Miracle-dz/UniFAR-FTUs huggingface dataset:
Texture Recognition and Classification Based on Deep Learning

Texture image classification has always been a very active research topic in computer vision and pattern recognition. In this paper, based on the deep learning advanced framework– Keras, we use Convolutional Neural Networks (CNN) to classify 12 kinds of texture images. Because there are few original datasets and the quantity is not balanced, We used such as reflection enhancement, elastic transformation, random lighting and other data augmentation techniques to enhance and expand some texture images. On the one hand, it balances the number of various types of texture images. On the other hand, it enhances the generalization ability of the datasets. It plays a key role in the training of the model and improves the accuracy of the model. The final test accuracy is close to 90%, which is more advanced and convenient than the traditional texture image classification method, and the accuracy rate is higher.
Large Field of View Binocular Stereo Vision Sensor Calibration Method Based on 3D Virtual Target

In this paper, to address the problem of high calibration accuracy for the binocular stereo vision sensor of large field of view, a binocular stereo vision sensor calibration method using a three-dimensional virtual target constructed by a precision two-axis turntable, laser tracker and a single calibration reference point is studied. The precision two-axis turntable drives the camera capturing a fixed single optical reference point to make two-dimensional rotation, which construct a precise reference control field and achieve calibration. The internal and external reference calibration of the binocular stereo vision sensor is automatically and efficiently completed by the program control, so the method does not need to adjust or know the positional relationship between the reference point and the camera or the turntable. The error simulation analysis and calibration experiments verify that the calibration method proposed in this paper has high reliability and calibration accuracy.
Holistic Multi-Modal Memory Network for Movie Question Answering

Answering questions using multi-modal context is a challenging problem, as it requires a deep integration of diverse data sources. Existing approaches only consider a subset of all possible interactions among data sources during one attention hop. In this paper, we present a holistic multi-modal memory network (HMMN) framework that fully considers interactions between different input sources (multi-modal context and question) at each hop. In addition, to hone in on relevant information, our framework takes answer choices into consideration during the context retrieval stage. Our HMMN framework effectively integrates information from the multi-modal context, question, and answer choices, enabling more informative context to be retrieved for question answering. Experimental results on the MovieQA and TVQA datasets validate the effectiveness of our HMMN framework. Extensive ablation studies show the importance of holistic reasoning and reveal the contributions of different attention strategies to model performance.
Long-term impact of oral vancomycin, ciprofloxacin and metronidazole on the gut microbiota in healthy humans

OBJECTIVES The impact of combination antibiotic therapy on the composition of the intestinal microbiota remains ill-defined. We aimed to assess the effect of a 1 week antibiotic regimen on the intestinal microbiota of healthy humans for a period of up to 31 months. PATIENTS AND METHODS Thirteen healthy adult men received either no treatment or oral broad-spectrum antibiotics (ciprofloxacin, vancomycin and metronidazole) for 7 days. At four timepoints (prior to treatment, on day 9, day 49 and 8-31 months later) faecal samples were collected and analysed using 16S RNA gene sequencing. RESULTS The short-term impact of broad-spectrum antibiotics on the gut microbiota was profound, with a loss of diversity and drastic shifts in community composition. In addition, antibiotics significantly reduced the abundance of bacterial taxa with important metabolic functions, such as the production of butyrate. The microbiota showed a remarkable return towards baseline after 8-31 months, but community composition often remained altered from its initial state. CONCLUSIONS These findings suggest that combined treatment with vancomycin, ciprofloxacin and metronidazole has a profound and long-lasting effect on microbiota composition, the consequences of which remain largely unknown.
Nondestructive Detection Of Anchorage Quality Of Rock Bolt Based On DS-DBN-SVM

Deep learning is a hot topic in the field of machine learning, which provides a new method for the nondestructive testing of bolt anchorage. Aiming at the importance of identifying the type of bolt defects, this paper proposes a DS-DBN-SVM(Differential Search-Deep belief network-Support vector machine) model for identifying the type of bolt anchorage defects. The DS algorithm is used to optimize the weights and thresholds of the DBN network. The original acceleration signal of the anchor is used as the input of the DS-DBN model to extract the high-level features of the signal. Finally, the SVM classffier is used for defect identification. To assess the effectiveness of the method, and compare it with the traditional classification methods. The results show that the defect recognition effect of this method is better than the traditional defect recognition method, and the recognition rate reaches 95.45%.
Systematic fast-track transition from oncological treatment to dyadic specialized palliative home care: DOMUS – a randomized clinical trial

Background: The focus of specialized palliative care is to improve quality of life for patients with incurable cancer and their relatives including an increased opportunity to make their own choice of place of care and death. Aim: To investigate whether a systematic fast-track transition from oncological treatment to specialized palliative care at home for patients with incurable cancer reinforced with a psychological dyadic intervention could result in more time spent at home and death at home. Secondary aims were to investigate effects on quality of life, symptomatology and survival. Design: A prospective, single-centre, randomized controlled trial (Clinicaltrials.gov: NCT01885637). Setting/participants: In all, 340 patients with incurable cancer and no or limited antineoplastic treatment options. Results: No statistically significant difference was found regarding number of deaths (4%, p = 0.460) and time spent at home (3%, p = 0.491). The secondary outcomes indicated that the intervention resulted in improved quality of life (−11.6 ± 25.5, p = 0.005, effect size = −0.44, 95% confidence interval = −0.77; −0.11), social functioning (−15.8 ± 31.4, p = 0.001, effect size = −0.50, 95% confidence interval = −0.84; −0.17) and emotional functioning (−9.1 ± 21.2, p = 0.039, effect size = −0.43, 95% confidence interval = −0.76; −0.10) after 6 months. A linear mixed-effect regression model confirmed a possible effect on emotional and social functioning at 6 months. Regarding survival, no differences were found between groups (p = 0.605). No adverse effects were seen as consequence of the intervention. Conclusions: The main findings indicated that the intervention had no effect on time spent at home or place of death. However, the intervention resulted in a weak improvement of quality of life, social functioning and emotional functioning after 6 months.
Impulsive choice does not predict binge-like eating in rats

Binge eating disorder is an addiction-like disorder characterized by recurrent, excessive food consumption within discrete periods of time, and it has been linked to increased trait impulsivity. Within impulsivity components, while impulsive action was shown to predict binge-like and addictive-like eating, the role of impulsive choice is instead unknown. The goal of this study was to determine if impulsive choice predicted, or was altered by binge-like eating of a sugary, highly palatable diet. We utilized a modified adjusting delay task procedure in free-fed rats to assess impulsive choice behavior, that is. the tendency to respond for a larger, delayed reward over a lesser, immediate reward. We found that baseline impulsive choice was not a predictor of binge-like eating in 1-h sessions of palatable diet operant self-administration. Furthermore, binge-like eating of the same palatable diet had no effect on later impulsive choice behavior. Thus, our data suggest that, unlike impulsive action, impulsive choice behavior does not predict binge-like eating in rats.
Evaluation of Neural Network-Based Sensing and Perception in Experimental Vehicles

As increasing use of neural networks (NN) and deep-learning (DL) can be observed in various fields, such as voice and image recognition, control, translation, data processing, function approximation, etc. These technologies can also be applied in a specific domain of experimental vehicles. This paper presents and summarizes our recent practical experiences gained during the usage of neural network as sensing and perception subsystem in an experimental vehicle. The paper does not targets general NN problems, only focuses on the narrow subdomain. The current paper also reviews and evaluates the available hyper parameter tunings, optimizers and neural network architectures and proposes tested solutions for enhanced performance. All of the used datasets, source codes and additional materials is available online.
Stock Transaction Analysis System Based on Hadoop and Capital Flow

Stock transaction analysis is an interesting topic to be investigated both for financial economist and data processing expert. In practice, various analysis systems have been studied to gain excellent result for prediction. However, few jobs have been done on the combination of financial index analysis and data processing techniques. The purpose of this paper is to test if the stock transaction behavior can be detected by using data processing technique on a synthetic index of capital flow. In this paper, the overall logic architecture and functional framework of analysis system have been introduced detailedly. In addition, a developed prototype system has been tested and the way how the main components are implemented is also illustrated. The experiments show that the prototypes system is capable of achieving better trade-off regarding the accuracy, availability and flexibility.
Subjective Experience of Interacting with a Social Robot at a Danish Airport1

This study investigates the subjective experience of interacting with a social robot at Aalborg Airport (AAL) by conducting a field study where 23 attributes in Human-Robot Interaction (HRI) were elicited. During two tests, Danish travellers were recruited by a remote controlled Double robot, which offered four wayfinding options specific for AAL. In the first test 30 subjects participated in a semi-structured interview about their experience. Observations and the subjects' statements were interpreted and coded using an affinity diagram. The affinity diagram resulted in 10 superordinate categories from which the 23 attributes were elicited and developed into scales. The scales were used in the second test at AAL, where 43 subjects rated HRI. The ratings were analysed with Principal Component Analysis (PCA). The developed scales presented in this paper might be used by robot designers for specific contexts and potentially for tailored user experience evaluations.
Design and Fuzzy Sliding Mode Admittance Control of a Soft Wearable Exoskeleton for Elbow Rehabilitation

Exoskeletons composed of rigid frames and joints have been widely implemented in rehabilitation application. However, the rigid mechanical structure introduces many problems, such as heavy weight, large inertia, and joint misalignment. To overcome these undesirable inherent disadvantages of rigid exoskeletons, the innovative contributions of this paper are to investigate the design and fuzzy sliding mode admittance (FSMCA) control of a soft wearable exoskeleton with compliance tendon-sheath actuation (CTSA) for multi-mode elbow rehabilitation training. The CTSA system is developed on the base of the Hill-based muscle model to imitate the working characteristics of human muscle and improve the compliance and coordination of human–robot cooperation. The FSMCA controller is capable of providing patient-passive and patient-active therapies under different training intensities and encouraging the active participation of the patients with various weakness levels. Further experimental investigations, including the trajectory tracking experiments with/without admittance regulation, the step response experiments with disturbance, the frequency response experiments, and the patient-active training experiments, are carried out by three healthy volunteers. Experimental results demonstrate the effectiveness of the proposed FSMCA control scheme in achieving high control accuracy and favorable frequency response characteristic. Besides, the training intensity can be qualitatively adjusted via appropriate admittance parameters.
TOP: An Efficient Two-Levels of Positive Resampling Framework for Class Imbalanced Data

In real-world applications such as fraud detection, target class values have an unequal size. This problem is called class-imbalanced data. Many strategies have been proposed to deal with this situation. Most of them focused on changing the data characteristics. For example, adjusting class distribution is one of the most popular approaches for this matter. In this work, we proposed TOP (TwO-levels of Positive resampling framework), an alternative framework to resolve such a problem. Our technique exploits DBSCAN mechanism and other resampling algorithms in order to maximize classification performance. It is able to dynamically draw two boundaries that represent similarity level between consideration positive and other instances. Many possible resampling techniques such as undersampling or over-sampling are allowed to perform inside those areas. We benchmarked TOP with three types of resampling techniques including over-sampling, down-sampling, and hybridsampling by training eleven machine learning algorithms on fifteen datasets. As a result, our technique outperformed other techniques in several evaluation metrics.
Why Do People With Schizophrenia Exercise? A Mixed Methods Analysis Among Community Dwelling Regular Exercisers

Individuals with schizophrenia have reduced rates of physical activity, yet substantial proportions do engage in independent and regular exercise. Previous studies have shown improvement in symptoms and cognitive function in response to supervised exercise programs in people with schizophrenia. There is little data on motivations of individuals who exercise independently, or their chosen type, duration, or setting of exercise. This study explores motivational parameters and subjective experiences associated with sustained, independent exercise in outpatients with a diagnosis of schizophrenia or schizoaffective disorder. Participants completed a semi-structured interview and then were given a prospective survey containing visual analog scales of symptom severity and the Subjective Exercise Experiences Scales to complete immediately before and after three sessions of exercise. Results from the semi-structured interview were analyzed by modified content analysis. The most important reason for exercise was self-image, followed closely by psychological and physical health. Among psychological effects, participants reported exercise was most helpful for mood and cognitive symptoms. The prospective ratings demonstrated 10–15% average improvements in global well-being, energy, and negative, cognitive and mood symptoms, with almost no change in psychosis, after individual exercise sessions. This suggests that non-psychotic parameters are more susceptible to inter-session decay of exercise effects, which may reinforce continued exercise participation.
Rotation Based Ensemble of One-Class Support Vector Machines

One-class support vector machine (OCSVM) is regarded as an important one-class classification method for tackling the problem of extreme class imbalance. However, combining several OCSVMs by the traditional ensemble approaches may not improve the performance of the single OCSVM because it is known as a strong classffier. In the paper, a rotation based ensemble method is proposed for integrating OCSVMs. , The training data of different OCSVMs in the ensemble is transformed by different rotation matrices. Therefore, the diversity of training data for the ensemble of OCSVMs can be guaranteed. Experiments conducted on ten benchmark data sets to validate the effectiveness of the proposed ensemble strategy.
Contextual Anomaly Detection for a Critical Industrial System Based on Logs and Metrics

Recent advances in contextual anomaly detection attempt to combine resource metrics and event logs to uncover unexpected system behaviors at run-time. This is highly relevant for critical software systems, where monitoring is often mandated by international standards and guidelines. In this paper, we analyze the effectiveness of a metrics-logs contextual anomaly detection technique in a middleware for Air Traffic Control systems. Our study addresses the challenges of applying such techniques to a new case study with a dense volume of logs, and finer monitoring sampling rate. Guided by our experimental results, we propose and evaluate several actionable improvements, which include a change detection algorithm and the use of time windows on contextual anomaly detection.
When Robot Personalisation Does Not Help: Insights from a Robot-Supported Learning Study

In the domain of robotic tutors, personalised tutoring has started to receive scientists' attention, but is still relatively underexplored. Previous work using reinforcement learning (RL) has addressed personalised tutoring from the perspective of affective policy learning. However, little is known about the effects of robot behaviour personalisation on user's task performance. Moreover, it is also unclear if and when personalisation may be more beneficial than a robot that adapts to its users and the context of the interaction without personalising its behaviour. In this paper we build on previous work on affective policy learning that used RL to learn what robot's supportive behaviours are preferred by users in an educational scenario. We build a RL framework for personalisation that allows a robot to select verbal supportive behaviours to maximise the user's task progress and positive reactions in a learning scenario where a Pepper robot acts as a tutor and helps people to learn how to solve grid-based logic puzzles. A between-subjects design user study showed that participants were more efficient at solving logic puzzles and preferred a robot that exhibits more varied behaviours compared with a robot that personalises its behaviour by converging on a specific one over time. We discuss insights on negative effects of personalisation and report lessons learned together with design implications for personalised robots.
Bayesian Theorem In Rough Set Background And Its Application To Recovering Image

Bayesian theorem is widely used in the fields of machine learning and computer vision. But the classic Bayesian theorem only focuses on the situations with a finite or countable partition of sample space, which sets a limitation on its applications. This paper first generalizes it by means of conditional mathematical expectation, and proceeds to propose a conjecture about it. Second, this paper introduces the concept of rough lower probability in approximation space; thereafter, the multiplication rule, the law of total probability and Bayesian theorem in classic probability are generalized into the rough background. Thirdly, the application of the generalized Bayesian theorem into the computer is suggested.
Deep Learning-Based Interval State Estimation of AC Smart Grids Against Sparse Cyber Attacks

Due to the aging of electric infrastructures, conventional power grid is being modernized toward smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks. However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies. Therefore, in this paper, scenario-based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. Then, in order to effectively detect the established cyber-attacks, an interval state estimation-based defense mechanism is developed innovatively. In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable. At last, a typical deep learning, i.e., stacked auto-encoder, is designed to properly extract the nonlinear and nonstationary features in electric load data. These features are then applied to improve the accuracy for electric load forecasting, resulting in a more narrow width of state variables. The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution. The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks.
Anti Accident System

Internet of Things (IoT) is one of the wide spreading region, which contains expansive no of smart devices and savvy devices. So these keen devices can likewise be utilized as a part of cutting edge vehicle observing and following framework. The fast development of innovation and foundation has made our lives less demanding. Despite the fact that the appearance of innovation has expanded, there is no diminishment in rush hour gridlock risks and the street accidents happen as often as possible. It causes colossal death toll and property on account of heedlessness. Our system is intended for observing the vehicle and whose adjacent vehicles in a zone at ongoing and give security condition to the explorer. For every vehicle the separation from different vehicles will be appeared in the show board. The show, as well as we give a light hit to appear if there is a vehicle near it. In this manner the drivers can decide if there will some other vehicles adjacent to this vehicle. A speedometer can be utilized as a part of an auto alert application with the goal that unsafe driving can be distinguished. This can be utilized as a crash or rollover indicator of the vehicle amid and after a crash. With signals alternate vehicles (hindrance) can be perceived. The proposed framework contains Global Positioning System (GPS) and Global System for Mobile Communication (GSM) which is utilized for vehicle following and checking component and for communication. The GPS gives current area of the vehicle, GSM is utilized for sending ready message. Microcontroller is the fundamental part in this system. Here we are utilizing 8051 microcontroller alongside different modules like GSM, GPS, Fuel gauge, Speedometer. Our system is mainly intended to control or to prevent the accident.