Miracle-dz/UniFAR-FTUs huggingface dataset:
Exosomes derived from acute myeloid leukemia cells promote chemoresistance by enhancing glycolysis‐mediated vascular remodeling

Acute myeloid leukemia (AML) is the most common type of leukemia in adults. AML cells secrete angiogenic factors to remodel vasculature and acquire chemoresistance; however, antiangiogenic drugs are often ineffective in AML treatment. Cancer cell‐derived exosomes can induce angiogenesis, but their role in vascular remodeling during AML is unclear. Here, we found that exosomes secreted by AML cells promoted proliferation and migration and tube‐forming activity of human umbilical vein endothelial cells (HUVECs), whereas HUVECs conferred chemoresistance to AML cells. AML cell‐derived exosomes contained vascular endothelial growth factor (VEGF) and VEGF receptor (VEGFR) messenger RNA and induced VEGFR expression in HUVECs. Furthermore, they enhanced glycolysis, which correlated with HUVEC proliferation, tube formation, and resistance to apoptosis. Thus, AML cells secrete VEGF/VEGFR‐containing exosomes that induce glycolysis in HUVECs leading to vascular remodeling and acquisition of chemoresistance. These findings may contribute to the development of novel therapeutic strategies targeting exosomes in AML.
Design and Implementation of a Configurable and Aperiodic Pseudo Random Number Generator in FPGA

Pseudo random number generators (PRNGs) play an important role in many fields such as encryption and hardware testing. One common structure of PRNGs is linear feedback shifting register (LFSR), which generate a n-bit sequence from a specific seed. However, the sequence of random numbers can be predicted given a fixed seed and some sequences of generated numbers. Considering the drawbacks of available LSFR-based PRNGs, in this paper we devised a configurable and aperiodic LSFR-based PRNG, which utilize the unpredictability of metastable state widely existed in digital circuits. In this case, the sequence of random numbers is almost unpredictable and can meet the requirements of most applications especially critical ones. Then we implemented it on FPGA and the simulation results indicate that the proposed PRNG has better performance in generating random numbers with high randomness.
A Raster-Image-Based Approach for Understanding Associations of Urban Sensing Data

Torrential rains, the complicated network of roads, and the high density of vehicles contribute partly the number of traffic accidents. In order to understand the correlation between these factors towards building a risk map that can alert drivers of dangerous zones, the visual patterns reasoning system is proposed. By converting sensing data collected from different resources to raster images, the accident patterns can be treated as visual patterns that can conserve spatiotemporal information of events. Deep learning techniques are utilized to build a model based on these raster images towards detecting places with a high probability of accidents. Image clustering is applied to learn a representation of each type of correlations. Thus, the visual pattern of high-probability traffic accidents can be reasoned in the natural language format. The very first result of the ongoing project shows the promising research direction. Currently, only spatial information is stored in raster images and no comparison to related methods is made. In the future, temporal information will be integrated with spatial information to create raster videos towards understanding a correlation of environment, congestion, and human reaction before and after accidents happened. This could help to predict time and place which has a high probability of accidents, and a situation of congestion after accidents happened.
Improved Weight Slope One Algorithm of Integrating Expert Similarity and Item Characteristics

Slope One algorithm widely used as a collaborative filtering recommendation algorithm based on the item, have characteristics of high recommendation accuracy and being easy to implement. However, without considering information like expert similarity and item characteristics, unimportant users and items have been included in this algorithm. It impacts the users' rating accuracy. Therefore, on the basis of the weighted Slope One algorithm, on one hand, experts and K-nearest neighbor users are comprehensively considered. On the other hand, item characteristics and common item information are integrated in this paper. The user's comprehensive rating to the item is predicted mainly by experts and item characteristics. The experimental results on two different datasets of MovieLens show that the improved algorithm could not only improve the prediction accuracy, but also effectively deal with the sparsity of the rating matrix.
Interference Coordination and Resource Allocation Planning With Predicted Average Channel Gains for HetNets

Future average channel gains are recently reported predictable within a minute-level horizon for a mobile user. Predictive resource allocation for non-realtime service with future average channel gains in a time window of one or more minutes long has been demonstrated effective in improving user experience and network throughput as well as reducing energy consumption. Nonetheless, existing studies for predictive resource allocation never consider inter-cell interference (ICI), which severely limits the user experience and network performance in densely-deployed cellular networks. This paper investigates predictive resource allocation in heterogeneous networks, where some base stations generate strong ICIs to some mobile users requesting non-realtime service. Optimizing the resource allocation in a predictive manner for interference networks is challenging, since how to allocate future resources depends on the future signal to interference-plus-noise ratio, which in turn relies on the assigned resources. To deal with this difficulty, we introduce a predictive interference coordination scheme to divide all BSs and users into groups, where the BS-user pairs in each group can communicate simultaneously in a second-level frame. Then, we optimize a common resource allocation plan for all users in each group. The plan essentially determines which BSs in the group should be muted and the users should be associated with which active BS in each frame of the prediction window. By resorting to graph theory, we obtain the optimal solution and derive a low-complexity algorithm. Simulation results show that the proposed scheme outperforms existing relevant methods in terms of user satisfactory rate in heterogeneous networks with heavy traffic load.
Trust in Medical Human-Robot Interactions based on Kinesthetic guidance

In medical human-robot interactions, trust plays an important role since for patients there may be more at stake than during other kinds of encounters with robots. In the current study, we address issues of trust in the interaction with a prototype of a therapeutic robot, the Universal RoboTrainer, in which the therapist records patient-specific tasks for the patient by means of kinesthetic guidance of the patients arm, which is connected to the robot. We carried out a user study with twelve pairs of participants who collaborate on recording a training program on the robot. We examine a) the degree with which participants identify the situation as uncomfortable or distressing, b) participants' own strategies to mitigate that stress, c) the degree to which the robot is held responsible for the problems occurring and the amount of agency ascribed to it, and d) when usability issues arise, what effect these have on participants' trust. We find signs of distress mostly in contexts with usability issues, as well as many verbal and kinesthetic mitigation strategies intuitively employed by the participants. Recommendations for robots to increase users' trust in kinesthetic interactions include the timely production of verbal cues that continuously confirm that everything is alright as well as increased contingency in the presentation of strategies for recovering from usability issues arising.
Survey of Drones for Agriculture Automation from Planting to Harvest

Farmers explore the capabilities for applications of Robotic Process Automation (RPA) with image processing, pattern recognition and machine learning, so its logical to ask where best to apply this technology for maximum effect. Machines can do automated tasks better, cheaper and faster. One can use the camera from the sky and measure things on the earth especially on cropland from drones and satellites. Over time, drones have increased in capabilities and fallen in cost, and their use has greatly expanded especially in complex terrain. High quality remote sensing with spectral imaging using drones makes them interesting for regular use in Precision Agriculture (PA). Drones are often used in agriculture in ways that were highly controversial only a short time ago even there are no unified legislation on drones usage in agriculture. In this paper we address problem of Remote Sensing (RS) technologies combined with Unmanned Aerial System (UAS) platforms to support and develop selected agriculture operations like map or sensor-based Variable Rate Application (VRA).
Modeling and Decoupled Control of Inductive Power Transfer to Implement Constant Current/Voltage Charging and ZVS Operating for Electric Vehicles

To prevent the degradation of battery life and reduce switching loss in inductive power transfer (IPT) systems for electric vehicles (EVs), it is vital to realize constant current/voltage (CC/CV) charging and zero-voltage-switching (ZVS) operation. However, due to the existence of couplings between the charging and ZVS operating control loops, the controllers are neither optimal nor robust. In this paper, a model-based decoupled control method is proposed to improve the performance of the charging and ZVS operating controllers. Since the operating point of the CC and CV modes are different, two dynamic models for the corresponding charging modes are built and linearized. Besides, the couplings between the charging and ZVS operating control loops are analyzed, and in turn, decoupled by designing compensators. To optimize the system performance, we chose PI controllers, and their parameters are obtained by calculating the settling time and overshoot of the close-loop dominant poles according to the control performance requirements. In the experiment, a 500-W series–series IPT system prototype is built and tested to verify the improvement of the control performance. Compared with the coupled control method and the traditional closed-loop control feedback method, the recorded response curves and transient waveforms demonstrate that the proposed decoupled control method brings a better performance to the system.
A Modified Approach Towards Personalized Travel Recommendation System Using Sentiment Analysis

Social media data provide enormous amount of opportunities to work on different fields and one of them is travel recommendation. This paper surveys most of the travel recommendation approaches with their limitation as well as what can be done to rectify them and also focuses on methodologies, techniques that should be included in a recommender system. We have proposed a customized travel sequence suggestion from both encounters of a travel journals and photographs taken from social media and the system sort the data (according to geolocation, Seasons and date on which the photo was taken) connected with these photographs. Content based filtering is used as it provides more features as compared to collaborative filtering and important one of them is to reduce impact of sparsity problem. The proposed approach is not at all similar to the existing approaches as it not only considers client's travel enthusiasm but also factors like time, season and cost as well. Social media is famous and a very important medium for expressing your views and communicating with people around the world. When many tweets are taken into consideration they can provide the opinion of public towards an events or object or even a location. Sentiment analysis is used to differentiate between the bad sentiments and the good sentiments, which will provide reviews about the POI and by which the traveller can decide whether to go there or not.
The Effect of Optical Crosstalk on Accuracy of Reflectance-Type Pulse Oximeter for Mobile Healthcare

According to the theoretical equation of the pulse oximeter expressed by the ratio of amplitude (AC) and baseline (DC) obtained from the photoplethysmographic signal of two wavelengths, the difference of the amount of light absorbed depending on the melanin indicating the skin color is canceled by normalizing the AC value to the DC value of each wavelength. Therefore, theoretically, skin color does not affect the accuracy of oxygen saturation measurement. However, if there is a direct path for the light emitting unit to the light receiving unit instead of passing through the human body, the amount of light reflected by the surface of the skin changes depending on the color of the skin. As a result, the amount of crosstalk that varies depending on the skin color affects the ratio of AC to DC, resulting in errors in the calculation of the oxygen saturation value. We made crosstalk sensors and crosstalk-free sensors and performed desaturation experiments with respiratory gas control on subjects with various skin colors to perform oxygen saturation measurements ranging from 60 to 100%. Experimental results showed that there was no difference in the measurement error of oxygen saturation according to skin color in the case of the sensor which prevented crosstalk (−0.8824 ± 2.2859 for Asian subjects, 0.6741 ± 3.2822 for Caucasian subjects, and 0.9669 ± 2.2268 for African American subjects). However, a sensor that did not prevent crosstalk showed a large error in dark skin subjects (0.8258 ± 2.1603 for Asian subjects, 0.8733 ± 1.9716 for Caucasian subjects, and −3.0591 ± 3.9925 for African Americans). Based on these results, we reiterate the importance of sensor design in the development of pulse oximeters using reflectance-type sensors.
Forecasting People’s Needs in Hurricane Events from Social Network

Social networks can serve as a valuable communication channel for asking for help, offering assistance, and coordinating rescue activities in disaster because it allows users to continuously update critical information in the fast-changing disaster environment. This paper presents a novel sequence to sequence based framework for forecasting people’s needs during disasters using social media and weather data. It consists of two Long Short-Term Memory (LSTM) models, one of which encodes input sequences of weather information and the other plays as a conditional decoder that decodes the encoded vector and forecasts the survivors’ needs. Case studies using data collected during Hurricane Sandy in 2012, Hurricane Harvey and Hurricane Irma in 2017 demonstrate that the proposed approach outperformed the statistical language model n-gram, LSTM generative model, and convolutional neural network (CNN) based model. This research indicates its great promise for enhancing disaster management such as evacuation planning and commodity delivery.
Circulating Irisin Levels in Children With GH Deficiency Before and After 1 Year of GH Treatment

Purpose To evaluate circulating irisin levels in children with GH deficiency (GHD) and any relation with clinical and metabolic parameters. Patients Fifty-four prepubertal children (mean age, 7.4 ± 0.8 years) with idiopathic GHD treated with GH for at least 12 months and 31 healthy short children as control subjects. Methods Body height, body mass index (BMI), waist circumference (WC), IGF-I, HbA1c, lipid profile, fasting and after-oral glucose tolerance test glucose and insulin, insulin sensitivity indices, and irisin levels were evaluated at baseline and after 12 months of GH replacement (GHR). Results At baseline, children with GHD, in addition to having lower growth velocity (P < 0.001), GH peak after stimulation tests (both P < 0.001), and IGF-I (P < 0.001), showed significantly lower irisin (P < 0.001) and higher BMI (P < 0.001) and WC (P = 0.001), without any difference in metabolic parameters, than control subjects. After GHR, children with GHD showed a significant increase in height (P < 0.001), growth velocity (P < 0.001), IGF-I (P < 0.001), fasting glucose (P = 0.002) and insulin (P < 0.001), homeostasis model assessment estimate of insulin resistance (P < 0.001), and irisin (P = 0.005), with a concomitant decrease in BMI (P = 0.001) and WC (P = 0.003). In multivariate analysis, the independent variables significantly associated with irisin were BMI (P = 0.002) and GH peak (P = 0.037) at baseline and BMI (P = 0.005), WC (P = 0.018), and IGF-I (P < 0.001) during GHR. Conclusions We report that GHR leads to an increase in irisin levels, strongly related to a decrease in BMI and WC, and to an increase in IGF-I; these changes are among the main goals of GHR. These data confirm the favorable effects of GHR in children.
Refining Cosine Distance Features for Robust Speaker Verification

Cosine distance features (CDF) have been proposed recently as a means to increase the performance of the speaker verification system. In CDF, we measure the cosine similarity of the i-vector of the input speech utterance with the i-vectors of the reference speakers. It is found that, the performance could be improved if the reference speakers are acoustically similar to the target speaker. There are different set of reference speakers for every target speakers if we select acoustically similar reference speakers, which is leading to speaker specific CDF(SSCDF). In this work, we explore the possibilities of further improving the performance of the SSCDF for the same number of reference speakers. We have developed two sub-systems with the reduced number of reference speakers (acoustically similar to the target speakers) and then combined/fused the decision scores of the two sub-systems. It is found that the fused SSCDF system out-performed the SSCDF consistently for reduced feature dimension and therefore using less number of reference speakers.
Modelica Based Object-Oriented Modeling and PD-Computed Torque Control of a 2-DOF Robotic Arm

In this paper, an object-oriented model was developed using Modelica for a 2-DOF robotic arm. The model was validated through testing the performance of a PD computed-torque disturbance rejection method to handle the trajectory tracking control of the robotic arm. The simulation results obtained in Dymola proved the validity of the developed model and showed the effectiveness of the designed PD-computed torque controller in enhancing the trajectory tracking performance of the robotic arm.
Phase-Matched Frequency Adaptive Repetitive Controller for a Grid-Supporting STATCOM

STATCOMs are widely used to provide voltage support in electrical distribution systems. However, if the grid voltage is distorted, the output current of a STATCOM may also be distorted unless a harmonic current controller is added to the control system. Repetitive controllers (RCs) provide an attractive solution for harmonic control in electronic power converters. These controllers are simple to design and implement but, unfortunately, their performance deteriorate with frequency deviations. This paper presents a solution called Phase-Matched filter to solve the frequency-adaptation problem of RCs. Compared to other filter methods already presented in the literature the proposed method provides additional degrees of freedom for the design. It will be shown the proposed filter improves the steady-state performance of RCs when the grid frequency varies. The proposed solution was applied to a STATCOM that provided voltage support in a weak grid. The main control system improvements were tested by simulation, and on a 15 kVA prototype.
Quantum Supervised Clustering Algorithm for Big Data

Supervised clustering is the process of grouping unlabeled objects by using some specific objectives and any supervised learning technique. In traditional clustering algorithms, the similarity measure is based on some distance finding formulations. In supervised clustering, this similarity measure is trained with the help of asupervised learning method. In this paper, we have designed a quantum supervised clustering algorithm and analyzed the overall runtime complexity of the algorithm. In our approach, we have trained the similarity measure model with quantum support vector machine. This quantum mechanically trained similarity measure model is used in the quantum K-Means algorithm, instead of the traditional distance-based similarity measure functions, for clustering unlabeled objects. Our analysis demonstrates that the proposed approach exhibits exponential speed up when compared to the classical implementation.
Routing in Reinforcement Learning Based Cognitive Radio Networks

Cognitive Radio (CR) technology is a promising technology that allows unlicensed users to access licensed spectrum bands opportunistically in a dynamic and noninterfering manner. Thus, using Cognitive Radio Networks (CRNs) spectrum efficiency can be increased by allowing the secondary users (SUs) to access the licensed band dynamically and opportunistically without interfering the primary users (PUs). Cognitive Radio Networks can be defined in the context of machine learning as the network which improves its performance through experience gained over a period of time without complete information about the environment in which it operates. Reinforcement learning is one such type of machine learning concerned with how software agents or learning agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Thus, the dynamism and opportunism can be learned by reinforcement learning (RL).
Primal-Dual Parallel Algorithm for Optimal Content Delivery in Cloud CDNs

Content delivery networks have been providing content delivery services for the last two decades using their own infrastructure. Now-a-days content delivery networks have the better option of using storage cloud sites as edge servers. The problems of replicating the content required by the users on optimal sites in Cloud and assigning the sites to users are considered in this work. Given a set of current user requests and cloud sites potential to the user, the combined problem of finding the optimal sites for content placement and content dissemination is set-cover problem. The Previous works solved this problem by using greedy algorithm. Primal-dual parallel algorithm for optimal content delivery in Cloud content delivery networks is proposed in this work. The proposed algorithm is an efficient parallel algorithm that requires only local information. Primal-dual algorithm takes less time than greedy algorithm and the experimental results demonstrate the fact.
Industrial Internet of Things: Challenges, Opportunities, and Directions

Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.