thinking in a broad variety of general education courses. The framework is designed to be used by faculty without formal training in information technology in order to understand and integrate computational thinking into their own general education courses. The framework includes examples of computational thinking in a variety of general education courses, as well as sample in-class activities, assignments, and other assessments for the courses. The examples in the different courses are related and differentiated using categories taken from Denning Great Principles of Computing, so that similar types of computational thinking appearing in different contexts are brought together. This aids understanding of the computational thinking found in the courses and provides a template for future work on new course materials. Specific examples of computational thinking in the design category are provided in the context of three distinct courses.
The City Browser : Utilizing Massive Call Data to Infer City Mobility Dynamics

This paper presents the City Browser, a tool developed to analyze the complexities underlying human mobility at the city scale. The tool uses data generated from mobile phones as a proxy to provide several insights with regards to the commuting patterns of the population within the bounds of a city. The three major components of the browser are the data warehouse, modules and algorithm, and the visualization interface. The modules and algorithm component utilizes Call Detail Records (CDRs) stored within the data warehouse to infer mobility patterns that are then communicated through the visualization interface. The modules and algorithm component consists of four modules: the spatial-temporal decomposition module, the home/work capturing module, the community detection module, and the flow estimation module. The visualization interface manages the output of each module to provide a comprehensive view of a city’s mobility dynamics over varying time scales. A case study is presented on the city of Riyadh in Saudi Arabia, where the browser was developed to better understand city mobility patterns.
A Pose-Invariant Descriptor for Human Detection and Segmentation

We present a learning-based, sliding window-style approach for the problem of detecting humans in still images. Instead of traditional concatenation-style image location-based feature encoding, a global descriptor more invariant to pose variation is introduced. Specifically, we propose a principled approach to learning and classifying human/non-human image patterns by simultaneously segmenting human shapes and poses, and extracting articulation-insensitive features. The shapes and poses are segmented by an efficient, probabilistic hierarchical part-template matching algorithm, and the features are collected in the context of poses by tracing around the estimated shape boundaries. Histograms of oriented gradients are used as a source of low-level features from which our pose-invariant descriptors are computed, and kernel SVMs are adopted as the test classifiers. We evaluate our detection and segmentation approach on two public pedestrian datasets.
Invertible Conditional GANs for image editing

Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes. Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications.
A Novel Rotor Design of Interior Permanent-Magnet Synchronous Motors to Cope with Both Maximum Torque and Iron-Loss Reduction

A novel rotor design of interior permanent-magnet synchronous motors (IPMSMs) has been developed in order to cope with both maximum torque and iron-loss reduction for wide-speed-range applications. First, the flux barrier and permanent-magnet shapes of an IPMSM are determined by using an automatic optimization method. In this optimization, the iron loss of the IPMSM under flux-weakening control at high speeds is minimized under the constrained condition of maximum torque at low speeds. As a result, a novel rotor, which has inner magnetic pole bodies, is obtained. The effectiveness of this design is discussed from both results of the finite-element analysis (FEA) and the measurement of a prototype motor. It is revealed that the iron loss of the proposed IPMSM at high speeds is significantly reduced, whereas the maximum torque decreases by only a few percent.
Clustering with Lower Bound on Similarity

We propose a new method, called SimClus, for clustering with lower bound on similarity. Instead of accepting k the number of clusters to find, the alternative similarity-based approach imposes a lower bound on the similarity between an object and its corresponding cluster representative (with one representative per cluster). SimClus achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic datasets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.
Balancing Utility and Security: Securing Cloud Federations of Public Entities

Following their practical needs and legal constraints, recent application of the cloud paradigm among public administrations has been focused on the deployment of private clouds. Due to the increasing amount of data and processing requirements, many organizations are considering possibilities to additionally optimize their infrastructures and collaborative processes by employing private cloud federations. In this work, we present our contribution based on three real-world use cases implemented in the course of the SUNFISH project. We consider intraand inter-organizational processes which demand secure and transparent infrastructure and data sharing. Based on derived requirements for data security and privacy in cloud federations, we propose a security governance architecture which enables a multi-layered, context and process-aware policy enforcement in heterogeneous environments. The proposed architecture relies on the micro-services paradigm to support scalability and provides additional security by integrating reactive and transformative security controls. To prove the feasibility of this work we provide performance evaluation of our implementation.
A multimodal feature learning approach for sentiment analysis of social network multimedia

In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.
Understanding Color Image Processing by Machine Vision for Biological Materials

Handling (Post harvest) process of fruits is completed in several steps: washing, sorting, grading, packing, transporting and storage. The fruits sorting and grading are considered the most important steps of handling. Product quality and quality evaluation are important aspects of fruit and vegetable production. Sorting and grading are major processing tasks associated with the production of fresh-market fruit types. Considerable effort and time have been invested in the area of automation.
Head-shoulder detection using joint HOG features for people counting and video surveillance in library

Pedestrian detection is an important problem in video surveillance. While pedestrians often have diverse postures and mutual occlusion which make the detection quite difficult, their head-shoulder portions are relatively stable. Thus we choose to use head-shoulder outline features of a pedestrian for detecting. First, we apply a hierarchical classification method using Haar features and HOG features to head-shoulder location detection. Second, we define a combined feature named Joint HOG based on the symmetry of head-shoulder portion. Third, we filter out most negative samples by using the Haar classifier. Finally, we execute an elaborate HOG verification and thus obtain the head-shoulder target box expected. Experimental results show that our method achieved a real-time processing accuracy rate of nearly 90%, arguing that it is applicable to people counting and video surveillance in library.
Prediction of Bitcoin prices with machine learning methods using time series data

In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012–2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training phase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model.
Highly reliable M1X MLC NAND flash memory cell with novel active air-gap and p+ poly process integration technologies

Our Middle-1X nm MLC NAND (M1X) flash cell is intensively characterized with respect to reliability and manufacturability. For the first time, the novel active air-gap technology is applied to alleviate the drop of channel boosting potential of program inhibition mode, BL-BL interference is reduced to our 2y nm node level by this novel integration technology. Furthermore, it also relaxes the effect of process variation like EFH (Effective Field oxide Height) on cell Vt distribution. Better endurance and retention characteristics can be obtained by p+ doped poly gate. By optimization of active air-gap profile and poly doping level, M1X nm MLC NAND flash memory has been successfully implemented with superior manufacturability and acceptable reliability.
A linear algorithm for finding Hamiltonian cycles in 4-connected maximal planar graphs

The Hamiltonian cycle problem is one of the most popular NP-complete problems, and remains NP-complete even if we restrict ourselves to a class of (3-connected cubic) planar graphs . Therefore, there seems to be no polynomial-time algorithm for the Hamiltonian cycle problem. However, for certain (nontrivial) classes of restricted graphs, there exist polynomial-time algorithms [3,4,6]. In fact, employing the proof technique used by Tutte [lo], Gouyou-Beauchamps has given an 0(n3) time algorithm for finding a Hamiltonian cycle in a 4-connected planar graph G, where n is the number of vertices of G [6]. Although such a graph G always has a Hamiltonian cycle [lo], it is not an easy matter to actually find a Hamiltonian cycle of G. However, for a little more restricted class of graphs, i.e., the class of 4-connected maximal planar graphs, we can construct an efficient algorithm. One can easily design an O(n*) time algorithm to find a Hamiltonian cycle in a 4-connetted maximal planar graph G with n vertices, entirely based on Whitney’s proof of his theorem [l 11. In this paper, we present an efficient algorithm for the problem, based on our simplified version of Whitney’s proof of his result. We employ ‘divide and conquer’ and some other techniques in the algorithm. The computational complexity of our algorithm is linear, thus optimal to within a constant factor.
Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications

The broad success of Hadoop has led to a fast-evolving and diverse ecosystem of application engines that are building upon the YARN resource management layer. The open-source implementation of MapReduce is being slowly replaced by a collection of engines dedicated to specific verticals. This has led to growing fragmentation and repeated efforts with each new vertical engine re-implementing fundamental features (e.g. fault-tolerance, security, stragglers mitigation, etc.) from scratch.
 In this paper, we introduce Apache Tez, an open-source framework designed to build data-flow driven processing runtimes. Tez provides a scaffolding and library components that can be used to quickly build scalable and efficient data-flow centric engines. Central to our design is fostering component re-use, without hindering customizability of the performance-critical data plane. This is in fact the key differentiator with respect to the previous generation of systems (e.g. Dryad, MapReduce) and even emerging ones (e.g. Spark), that provided and mandated a fixed data plane implementation. Furthermore, Tez provides native support to build runtime optimizations, such as dynamic partition pruning for Hive.
 Tez is deployed at Yahoo!, Microsoft Azure, LinkedIn and numerous Hortonworks customer sites, and a growing number of engines are being integrated with it. This confirms our intuition that most of the popular vertical engines can leverage a core set of building blocks. We complement qualitative accounts of real-world adoption with quantitative experimental evidence that Tez-based implementations of Hive, Pig, Spark, and Cascading on YARN outperform their original YARN implementation on popular benchmarks (TPC-DS, TPC-H) and production workloads.