nils-herrmann/clean_scirepeval huggingface dataset:
Representation Learning for Speech Emotion Recognition

Speech emotion recognition is an important problem with applications as varied as human-computer interfaces and affective computing. Previous approaches to emotion recognition have mostly focused on extraction of carefully engineered features and have trained simple classifiers for the emotion task. There has been limited effort at representation learning for affect recognition, where features are learnt directly from the signal waveform or spectrum. Prior work also does not investigate the effect of transfer learning from affective attributes such as valence and activation to categorical emotions. In this paper, we investigate emotion recognition from spectrogram features extracted from the speech and glottal flow signals; spectrogram encoding is performed by a stacked autoencoder and an RNN (Recurrent Neural Network) is used for classification of four primary emotions. We perform two experiments to improve RNN training : (1) Representation Learning Model training on the glottal flow signal to investigate the effect of speaker and phonetic invariant features on classification performance (2) Transfer Learning RNN training on valence and activation, which is adapted to a four emotion classification task. On the USC-IEMOCAP dataset, our proposed approach achieves a performance comparable to the state of the art speech emotion recognition systems.
A Human--Exoskeleton Interface Utilizing Electromyography

This paper presents a human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation. These signals are recorded with electrodes attached to the skin on top of selected muscles and reflect the activation of the observed muscle. They are evaluated by a sophisticated but simplified biomechanical model of the human body to derive the desired action of the operator. A support action is computed in accordance to the desired action and is executed by the exoskeleton. The biomechanical model fuses results from different biomechanical and biomedical research groups and performs a sensible simplification considering the intended application. Some of the model parameters reflect properties of the individual human operator and his or her current body state. A calibration algorithm for these parameters is presented that relies exclusively on sensors mounted on the exoskeleton. An exoskeleton for knee joint support was designed and constructed to verify the model and to investigate the interaction between operator and machine in experiments with force support during everyday movements.
Background–Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering

Background estimation and foreground segmentation are important steps in many high-level vision tasks. Many existing methods estimate background as a low-rank component and foreground as a sparse matrix without incorporating the structural information. Therefore, these algorithms exhibit degraded performance in the presence of dynamic backgrounds, photometric variations, jitter, shadows, and large occlusions. We observe that these backgrounds often span multiple manifolds. Therefore, constraints that ensure continuity on those manifolds will result in better background estimation. Hence, we propose to incorporate the spatial and temporal sparse subspace clustering into the robust principal component analysis (RPCA) framework. To that end, we compute a spatial and temporal graph for a given sequence using motion-aware correlation coefficient. The information captured by both graphs is utilized by estimating the proximity matrices using both the normalized Euclidean and geodesic distances. The low-rank component must be able to efficiently partition the spatiotemporal graphs using these Laplacian matrices. Embedded with the RPCA objective function, these Laplacian matrices constrain the background model to be spatially and temporally consistent, both on linear and nonlinear manifolds. The solution of the proposed objective function is computed by using the linearized alternating direction method with adaptive penalty optimization scheme. Experiments are performed on challenging sequences from five publicly available datasets and are compared with the 23 existing state-of-the-art methods. The results demonstrate excellent performance of the proposed algorithm for both the background estimation and foreground segmentation.
Corticospinal excitability during observation and imagery of simple and complex hand tasks: Implications for motor rehabilitation

Movement observation and imagery are increasingly propagandized for motor rehabilitation. Both observation and imagery are thought to improve motor function through repeated activation of mental motor representations. However, it is unknown what stimulation parameters or imagery conditions are optimal for rehabilitation purposes. A better understanding of the mechanisms underlying movement observation and imagery is essential for the optimization of functional outcome using these training conditions. This study systematically assessed the corticospinal excitability during rest, observation, imagery and execution of a simple and a complex finger-tapping sequence in healthy controls using transcranial magnetic stimulation (TMS). Observation was conducted passively (without prior instructions) as well as actively (in order to imitate). Imagery was performed visually and kinesthetically. A larger increase in corticospinal excitability was found during active observation in comparison with passive observation and visual or kinesthetic imagery. No significant difference between kinesthetic and visual imagery was found. Overall, the complex task led to a higher corticospinal excitability in comparison with the simple task. In conclusion, the corticospinal excitability was modulated during both movement observation and imagery. Specifically, active observation of a complex motor task resulted in increased corticospinal excitability. Active observation may be more effective than imagery for motor rehabilitation purposes. In addition, the activation of mental motor representations may be optimized by varying task-complexity.
Semi-supervised learning by disagreement

In many real-world tasks, there are abundant unlabeled examples but the number of labeled training examples is limited, because labeling the examples requires human efforts and expertise. So, semi-supervised learning which tries to exploit unlabeled examples to improve learning performance has become a hot topic. Disagreement-based semi-supervised learning is an interesting paradigm, where multiple learners are trained for the task and the disagreements among the learners are exploited during the semi-supervised learning process. This survey article provides an introduction to research advances in this paradigm.
Regular polygon detection

This paper describes a new robust regular polygon detector. The regular polygon transform is posed as a mixture of regular polygons in a five dimensional space. Given the edge structure of an image, we derive the a posteriori probability for a mixture of regular polygons, and thus the probability density function for the appearance of a mixture of regular polygons. Likely regular polygons can be isolated quickly by discretising and collapsing the search space into three dimensions. The remaining dimensions may be efficiently recovered subsequently using maximum likelihood at the locations of the most likely polygons in the subspace. This leads to an efficient algorithm. Also the a posteriori formulation facilitates inclusion of additional a priori information leading to real-time application to road sign detection. The use of gradient information also reduces noise compared to existing approaches such as the generalised Hough transform. Results are presented for images with noise to show stability. The detector is also applied to two separate applications: real-time road sign detection for on-line driver assistance; and feature detection, recovering stable features in rectilinear environments.
City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.
Vulnerability Assessment of Cybersecurity for SCADA Systems

Vulnerability assessment is a requirement of NERC's cybersecurity standards for electric power systems. The purpose is to study the impact of a cyber attack on supervisory control and data acquisition (SCADA) systems. Compliance of the requirement to meet the standard has become increasingly challenging as the system becomes more dispersed in wide areas. Interdependencies between computer communication system and the physical infrastructure also become more complex as information technologies are further integrated into devices and networks. This paper proposes a vulnerability assessment framework to systematically evaluate the vulnerabilities of SCADA systems at three levels: system, scenarios, and access points. The proposed method is based on cyber systems embedded with the firewall and password models, the primary mode of protection in the power industry today. The impact of a potential electronic intrusion is evaluated by its potential loss of load in the power system. This capability is enabled by integration of a logic-based simulation method and a module for the power flow computation. The IEEE 30-bus system is used to evaluate the impact of attacks launched from outside or from within the substation networks. Countermeasures are identified for improvement of the cybersecurity.
Phishing Detection Using Neural Network

The goal of this project is to apply multilayer feedforward neural networks to phishing email detection and evaluate the effectiveness of this approach. We design the feature set, process the phishing dataset, and implement the neural network (NN) systems. We then use cross validation to evaluate the performance of NNs with different numbers of hidden units and activation functions. We also compare the performance of NNs with other major machine learning algorithms. From the statistical analysis, we conclude that NNs with an appropriate number of hidden units can achieve satisfactory accuracy even when the training examples are scarce. Moreover, our feature selection is effective in capturing the characteristics of phishing emails, as most machine learning algorithms can yield reasonable results with it.
Knee and hip kinetics during normal stair climbing.

Understanding joint kinetics during activities of daily living furthers our understanding of the factors involved in joint pathology and the effects of treatment. In this study, we examined hip and knee joint kinetics during stair climbing in 35 young healthy subjects using a subject-specific knee model to estimate bone-on-bone tibiofemoral and patello-femoral joint contact forces. The net knee forces were below one body weight while the peak posterior-anterior contact force was close to one body weight. The peak distal-proximal contact force was on average 3 times body weight and could be as high as 6 times body weight. These contact forces occurred at a high degree of knee flexion where there is a smaller joint contact area resulting in high contact stresses. The peak knee adduction moment was 0.42 (0.15) Nm/kg while the flexion moment was 1.16 (0.24) Nm/kg. Similar peak moment values, but different curve profiles, were found for the hip. The hip and knee posterior-anterior shear forces and the knee flexion moment were higher during stair climbing than during level walking. The most striking difference between stair ascent and level walking was that the peak patello-femoral contact force was 8 times higher during stair ascent. These data can be used as baseline measures in pathology studies, as input to theoretical joint models, and as input to mechanical joint simulators.
On the Discrimination-Generalization Tradeoff in GANs

Generative adversarial training can be generally understood as minimizing certain moment matching loss defined by a set of discriminator functions, typically neural networks. The discriminator set should be large enough to be able to uniquely identify the true distribution (discriminative), and also be small enough to go beyond memorizing samples (generalizable). In this paper, we show that a discriminator set is guaranteed to be discriminative whenever its linear span is dense in the set of bounded continuous functions. This is a very mild condition satisfied even by neural networks with a single neuron. Further, we develop generalization bounds between the learned distribution and true distribution under different evaluation metrics. When evaluated with neural distance, our bounds show that generalization is guaranteed as long as the discriminator set is small enough, regardless of the size of the generator or hypothesis set. When evaluated with KL divergence, our bound provides an explanation on the counter-intuitive behaviors of testing likelihood in GAN training. Our analysis sheds lights on understanding the practical performance of GANs.
Fast Alternating Direction Optimization Methods

Alternating direction methods are a common tool for general mathematical programming and optimization. These methods have become particularly important in the field of variational image processing, which frequently requires the minimization of non-differentiable objectives. This paper considers accelerated (i.e., fast) variants of two common alternating direction methods: the Alternating Direction Method of Multipliers (ADMM) and the Alternating Minimization Algorithm (AMA). The proposed acceleration is of the form first proposed by Nesterov for gradient descent methods. In the case that the objective function is strongly convex, global convergence bounds are provided for both classical and accelerated variants of the methods. Numerical examples are presented to demonstrate the superior performance of the fast methods for a wide variety of problems.
24.5 A 4.5nW wake-up radio with −69dBm sensitivity

Wake-up receivers (WuRXs) are low-power radios that continuously monitor the RF environment to wake up a higher-power radio upon detection of a predetermined RF signature. Prior-art WuRXs have 100s of kHz of bandwidth  with low signature-to-wake-up-signal latency to help synchronize communication amongst nominally asynchronous wireless devices. However, applications such as unattended ground sensors and smart home appliances wake-up infrequently in an event-driven manner, and thus WuRX bandwidth and latency are less critical; instead, the most important metrics are power consumption and sensitivity. Unfortunately, current state-of-the-art WuRXs utilizing direct envelope-detecting [2] and IF/uncertain-IF [1,3] architectures  achieve only modest sensitivity at low-power (e.g., −39dBm at 104nW ), or achieve excellent sensitivity at higher-power (e.g., −97dBm at 99µW ) via active IF gain elements. Neither approach meets the needs of next-generation event-driven sensing networks.
Tavaxy: Integrating Taverna and Galaxy workflows with cloud computing support

Over the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts. In this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure. Tavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis. The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at http://www.tavaxy.org .
Image encryption using chaotic logistic map

In recent years, the chaos based cryptographic algorithms have suggested some new and efficient ways to develop secure image encryption techniques. In this communication,wepropose a newapproach for image encryption basedon chaotic logisticmaps in order tomeet the requirements of the secure image transfer. In the proposed image encryption scheme, an external secret key of 80-bit and two chaotic logistic maps are employed. The initial conditions for the both logistic maps are derived using the external secret key by providing different weightage to all its bits. Further, in the proposed encryption process, eight different types of operations are used to encrypt the pixels of an image and which one of them will be used for a particular pixel is decided by the outcome of the logistic map. To make the cipher more robust against any attack, the secret key is modified after encrypting each block of sixteen pixels of the image. The results of several experimental, statistical analysis and key sensitivity tests show that the proposed image encryption scheme provides an efficient and secure way for real-time image encryption and transmission. q 2006 Elsevier B.V. All rights reserved.
Web-based Smart Home Automation : PLC-controlled Implementation

The most important source of motivation in continuity of technological developments is to upgrade human living standards. The technological development provides and increases human-beings’ safety and comfort directly and indirectly. Developing technologies for this purpose directly affects the life standards by means of smart home systems design. It is possible to classify smart home systems into two as local and remote. In this study, a smart home automation system design was carried out by using Delta DVP28SV model PLC (Programmable Logic Controller). Smart home system can be controlled in two different ways either by any internet-connected device or an operator panel assembled on PLC. Control of the ventilation, lighting and security units in the smart home were carried out. Instantaneous states and variations of the smart home system which is feedback based are monitored and can be changed from the user interface prepared with the C# programming language. Unusual circumstances occurred in security units have been reported to the user with SMS (Short Message Service).
Deformable model for estimating clothed and naked human shapes from a single image

Estimation of human shape from images has numerous applications ranging from graphics to surveillance. A single image provides insufficient constraints (e.g. clothing), making human shape estimation more challenging. We propose a method to simultaneously estimate a person’s clothed and naked shapes from a single image of that person wearing clothing. The key component of our method is a deformable model of clothed human shape. We learn our deformable model, which spans variations in pose, body, and clothes, from a training dataset. These variations are derived by the non-rigid surface deformation, and encoded in various low-dimension parameters. Our deformable model can be used to produce clothed 3D meshes for different people in different poses, which neither appears in the training dataset. Afterward, given an input image, our deformable model is initialized with a few user-specified 2D joints and contours of the person. We optimize the parameters of the deformable model by pose fitting and body fitting in an iterative way. Then the clothed and naked 3D shapes of the person can be obtained simultaneously. We illustrate our method for texture mapping and animation. The experimental results on real images demonstrate the effectiveness of our method.
A deep learning approach to structured signal recovery

In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach.
Automatically Selecting Answer Templates to Respond to Customer Emails

Contact center agents typically respond to email queries from customers by selecting predefined answer templates that relate to the questions present in the customer query. In this paper we present a technique to automatically select the answer templates corresponding to a customer query email. Given a set of query-response email pairs we find the associations between the actual questions and answers within them and use this information to map future questions to their answer templates. We evaluate the system on a small subset of the publicly available Pine-Info discussion list email archive and also on actual contact center data comprising customer queries, agent responses and templates.