Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction of Antioxidants from Winemaking Byproducts: Effect of the Solvent on Phenolic Composition, Antioxidant and Anti-Cholinesterase Activities, and Electrochemical Behaviour

Extraction solvent is a very important factor in the recovery of antioxidants from natural matrices. In this study, the effect of three solvents (ethanol, ethanol/water and water) on the phenolic composition, antioxidant and anti-cholinesterase activities and electrochemical behaviour of four winemaking byproducts (seeds, skins, stems, and pomace) was evaluated. Phenolic composition was determined by the Folin–Ciocalteu method and ultra-high-performance liquid chromatography (UHPLC), antioxidant activity by the capacity to scavenge 2,2-diphenyl-1-picrylhydrazyl and hydroxyl radicals, anti-cholinesterase activity by the Ellman’s method, and electrochemical behaviour by cyclic voltammetry. Eight phenolic compounds were quantified with higher content in water/ethanol extracts (e.g., epicatechin in pomace: 17 mg/100 g vs. 7 and 6 mg/100 g in ethanol and water extracts, respectively), although there were some exceptions (e.g., gallic acid in seeds was most abundant in water extracts). Moreover, the highest total phenolic content (TPC) and antioxidant activity were found in ethanol/water extracts (between 2 and 30-fold the values of the other extracts). Overall, the most active extracts in inhibiting both acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes were ethanol/water and ethanol extracts from seeds (between 31.11 and 53.90%). The electrochemical behaviour allowed for differentiating the extracts depending on the solvent and the byproduct. Our findings indicate that winemaking byproducts represent a source of phenolic compounds with antioxidant and anti-cholinesterase activities and suggest that cyclic voltammetry is a promising technique to evaluate the phenolic extraction process from these byproducts.
Arsenite induces dysfunction of regulatory T cells through acetylation control of the Foxp3 promoter

Arsenic is known to cause damage to the body’s immune system by inducing epigenetic changes. However, the molecular mechanism of this damage remains elusive. Here, we report that arsenic disrupts the morphology of lymphocytes, decreases cell viability, and results in abnormal proportions of T lymphocyte subsets. Moreover, our results revealed that arsenic can reduce global acetylation of histone H4 at K16 (H4K16 ac) in lymphocytes via decreasing the level of males absent on the first but upregulates mRNA and protein levels of the forkhead/winged-helix box P3 (Foxp3) gene by increasing the acetylation of histone H4 at K16 (H4K16) at the promoter of Foxp3. Finally, arsenic-induced dysfunction of regulatory T cells (Tregs) could be ameliorated by trichostatin A. Our research indicates that arsenic-induced immunosuppressive effect in human lymphocytes may be related to the acetylation of H4K16 at the promoter of Foxp3 and that histone deacetylase inhibitors may play a role in the prevention and treatment of immune injury caused by arsenic.
A Novel Output Device for visually impaired and blind people’s aid systems

In this paper, we introduce a novel device which serves as visually impaired aid systems’ output device. The novelty in this work is the fact that the device is perceivable by the touch sense. The design and the conception of our proposed device allow the visually impaired aid systems to encode their outputs in several ways to respond to different applications. This device provides in concise and simple way a semantic information which permits to increase the user autonomy. In this work, we will use it for scene understanding and indoor navigation using only depth data as input. Conducted experiments are presented and discussed.
Towards multi-objective mixed integer evolution strategies

Many problems are of a mixed integer nature, rather than being restricted to a single variable type. Although mixed integer algorithms exist for the single-objective case, work on the multi-objective case remains limited. Evolution strategies are stochastic optimisation algorithms that feature step size adaptation mechanisms and are typically used in continuous domains. More recently they were generalised to mixed integer problems. In this work, first steps are taken towards extending the single-objective mixed integer evolution strategy for the multi-objective case. First results are promising, but step size adaptation for the multi-objective case can likely be improved.
Patients’ reasons for non-use of digital patient-reported outcome concepts: A scoping review

Data from digitally administered patient reported outcomes (PROs) is used more and more in routine healthcare for long-term conditions as a part of daily clinical practice. This literature study reviews empirical studies of digital PRO to examine patients’ reasons for non-use of digitally administered PRO data in routine care. This scoping review searched through PubMed, Embase, Web of Science and PsycINFO databases, reporting on study population, intervention, duration of intervention and motivational factors alongside stated reasons for nonparticipation or dropout for each study. The patients’ reasons for not participating, either from study start or by dropout, were analysed through a thematic approach. Fifty-one studies were included, published from 2010 to 2019, mostly from Europe and the Unites States covering different long-term conditions. The reasons for non-use are manifold and cover the themes of ability to use PRO, engagement, emotional distress and technical barriers. Several reasons are given explaining why patients with long-term conditions are not using digitally administered PRO as intended. This should be taken into account in the design phase of digital PRO interventions and considered in conversations with patients during the intervention.
Blockchain Meets AMI: Towards Secure Advanced Metering Infrastructures

Smart grids (SGs) and advanced metering infrastructures (AMIs) are considered as the new evolution of classical electrical grids. The recent emergence of smart meters is paving the way for the proliferation of smart grids, where billions of smart meters are interconnected to provide novel pervasive services (e.g., real time pricing application and real time energy consumption), and automate diagnostic and daily energy metering (i.e., gas, electric) tasks (e.g., billing, monitoring, planning and predicting of energy usage). The recent explosion in the number of insecure smart meters is changing the view towards SG from enabler of smart homes into a powerful amplifying tool that creates new vectors for cyberattacks (i.e., smart-homes Distributed Denial-of-Service (DDoS) attacks) at large scale. This motivated us to design a new flexible, secure, efficient and trustworthy access control scheme based on blockchain and smart contract. Although access control exists in AMI, it is based on a centralized model (i.e., router/gateway, firewall) which introduces a bottleneck (i.e., single point of failure) and causes the collapse of the system. In this paper, we propose a new decentralized-based access control architecture for SG based on blockchain; it uses smart contracts (i.e., Ethereum’s smart contracts) in order to manage permissions in a fully distributed and trustworthy manner. The architecture is implemented, tested and deployed on the Ethereum official test network Ropsten [1]. The results confirm that the proposed blockchain based access control scheme achieves security, flexibility, efficiency, and cost effectiveness making it a promising solution to mitigate DDoS attacks in SGs.
VR Alpine Ski Training Augmentation using Visual Cues of Leading Skier

Alpine skiing has strong environmental dependencies and the way of teaching the movement is believed to be incremental and cyclical. Training alpine skiing on simulators is a challenging work, especially when supporting experienced learner to improve to higher level. In this paper, we propose several vision augmentations for learning from a recorded expert skier motion in the way to replay the motion as a virtual leading skier.The system uses an stationary indoor ski simulator and a VR System for prototyping, two VR trackers are used to capture the motion of skis so that users can control the skis on the virtual slope. For training, we captured the motion of professional athletes and replay it to let the users follow the experts in the slope. To support users, 6 different visual cues are introduced from different perspectives of learning skiing, such as the feet angle or the lateral position.To explore the utility of visual cues and to study how users could learn the motion patterns from the expert-skier effectively, we performed qualitative and quantitative evaluations. In addition, we also studied several visual feedback aiming to help the learning process. The work provides the basis for developing and understanding the possibilities and limitations of VR ski ski training, which also has the potential to be extended to AR/MR use in real world.
Dynamic Network Slice Reconfiguration by Exploiting Deep Reinforcement Learning

It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond systems need to support. To guarantee performance isolation while maximizing network resource utilization under traffic uncertainty, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by the numerous variables. In this paper, we investigate network slice reconfiguration with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). To address the curse of dimensionality of the problem, we propose to incorporate the Branching Dueling Q-network (BDQ) into DRL, to avoid some unnecessary calculations of Q-value by separating the Q-network into a shared value branch and a number of distributed advantage branches. Furthermore, the value branch and the advantage branch of each dimension are aggregated to derive the corresponding dimension’s sub-Q-value. Then the best reconfiguration action is composed of the subactions in individual dimensions which are selected by $\epsilon -$greedy policy. Finally, we design an intelligent online network slice reconfiguration policy based on BDQ and extensive simulation experiments are conducted to validate the effectiveness of the proposed slice reconfiguration policy.
A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.
An Optimized Controller Placement Algorithm in 5G Based on SDN

Software Defined NetWork(SDN) is a new paradigm of decoupling the control logic from packet forwarding devices. We can reduce the communication delay in the network by optimizing the location of the controller and improve the communication performance of the network. From the perspective of reducing the average communication latency, this paper studies the deployment of controllers in the multiple network areas. An optimized DPC algorithm is then proposed to address the problem. Specifically, on the basis of DPC algorithm, we borrowed the idea of triangular stability from the BeeDPC algorithm, introduced SC metrics and related evaluation indicators, that is, the degree of separation between sub-network regions and the degree of aggregation within the sub-network region, and get a more reasonable controller of deployment scheme which reduces the limitations of the DPC algorithm. Extensive simulations are conducted and results demonstrate the optimized DPC algorithm can effectively reduce the average latency between the control layer and the data layer compared with K-means algorithm, optimized K-means algorithm, and optimal placement algorithm, improve network performance and reliability.
Neighbor Cell List Optimization in Handover Management Using Cascading Bandits Algorithm

Frequent handover is a key challenge in 5G Ultra-Dense Networks (UDN). In this paper, we show the significance of configuring Neighbor Cell List (NCL) in handover procedure. To cope with the high dynamic of UDN, we propose an online-learning method, namely the Cost-aware Cascading Bandits NCL configuration (CCB-NCL) algorithm, which applies the cascading model and Multi-Armed Bandits (MAB) theory to configure the efficient Neighbor Cell List (eNCL) and improves the handover performance by assisting the User Equipment (UE) to choose the optimal target Base Station (BS). We provide rigorous proof of regret bound to show the asymptotic convergence of the proposed CCB-NCL algorithm. The robustness and efficiency of the proposed algorithm are both demonstrated in different network scenarios, where varies BS densities, BS dynamic and network heterogeneity are considered respectively. In the simulation work, we reproduce two existing methods of configuring NCL in handover management, named dynamic threshold based solution and received signal strength based solution. In comparison with the existing solutions, the proposed algorithm can reduce the overlarge signaling cost and unnecessary delay in the preparation phase of handover procedure by significantly shortening the length of NCLs and reducing the number of scanned BSs. Extensive simulations are conducted in different scenarios to validate the robustness of the proposed algorithm and the results show that the proposed CCB-NCL algorithm is a superior approach to efficient handover management.
Secrecy Performance Analysis of Cooperative NOMA system with multiple DF Relays

In this paper, we derive the Secrecy Outage Probability (SOP) and Strictly Positive Secrecy Capacity (SPSC) of cooperative Non Orthogonal Multiple Access (NOMA) in the presence of K Decode and Forward (DF) relays and Rayleigh fading channels. We also optimize the powers allocated to far and near users to maximize the SPSC or minimize the SOP. Our results are valid for any number of relays with arbitrary positions.
An a-contrario Biometric Fusion Approach

Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data.
VNF Dynamic Scaling and Deployment Algorithm Based on Traffic Prediction

NFV separates network functions from hardware-dependent middle boxes, which can significantly reduce costs and improve network management flexibility. It has been widely used in operator networks. However, due to traffic fluctuation in the network, using virtual network functions to provide flexible services is still challenging. In addition, most VNF scaling methods are passive in nature, which may cause high latency and fail to meet the QoS requirements of services. Therefore, this paper first proposes a GRU-based traffic prediction model and scales in/out VNF instances in advance based on the prediction result. Then we design a VNF buffering mechanism to avoid frequently releasing and creating VNF instances. Furthermore, based on the scaling results of VNF, we apply a DRL algorithm called A3C to train the agent and then obtain the optimal strategy of deploying new instances. Simulation results show that compared with other methods, the proposed proactive method can respond to traffic fluctuation in advance and reduce the total operating costs.
No One Left Behind: Avoid Hot Car Deaths via WiFi Detection

According to the safety organization Kids and Cars, in US, an average of 38 children die each year in hot cars, seemingly forgotten by a distracted parent. Existing car seat alarm designs either compromise people’s privacy (camera based designs), or fail to distinguish children sitting in the back from heavy stuff put on rear seats, and keep sending false alerts (pressure sensor based designs). In an effort to prevent such tragedies, we propose to utilize the fine-grained channel state information (CSI) from commercial off-the-shelf WiFi devices to detect if a child has been forgotten in rear seat of the car. Our child detection system only needs WiFi signal and applies both phase and amplitude measurement of the CSI. Based on this, our system can capture the movements of children, and effectively detect the children who are forgotten in rear seat and distinguish them from pets or other heavy stuff in rear seat with deep learning algorithms. In comparison with KNN based child detection method, the experiment results show that the performance of our deep learning based system increases dramatically, and the detection accuracy can reach more than 95%.
Protecting Consumption Habits with Differential Privacy

Online bank system has been widely deployed to provide financial service worldwide. Big data mining brings serious privacy issues to consumers because consumption records may disclose their activities. Existing authentication and encryption algorithms in privacy-preserving deposit transaction schemes are mostly limited to the case that each consumption has to be reported to online banks directly and an insider adversary can monitor a consumer’s bank account during the financial transaction period. Motivated by the above problems, we propose a deposit transaction scheme based on the differential privacy. Considering a mobile payment system as a noise generator, this scheme aims to reduce the relevancy between real consumption and deposit transaction. In addition, we provide in-depth theoretical analysis that our scheme can satisfy the definition of the differential privacy. Experimental results illustrate the consumption pattern can be protected with the differential privacy.
Chemical Synthesis Elucidates the Key Antigenic Epitope of the Autism-related Bacterium Clostridium bolteae Capsular Octadecasaccharide.

The gut pathogen Clostridium bolteae has been associated with the onset of autism spectrum disorder (ASD). To create vaccines against C. bolteae , it is important to identify exact protective epitopes of the immunologically active capsular polysaccharide (CPS). Here, we prepared a series of C. bolteae CPS glycans up to an octadecasaccharide. Key to achieving the total syntheses is a [2+2] coupling strategy based on a β-d-Rha p -(1→3)-α-d-Man p repeating unit that in turn was accessed via a stereoselective β-d-rhamnosylation. The 4,6- O -benzylidene-induced conformational locking is a powerful strategy for forming β-d-mannose-type glycoside. An indirect strategy based on the C2 epimerization of β-d-quinovoside was efficiently achieved by Swern oxidation and borohydride reduction. Sequential glycosylation, regioselective and global deprotection produced disaccharide, tetrasaccharide, all the way to octadecasaccharide. Glycan microarrays analysis of sera from rabbits immunized with inactivated C. bolteae bacteria revealed a humoral immune response to di- and tetrasaccharide but none of the longer sequences. Tetrasaccharide may be a key motif for designing glycoconjugate vaccines against C. bolteae .
Research on the Mapping Mathematical Model of Points in Polygon Region and Rectangle Region

According to the basic principle of Schwarz Christoffel transformation, the mathematical model of polygon region boundary mapping to rectangle region conformal mapping was established, and the numerical calculation method of mapping mathematical model was proposed. In the whole mapping process, the zonal transition region was needed. In the process of calculating the mapping from polygonal region to zonal transition region, two-dimensional particle swarm optimization algorithm was used to get the initialization points of the transition region. According to the mapping mathematical model and boundary mapping results, the initial points in the zonal transition region were taken as the end point of integration, and the nearest points between the initial points and the boundary of the zonal transition region were taken as the starting point of integration. Gauss Jacobian integration method was used to get the calculated points in the polygonal region. Taking the sum of square error between actual and calculated points as objective function, the optimized PSO algorithm was used to obtain the calculated points in the zonal transition region. With the corresponding rules of transformation scale from the zonal transition region to the rectangular region, the initialization method of point position in the rectangular region was proposed. With Newton method, jacobian elliptic function was solved for the mapping point position in the rectangular region.
Facial motion analysis using template matching

Motion is a real issue in video sequence analysis since it describes an object in three dimensions whereas the images are defined by a 3D scene projection in a 2D plane. An important problem in tracking of a point between two successive images is that only one pixel can’t be tracked since the intensity pixel value can both change due to noise and can be confused with adjacent pixels. As a result, it’s often impossible to determine the pixel location in the next frame, based only on the local information.In this article, we are interested in facial motion tracking for talking faces. When talking, points on the face move and their surround intensity change in a complex way. Therefore, we propose a comparison study of three methods for facial motion tracking to estimate the trajectory of each facial point on a talking face by analyzing templates intensities levels belonging two successive images. The first method is based on block matching: Normalized Sum of Squared Differences (NSSD), the second on normalized cross correlation (NCC), whereas the third method concerns the Kanade–Lucas–Tomasi Tracking (KLT tracker). Results obtained are compared, based on tracked error on the trajectory of a video by measuring the root-mean-squared intensity difference between the current and the last template.