Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Friction Compensation for Humanoid Robots without Joint-Torque Sensors

This paper presents a novel approach for friction compensation on humanoid robots originally designed for position control in order to enable torque-based control methods on such systems. Due to their design, this kind of robots lacks joint-torque sensors and is equipped with high-reduction gearboxes. Nevertheless, we can still apply torque commands using torque estimation from motor currents. Moreover, the high gear reduction ratio produces high dynamic friction which significantly affects the robots drives and must be taken into account. Considering the LuGre friction model, an adaptive friction compensator based on a second-order sliding mode is developed and illustrated on a humanoid robot. The proposed method only relies on IMU data as well as joint position and velocity measurements from the joint encoders and is applied to the 12 DoFs of the robot's legs for CoM motion. The proposed control approach does not require the FT sensors mounted on the ankles.
Worker Classification based on Answer Pattern for Finding Typical Mistake Patterns

One of the problems in crowdsourcing is the development of appropriate instructions for workers. To improve task instructions, we must find typical mistake patterns. However, manually identifying these patterns is a cumbersome task. This study shows that a relatively simple approach classifying workers in terms of their understanding of task instructions is promising for addressing this issue. The verification results by domain experts suggest that the output is useful for improving task instructions.
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier

Insider threats continue to present a major challenge for the information security community. Despite constant research taking place in this area; a substantial gap still exists between the requirements of this community and the solutions that are currently available. This paper uses the CERT dataset r4.2 along with a series of machine learning classifiers to predict the occurrence of a particular malicious insider threat scenario - the uploading sensitive information to wiki leaks before leaving the organization. These algorithms are aggregated into a meta-classifier which has a stronger predictive performance than its constituent models. It also defines a methodology for performing pre-processing on organizational log data into daily user summaries for classification, and is used to train multiple classifiers. Boosting is also applied to optimise classifier accuracy. Overall the models are evaluated through analysis of their associated confusion matrix and Receiver Operating Characteristic (ROC) curve, and the best performing classifiers are aggregated into an ensemble classifier. This meta-classifier has an accuracy of 96.2% with an area under the ROC curve of 0.988.
Diftogram: A Better Texture Discriminator

This article aims to design the most efficient texture discriminator by proposing two alterations to the existing algorithms. The first argument presented in this article is that the direct use of Unser’s difference histogram (not sum) as input to a classifier yields more successful results than Haralick’s method. The second proposal is the introduction of two different methods for the selection of the pixel distance and the orientation. Both mathematical and empirical evidence is presented, proving that these methods improve the texture discrimination rate by %250–%300 while reducing the computational complexity by %50.
Applied attention-based LSTM neural networks in stock prediction

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. This paper proposes an attention-based long short-term memory model to predict stock price movement and make trading strategies
Natural Language Generation Using Dependency Tree Decoding for Spoken Dialog Systems

In this paper, we propose a new natural language generation (NLG) method for spoken dialog systems and demonstrate its capacity. Studies on NLG often employ sequence decoding, which generates the words comprising a sentence in sequential order and uses the input generated by each word in the previous step. In contrast, we propose a decoding method that employs a sequence generated by traversing a dependency tree with feed input to a pair consisting of a parent and sibling in the dependency tree. As a result, the most important words are generated first, thereby enabling words with greater relevance to be fed into the process. At prediction time, our model generates dependency trees and converts the trees into sentences. The proposed decoding method was evaluated by re-implementing a semantically controlled long short-term memory structure for NLG, and the input and predicted sequence were converted to allow dependency tree decoding. The experimental results indicated that our suggested approach, i.e., dependency tree decoding, dramatically elevates the BLEU-score and naturalness. Furthermore, when creating sentences with ${n}$ -best using dependency tree decoding, the word diversity of the output sentences was increased by approximately 6%, offering a more diverse sentence pattern.
The DEEP-ER Project: I/O and Resiliency Extensions for the Cluster-Booster Architecture

The recently completed research project DEEP-ER has developed a variety of hardware and software technologies to improve the I/O capabilities of next generation high-performance computers, and to enable applications recovering from the larger hardware failure rates expected on these machines. The heterogeneous Cluster-Booster architecture - first introduced in the predecessor DEEP project - has been extended by a multi-level memory hierarchy employing non-volatile and network-attached memory devices. Based on this hardware infrastructure, an I/O and resiliency software stack has been implemented combining and extending well established libraries and software tools, and sticking to standard user-interfaces. Real-world scientific codes have tested the projects' developments and demonstrated the improvements achieved without compromising the portability of the applications.
A New Architecture for Distributed Computing in Named Data Networking

With the development of new technologies such as big data, data mining and artificial intelligence, there is a great demand for distributed computing. Named Data Networking (NDN) is a promising future Internet architecture and is receiving more and more research attentions. In this paper, we propose a new architecture to support distributed computing in NDN. Due to the nature of NDN, the computing nodes can be selected in proximity to consumers/users. In addition, we propose a new privacy protection scheme that allows users to anonymously access network resources, e.g., computing resources. The effectiveness and overhead of this architecture and the devised privacy protection scheme are evaluated. The results demonstrate the feasibility of the proposed architecture.
Automatic Diet Recording Based on Deep Learning

Diet assessment plays an important role in the prevention and treatment of many diseases, but it takes too much time and money to do a thorough long-term diet record. In order to make the recording efficient and affordable, we aim at building an automatic diet recording system, which can improve the automation level of diet recording through deep learning algorithms. And two contributions have been made in this paper. First, several deep learning models are trained and evaluated on the commonly used food image datasets, and after fusing several popular convolution neural network models, the top-1 accuracy on UEC FOOD 100, UEC FOOD 256, Food-101 and ChineseFoodNet datasets can reach 84.0%, 75.9%, 84.5% and 79.8% respectively. Second, based on the trained deep learning models, we build an automatic diet recording prototype system, which performs well during the test in a real canteen. The work of this paper suggests that deep learning can achieve a high accuracy in the automatic identification of dietary objects, which will help to promote the research in the field of disease prevention and rehabilitation.
A Multi-loss Deep Network Based on Pyramid Pooling for Person Re-identification

Person re-identification (re-ID) can be regarded as a retrieval problem. The challenge of this task is mainly lying in that people's appearance often undergoes dramatic changes across camera views due to severe occlusions, illumination, complex background clutter, and large pose variation. Recently, many deep learning approaches are successfully employed in person re-ID, which have achieved state-of-the-art performance in a period. However, it easily neglects the local discriminative details on the images and is not enough to cover discriminative information. Therefore, we employ ResNet50 as a basic network and propose a multi-loss siamese network with pyramid pooling structure for person re-ID. Our network can get a better discriminative representation and achieve a better performance on two large public datasets including Market1501 and CUHK03. We also report competitive accuracy compared with previous approach, which demonstrates the effectiveness of proposed method.
Deriving Real-time City Crowd Flows by Heterogeneous Big Urban Data

Real-time city crowd flows are extremely important for facility placement, transportation management, and public safety. In this paper, we show how to derive the real-time city crowd flows with heterogeneous urban data. Unlike existing prediction-based approaches, our proposal does not rely on the training data and learning models. We propose a computation framework for it by exploiting the massive heterogeneous urban data, which includes both immutable data (i.e., bus and subway stations, commercial-based regions) and mutable data (i.e., real-time taxi, bus, subway transactions and trajectories). In addition, our solution provides accurate and timely city crowd-flows.To provide a practical solution for it, we first partition the city into commercial-based regions with geographic information (e.g., road network, administrative regions). Then, we devise three major components (i.e., urban data fusion model, heterogeneous urban data integration model, and effective crowd flows computation model) in the computation framework to process massive heterogeneous urban data effectively and derive city crowd flows accurately. Finally, we conduct extensive experiments to demonstrate the effectiveness and efficiency of our proposed solution with real heterogeneous urban data in Shenzhen, China.
Threat Analysis of Natural Gas Control System Based on Physical Process Constraints

Natural gas is one of the essential energy sources for daily life. The increased reliance on energy makes control systems attractive targets for attackers. This paper explains how the control network works and describes the negative influence of attacking physical process. The paper proposes denial-of-service (DoS) attacks model and integrity attacks for the control system. In addition, our work focus on analyzing triethylene glycol (TEG) dehydration process flow for discovering the influencing factors, operational constraints and safety constraints in the dehydration process. Then, we evaluate physical and economic influences for DoS attacks and integrity attacks on TEG dehydration process in our physical testbed. The consequence shows that these kinds of attacks scenarios will drive the plant into an unsafe state. Finally, we propose an effective mechanism to defense DoS attacks and integrity attacks on TEG dehydration process.
Autonomous Bipedal Humanoid Grasping with Base Repositioning and Whole-Body Control

Autonomous behaviors in humanoid robots are generally implemented by considering the robot as two separate parts, using the lower body for locomotion and balancing, and the upper body for manipulation actions. This paper provides a unified framework for autonomous grasping with bipedal robots using a compliant whole-body controller. The grasping action is based on parametric grasp planning for unknown objects using shape primitives, which allows a generation of multiple grasp poses on the object. A reach ability analysis is used to select the final grasp, and also for triggering a base repositioning behavior that locates the robot on a better position for grasping the desired object more confidently, considering all grasps and the uncertainty in reaching the desired position. The whole-body controller accounts for perturbations at any level and ensures a successful execution of the intended task. The approach is implemented in the humanoid robot TORO, and different experiments demonstrate its robustness and flexibility.
Feature Selection with Fast Correlation-Based Filter for Breast Cancer Prediction and Classification Using Machine Learning Algorithms

Breast cancer is the first female cancer responsible for high mortality worldwide. Despite the progress that has made it possible to better understand the mechanisms of cancer development, the causes of breast cancer are currently unknown. Nevertheless, studies have identified some risk factors that promote breast cancer and a healthy lifestyle can reduce risk. In Morocco, breast cancer is the first cancer in women. It represents 34.3% of all female cancers. In this work, the Fast Correlation-Based Feature selection (FCBF) method is used to filter irrelevant and redundant characteristics in order to improve the quality of cancer classification, and we will provide an overview of the evolution of key data in the health system and apply five learning algorithms to a breast cancer data set. The purpose of this research work is to predict breast cancer, using several machine-learning algorithms that are Random Forest, Naïve Bayes, Support Vector Machines SVM, K-Nearest Neighbors K-NN, and Multilayer Perception MLP, in order to select the most effective algorithm with and without FCBF. The experimental results show that SVM gives the highest accuracy of 97.9% without FCBF but if we apply this method we find that the SVM and MLP show the best results in comparison with other algorithms. The results will help to choose the best learning algorithm machine classification for breast cancer prediction.
A Multi-Granular Relative Density Model for Class Noise Detection

Class noise can cause overfitting of learning methods and deteriorate their generalizability. Although relative density can detect class noise effectively, it has a time complexity of O(N2) and a low efficiency. To address this problem, by introducing the granular computing into the relative density model, this paper proposes a Multi-Granularity Relative Density (MGRD) model for class noise detection. In an experiment, we tested the effect of parameters on its performance under classifiers. The experimental results on benchmark data sets demonstrated that it had a higher efficiency than conventional methods. In addition, it exhibited better generalizability in comparison with the conventional methods because of the good robustness of granular computing on many cases.
Artificial Neural Network Based Test Escape Screening Using Generative Model

In test of large scale integration (LSI) circuit, test escape is always regarded as a critical issue since significant cost is imposed to manufacturing cost. In this paper, we propose a novel outlier screening method for test escape. The proposed method exploits variational autoencoder (VAE) that is widely used to design complex generative model in artificial neural network field. While a typical autoencoder (AE) simply extracts features of training data, the VAE does it as probability distribution, and thus it can avoid potential risk of overfitting by using the probability distributions as a regularizer. Moreover, the proposed method effectively detects test escapes by utilizing the probability distributions as likelihood function of good chips. Through experiments using an industrial production test data, we demonstrate that the proposed method detects test escapes more than approximately 8.5 times as compared to that using a conventional AE-based method.
Efficient Stacked Cylindrical Dielectric Resonator Antenna for Anticollision Short Range Radar at 79GHz

This work presents the design of a high gain Stacked Cylindrical Dielectric Resonator Antenna (Sc. CDRA) fed by a micro-strip line for anti-collision radar SRR application at 79 GHz. The first proposed stacked antenna, which is resonating at 79 GHz and has a simulated gain of 8.39dB, is formed by two stacked cylindrical dielectric resonators (DR) materials of dielectric constants values respectively 17.9 and 16.9; but relatively near maximum values, of the main and the side lobs, remains a disadvantage of this first design. In order to increase the antenna’ performances such as lowering return loss values, enhancing gain and, particularly, reducing radiation patterns side lobs magnitudes, we have added another cylindrical DR having a low permittivity value of 9. The simulated single Three Sc CDRA has a high gain (11.8 dB) and a narrow radiation beam where half power beam width (HPBW) is 35 degree at 79 GHz. Far field radiation patterns and return loss parameter of the structure have been investigated to extract the optimal design where results demonstrated a very low S11values and desired directional properties of antenna radiation pattern at 79 GHz.
A Leader-Following Formation Control Bearing-Based Approach

In this paper, bearing rigidity theory and Routh-Hurwitz stability criterion are used to study leader-following formation maneuver. The target formation is constrained by inter-neighbor bearings. Due to the invariance of bearings, control of first-leader can achieve translational formation control and changing the relative position constraint between first leader and second leader can achieve scaling formation control. Considering the practical use of a multi-agent system, we adopt a realistic model for followers which differs from the conventional single-integrator or double-integrator model. A control law for second leader and distributed control laws for followers are proposed and the global formation stability is analyzed. Numerical simulations are presented to support the theoretical results.
Computational Efficient Balance Control for a Lightweight Biped Robot with Sensor Based ZMP Estimation

This paper presents a computational efficient balance control algorithm developed for a lightweight biped. A LIP model of the robot is combined with the ZMP calculation to derive a joint space control action based on a PD controller. Furthermore, a method is implemented to estimate the ZMP directly from the center of pressure measured using the force sensors installed under the feet of the robot. This, allows a real time implementation of the controller without using the robot direct kinematics, reducing model inaccuracies and improving the controller reactivity. Simulation results and tests on the real robot prototype shows that the control system is able to compensate for external disturbances forces up to 10N reducing the oscillations of 60%.
CoDe-DTI: Collaborative Deep Learning-based Drug-Target Interaction Prediction

Drug-target interaction (DTI) prediction is a problem that identifies novel protein-ligand interactions from previous information. DTI plays an important role in computer-aided drug discovery because it is related to many aspects of drug discovery, such as virtual screening, target prediction, side effect prediction, and drug repositioning. Previous methods can be divided into two types: content-based methods and collaborative filtering. However, both types have problems, namely, a lack of diversity and “cold-start” problems. In this study, we developed a new method named CoDe-DTI (COllaborative DEep learning-based Drug Target Interaction predictor) that combines both methods to avoid these problems. CoDe-DTI is based on collaborative deep learning, which introduces the information of chemical structures into the latent variables by combining probabilistic matrix factorization with a denoising autoencoder. Fivefold cross validation showed that CoDe-DTI significantly outperformed other machine learning-based methods regarding hit rate (top 5%). Comparing between drugwise cross validation and interactionwise cross validation, CoDe-DTI still works even when there is no interaction information of the input ligand exists. The source code for CoDe-DTI is available at: https://github.con/sekijima-lab/CoDe-DTI.
Conceptual Design of a Variable Stiffness Mechanism in a Humanoid Ankle Using Parallel Redundant Actuation

Future robots will rely more than today on high precision, better energy efficiency and safe handling (e.g. human-machine interaction). An inevitable step in the development of new robots is therefore the improvement of existing mechanisms, since better sensors and algorithms do not satisfy the demands alone. During the last three decades, Parallel Redundant Mechanisms (PRM) came more into the focus of research, as they are advantageous in terms of singularity avoidance, fast movements and energy efficiency. Subsequently, yet another technology - the Variable Impedance Actuator (VIA) - emerged which proposes to change its inherent stiffness allowing an adaptation to its environment and to handle for example dynamic movements or shock absorptions. This work aims to create a new mechanism where a stiffness and position control for 2 degrees of freedom (DOF) is achieved by 3 actuators with flexible elements. It is thus a combination of the PRM and VIA, while taking advantage of both technologies but asking for a more sophisticated mathematical description. Practical implementation is intended for a humanoid ankle mechanism. Kinetostatic and stiffness models are derived and incorporated into the simulation of the mechanism. The simulations show that improvements in terms of singularity removal and dexterity are achieved. Furthermore, the adaptation of human like gait performances is presented.
A quasi-online geometric errors compensation method on CNC machine tool

The design of the quasi-online geometric errors compensation method on CNC machine tool using the Ballbar device is described. Nowadays, control and compensation of CNC machine tools are essential phases of their operation. The control is usually manual; the operator and the measuring apparatus jointly perform the measurement and subsequent machine compensation. The present paper describes the procedure and the experiment designed to assess machine condition under various operating conditions. The principle of performed machine tests is based on circular interpolation in manual regime. Prior to experiments, relevant evaluations are made, and quasi-online compensation is devised. For circularity measurement, the DMU 125 P DuoBlock CNC machining centre was chosen
Investigation of estimator algorithms for high speed drive systems

In high speed drives both the sampling over reference frequency ratio F and the carrier over reference frequency mf is a low value. The low mf ratio results voltage and current harmonic spectra far more unfavorable than at standard ratios. The low F ratio can result uncertainty and inaccuracy in the calculation of the magnitude and angle of stator or rotor flux vector or in the estimation of the actual value of the speed.The current paper derives the discrete equations of different flux and speed estimator algorithms by using Tustin approximation. The performance of the algorithms, both in open and closed loop, is demonstrated via numerical simulation using a high speed motor drive with a low sampling to fundamental frequency ratio.