Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliable Customized Privacy-Preserving in Fog Computing

Fog computing is an emergent computing paradigm that extends the cloud paradigm to the edge. With the explosive growth of smart devices and massive data generated everyday, cloud computing no longer matches the requirements of the Internet of Things (IoT) era, such as low latency, uninterrupted service and location awareness. Thus, fog computing has been introduced as a complement of the current cloud computing model to meet the requirements in IoT. Fog computing is a relatively new networking paradigm and considered as a promising solution to support IoT scenarios. On the one hand, fog computing inherits many features from cloud; on the other hand, fog computing also inherits some challenges and issues from cloud computing: privacy issue is one of them. In this paper, we propose a personalized differential privacy model based on the distance between two fog nodes in a fog network. We also identify the collusion attack in differential privacy framework which compromised the personalized Laplace function. Based on that, we develop a personalized differential privacy model, which not only eliminate this particular attack but also optimize the trade-off between privacy preserving and data utility.
Cloud-RAN functional split for an efficient fronthaul network

The evolution of telecommunication network towards cloud-native environments enables flexible centralization of the base band processing of radio signals. There is however a trade-off between the centralization benefits and the fronthaul cost for carrying the radio data between distributed antennas and data processing centers, which host the virtual RAN functions. In this paper, we present a specific split solution for an efficient fronthaul, which enables reducing the consumed bandwidth while being compliant with advanced cooperative radio technologies (interference reduction and data rate improvements). The proposed split has been implemented on the basis of Open Air Interface code and shows important gains in the required fronthaul bandwidth as well as significant latency reduction in the processing of radio frames.
MOOC Student Success Prediction Using Knowledge Distillation

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. Due to the low success rate of MOOCs, student success prediction has received much attention recently. Yet many utilize post-hoc prediction architectures, where model fitting requires features which are not knowable until a course completes. These works do not match the actual environment in which the prediction model is used, which may lead to unsatisfactory results. In order to build a reliable model that can be used in the actual environment, we propose a new student success prediction algorithm based on knowledge distillation. This algorithm can make the model obtain good prediction results with only a few basic features, and solve the problem of using features which are not knowable until a course completes.
On the Impact of SSDF Attacks in Hard Combination Schemes in Cognitive Radio Networks

One of the critical threats menacing the Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) is the Spectrum Sensing Data Falsification (SSDF) reports, which can deceive the decision of Fusion Center (FC) about the Primary User (PU) spectrum accessibility. In CSS, each CR user performs Energy Detection (ED) technique to detect the status of licensed frequency bands of the PU. This paper investigates the performance of different hard-decision fusion schemes (OR-rule, AND-rule, and MAJORITY-rule) in the presence of Always Yes and Always No Malicious User (AYMU and ANMU) over Rayleigh and Gaussian channels. More precisely, comparative study is conducted to evaluate the impact of such malicious users in CSS on the performance of various hard data combining rules in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are carried out to show that the hard-decision fusion scheme with MAJORITY-rule is the best among hard-decision combination under AYMU attacks, OR-rule has the best detection performance under ANMU.
New evidence for a role of vessel‐associated cells and phloem in the rapid xylem refilling of cavitated stems of Laurus nobilis L.

Xylem recovery from embolism was studied in Laurus nobilis L. stems that were induced to cavitate by combining negative xylem pressure potentials (PX = −1.1 MPa) with positive air pressures (PC) applied using a pressure collar. Xylem refilling was measured by recording the percentage loss of hydraulic conductance (PLC) with respect to the maximum 2 min, 20 min and 15 h after pressure release. Sodium orthovanadate (an inhibitor of many ATP-ases) strongly inhibited xylem refilling while fusicoccin (a stimulator of the plasma membrane H+-ATPase) promoted complete embolism reversal. So, the refilling process was interpreted to result from energy-dependent mechanisms. Stem girdling induced progressively larger inhibition to refilling the nearer to the embolized stem segment phloem was removed. The starch content of wood parenchyma was estimated as percentages of ray and vasicentric cells with high starch content with respect to the total, before and after stem embolism was induced. A closely linear positive relationship was found to exist between recovery from PLC and starch hydrolysis. This, was especially evident in vasicentric cells. A mechanism for xylem refilling based upon starch to sugar conversion and transport into embolized conduits, assisted by phloem pressure-driven radial mass flow is proposed.
Persistent minimal sequences of SARS-CoV-2

Abstract Motivation Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 14 million cases and more than half million deaths. Given the absence of implemented therapies, new analysis, diagnosis, and therapeutics are of great importance. Results Analysis of SARS-CoV-2 genomes from the current outbreak reveals the presence of short persistent DNA/RNA sequences that are absent from the human genome and transcriptome (PmRAWs). For the PmRAWs with length 12, only four exist at the same location in all SARS-CoV-2. At the gene level, we found one PmRAW of size 13 at the Spike glycoprotein coding sequence. This protein is fundamental for binding in human ACE2 and further use as an entry receptor to invade target cells. Applying protein structural prediction, we localized this PmRAW at the surface of the Spike protein, providing a potential targeted vector for diagnostics and therapeutics. Additionally, we show a new pattern of relative absent words (RAWs), characterized by the progressive increase of GC content (Guanine and Cytosine) according to the decrease of RAWs length, contrarily to the virus and host genome distributions. New analysis shows the same property during the Ebola virus outbreak. At a computational level, we improved the alignment-free method to identify pathogen-specific signatures in balance with GC measures and removed previous size limitations. Availability and Implementation https://github.com/cobilab/eagle. Supplementary informationbtaa686_Supplementary-data Click here for additional data file. Supplementary data are available at Bioinformatics online.
Indirect Structural Connectivity Identifies Changes in Brain Networks After Stroke

Background / Purpose: The purpose of this study was 1) to identify changes in structural connectivity after stroke and 2) to relate changes in indirect connectivity to post-stroke impairment. METHODS A novel measure of indirect connectivity was implemented to assess the impact of stroke on brain connectivity. Probabilistic tractography was performed on 13 chronic stroke and 16 control participants to estimate connectivity between gray matter regions. The Fugl-Meyer assessment of motor impairment was measured for stroke participants. Network measures of direct and indirect connectivity were calculated, and these measures were linearly combined with measures of white matter integrity to predict motor impairment. RESULTS We found significantly reduced indirect connectivity in the frontal and parietal lobes, ipsilesional subcortical regions and bilateral cerebellum after stroke. When added to the regression analysis, the volume of gray matter with reduced indirect connectivity significantly improved the correlation between image parameters and upper extremity motor impairment (R2=0.71, p<0.05). CONCLUSION This study provides evidence of changes in indirect connectivity in regions remote from the lesion, particularly in the cerebellum and regions in the fronto-parietal cortices, and these changes correlate with upper extremity motor impairment. These results highlight the value of using measures of indirect connectivity to identify the effect of stroke on brain networks.
Blood Profile of West African Dwarf Goats Fed Panicum maximum Supplemented with Afzelia africana and Newbouldia laevis

The haematological and biochemical status of twenty West African dwarf goats fed Panicum maximum supplemented with foliage from Afzelia africana and Newbouldia laevis was investigated. Values for PCV, Hb concentration and RBC count differed significantly (P 0.05) between the diets. Sodium was highest in 25Afz:75Pm diet and varied significantly (P < 0.05) compared to 50Nwb:50Pm diet. Potassium in the serum of the studied animals was significantly higher (P < 0.05) in 25Afz:75Pm diet than in 25Nwb:75Pm and 100Pm (control) diets but did not differ from diets 50Afz:50Pm and 50Nwb:50Pm. In terms of total protein level, only the 100Pm (control) diet differed significantly from the supplemented diets. Activities of th e enzymes alanine transaminase, aspartate transaminase and alkaline phosphatase in the sampled sera did not vary between the diets except for aspartate transaminase where, only the 50Nwb:50Pm diet differed from the other diets. These results to a large extent suggest the positive potential of the studied plant leaves in the feeding of goats without adverse effects.
A New Robust Identification Method for Transmission Line Parameters Based on ADALINE and IGG Method

Accurate transmission line parameters are the basis of power system calculations. Aiming at obtaining accuracy online transmission line parameters in the case of large random noises even bad data in Phasor Measurement Unit (PMU) measurements, which occurs frequently in the practice, a new adaptive robust identification method combining adaptive linear neuron (ADALINE) and traditional robust IGG (Institute of Geodesy & Geophysics, Chinese Academy of Sciences) method is proposed. In detail, first, the identification model of transmission line parameters is presented based on the multi-period PMU measurements at both ends of the transmission line. Then, a parameter solving model based on ADALINE is established. Furthermore, to fully use measurement information, the adaptive robust ADALINE (ARA) are proposed, which applies the robust IGG weight function (Scheme I) to ADALINE to realize “three segments” robust identification. In addition, to improve the robustness, the expectation and variance of the equation residual sequence are estimated adaptively with the median principle to adjust the threshold for the IGG function to assure robustness (TAR), which is independent of the known information for the error of the measurement equipment. The cases based on PSCAD simulated data and measured data show the effectiveness and engineering practicality of the proposed method.
Cloned DNA probes distinguish endemic and exotic Entomophaga grylli fungal pathotype infections in grasshopper life stages

Entomophaga grylli is a fungal pathogen of grasshoppers and at least three pathotypes are recognized world‐wide. Pathotypes 1 and 2 are endemic to North America while the Australian pathotype 3 had been released into two field sites in North Dakota between 1989 and 1991. Grasshoppers were collected over the summer at the field sites in 1992 and assessed for pathotype infection by cloned DNA probe analysis. The three most predominant grasshopper species that were infected (Melanoplus sanguinipes, M. bivittatus and Camnula pellucida) were assessed for pathotype infection with respect to their life stages (nymphal instars and adult males and females). Pathotype 1 predominantly infected grasshoppers in the subfamilies Oedipodinae and Gomphocerinae and pathotype 2 predominantly infected grasshoppers in the subfamily Melanoplinae. Early‐instar M. sanguinipes and M. bivittatus had higher pathotype 2 infection frequencies, while late‐instar and adult C. pellucida had higher pathotype 1 infection frequencies. Cross‐infection by the pathotypes did occur in up to 3% of the individuals, on a per species basis, and primarily in later instar and adult grasshoppers. Pathotype 3 infections occurred in later instar and adults of the three grasshopper species. Infection of grasshoppers by E. grylli pathotypes is discussed with reference to the fungal life cycles.
NASPGHAN Position Paper: Plant-Based Milks.

Parents and caretakers are increasingly feeding infants and young children plant-based "milk" alternatives to cow milk. The US Food and Drug Administration currently defines "milk" and related milk products by the product source and the inherent nutrients provided by bovine milk. Substitution of a milk that does not provide a similar nutritional profile to cow milk can be deleterious to a child's nutritional status, growth and development. Milk's contribution to the protein intake of young children is especially important. For almond or rice milk, an 8 oz serving provides only about 2% or 8%, respectively, of the protein equivalent found in a serving of cow milk. Adverse effects from the misuse of certain plant-based beverages have been well-documented and include failure to gain weight, decreased stature, kwashiorkor, electrolyte disorders, kidney stones and severe nutrient deficiencies including iron deficiency anemia, rickets and scurvy. Such adverse nutritional outcomes are largely preventable. It is the position of the NASPGHAN Nutrition Committee, on behalf of the society, that only appropriate commercial infant formulas be used as alternatives to human milk in the first year of life. In young children beyond the first year of life requiring a dairy-free diet, commercial formula may be a preferable alternative to cow's milk, when such formula constitutes a substantial source of otherwise absent or reduced nutrients (e.g., protein, calcium, vitamin D) in the child's restricted diet. Consumer education is required to clarify that plant-based milks do not represent an equivalent source of such nutrients. In this position paper we provide specific recommendations for clinical care, labeling and needed research relative to plant-based milks.
Acceptance of E-waste Recycling Among Young Adults: An Empirical Study

This paper integrates habits into the Theory of Planned Behavior and the Technology Acceptance Model to predict waste electrical and electronic equipment (WEEE) or the e-waste recycling intention among young adults. This study adopts the structural equation modelling (SEM) technique, using the partial least squares (PLS) approach as a multivariate statistical method to analyze the survey data. The findings show that the integrated model has good explanatory power and confirms its robustness in predicting the e-waste recycling intention among young adults. The role of habits and perceived usefulness are demonstrated as strong predictors of young adults' e-waste recycling intention, as well as their attitudes. The paper concludes with several social and practical implications that can foster e-waste recycling initiatives in the UAE.
A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology

The clinical evidence suggests that cognitive disorders are associated with vasculature dysfunction and decreased blood flow in the brain. Hence, a functional understanding of the linkage between brain functionality and the vascular network is essential. However, methods to systematically and quantitatively describe and compare structures as complex as brain blood vessels are lacking. 3D imaging modalities such as multiphoton microscopy enables researchers to capture the network of brain vasculature with high spatial resolutions. Nonetheless, image processing and inference are some of the bottlenecks for biomedical research involving imaging, and any advancement in this area impacts many research groups. Here, we propose a topological encoding convolutional neural network based on persistent homology to segment 3D multiphoton images of brain vasculature. We demonstrate that our model outperforms state-of-the-art models in terms of the Dice coefficient and it is comparable in terms of other metrics such as sensitivity. Additionally, the topological characteristics of our model’s segmentation results mimic manual ground truth. Our code and model are open source at https://github.com/mhaft/DeepVess.
Isolation of lectin and lectin derivatives from Haworthia limifolia : An in-vitro investigation

The ability for medicinal plants to aid in wound healing lends itself to cell-agglutinating and sugarspecific protein. These are collectively termed lectins. Although, the function of these lectins in plants are limiting, the benefit to human in terms of wound healing is of vital importance. With many new infections arising as a result of bacteria, fungi and viruses developing immunity against western medicines, the world is now turning towards natural medicines as an alternative source for cures. Harworthia limifolia is currently used by indigenous people for sun burns, burns, sores as well as a systemic remedy and spiritual benefits. The population in the wild has drastically being reduced due to the pressures of harvesting by traditional healers for trade purposes. The current study investigates the presence of lectins or lectin like derivatives in H. limifolia . The derivatives were tested against rat, rabbit and human serum, and a positive reaction with human α-2-macroglobulin was observed. Furthermore anti-tumor and wound healing properties have been validated.
A Map Matching Based Framework to Reconstruct Vehicular Trajectories from GPS Datasets

This paper proposes a new framework to reconstruct vehicle trajectories from GPS datasets. GPS-embedded vehicles generate massive vehicular trajectory data that are crucial for many applications, such as route recommendation, traffic analysis, urban planning, and Intelligent Transportation Systems (ITS). In order to achieve that, it is necessary to employ processing techniques such as noise filtering, segmentation, and map matching to prepare the data to be properly used by real applications. The proposed framework aims to reconstruct the trajectories completely, even from low sampled data, which naturally present gaps. Experimental results show the effectiveness and efficiency of the framework to reconstruct trajectories with different sampling rates and different characteristics.
Learning Sparse Neural Networks Through Mixture-Distributed Regularization

L0-norm regularization is one of the most efficient approaches to learn a sparse neural network. Due to its discrete nature, differentiable and approximate regularizations based on the concrete distribution [31] or its variants are proposed as alternatives; however, the concrete relaxation suffers from high-variance gradient estimates and is limited to its own concrete distribution. To address these issues, in this paper, we propose a more general framework for relaxing binary gates through mixture distributions. With the proposed method, any mixture pair of distributions converging to δ(0) and δ(1) can be applied to construct smoothed binary gates. We further introduce a reparameterization method for the smoothed binary gates drawn from mixture distributions to enable efficient gradient gradient-based optimization under the proposed deep learning algorithm. Extensive experiments are conducted, and the results show that the proposed approach achieves better performance in terms of pruned architectures, structured sparsity and the reduced number of floating point operations (FLOPs) as compared with other state-of-the-art sparsity-inducing methods.
Forecasting Crowd Distribution in Smart Cities

In this work we present a forecasting method that can be used to predict crowd distribution across the city. Specifically, we analyze and forecast cellular network traffic and estimate crowd on such basis. Our forecasting model is based on a neural network combined with time series decomposition techniques. Our analysis shows that this approach can give interesting results in two directions. First, it creates a forecasting solution that fits all the variability in our dataset without having to create specific features and without complex search procedures for optimal parameters. Second, the method performs well, showing to be robust even in the presence of spikes in the data thus enabling better applications such as event management and detection of crowd gathering.
Harmonic Analysis of Airgap Magnetic Fields in Doubly-Fed Flux Reversal Permanent Magnet Machines

Doubly-fed flux reversal permanent magnet (DF-FRPM) machine with two independent armature windings accommodating in stator and rotor slots separately exhibits larger torque density than conventional FRPM due to higher utilization rate of rotor space. In this paper, the investigation is focused on the airgap field modulation and torque contribution of dominating harmonics in DF-FRPM machine. Besides the open-circuit airgap flux density, both stator and rotor armature reaction are qualitatively analyzed by magneto-motive force (MMF)-permeance model considering stator and rotor saliency since the two independently supplied armature windings both make contributions to the torque production. The pole pairs and rotating speed of field harmonics are addressed. Dominating field harmonics that contribute to electromagnetic torque are identified and validated by finite element (FE) analysis.
D-SOP: Distributed Second Order Proximal Method for Convex Composite Optimization

This paper investigates a class of distributed optimization problems where the objective function is given by the sum of twice differentiable convex functions and a convex non-differentiable part. The setting assumes a network of communicating agents in which each individual agent’s objective is captured by a summand of the aggregate objective function, and agents cooperate through an information exchange with their neighbors. We devise a second order method by transforming the problem into a continuously differentiable form using proximal operators, and truncating the Taylor expansion of the Hessian inverse so that a distributed implementation of the algorithm is possible. We prove global linear convergence (without backtracking), under usual strong convexity assumptions, and further demonstrate the effectiveness of our scheme through numerical simulations.
Leveraging Digital Twins to Enhance Performance of IoT in Disadvantaged Networks

The U.S. Army is investing in research to deliver enhanced sensing, situational awareness, computing, and decision-making capabilities via IoT devices on the battlefield. One of the current shortfalls with existing capabilities involves the use of large wireless mesh networks to support disadvantaged users. Networks, like the Soldier Radio Waveform (SRW) suffer from increased latency and packet overhead that affect system performance and severely limit the operational employment of the system. In this paper, a novel hybrid topology management technique using a digital twin of the propagation environment and rapid assessment, cluster creation, and node assignment process has been proposed and investigated to show Software-Defined Networking and K-Means Clustering machine learning have potentials for increasing the number of users able to connect to wireless mesh networks. Preliminary results show that this technique can reduce the path length in large graphs at the expense of edge connectivity while delivering a level of service to all users that was previously unavailable.