Miracle-dz/UniFAR-FTUs huggingface dataset:
Texture weighted fuzzy C-means algorithm for 3D brain MRI segmentation

The segmentation of human brain Magnetic Resonance Image (MRI) is an essential component in the computer-aided medical image processing research. Fuzzy C-Means (FCM) algorithm is one of the practical algorithms for brain MRI segmentation. However, Intensity Non-Uniformity (INU) problem in brain MRI is still challenging to existing FCM. In this paper, we propose the Texture weighted FCM (TFCM) algorithm performed with Local Binary Patterns on Three Orthogonal Planes (LBP-TOP). By incorporating texture constraints, TFCM could take into account more global image information. The proposed algorithm is divided into following stages: Volume of Interest (VOI) is extracted by 3D skull stripping in the pre-processing stage. The initial FCM clustering and LBP-TOP feature extraction are performed to extract and classify each cluster's features. At the last stage, FCM with texture constraints refines the result of initial FCM. The proposed algorithm has been implemented to evaluate the performance of segmentation result with Dice's coefficient and Tanimoto coefficient compared with the ground truth. The results show that the proposed algorithm has the better segmentation accuracy than existing FCM models for brain MRI.
Improving Utilization and Parallelism of Hadoop Cluster by Elastic Containers

Modern datacenter schedulers apply a static policy to partition resources among different tasks. The amount of allocated resource won't get changed during a task's lifetime. However, we found that resource usage during a task's runtime demonstrates high dynamics and it only reaches full usage at few moments. Therefore, the static allocation policy doesn't exploit the dynamic nature of resource usage, leading to low system resource utilization. To address this hard problem, a recently proposed task-consolidation approach packs as many tasks as possible on the same node based on real-time resource demands. However, this approach may cause resource over-allocation and harm application performance. In this paper, we propose and develop ECS, an elastic container based scheduler that leverages resource usage variation within the task lifetime to exploit the potential utilization and parallelism. The key idea is to proactively select and shift tasks backward so that the inherent paralleled tasks can be identified without over-allocation. We formulate the scheduling scheme as an online optimization problem and solves it using a resource leveling algorithm. We have implemented ECS in Apache Yarn and performed evaluations with various MapReduce benchmarks in a cluster. Experimental results show that ECS can efficiently utilize resource and achieves up to 29% reduction on average job completion time while increasing CPU utilization by 25%, compared to stock Yarn.
An Empirical Study of Methods for SPN Learning and Inference

In this study, we provide an empirical comparison of methods for sum-product network (SPN) learning and inference. LearnSPN is a popular algorithm for learning SPNs that utilizes chop and slice operations. As g-test is a standard chopping method and Gaussian mixture models (GMM) using expectation-maximization is a common slicing method, it seems to have been assumed in the literature that this is the best pair in LearnSPN. On the contrary, our results show that g-test for chopping and k-means for slicing yields SPNs that are just as accurate. Moreover, it has been shown that implementing SPN leaf nodes as Chow-Liu Trees (CLTs) yields more accurate SPNs for the former pair. Our experiments show the same for the latter pair, and that neither pair dominates the other. Lastly, we report an analysis of SPN topology for unstudied pairs. With respect to inference, we derive partial propagation (PP), which performs SPN exact inference without requiring a full propagation over all nodes in the SPN as currently done. Experimental results on SPN datasets demonstrate that PP has several advantages over full propagation in SPNs, including relative time savings, absolute time savings in large SPNs, and scalability.
Transition-Aware Housekeeping Task Monitoring Using Single Wrist-Worn Sensor

Population aging is one of the general issues of public health over the world. Such demographic shifts pose challenges to healthcare system. Several wearable-based activity monitoring systems have been developed to improve the quality of healthcare and provide monitoring information for health professionals, such as the information of kitchen task, dressing task, food and fluid intake, and medication intake. However, few works pay attention to the housekeeping task, as the housekeeping performance correlates to the cognitive and functional health status in elderly people. Since typical clinical approaches of measuring and assessing housekeeping task performance suffer issues in long-term observation and manual error, a transition-aware household task monitoring system is proposed to support clinical professionals to gather fine-grained housekeeping task information for clinical assessment in this paper. Novel algorithms and models are proposed and designed based on knowledge and hierarchical approaches, including preliminary target activity recognition, transition detection, transition point identification, activity model, and activity inference. In addition, the typical activity classification and transition detection approach are implemented to compare with the proposed system. In the experiment, five healthy elderly participants are invited to take part in performing a set of four housekeeping activities, and there are 948 collected instances. The monitoring system is validated by using leave-one-subject-out cross-validation approach. The experimental results show that the best overall accuracy, recall, and precision of the proposed system can achieve 81.63%, 78.40%, and 78.58% when the window size is 2.0 s.
Optimizing Activity Recognition in Stroke Survivors for Wearable Exoskeletons

Stroke affects the mobility, hence the quality of life of people victim of this cerebrovascular disease. Part of research has been focusing on the development of exoskeletons bringing support to the user's joints to improve their gait and to help regaining independence in daily life. One example is Xosoft, a soft modular exoskeleton currently being developed in the framework of the European project of the same name. On top of its assistive properties, the soft exoskeleton will provide therapeutic feedback via the analysis of kinematic data stemming from inertial sensors mounted on the exoskeleton. Prior to these analyses however, the activities performed by the user must be known in order to have sufficient behavioral context to interpret the data. Four activity recognition chains, based on machine learning algorithm, were implemented to automatically identify the nature of the activities performed by the user. To be consistent with the application they are being used for (i.e. wearable exoskeleton), focus was made on reducing energy consumption by configuration minimization and bringing robustness to these algorithms. In this study, movement sensor data was collected from eleven stroke survivors while performing daily-life activities. From this data, we evaluated the influence of sensor reduction and position on the performances of the four algorithms. Moreover, we evaluated their resistance to sensor failures. Results show that in all four activity recognition chains, and for each patient, reduction of sensors is possible until a certain limit beyond which the position on the body has to be carefully chosen in order to maintain the same performance results. In particular, the study shows the benefits of avoiding lower legs and foot locations as well as the sensors positioned on the affected side of the stroke patient. It also shows that robustness can be brought to the activity recognition chain when the data stemming from the different sensors are fused at the very end of the classification process.
Vernier: Accurate and Fast Acoustic Motion Tracking Using Mobile Devices

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. Consequently, they are inherently difficult to achieve both high accuracy and low delay. We propose Vernier, an efficient and accurate acoustic tracking method on commodity mobile devices. In the heart of Vernier lies a novel method to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance on COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Evaluation results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3×improvement to existing phase based approaches and 10×to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.
Comparison of intestinal microbes in female and male Chinese concave‐eared frogs (Odorrana tormota) and effect of nematode infection on gut bacterial communities

The Chinese concave‐eared frog (Odorrana tormota) is a rare and threatened species with remarkable sexual dimorphism. Intestinal microbes are understood to play important roles in animal physiology, growth, ecology, and evolution. However, little is known about the intestinal microbes in female and male frogs, as well as the contributing effect by gut infesting nematodes to the co‐habiting bacteria and their function in degradation food rich in chitin. Here, this study analyzed the microbiota of the intestinal tract of both female and male, healthy as well as nematode‐infested concave‐eared frogs using high throughput 16S rRNA sequencing and metagenomic techniques. The results showed that the bacterial composition of the microbiota at the phylum level was dominated by Firmicutes, Verrucomicrobia, Bacteroidetes, and Proteobacteria. The study also revealed that the community composition below the class level could be represent sex differences, particularly with regard to Enterobacteriales, Enterobacteriaceae, Peptostreptococcaceae, and Rikenellaceae, among others. Carbohydrate‐active enzyme‐encoding genes and modules were identified in related gut bacteria by metagenomic analysis, with Bacteroidia, Clostridia, and gammaproteobacteria predicted to be the main classes of chitin‐decomposing bacteria in the frog intestine. In addition, the abundance of some bacteria significantly increased or decreased in nematode‐infected hosts compared with healthy individuals, including Verrucomicrobia, Verrucomicrobiae, Negativicutes, Actinobacteria, and Bacilli, among others. This indicates that nematode infection may affect the richness and composition of some gut bacteria.
Soft Clustering Guided Image Smoothing

Image smoothing, which aims to remove unwanted textures and preserve desired structures, plays an important role in many multimedia and computer vision tasks. The key to image smoothing, despite different applications, is to distinguish the structures from the textures. This paper presents a novel image smoothing method, following the principle that, for a certain pixel, its neighbors in both space and intensity should contribute more on smoothing, while the distant ones be insulated for avoiding over-smoothing. Intuitively, clustering is a good candidate to achieve the goal. However, due to rich textures and clutters within images, simply performing the clustering on the input likely obtains inaccurate results, and thus leads to unsatisfied smoothing results. In addition, for our task, using traditional hard clustering techniques is at high risk of generating staircase artifacts. For addressing these issues, an algorithm is customized, which on the one hand adopts the soft clustering to more faithfully assign pixels, on the other hand iterates the soft clustering and smoothing, expecting to improve each other. Experiments on several challenging images are provided to show the efficacy of our method, and its superiority over other prevailing approaches.
Social-Guided Representation Learning for Images via Deep Heterogeneous Hypergraph Embedding

Representation learning for images is widely recognized as critical to the performance of end tasks such as image classification and cross-modal retrieval. However, most existing methods extract features only from visual content, far from adequate in interpreting semantics latent in images. For social images, there also exists rich social context information, e.g. owners, tags and groups, which provides cues for interpreting semantics. In this paper, we propose a representation learning framework via deep heterogeneous hypergraph embedding (DHHE), considering both visual content and social contexts. In particular, images and their contexts are first represented as a heterogeneous hypergraph, which is then embedded into a low-dimensional space. To incorporate visual information and generalize for unseen images, we learn the mapping from visual content to the semantic space. We conduct experiments with the tasks of classification, cross-modal retrieval and recommendation, which demonstrates the effectiveness of our approach and the merits of social guidance.
Presence of Speech Region Detection using Vowel-like Regions and Spectral Slope Information

In this work a novel speech region detection method is proposed that is robust for implementation of speaker verification (SV) systems in practical scenario. Vowel-like region (VLR)s have been found to work better in case of degraded conditions that helps for SV. The proposed method for speech region detection first identifies the VLRs of a given speech utterance and linear prediction (LP) spectrum is computed for those regions. It is observed that the spectral slopes for speech and non-speech regions are quite discriminative which can be useful for classification of the two categories. A study is made for the discrimination of the two classes and a threshold is obtained to categorize into speech or non-speech regions. The proposed approach is evaluated over a text-dependent SV database collected from a practical system developed over online telephone based network. It is observed that the above mentioned approach for robust speech region detection works better than the existing approaches for SV systems in a practical scenario.
Design of an FOPI Controller for Inverted Decoupled TITO Coupled-tank System using Grey Wolf Algorithm: A real-time Implementation

In this paper, a design approach of Fractional Order Proportional-Integral (FOPI) controller is proposed for decoupled two tank process. The decoupler is designed using inverted decoupling technique. As there are three unknown parameters of the FOPI controller, we have used phase margin, gain crossover frequency and velocity error constant specifications to get a objective function and constraints for optimization based control design approach. After obtaining the objective function, the optimization problem is solved by Grey Wolf Optimization (GWO) algorithm. To show the effectiveness of the approach of designing an FOPI controller for the decoupled process with respect to PI, both simulation and real-time analysis is done. It is observed that the design approach is simple and easy to implement in real-time environment.
SMTP MTA Strict Transport Security (MTA-STS)

SMTP Mail Transfer Agent Strict Transport Security (MTA-STS) is a mechanism enabling mail service providers to declare their ability to receive Transport Layer Security (TLS) secure SMTP connections, and to specify whether sending SMTP servers should refuse to deliver to MX hosts that do not offer TLS with a trusted server certificate.
Targeting the extracellular matrix for delivery of bioactive molecules to sites of arthritis

Modifications to the extracellular matrix (ECM) can be either causal or consequential of disease processes including arthritis and cancer. In arthritis, the cartilage ECM is adversely affected by the aberrant behaviours of inflammatory cells, synoviocytes and chondrocytes, which secrete a plethora of cytokines and degradative proteases. In cancer, the ECM and stromal cells are linked to disease severity, and metalloproteinases are implicated in metastasis. There have been some successes in the field of targeted therapies, but efficacy depends upon the type and stage of disease. ECM targets are becoming increasingly attractive for drug delivery, owing to changes in ECM structure and composition in the diseased state, and the long in vivo half‐life of its components. This review will highlight various strategies for targeting therapeutics to arthritic joints, including antibody and peptide‐mediated drug delivery platforms to aid delivery to the ECM and retention at disease sites.
Interaction Force Estimation Using Camera and Electrical Current Without Force/Torque Sensor

In this paper, we propose a method for the estimation of the interaction forces between the motorized system and object through the visual and electric information. In particular, we propose a new interaction force sensing method based on sequential images and the electrical current from the motor during the interaction between the system and environment to estimate the interaction force using deep learning. In the previous method, to measure the interaction force using only visual information, the prediction is inaccurate when the system interacts with an undeformable target, even though the aspect of the change appears small in the image. We use a neural network structure for estimating the interaction force from the time-series data of visual and electric information using deep learning, which combines the convolution neural network and long short-term memory models. From the evaluation to show the feasibility of the interaction force estimation, the proposed learning models successfully estimate the forces for four targets (rigid box, rigid box on sponge, sponge, and stapler), which are both deformable and undeformable objects. The proposed method demonstrates the best results in the interaction force estimation between the motorized system and object.
Development of a Wearable Tremor Suppression Glove

Current treatments for parkinsonian tremor, such as medication and brain surgery, have shown varying levels of effectiveness and carry the risk of significant side effects and complications. Studies on wearable tremor suppression devices have shown positive results in the use of mechanical and electrical suppression on tremor management of the upper limbs. Wearable technology for tremor suppression is a promising solution for patients who do not respond to medication and do not present severe enough symptoms to undergo surgery. Available tremor suppression devices are mainly for elbow and wrist tremor. Devices for finger tremor suppression have not been developed despite the fact that finger tremor is also present. In this study, a wearable tremor suppression glove prototype was designed and validated with recorded tremor data from patients with Parkinson's disease. Two validation experiments were conducted to assess the performance of the proposed device when suppressing tremor motion and following voluntary motion. The tremor suppression assessment showed an overall tremor amplitude reduction of $\pmb{85.0\% \pm 8.1\%}$, and the power reductions for the Ist, 2nd, and 3rd harmonics are $\pmb{87.9\%\pm . 6\%, 92.0\%\pm . 4\%}$, and $\pmb{81.7\% \pm 13.0\%}$, respectively. Following voluntary motion was possible with a RMSE of $\pmb{14.2\%\pm . 5\%}$ and a correlation coefficient of $\pmb{0.97\pm 0.01}$. Both assessments have shown positive results for the validation of the proposed device; however, further work is needed to improve the performance of the proposed device prior to human trials.
Conflicted between Goal-Directed and Habitual Control, an fMRI Investigation

Abstract “Slips of action” occur in everyday life when we momentarily lose sight of a goal (for example, when in a rush or distracted). Associative models propose that these habitual responses can be activated via a direct stimulus-response (S-R) mechanism, regardless of the current hedonic value of the outcome. The slips-of-action task (SOAT) has been extensively used in both healthy and pathological populations to measure habit tendencies, the likelihood of making erroneous responses for devalued outcomes. Inspection of behavioral performance does not reveal, however, whether the impairments were due to impaired goal-directed control or aberrantly strong habit formation. In the current study, we used functional MRI while human participants performed both the instrumental training and SOAT test phases, to elucidate the relative contributions of these mechanisms to performance on the SOAT. On trials in which conflict arises between competing goal-directed and habitual responses, we observed increased activation across areas including the anterior cingulate cortex, paracingulate gyrus, lateral orbitofrontal cortex (OFC), insula, and inferior frontal gyrus (IFG). Responding for devalued outcomes was related to increased activation in the premotor cortex and cerebellum, implicating these regions in habitual responding. Increased activation in the caudate, dorsolateral prefrontal cortex (dlPFC), and frontal pole during training was associated with better performance during the test phase, indicative of goal-directed action control. These results endorse interpretation of the SOAT in terms of competing goal-directed and habitual mechanisms and highlight that cognitive control processes present an additional bottleneck for successful performance on this task.
Combating Cross-Technology Interference for Robust Wireless Sensing with COTS WiFi

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices.Many research efforts have been devoted to promote WiFi sensing by innovating applications, refining models and optimizing algorithms. A critical issue of Cross-Technology Interference (CTI), however, is surprisingly unnoticed and largely unexplored in the existing literature. In this paper, we demonstrate that CTI poses severe impacts on CSI measurements and further degrades the performance of CSI-based sensing. Based on in-depth understanding of such impacts, we present PERFIC to deal with CTI for CSI on commercial WiFi. We first exploit the inherent cyclostationarity property of different signals to detect CTI and further identify the specific distorted subcarriers on CSI. For each interfered CSI, we then propose to mitigate the impacts of CTI by amending the abnormal subcarriers. We conduct experiments on typical wireless sensing applications, including human detection and activity classification, using off-the-shelf WiFi devices. The results demonstrate that PERFIC yields a remarkable performance gain of >30% with high efficiency and outperforms existing robust classifiers.By providing interference-free CSI that is amendable to existing and emerging CSI-based sensing applications, PERFIC underpins new insights for improving the sensitivity and reliability of wireless sensing.