Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection

Today, machine learning and deep learning have paved the way for vital and critical applications such as abnormal detection. Despite the modernity of transfer learning, it has proved to be one of the crucial inventions in the field of deep learning because of its promising results. For the purpose of this study, transfer learning is utilized to extract human motion features from RGB video frames to improve detection accuracy. A convolutional neural network (CNN) based on Visual Geometry Group network 19 (VGGNet-19) pre-trained model is used to extract descriptive features. Next, the feature vector is passed into Binary Support Vector Machine classifier (BSVM) to construct a binary-SVM model. The performance of the proposed framework is evaluated by three parameters: accuracy, area under the curve, and equal error rate. Experiments performed on two different datasets comprising highly different context abnormalities accomplished an accuracy of 97.44% and an area under the curve (AUC) of 0.9795 for University of Minnesota (UMN) dataset and accomplished an accuracy of 86.69% and an AUC of 0.7987 for University of California, San Diego Pedistrain1 (UCSD-PED1) dataset. Moreover, the performance of the pre-trained network VGGNet-19 with handcrafted feature descriptors and with other CNN pre-trained networks, respectively, has been investigated in this study for abnormal behavior detection. The results demonstrated that VGGNet-19 has better performance than histogram of oriented gradients, background subtraction, and optical flow. In addition, the VGGNet-19 shows higher detection accuracy than other pre-trained networks: GoogleNet, ResNet50, AlexNet, and VGGNet-16.
Recognizing Polyps in Wireless Endoscopy Images Using Deep Stacked Auto Encoder With Constraint Image Model in Flexible Medical Sensor Platform

In the recent past, Wireless endoscopy (WE) helps physicians to study the digestive tract at the cost of a wide range of images without surgery. The major challenge arises from the complication of robust image characterization in computer-aided WE pictorial diagnostics. The purpose of the research is to provide a biased definition of WE images and to enable medical professionals to automatically identify polyp images. In this paper, the learning feature approach called Deep Stacked Auto Encoder with Constraint Image (DSAECI) for recognizing polyps in WE images has been proposed. This DSAECI differs from the Traditional Method of Auto Encoder (TMAE) due to the introduction of constraint image, created by the nearest neighbor’s image and describing inherent object structures. The multiple limitations of images force users to keep images in the same category far away, which share similar learned characteristics and images in various categories and utilized the Flexible medical sensor platform for data analysis. The learned characteristics thus retain large inter-variances and small intra-images. The average total accuracy (OA) of our WE images method is 98.00%. The full results showed that the proposed DSAECI can correctly identify polyps in a WE-image and provide definition characterization for web images. In clinical trials, this approach could be further used to help doctors interpret repetitive images.
Research on Non-intrusive Load Identification Technology Based on Deep Learning

To address the problem that the non-intrusive load monitoring algorithm has limitations and low recognition rate, the load identification method based on convolution neural network is proposed. Firstly, the method obtains load start-stop state by an incident detection algorithm, which includes steps of zero-crossing, curve similarity, threshold judgment, and current waveform acquisition. Secondly, the current waveform is converted into a grayscale graph, and the load waveform identification of the varying current waveform is performed according to the grayscale database of each load and the convolutional neural network. Then, the identification of the load type during the load start-stop state is completed. Finally, through the simulation experiments of single load identification, multi-load identification, resident user load identification and different load recognition algorithms, the average value of load accuracy based on convolutional neural network is above 93%, proving that the rationality and immediacy of the proposed algorithm could meet the load identification requirements.
Category Classification of Deformable Object using Hybrid Dynamic Model for Robotic Grasping

This work studies the problem of classification of a hung garment in the unfolding procedure by a home service robot. The sheer number of unpredictable configurations that the deformable object can end up in makes the visual identification of the object shape and size difficult. In this paper, we propose a hybrid dynamic model to recognize the pose of hung garment using a single manipulator. A dataset of hung garment is generated by capturing the depth images of real garments at the robotic platform (real images) and also the images of garment mesh model from offline simulation (synthetic images) respectively. Deep convolutional neural network is implemented to classify the category and estimate the pose of garment. Experiment results show that the proposed method performs well and is applicable to different garments in robotic manipulation.
Excitation Features of Speech for Speaker-Specific Emotion Detection

In this article, we study emotion detection from speech in a speaker-specific scenario. By parameterizing the excitation component of voiced speech, the study explores deviations between emotional speech (e.g., speech produced in anger, happiness, sadness, etc.) and neutral speech (i.e., non-emotional) to develop an automatic emotion detection system. The excitation features used in this study are the instantaneous fundamental frequency, the strength of excitation and the energy of excitation. The Kullback-Leibler (KL) distance is computed to measure the similarity between feature distributions of emotional and neutral speech. Based on the KL distance value between a test utterance and an utterance produced in a neutral state by the same speaker, a detection decision is made by the system. In the training of the proposed system, only three neutral utterances produced by the speaker were used, unlike in most existing emotion recognition and detection systems that call for large amounts of training data (both emotional and neutral) by several speakers. In addition, the proposed system is independent of language or lexical content. The system is evaluated using two databases of emotional speech. The performance of the proposed detection method is shown to be better than that of reference methods.
A Semi-Supervised Paraphrase Identification Model Based on Multi-Granularity Interaction Reasoning

Conventional paraphrase identification (PI) models based on deep learning usually focus on text representation and ignore the mining and matching of multi-granular interaction features. In addition, supervised learning relies on a large labeled data. However, labeled training set for PI is small in comparison with the high complexity of the task. To solve the problems, we propose a semi-supervised deep learning framework for PI. We use a neural encoder with word-by-word attention mechanism to reason equivalence or contradiction over pairs of words, phrases and sentences. We employ a two-stage training procedure. First, we use a language modeling objective to learn the initial parameters on the unlabeled corpora of more than one million pairs of sentences. This is followed by a supervised training, where we adapt these parameters to a specific classification task with labeled data. Experimental results on MRPC (Microsoft Research Paraphrase Corpus) and SICK (Sentences Involving Compositional Knowledge) datasets demonstrate the effectiveness of our approach. Compared with the previous neural network models, we achieve absolute improvements in accuracy of 7.6% and <inline-formula> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> of 5.4% on MRPC, Pearson’s <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> of 4.5% and Spearman’s <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> of 5.1% on SICK.
A SiPM-Based Clinical MRI-Compatible SPECT Insert

The first SiPM-based clinical SPECT ring for brain simultaneous imaging in standard MR scanners has been put into operation. Reliable operation of the instrument has been achieved by means of its optimization, such as the introduction of a dual-cooling system (liquid-gas) of the DAQ electronics. The static gantry is composed of 20 MRI-compatible γ-detection modules mounting 5 cm × 10 cm CsI(Tl) scintillators for a total of 1440 pixels (targeting a transaxial FoV of 20 cm). Here we present the results of a preliminary characterization showing 220 kcps max. count rate, 365cps/MBq sensitivity with the multislit-slat collimator, ~17% energy resolution and an average extrinsic spatial resolution of ~8 mm in the trans-axial plane.
Predicting Invasive Ductal Carcinoma in breast histology images using Convolutional Neural Network

Over the past ten years, there has been a rise in using deep learning for medical image analysis such as CNN. Deep learning is used extensively in the field of healthcare to identify patterns, classify and segment tumors and so on. The classification of breast cancer is a well-known problem that attracts the attention of many researchers in the field of healthcare because breast cancer is the second major cause of cancer-related deaths in women. The most common subtype of all breast cancers is the Invasive Ductal Carcinoma (IDC). There are many ways to identify this type of breast cancer such as a biopsy where tissue is removed from patient and studied under microscope. The biopsy is followed by a diagnosis which is based on the qualification of the pathologists, who will look for abnormal cells. The next task for pathologists is to assign an aggressiveness grade to a whole mount sample. To do this, pathologists focus on the region of interest which contain the IDC. Therefore, one of the popular pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide. In this paper, we have experimentally tested two CNN models using depthwise separable convolution and standard convolution to enhance the accuracy of the convolutional neural network. We tested different types of activation functions such as ReLU, Sigmoid, and Tanh. As well as applying gaussian noise to test the robustness of the two models. The results show convolutional neural networks outperformed the softmax classifier, with standard convolution and ReLU where we achieved ~87.5% classification accuracy, ~93.5% sensitivity, and ~71.5% specificity.
Docker Security: A Threat Model, Attack Taxonomy and Real-Time Attack Scenario of DoS

As the last decade experienced an explosion in the development and use of virtualization technologies, the need for an efficient and secure virtualization solution has also been increased. All the solutions that emerged can be classified into two major classes i.e. hypervisor-based virtualization and container-based virtualization. Container technologies have been around for a very long time but Docker is a relatively new and the most dominant candidate among all the other technologies. Along with so many advantages, it has a few disadvantages as well in which its security is the primary and the most crucial concern. In this paper, we propose a threat model for Docker with all the possible attack scenarios in Docker-based host systems. Furthermore, the paper also provides a detailed classification of attacks that can take place on various layers of Docker along with the description of each one of them. Lastly, the paper presents a real-time case study on Denial of Service (DoS) attack in the Docker environment.
Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

In this paper, a new hybrid model is proposed using Subset Selection by Maximum Dissimilarity (SSMD) and adaptive neuro-fuzzy inference system (ANFIS) hybridized with the firefly algorithm (FFA) to predict the longitudinal dispersion coefficient (<inline-formula> <tex-math notation="LaTeX">$K_{x}$ </tex-math></inline-formula>). The proposed framework (ANFIS-FFA), combines the specific structures and strengths of both ANFIS and FFA approaches. The FFA is used to derive the optimum ANFIS parameters. The <inline-formula> <tex-math notation="LaTeX">$K_{x}$ </tex-math></inline-formula> data set includes 503 cross-sectional data point from small to large rivers. For pre-processing of the data set, the SSMD method is used, which is superior to the classical trial and error method. The database covers a wide range of river width (0.2- 867m), and depths (0.034- 19.9 m). Fifteen different combinations of river width (B), depth (H), flow velocity (U) and shear velocity (<inline-formula> <tex-math notation="LaTeX">$\text{U}_{\ast }$ </tex-math></inline-formula>) are implemented as inputs to create fifteen estimative models. The output of the ANFIS-FFA model is compared with the ANFIS and previously published equations to check the performance of the proposed model. The results show that the highest accuracy is attained by the M1 model, with all geometric and hydrodynamic parameters as input variables in comparison with ANFIS and previous equations. The <inline-formula> <tex-math notation="LaTeX">$\text{R}^{2}$ </tex-math></inline-formula> value, RMSE, MAE and NSE for ANFIS-FFA model are 0.67, 113.14 <inline-formula> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula>/s, 48 <inline-formula> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula>/s, and 0.63 for proposed dimensional model, and 0.35, 874.5, 520.8, and 0.1 in non-dimensional ANFIS-FFA model, respectively. These values were 0.37, 463.34 <inline-formula> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula>/s, 85.69 <inline-formula> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula>/s, and −5.19 for dimensional ANFIS model, and 0.11, 3269.88, 1932.09 and −11.54 for non-dimensional ANFIS model, respectively. Overall, hybridization caused 81%, 75%, 76% improvement in <inline-formula> <tex-math notation="LaTeX">$\text{R}^{2}$ </tex-math></inline-formula>, RMSE and MAE. In another contribution of the paper, by using the matrix form of developed ANFIS-FFA optimized parameters, a novel explicit calculation procedure for estimation of <inline-formula> <tex-math notation="LaTeX">$K_{x}$ </tex-math></inline-formula> is derived. Based on the results, the proposed ANFIS-FFA model exhibits significant improvements than the classical ANFIS and highlights that optimizing by nature-inspired optimization algorithms plays a critical role in strengthening the ANFIS estimations generality.
Using Kurtosis for Objective Assessment of the Musical Signals Clipping Degree

When transmitting music signals via infocommunication channels, there is a risk of distortion of these signals due to clipping. The consequence of these distortions is a decrease in subjective assessments of the quality of musical signals, so clipping detection is an important issue. In this paper, the kurtosis and the inverse value of kurtosis are proposed as measures of an objective assessment of the musical signals clipping degree. Matching maps of objective and subjective clipping degree estimates are monotonic functions that do not contain sharp kinks, which allow not only evaluating clipping degree, but also estimating music signals quality. An important advantage of the proposed measures of the clipping degree is the ability to simplify and speed up significantly the calculations due to the absence of the need of preliminarily estimation the probability density function of the analyzed musical signal.
Top-Down Process Mining From Multi-Source Running Logs Based on Refinement of Petri Nets

Today’s information systems of enterprises are incredibly complex and typically composed of a large number of participants. Running logs are a valuable source of information about the actual execution of the distributed information systems. In this paper, a top-down process mining approach is proposed to construct the structural model for a complex workflow from its multi-source and heterogeneous logs collected from its distributed environment. The discovered top-level process model is represented by an extended Petri net with abstract transitions while the obtained bottom-level process models are represented using classical Petri nets. The Petri net refinement operation is used to integrate these models (both top-level and bottom-level ones) to an integrated one for the whole complex workflow. A multi-modal transportation business process is used as a typical case to display the proposed approach. By evaluating the discovered process model in terms of different quality metrics, we argue that the proposed approach is readily applicable for real-life business scenario.
Decoding auditory and tactile attention for use in an EEG-based brain-computer interface

Brain-computer interface (BCI) systems offer a nonverbal and covert way for humans to interact with a machine. They are designed to interpret a user's brain state that can be translated into action or for other communication purposes. This study investigates the feasibility of developing a hands- and eyes-free BCI system based on auditory and tactile attention. Users were presented with multiple simultaneous streams of auditory or tactile stimuli, and were directed to detect a pattern in one particular stream. We applied a linear classifier to decode the stream-tracking attention from the EEG signal. The results showed that the proposed BCI system could capture attention from most study participants using multisensory inputs, and showed potential in transfer learning across multiple sessions.
Modeling Coral Reef Susceptibility Using GIS Multi-Criteria Analysis

In this study a multi-criteria analysis model of GIS has been developed to map the susceptibility of coral reef based on various environmental indicators. For this there is a need to understand the pattern of coral susceptibility to environmental indicators including sea surface temperature (SST) which is continuously changing due to the global climate. The Northern Indian Ocean (NIO) has been selected as the study area which includes waters of Arabian Sea and Bay of Bengal. The purpose of this study is to highlight most vulnerable areas of coral bleaching in the NIO by using multicriteria analysis (MCA). Remotely sensed environmental indicators including SST, aragonite saturation state, bathymetry, photosynthetic active radiation (PAR), wind velocity, and reef slope were used to observe the susceptibility of coral reefs in NIO. Existing bleaching sites of corals were overlaid with susceptibility map obtained by MCA. The coast of Sri Lanka, South India, Maldives, Oman and British Indian ocean territory were found to be in the highly susceptible zone of coral bleaching. GIS-based MCA proved to be an efficient technique to analyze the suitable or unsuitable conditions for coral reef susceptibility.
Optimization of SRM Direct Instantaneous Torque Control Strategy based on Improved Firefly Algorithm

The direct instantaneous torque control has weak anti-interference ability and insufficient torque ripple optimization. In this paper, a method based on improved firefly algorithm to optimize SRM direct instantaneous torque control is proposed. On the basic of proportional integral control, the improved firefly algorithm is used to optimize the parameters which will help to find the optimal simulation parameters automatically. The method proposed in this paper has reduced torque ripple and improved dynamic response of the system. The experimental simulation verifies its effectiveness.
Overall Survival and Health Care Costs of Medicare Patients with Previously Treated Chronic Lymphocytic Leukemia

Background: Bendamustine-based regimens are often used in the management of patients with chronic lymphocytic leukemia (CLL) but few studies have analyzed the comorbidity- and/or adverse event (CAE)-related healthcare costs in patients receiving these regimens in a real-world setting. Aims: To describe all-cause and CAE-related healthcare costs in relapse/refractory (R/R) elderly patients with CLL treated with bendamustine-based regimens in a real-world setting. Methods: Adult patients with R/R CLL who received bendamustine-based regimens on/after January 2010 were selected from the Medicare Limited Data Set (LDS) 5% Standard Analytic Files. Selected patients were classified into cohorts based on the two most prevalent bendamustine-based regimens observed (index treatment): 1) bendamustine + rituximab (BR cohort) and 2) bendamustine monotherapy (B-mono cohort). For each cohort, all-cause and CAE-related healthcare costs, while on treatment, were reported per-patient-per-month (PPPM). Overall survival (OS) rates following initiation of the index treatment were described using age- and gender-adjusted Kaplan-Meier curves. Results: A total of 275 patients were included in the BR cohort and 100 patients in the B-mono cohort. Most patients were male and the mean age was approximately 75 years old. During treatment, total all-cause healthcare costs were $14,520 PPPM for the BR cohort and $13,125 PPPM for the B-mono cohort—outpatient costs (mainly driven by CLL-drug costs) represented 86.1% of the total all-cause healthcare costs for the BR cohort and 69.8% for the B-mono cohort. CAE costs accounted for 58.3% of the total all-cause healthcare costs for the BR cohort and 66.9% for the B-mono cohort. Median OS was 35 months in the BR cohort and 21 months in the B-mono cohort. Conclusion: In this population of elderly patients with R/R CLL treated with bendamustine-based regimens, CAEs were common and translated into important medical costs. Median OS was also relatively short suggesting an unmet medical need.
Modelling of Scatter in the System Matrix for 3D PET Image Reconstruction: a Comparative Study

Compton scattering in positron emission tomography (PET) is a major image degrading effect. In this work, an analytical model for Compton scatter is included in the system matrix of two iterative image reconstruction techniques, maximum likelihood expectation maximization (MLEM) and origin ensemble (OE); their performance with respect to conventional MLEM and MLEM with analytic single scatter simulation (SSS) based scatter correction is validated according to the NEMA NU 2-2007 protocol. Background variability is reduced by 9.4% on average compared to MLEM with SSS. Contrast recovery coefficients (CRCs) are improved for larger sources.
Acquisition Correction and Reconstruction for a Clinical SPECT/MRI Insert

The development of the first clinical simultaneous Single Photon Emission Computed Tomography (SPECT) and Magnetic Resonance Imaging (MRI) system was carried out within the INSERT project. The INSERT scanner was constructed under the initial project, but its performance was not fully evaluated; here we have reconstructed the first images on the SPECT system. Calibration and acquisition protocols were developed and used to establish the clinical feasibility of the system. The image reconstruction procedures were implemented on the first phantom images in order to assess the system’s imaging capabilities. This study solved issues involving incomplete data sets and pixel failure in the prototype detector system. The final images determined a measure of trans-axial image resolution, giving average values of 9.14 mm and 6.75 mm in the radial and tangential directions respectively. The work carried out on the complete system produced several clinical phantom images which utilized the capabilities of both SPECT and MRI.