Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Decoding of Eccentricity and Direction Information for a Single-Flicker SSVEP BCI

The feasibility of a steady-state visual evoked potential (SSVEP) brain–computer interface (BCI) with a single-flicker stimulus for multiple-target decoding has been demonstrated in a number of recent studies. The single-flicker BCIs have mainly employed the direction information for encoding the targets, i.e., different targets are placed at different spatial directions relative to the flicker stimulus. The present study explored whether visual eccentricity information can also be used to encode targets for the purpose of increasing the number of targets in the single-flicker BCIs. A total number of 16 targets were encoded, placed at eight spatial directions, and two eccentricities (2.5° and 5°) relative to a 12 Hz flicker stimulus. Whereas distinct SSVEP topographies were elicited when participants gazed at targets of different directions, targets of different eccentricities were mainly represented by different signal-to-noise ratios (SNRs). Using a canonical correlation analysis-based classification algorithm, simultaneous decoding of both direction and eccentricity information was achieved, with an offline 16-class accuracy of 66.8 ± 16.4% averaged over 12 participants and a best individual accuracy of 90.0%. Our results demonstrate a single-flicker BCI with a substantially increased target number towards practical applications.
Radiocommunication event allocation model for a selected sea area

Abstract This article presents the structure of a model of the allocation of radiocommunication events at coastal radio stations, land-based satellite stations and on vessels in sea area A3. The propagation of radio waves in the HF band has been analyzed to examine the range of various radio stations and their capabilities of establishing communication between each other. We also present methods of modeling and displaying the deployment of individual stations, of presenting radiocommunication events as a function of time using time diagrams, as well as the manner of the chronological presentation of radiocommunication events and related decisions. It has been shown that there is a relationship between the effectiveness of communication and propagation conditions that is strongly dependent on the time-of-day. We present the need to develop a decision support system for the radio operator on the bridge.
Combining goal model with reviews for supporting the evolution of apps

To support the iterative development process of Apps, the goal model is not only established to describe the requirements at the early stage but also used for identifying the updating strategy in every iteration. In this process, reviews from users provide valuable information for developers to analyse the model with users sentiments. In this study, the authors combine the goal model with reviews for supporting the evolution of Apps. First, the authors introduce the reviews into the goal model as a new factor by comparing keywords. Second, the users sentiments in reviews are mined, and two kinds of information are gained by analysing the model to help developers make decisions on which goals to be improved in next version: one kind of information is about users sentiments on the goals to evaluate whether users like them; another kind is the impact of updating one goal to others. To validate the proposed approach, they conducted experiments and a survey based on the Apps in Google Play. The results show that the proposed approach can establish relationships between goals and reviews reasonably and further provide useful information for optimising the evolution strategy of the App.
THE EFFECT OF THE BLOOD PRESSURE FEEDBACK INTERVENTION TO PHYSICIANS ON THE IMPROVEMENT OF THE BLOOD PRESSURE CONTROL

The study aimed to assess the effect of the blood pressure (BP) feedback intervention to physicians on the improvement of the blood pressure control of hypertension subjects. The study was done with controlled repeated intervention design. The adult hypertensive non-hemodialysis subjects from 4 Indonesian hospitals were included as intervention and control subjects. Outcomes were measured as the improvement of systolic BP (SBP). The subjects in intervention (n=385) vs. non-intervention (n=271) groups had similar age and proportion of males (p>0.05); proportion of cardiovascular comorbid 78.7% vs. 91.5% (p<0.01) and the baseline SBP at 144.1±15.8 vs. 139.6±13.8mmHg (p<0.01). The final SBP 138.2±17.2 vs.140. 6±15.4mmHg (adjusted p<0.01); the difference between (∆) final-baseline SBP: 5.9±20.3 vs. (-)0.9±20.0mmHg (adjusted p<0.01); ∆final-target SBP: (-)6.1±17.3 vs. (-)9.6±15.5 (adjusted p<0.01). There were more intervention subjects with good controlled final SBP; odds ratio (OR) 1.4(CI95%:1.0-1.9, adjusted p<0.05). Based on the ∆final-baseline SBP, the ∆final-target SBP, and OR SBP reached the target; theintervention subjects had significant SBP improvement.
Fuzzy Logic Approximation and Deep Learning Neural Network for Fish Concentration Maps

— This paper proposes an algorithm to obtain topographic maps of lakes, maps of fish concentration and a map of predator location based on the results of an intelligent sonar data processing. The algorithm is based on the following steps: input frame separation into overlapping blocks, blocks-processing using convolutional neural networks (CNN) YOLO v2, and merging extracted bounding boxes around one object. To construct maps of the distribution of features along the lake, we propose a novel method for constructing the approximation of GPS-referenced CNN results based on the original implementation of fuzzy logic.
Integrating Fronthaul and Backhaul Networks: Transport Challenges and Feasibility Results

In addition to CPRI, new functional splits have been defined in 5G creating diverse fronthaul transport bandwidth and latency requirements. These fronthaul requirements shall be fulfilled simultaneously together with the backhaul requirements by an integrated fronthaul and backhaul transport solution. In this paper, we analyze the technical challenges to achieve an integrated transport solution in 5G and propose specific solutions to address these challenges. These solutions have been implemented and verified with pre-commercial equipment. Our results confirm that an integrated fronthaul and backhaul transport dubbed Crosshaul can meet all the requirements of 5G fronthaul and backhaul in a cost-efficient manner.
EdgeBox: Confidential Ad-Hoc Personalization of Nearby IoT Applications

Steady progress in ubiquitous technologies and machine learning (ML) facilitates ever-new and better personalized IoT applications (e.g., digital assistants). However, current architectural concepts for personalization are either impractical or cannot support both sides without leaking sensitive data, namely the user's personal data or the provider's protected ML models. In this paper, we propose a novel approach termed EdgeBox for ad hoc personalizing nearby IoT applications while allowing both, (1) keeping the providers' ML models confidential and (2) privacy-preserving processing of personal user data, even on untrusted IoT devices. In short, confidential parts of the EdgeBox (e.g., the personalization task) are performed in an isolated trusted execution environment (TEE) within the IoT device's processor; the remote attested code in the TEE allows to confidentially load latest provider models and stream the required user data over wireless direct link ad-hoc protocol on demand. We evaluate our implemented proof-of-concept prototype (ad-hoc-initialized vs. pre-initialized) w.r. t. setup time and performance overhead. The results show real-time capabilities with an acceptable overhead, enabling a novel and confidential way to ad hoc personalize a user's dynamic environment in everyday life, supporting several assistant use cases.
Single pixel hyperspectral bioluminescence tomography based on compressive sensing

Photonics based imaging is a widely utilised technique for the study of biological functions within pre-clinical studies. Specifically, bioluminescence imaging is a sensitive non-invasive and non-contact optical imaging technique that is able to detect distributed (biologically informative) visible and near-infrared activated light sources within tissue, providing information about tissue function. Compressive sensing (CS) is a method of signal processing that works on the basis that a signal or image can be compressed without important information being lost. This work describes the development of a CS based hyperspectral Bioluminescence imaging system that is used to collect compressed fluence data from the external surface of an animal model, due to an internal source, providing lower acquisition times, higher spectral content and potentially better tomographic source localisation. The work demonstrates that hyperspectral surface fluence images of both block and mouse shaped phantom due to internal light sources could be obtained at 30% of the time and measurements it would take to collect the data using conventional raster scanning methods. Using hyperspectral data, tomographic reconstruction of internal light sources can be carried out using any desired number of wavelengths and spectral bandwidth. Reconstructed images of internal light sources using four wavelengths as obtained through CS are presented showing a localisation error of ∼3 mm. Additionally, tomographic images of dual-colored sources demonstrating multi-wavelength light sources being recovered are presented further highlighting the benefits of the hyperspectral system for utilising multi-colored biomarker applications.
Constructing Cooking Ontology for Live Streams

We build a cooking domain knowledge by using an ontology schema that reflects natural language processing and enhances ontology instances with semantic query. Our research helps audiences to better understand live streaming, especially when they just switch to a show. The practical contribution of our research is to use cooking ontology, so we may map clips of cooking live stream video and instructions of recipes. The architecture of our study presents three sections: ontology construction, ontology enhancement, and mapping cooking video to cooking ontology. Also, our preliminary evaluations consist of three hierarchies—nodes, ordered-pairs, and 3-tuples—that we use to referee (1) ontology enhancement performance for our first experiment evaluation and (2) the accuracy ratio of mapping between video clips and cooking ontology for our second experiment evaluation. Our results indicate that ontology enhancement is effective and heightens accuracy ratios on matching pairs with cooking ontology and video clips.
Smart ECG Monitoring Through IoT

The emergence of internet of things allows the integration of health systems by enabling real-time monitoring with a low cost. Therefore, one of the essential targets in this work is the realization of a new smart real-time electrocardiogram remote monitoring system based on cloud networks. This health wireless system allows the acquisition of electrocardiogram signal with the temperature and acceleration measurement of the patient's body using the inertial measurement unit module sensor. A strong access schemes is employed to transfer the data from sensors to cloud environment by keeping the protection of e-health information. The second objective in this chapter is designing a flexible and stretchable health circuit basing on design considerations, aiming the combination of flexible, elastic, and rigid materials around minimal constraints and maximum mechanical dependability in the structures. The flexible fabrication part was inspired from the biocompatible process technology.
Secure Cloud Data Deduplication with Efficient Re-Encryption

Data deduplication technique has been widely adopted by commercial cloud storage providers, which is both important and necessary in coping with the explosive growth of data. To further protect the security of users’ sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware encrypted deduplication scheme (REED) is vulnerable to an attack which we call it stub-reserved attack. Furthermore, we propose a secure data deduplication scheme with efficient re-encryption based on the convergent all-or-nothing transform (CAONT) and randomly sampled bits from the Bloom filter. Due to the intrinsic property of one-way hash function, our scheme can resist the stub-reserved attack and guarantee the data privacy of data owners’ sensitive data. Moreover, instead of re-encrypting the entire package, data owners are only required to re-encrypt a small part of it through the CAONT, thereby effectively reducing the computation overhead of the system. Finally, security analysis and experimental results show that our scheme is secure and efficient in re-encryption.
Model Predictive Control of a Doubly-fed Induction Generator, Connected to a Dc-grid, for Distributed Generation

This work presents a predictive control strategy applied to a doubly-fed induction generator (DFIG) connected to a dc microgrid, suitable for distributed generation purposes and microgrid environment. The topology of connecting the DFIG to the dc grid consists in the replacement of one voltage source inverter by a diode rectifier. However, the inclusion of the diode rectifier gives rise to a large torque ripple, characterized by a component oscillating at six times the stator frequency. This harmonic component has negative effects on the mechanical components of the drive system and as such, efforts are needed to eliminate those torque oscillations. Classical control strategies, based on field orientation try to address this issue by the inclusion of resonant controllers, or by decomposing and suppressing this harmonic component in multiple reference frames. However, those methods have inherent disadvantages, such as: great tuning effort, inferior dynamic response and difficulty to include restrictions in the system. Furthermore, the existence of multiple control loops dictates that separate bandwidth for them must be ensured, as the stability of the system is closely related to the bandwidth of the inner loop. The use of model predictive control (MPC) in the proposed control system system avoids all the difficulties associated with vector control and is able to practically fully eliminate the low-frequency torque oscillations. Attending that predictive control strategies are based in discrete models, their performance is heavily dependent on the model parameters accuracy and of a correct parameter estimation. On the other hand, the complexity of the discrete model must also be minimized, to make the algorithm feasible for practical use. As such, a comparison is also provided on the steadystate and dynamic performance of the system, when the forward Euler and Taylor series expansion discretization methods are used.
The Optimum Location for Access Point Deployment Based on RSS for Indoor Communication

- In indoor wireless communication networks, the optimal locations had been known to deploy the access points (AP's) which has a significant impact on improving various aspects of network operation, management, and coverage. In addition, develop the behavioral characteristics of the wireless network. The most used approach for localization purposes was based on Received Signal Strength (RSS) measurements, which is widely used in the wireless network. As well as, it can be easily accessed from different operating systems. In this paper, we proposed an optimal AP localization algorithm based on RSS measurement obtained from different received points. This localization algorithm works as a complementary to the 3D Ray tracing model based REMCOM wireless InSite software and considered two-step localization approach, data collection phase, and localization phase. Obtained result give relatively high accuracy to select the optimum location for AP compare with other selected locations. It is worth to mention that effect of different building materials on signal propagation has been considered with specifying the optimum location of deployment. Furthermore, channel characterizations that based on path losses have been considered as a confirmation for the optimum location being selected.
Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods

Evidence accumulation models are a useful tool to allow researchers to investigate the latent cognitive variables that underlie response time and response accuracy. However, applying evidence accumulation models can be difficult because they lack easily computable forms. Numerical methods are required to determine the parameters of evidence accumulation that best correspond to the fitted data. When applied to complex cognitive models, such numerical methods can require substantial computational power which can lead to infeasibly long compute times. In this paper, we provide efficient, practical software and a step-by-step guide to fit evidence accumulation models with Bayesian methods. The software, written in C++, is provided in an R package: 'ggdmc'. The software incorporates three important ingredients of Bayesian computation, (1) the likelihood functions of two common response time models, (2) the Markov chain Monte Carlo (MCMC) algorithm (3) a population-based MCMC sampling method. The software has gone through stringent checks to be hosted on the Comprehensive R Archive Network (CRAN) and is free to download. We illustrate its basic use and an example of fitting complex hierarchical Wiener diffusion models to four shooting-decision data sets.
A Model-Driven Approach for Configurable Evaluation of Traceability Information

—Requirement traceability is the ability to explicate and pursue the relations between all artifacts that specify, implement, test, or document a software solution. Besides being required by laws and regulations in safety-critical industries, the traceability information models can give insight on project progress and quality. Yet, only a few companies are utilizing this competitive advantage due to missing tool support. The paper introduces an integrated solution to deﬁne and execute company-or project-speciﬁc analysis statements written in a dedicated domain-speciﬁc language. First, the capabilities of the Traceability Analysis Language are demonstrated by deﬁning coverage, impact and consistency analysis. Every analysis is deﬁned as a rule expression that compares a customizable metric’s value (aggregated from the traceability information model) against an individual threshold. The focus of the Traceability Analysis Language is to make the deﬁnition and execution of information aggregation and evaluation from a traceability information model conﬁgurable and thereby allow users to deﬁne their own analyses based on their regulatory, project-speciﬁc, or individual needs. Further, the analyses are applied to a model according to the Automotive Software Process Improvement and Capability Determination (A-SPICE) standard. Second, the underlying grammar, as well as the mechanisms to make data retrieval conﬁgurable, are explained. Finally, the paper reports on case study ﬁndings at a tier one automotive supplier company. The case study revealed that the possibility to introduce custom data retrieval functions is crucial in real-world scenarios. Further, the case study showed that the traceability analysis language supported the tier one automotive supplier in the process of being A-SPICE re-certiﬁed.
Measuring BGP Route Propagation Times

We present a methodology to measure the propagation time of BGP routes with BGP route collectors and beacons which accounts for the clock offset error between route source and destinations. We apply it to the RIPE RIS data set comparing 2012 and 2018, showing that the IPv4 route prefix reachability interval and the time to receive the preferred route have not changed much, but the time to remove an IPv4 route has increased and it is high (a mean of 121s to remove a route). In 2018, IPv6 route propagation times are shorter than IPv4 ones.
Cellular Optimization of Nanofat: Comparison of Two Nanofat Processing Devices in Terms of Cell Count and Viability

Abstract Background Nanofat was introduced by Tonnard and Verpaele in 2013. Their initial observations in intradermal applications showed improvement in the appearance of the skin. Since then, a number of Nanofat devices have been introduced. The cellular content in the processing of Nanofat is not the same in every device, yet the cellular composition is responsible for the biologic action of Nanofat. The authors sought to find a different means to produce a matrix rich Nanofat to optimize the cellular content. Objectives The primary objective of this study was to compare cell counts, cultures, and cell viabilities produced by LipocubeNano (Lipocube, Inc., London, UK) in comparison to Tulip’s NanoTransfer (Tulip Medical, San Diego, CA) processing methods. Methods Twenty milliliters of fat were harvested from 10 patients in order to test two methods of Nanofat production. Ten milliliters of fat were used to assess each method and, after the final product was obtained, enzymatic digestion for stromal vascular fraction (SVF) isolation was performed. A Muse Flow-cytometer was used to measure cell counts and cell viabilities, cell cultures were performed, and cell images were taken with a florescent microscope. Results The LipocubeNano was shown to be superior to Tulip’s NanoTransfer system of progressive downsizing with final filtering, which appeared to trap more fibrous tissue leading to lower amounts of SVF. LipocubeNano resulted in higher cell counts (2.24 × 106/cc), whereas Tulip’s NanoTransfer method resulted in a lower cell count at 1.44 × 106/cc. Cell viability was the same (96.05%) in both groups. Conclusions Nanofat from LipocubeNano has a higher regenerative cell count and more SVF cells than the other common mechanical method of Nanofat processing. This new means of mechanical processing preserves more matrix, optimizing the cellular content of the Nanofat, thus having potentially a higher regenerative effect. Level of Evidence: 5
Structured Graph Reconstruction for Scalable Clustering

Spectral clustering is a quite simple but effective method for solving graph clustering problem. It projects the original data points into a lower dimensional space with spectral embedding, and then relies on an algorithm to obtain the cluster labels. Since it involves eigendecomposition of the graph Laplacian matrix for embedding, spectral clustering has high time complexity and is not able to process large scale data. The performance of spectral clustering is also limited by a post-processing algorithm such as kmeans. To tackle the two issues, we propose a method called Orthogonal and Nonnegative Graph Reconstruction (ONGR) for large scale clustering. The two constraints serve as a structure constraint with which the graph reconstructed by the indicator matrix is structured. The proposed method has linear time complexity with respect to the data size that it mainly needs to implicitly construct a graph and iteratively perform economical singular value decomposition for a small size matrix. Moreover, the interpretability of the indicator matrix is offered due to the nonnegative constraint, and thus our method can provide the cluster labels with no post-processing. The experiments on benchmark datasets show the effectiveness of the proposed scalable clustering method.