Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Layer Weighted Fuzzy Support Vector Regression for Emotional Intention Understanding in Human–Robot Interaction

A three-layer weighted fuzzy support vector regression (TLWFSVR) model is proposed for understanding human intention, and it is based on the emotion-identification information in human–robot interaction. The TLWFSVR model consists of three layers, including adjusted weighted kernel fuzzy c-means for data clustering, fuzzy support vector regressions (FSVR) for information understanding, and weighted fusion for intention understanding. It aims to guarantee the quick convergence and satisfactory performance of the local FSVR via adjusting the weights of each feature in each cluster, in such a way that importance of different emotion-identification information is represented. Moreover, smooth human-oriented interaction can be obtained by endowing robot with human intention understanding capability. Experimental results show that the proposed TLWFSVR model obtains higher intention understanding accuracy and less computational time than that of two-layer fuzzy support vector regression, support vector regression, and back propagation neural network (BPNN), respectively. Additionally, the preliminary application experiments are performed in the developing human–robot interaction system, called emotional social robot system, where 12 volunteers and 2 mobile robots experience a scenario of “drinking at a bar. ” Application results indicate that the bartender robot is able to understand customers’ order intentions.
Gliosarcoma: Neuroimaging and Immunohistochemical Findings

Gliosarcoma (GSC) is an intra‐axial lesion which often abuts a dural margin and is composed of glial and mesenchymal elements. This lesion is considered a variant of isocitrate dehydrogenase (IDH)‐wild type glioblastoma (GBM). The purpose of this study is to evaluate the imaging and molecular features of GSC in a large patient cohort.
Beverage consumption patterns and energy contribution from beverages per meal type: results from a national dietary survey in Sweden

Abstract Objective Many studies of food intake have been performed and published in Sweden, but to our knowledge no studies have extensively explored the beverage consumption of the Swedish adult population. The present study aimed to describe the beverage consumption and the contribution of beverage energy (including alcohol energy) to total energy intake according to gender, region of living, meal type and day for a Swedish adult population. Design National dietary survey Riksmaten (2010–2011), collected by the Swedish National Food Agency. Setting Sweden. Subjects A total of 1682 participants (57 % women) reported dietary intake data during four consecutive days, specified by portion size, meal, time point, day of the week and venue. Meals were categorized as breakfast, lunch, dinner and ‘other’. Result The beverage reported to be consumed the most was water (ml/d), followed by coffee. Men had a higher consumption of juice, soft drinks, beer, spirits and low-alcohol beer, while the consumption of tea and water was higher for women. For both genders, milk contributed the most to beverage energy intake. Energy percentage from beverages was higher at lunch and dinner during weekends for both genders. Participants from the biggest cities in Sweden had a higher consumption of wine for both genders and tea for men than participants from other regions. Conclusions A considerable part of total energy intake was contributed by beverages, especially for men. Beverages can contribute to a more enjoyable diet, but at the same time provide energy, sugar and alcohol in amounts that do not promote optimal health.
Differentiated Effects of Robot Hand Training With and Without Neural Guidance on Neuroplasticity Patterns in Chronic Stroke

Robot-assisted training combined with neural guided strategy has been increasingly applied to stroke rehabilitation. However, the induced neuroplasticity is seldom characterized. It is still uncertain whether this kind of guidance could enhance the long-term training effect for stroke motor recovery. This study was conducted to explore the clinical improvement and the neurological changes after 20-session guided or non-guided robot hand training using two measures: changes in brain discriminant ability between motor-imagery and resting states revealed from electroencephalography (EEG) signals and changes in brain network variability revealed from resting-state functional magnetic resonance imaging (fMRI) data in 24 chronic stroke subjects. The subjects were randomly assigned to receive either combined action observation (AO) with EEG-guided robot-hand training (RobotEEG_AO, n = 13) or robot-hand training without AO and EEG guidance (Robotnon−EEG_Text, n = 11). The robot hand in RobotEEG_AO group was activated only when significant mu suppression (8–12 Hz) was detected from subjects' EEG signals in ipsilesional hemisphere, while the robot hand in Robotnon−EEG_Text group was randomly activated regardless of their EEG signals. Paretic upper-limb motor functions were evaluated at three time-points: before, immediately after and 6 months after the interventions. Only RobotEEG_AO group showed a long-term significant improvement in their upper-limb motor functions while no significant and long-lasting training effect on the paretic motor functions was shown in Robotnon−EEG_Text group. Significant neuroplasticity changes were only observed in RobotEEG_AO group as well. The brain discriminant ability based on the ipsilesional EEG signals significantly improved after intervention. For brain network variability, the whole brain was first divided into six functional subnetworks, and significant increase in the temporal variability was found in four out of the six subnetworks, including sensory-motor areas, attention network, auditory network, and default mode network after intervention. Our results revealed the differences in the long-term training effect and the neuroplasticity changes following the two interventional strategies: with and without neural guidance. The findings might imply that sustainable motor function improvement could be achieved through proper neural guidance, which might provide insights into strategies for effective stroke rehabilitation. Furthermore, neuroplasticity could be promoted more profoundly by the intervention with proper neurofeedback, and might be shaped in relation to better motor skill acquisition.
Feasibility of Automated Mobility Assessment of Older Adults via an Instrumented Cane

This study explored the feasibility of automated characterization of functional mobility via an Instrumented Cane System (ICS) within an older adult sample of cane users. An off-the-shelf offset cane was instrumented with inertial, force, and ultrasound sensors for noninvasive data collection. Eighteen patients from a neurological out-patient rehabilitation clinic and nine independently mobile controls participated in standard clinical evaluations of mobility using the ICS while under the care of an attending physical therapist. Feasibility of the ICS was gauged through two studies. The first demonstrated the capability of the ICS to reliably collect meaningful usage metrics, and the second provided preliminary support for the discriminability of high and low falls risk from system-reported metrics. Specifically, the cane significantly differentiated patients and controls (p < 0.05), and a measure of the variation in rotational velocity was associated with total scores on the Functional Gait Assessment (partial r = 0.61, p < 0.01). These findings may ultimately serve to complement and even extend current clinical assessment practices.
An Approximate Control Algorithm for Zero-Sum Differential Games Using Adaptive Critic Technique

This paper investigates the optimal control for a class of continuous-time systems in the framework of zero-sum differential game. A novel adaptive dynamic programming (ADP) algorithm is proposed to approximate the optimal value function through a critic neural network (NN). First, a class of control systems with external disturbances are formulated as two-player zero-sum differential game. Then, the proposed approximate structure is implemented to obtain the approximate optimal control policy and the worst-base disturbance policy. Moreover, Lyapunov theory is utilized to demonstrate the uniform ultimate bounded stability of the closed-loop system. Finally, two simulation examples are given to demonstrate the effectiveness of the developed control scheme.
Comparison of the Techniques of Secondary Intraocular Lens Implantation after Penetrating Keratoplasty

Aim To conduct a retrospective analysis of secondary IOL implantation in patients who underwent PK with no simultaneous IOL implantation. Materials and Methods The retrospective study of the secondary implantation of IOLs was conducted in 46 eyes that underwent a primary operation with PK and cataract/lens extraction with no IOL implantation due to capsule rupture or combining corneal or intraocular complications. The minimum period from PK was 12 months. All secondary IOL implantations were performed from January 2011 to August 2017. Aphakic postkeratoplasty patients were treated using one of the surgical techniques for secondary IOL implantation. In-the-bag IOL implantation was possible if the posterior capsule was complete. If the lens capsule remnants were sufficient to provide secure IOL support, an in-the-sulcus IOL implantation was performed. Scleral fixation was offered in eyes with extensive capsular deficiency or the presence of the vitreous body in anterior chamber. BCVA and expected and achieved refraction were evaluated; we included using two biometry devices, and results were compared. Results The corrected distance visual acuity (CDVA) before surgery ranged from 0.1 to 0.8 (mean 0.54 ± 0.17). After secondary IOL implantation, CDVA ranged from 0.2 to 0.8 (mean 0.43 ± 0.14) at postoperative 1 month and from 0.3 to 0.9 (mean 0.55 ± 0.15) at postoperative 6 months (p < 0.05). Comparison of the final refraction using two methods of biometry showed no statistically significant difference in the group that underwent scleral fixation of the IOL, similar to the findings for the in-the-bag and in-the-sulcus IOL implantation groups. In the scleral-fixation group, p=0.55 for the USG biometry technique and p=0.22 for the OB technique. p values for the IOL-implantation group were p=0.49 and p=0.44, respectively. Conclusion Both implantation methods are safe for the patients. Final refraction is depending on the technique and indication to keratoplasty. Both biometry techniques deliver precise data for IOL choice.
A High Frame Rate Wearable EIT System Using Active Electrode ASICs for Lung Respiration and Heart Rate Monitoring

A high specification, wearable, electrical impedance tomography (EIT) system with 32 active electrodes is presented. Each electrode has an application specific integrated circuit (ASIC) mounted on a flexible printed circuit board, which is then wrapped inside a disposable fabric cover containing silver-coated electrodes to form the wearable belt. It is connected to a central hub that operates all the 32 ASICs. Each ASIC comprises a high-performance current driver capable of up to 6 mA<sub>p-p</sub> output, a voltage buffer for EIT and heart rate signal recording as well as contact impedance monitoring, and a sensor buffer that provides multi-parameter sensing. The ASIC was designed in a CMOS 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> high-voltage process technology. It operates from ±9 V power supplies and occupies a total die area of 3.9 mm<sup>2</sup>. The EIT system has a bandwidth of 500 kHz and employs two parallel data acquisition channels to achieve a frame rate of 107 frames/s, the fastest wearable EIT system reported to date. Measured results show that the system has a measurement accuracy of 98.88% and a minimum EIT detectability of 0.86 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>/frame. Its successful operation in capturing EIT lung respiration and heart rate biosignals from a volunteer is demonstrated.
Microbiological Quality of Ready - to - Eat Food from Dhaka, Bangladesh

The objective of this research was to assess the microbiological quality of ready-to-eat food available in Dhaka city, Bangladesh, and check the risk factors associated with ingestion of ready-to-eat food from popular public places. This study was conducted in the Center of Excellence in the Department of Microbiology, Primeasia University, Dhaka, Bangladesh from August 2016 to February 2017. Forty-five samples belonging to 18 categories were collected aseptically in triplicates in pre-sterilized zip-lock bags or sterile bottles from Banani area from local street vendors. Samples were transported to and analysed in the Laboratory of Department of Microbiology, Primeasia University according to standard food analysis methods. Total viable count (TVC) and Total coliform count (TCC) were determined by using plate count agar (PCA) andMacConkey agar plates respectively. Antibiogram of the isolated strains was conducted with commercial antibiotics according to the Kirby-Bauer disc diffusion method on Mueller-Hinton agar medium. Identification of the coliforms together with antibiotic-resistance profile showed Escherichia coli, Enterobactersakazaki, Citrobacterfreundii and Salmonella typhimurium were present in various foods. E. coli and S.typhimurium showed increased sensitivity against Ampicillin 10 mg and Sulfamethoxazole 25 mg. The occurrence of antibiotic-resistance potential pathogens in ready-to-eat food poses a considerable health risk to consumers. Public awareness and timely assessment of food safety are needed to avoid the risks of food-borne infection and intoxication from ready-to-eat food.
Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.
Unsupervised Representation Learning of Image-Based Plant Disease with Deep Convolutional Generative Adversarial Networks

Rapid identification of plant disease is essential for food security. Deep learning, the latest breakthrough in computer vision, is promising for plant disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, a deep convolutional neural network and unsupervised methods are used to identify 14 crop species and 26 diseases. The trained model achieves an accuracy of 89.83% on a held-out test set, demonstrating the feasibility of this approach.
Reconstruction of Boolean Regulatory Models of Flower Development Exploiting an Evolution Strategy

One of the first popular applications of Boolean networks for gene regulatory networks corresponds to the Mendoza & Alvarez-Buylla network of flower development. In this paper, we consider this model and a reduced version to reconstruct synthetic threshold Boolean networks that have the same asymptotic behavior as these base models. For this, we employ an evolution strategy to search for neighboring solutions. We were able to find solutions with fewer edges as well as networks with more balanced distributions of basins of attractions. Overall, our results show the effectiveness of using evolutionary computation in this application to explore alternative solutions with desired properties.
&Dgr;Np63-driven recruitment of myeloid-derived suppressor cells promotes metastasis in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is particularly aggressive, with enhanced incidence of tumor relapse, resistance to chemotherapy, and metastases. As the mechanistic basis for this aggressive phenotype is unclear, treatment options are limited. Here, we showed an increased population of myeloid-derived immunosuppressor cells (MDSCs) in TNBC patients compared with non-TNBC patients. We found that high levels of the transcription factor &Dgr;Np63 correlate with an increased number of MDSCs in basal TNBC patients, and that &Dgr;Np63 promotes tumor growth, progression, and metastasis in human and mouse TNBC cells. Furthermore, we showed that MDSC recruitment to the primary tumor and metastatic sites occurs via direct &Dgr;Np63-dependent activation of the chemokines CXCL2 and CCL22. CXCR2/CCR4 inhibitors reduced MDSC recruitment, angiogenesis, and metastasis, highlighting a novel treatment option for this subset of TNBC patients. Finally, we found that MDSCs secrete prometastatic factors such as MMP9 and chitinase 3–like 1 to promote TNBC cancer stem cell function, thereby identifying a nonimmunologic role for MDSCs in promoting TNBC progression. These findings identify a unique crosstalk between &Dgr;Np63+ TNBC cells and MDSCs that promotes tumor progression and metastasis, which could be exploited in future combined immunotherapy/chemotherapy strategies for TNBC patients.
A Path Signature Approach to Online Arabic Handwriting Recognition

The Arabic script is one that has many properties that come together and result in what is commonly cited as one of the most beautiful scripts. Used by over 400 million people worldwide and with a history spanning over 1800 years, the Arabic script remains one of the most important languages in the world. Using tools from the theory of rough paths, combined with state of the art techniques from deep learning, we develop a recognition methodology for Arabic handwriting. Preliminary results using online Arabic handwritten characters show that the methodology developed can result in a significant decrease in error rate.
Towards BCI-Based Interfaces for Augmented Reality: Feasibility, Design and Evaluation

Brain-Computer Interfaces (BCIs) enable users to interact with computers without any dedicated movement, bringing new hands-free interaction paradigms. In this paper we study the combination of BCI and Augmented Reality (AR). We first tested the feasibility of using BCI in AR settings based on Optical See-Through Head-Mounted Displays (OST-HMDs). Experimental results showed that a BCI and an OST-HMD equipment (EEG headset and Hololens in our case) are well compatible and that small movements of the head can be tolerated when using the BCI. Second, we introduced a design space for command display strategies based on BCI in AR, when exploiting a famous brain pattern called Steady-State Visually Evoked Potential (SSVEP). Our design space relies on five dimensions concerning the visual layout of the BCI menu; namely: orientation, frame-of-reference, anchorage, size and explicitness. We implemented various BCI-based display strategies and tested them within the context of mobile robot control in AR. Our findings were finally integrated within an operational prototype based on a real mobile robot that is controlled in AR using a BCI and a HoloLens headset. Taken together our results (4 user studies) and our methodology could pave the way to future interaction schemes in Augmented Reality exploiting 3D User Interfaces based on brain activity and BCIs.
Poster Abstract: Towards Smaller Checkpoints for Better Intermittent Computing

We propose a set of differential techniques to allow transientlypowered embedded devices reduce the amount of data written on non-volatile memory during checkpoints used to cross times of energy unavailability. These techniques track modifications in the application state to isolate data from the slice of the previous checkpoint that remains unaltered. At the following checkpoint, our approach may thus only update the parts it detects as modified. This allows us to shift part of the energy budget from checkpointing overhead to useful computations, yielding better overall energy efficiency.
Organizational resources, KM process capability and strategic flexibility: a dynamic resource-capability perspective

Strategic flexibility is largely considered a source of competitive advantage, yet strategic flexibility in relation to organizational resources and knowledge management (KM) process capability is not well studied. To address this gap, this study aims to assesses the relationship of organizational resources (technical and social resources) and strategic flexibility through KM process capability. ,This paper is built on the assumptions of the resource-based view and the dynamic capability perspective of firm. Two types of organizational resources – technical and social – were identified from relevant literature. Data were collected from 23 small- and medium-sized firms (family owned firms) using a 37-item questionnaire. In addition to descriptive statistics, multiple hierarchical regressions and bootstrapping were used to test the study hypotheses. ,Findings suggest that organizational resources are positively and significantly related with strategic flexibility, and KM process capability partially mediates these relationships. ,The paper adds to strategic flexibility literature by exploring and assessing the linkage of organizational resources with strategic flexibility through KM process capability. ,Findings of this research may help organizations and practitioners in enhancing strategic flexibility of firm.
An Optimization of Video Sequence Stitching Method

Large field of view with high resolution has always been sought after for security monitoring personnel. Although the video sequence stitching has already been developed for several years, most existing method cannot achieve real-time stitching and fail to get excellent results. In this work, We used the same set of video frames to compare the time and the detection rate of three different feature point detection algorithms: SURF, Harris and ORB. In addition, we proposed a novel image fusion method. We calculated the region boundary and did morphological erosion operation. The pixels that need to be fused are determined, and the trigonometric functions are combined as the weights to be fused. Experiment shows that our optimized method not only achieve real-time stitching, but also meet the high quality requirements.