Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Selective Encryption method for Bigdata Sensing Stream using one way Hash Chain Algorithm

Resource controlled sensing devices are being used widely to build and set up self-organizing Wireless Sensor Networks for a variety of the essence applications such as smart agriculture. Smart building, smart energy, smart cities, smart healthcare. A Data Stream Manager (DSM) at the Server side gathers the information streams to perform ongoing examination and dynamic for these basic applications. A scornful rival may access or alter the information on the way. One of the difficult undertakings in such applications is to guarantee the obligation of the gathered information with the goal that any choices are made on the handling of right information. Guaranteeing high information dependability necessitates that the framework fulfills two key security properties: classification and uprightness. To guarantee the protection of gathered information, we have to keep delicate data from contacting some unacceptable individuals by guarantee that the power individuals are getting it. Senses data are constantly connected with various affectability levels dependent on the affectability of talented applications are sensed data types or the detecting Devices. Furnishing staggered information secrecy alongside information respectability for large detecting information streams with regards to approach continuous examination is a difficult issue. We proposed a Selective Encryption (SEEN) Method to secure big sensing data streams that fulfills the ideal different levels of secrecy and information genuineness. The Proposed method is based on two key concepts: common shared keys that are initialized and restructured by DataStream Manager without requiring retransmission and a flawless key refreshment. A scheme to secure a multi-hop protocol through the use of multiple one-way hash chains is proposed. The scheme is shown to be lower cost in power consumption, increased computational performance , and lower in communication costs able to secure multi-hop propagation of program.
Improvement of accuracy of UWB Positioning System within the intersection using a Long Short-Term Memory Network

In this paper the pedestrian positioning system in the intersection using UWB(Ultra-Wide Band) is studied. In this system, UWB signal is transmitted from the terminal owned by each pedestrian and it is received by base stations attached to traffic lights for pedestrian, and the distance to the pedestrian is measured. Distance between the pedestrian and the base station is estimated by TOA (Time Of Arrival) of the received signal. The position of the pedestrian is estimated by LSM (Least Square Method) using the distance measurement value. The accuracy of UWB positioning system deteriorates due to thermal noise and multipath. The distribution of this accuracy differs for each position, and it is complicated to create a mathematical model to correct. In this paper, appropriate correction is performed by learning different noise distributions for each position using LSTM (Long Short-Term Memory), a kind of RNN (Recurrent Neural Network). In addition, we propose a network that estimates the current value using previous observations in order to estimate the position even if the positioning of the pedestrian fails. The effectiveness of the proposed method is verified by comparing the performance with positioning using the Kalman filter. As a result of computer simulation, it is shown that the proposed method achieves high accuracy positioning performance.
An Integrated Framework for Vision-Based Relative Localization and Bandwidth Allocation

The provision of an accurate and efficient localization system is of great significance to Internet of Vehicles (IoV). In view of its promising applications including formation control and autonomous driving, the vision-based localization mechanism has attracted intensive attention for its outstanding performance in terms of accuracy and flexibility. However, the high mobility and fast switching features of IoV put a strain on communication resources, which can impair the real-time transmission of high-quality images for cooperative localization. In this paper, we propose ajoint position sensing and bandwidthconstrained communication framework for vision localization. First, we derive the relative squared position error bound (SPEB) by implementing subspace projection on the Fisher information matrix (FIM) of absolute positions given quantized observations. Then we exploit the relative geometry of vehicles and images to develop two localization algorithms, which estimate the relative positions of vehicles by incorporating the idea of Euclidean distance matrix. The effectiveness of the proposed localization algorithms is verified via numerical simulations, which offers guidelines for joint sensing and communication in future IoV scenarios.
A Novel Low-Power Notch-Enhanced Active Filter for Ultrawideband Interferer Rejected LNA

In this article, a novel low-power notch-enhanced active filter is presented using the feedback notch-enhanced (FNE) technique. The active notch filter is proposed based on an area-saving second-order<inline-formula> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> series resonator. The passive devices’ loss can be effectively eliminated by the generated dc power-independent negative resistance by inserting a current feedback transistor. Moreover, the capacitive source degeneration (CSD) technique is proposed to boost the filter <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> by shifting the series resonant frequency to the negative-resistance region. To demonstrate the feasibility and practicability of the proposed notch filter, a 3.1–10.6-GHz IEEE 802.11a interferer rejected low-noise amplifier (IR-LNA) is fabricated in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu {\hbox {m}}$ </tex-math></inline-formula> CMOS process. The multistage structure, consisting of a common-gate (CG) stage with a dual-resonance load and a cascode stage with the proposed active notch filter, is employed for wideband input matching, gain flatness, and interferer rejection (IR) simultaneously. The measurements show that the IR-LNA provides a maximum of 16.05-dB power gain, 2.08–3.70-dB noise figure (NF) in the desired band, and a maximum of 40.9-dB IR ratio (IRR) at 5.77 GHz, with a power consumption of 10.2 mW, while the active notch filter only consumes 456-<inline-formula> <tex-math notation="LaTeX">$\mu {\hbox {A}}$ </tex-math></inline-formula> current.
Synthesis and proton conductivity of sulfonated, multi-phenylated poly(arylene ether)s

A series of sterically-encumbered, sulfonated, poly(arylene ether) copolymers were synthesized and their proton conductivity examined. The series was prepared by copolymerizing a novel monomer, 2″,3″,5″,6″-tetraphenyl-[1,1′:4',1″:4″,1″':4″',1″″-quinquephenyl]-4,4″″-diol, with 4,4'-difluorobenzophenone and bisphenol A. Subsequent sulfonation and solution casting provided membranes possessing ion exchange capacities of 1.9 to 2.7 mmol/g and excellent mechanical properties (Young's modulus, 0.2–1.2 GPa; tensile strength, 35–70 MPa; elongation at break, 62–231%). Water uptake ranged from 34 to 98 wt% at 80 °C/100% RH. Proton conductivities ranged between 0.24 to 16 mS/cm at 80 °C/60% RH, and 3 to 167 mS/cm at 80 °C/95% RH. TEM analysis of the polymers, in the dehydrated state, revealed isolated spherical aggregates of ions, which presumably coalesce when hydrated to provide highly conductive pathways. The strategy of using highly-encumbered polymer frameworks for the design of mechanically-robust and dimensionally-stable proton conducting membranes is demonstrated. © 2014 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2014, 52, 2579-2587
Molecular packing in crystals of the aromatic hydrocarbons

The van der Waals energy, quadrupole-quadrupole coupling energy, and hydrogen-hydrogen repulsions have been calculated for the equilibrium structure of crystalline naphthalene and for several displaced structures. The displacements are small rotations of the molecules about their symmetry axes, phased so that the space-group symmetry and unit-cell dimensions are preserved. For structural variations of this type the hydrogen-hydrogen repulsions have a strong minimum within a few degrees angular variation from equilibrium, indicating that these repulsions are dominant and determine the crystal structure for this class of displacement. The attractive van der Waals and quadrupole interactions on the other hand are not minimized at the equilibrium structure; they vary slowly (by a few wavenumbers per degree rotation) and approximately linearly.
Impact of Low pH on Microbial Growth Rate, ATP Production, and NADH to NAD+ ratio

*Corresponding Author: Shymaa Enany Department of Microbiology and Immunology, Faculty of Pharmacy, Suez Canal University, Egypt. Tel. +201025801366 shymaa21@yahoo.com ORCID; Enany: 0000-00027827-6504 & ORCID Background: Bacterial metabolism is the tendency of bacteria to live, function, and replicate fittingly under their current culture and varied environment conditions. Microorganisms have intricated metabolic regulatory mechanisms to ameliorate environmental stresses. Objectives: We examined the effect of acidic pH, as one of stresses, on growth rate and metabolism of five different microorganisms. Methodology: ATP level, as an indicator for microbial viability, and alterations in NADH/NAD + ratio, which plays a critical role in microbial metabolism, were assessed. Results: Our results showed that alterations in pH influence metabolism of different bacterial species with different extent. The growth rate of Pseudomonas aeruginosa, Escherichia coli and Bacillus Subtilis were diminished with an elevation in ATP and NADH/NAD + ratio at low pH. Contrary, MRSA and MSSA showed trivial alterations for ATP and NADH/NAD + ratio. Conclusion: Ultimately, this study affirmed differences in metabolism between different species and confirmed that alterations in pH influenced the metabolism and hence the pathogenesis.
BIG DATA ANALYTIC USING MACHINE LEARNING ALGORITHMS FOR INTRUSION DETECTION SYSTEM: A SURVEY

Through the increasing amounts of data day by day, big data analytics has appeared as an important trend for many organizations. These collected data can have important information that possibly will be key to solving extensive problems, such as cybersecurity, healthcare, marketing, intrusion detection, and fraud. An intrusion detection system (IDS) work as observers and evaluates the data in order to detect at all intrusion that may occur in the network or system. The huge data that is a high speed, high volume, and variety of data produced through the network has prepared for the process of the data analysis to detect any attacks through traditional techniques that cause big challenges. Big data analytic uses in IDS to increase accuracy and more efficient for detecting the attacks. we present a survey of eight machine learning algorithms use for IDS, intrusion detection techniques, advantages and disadvantages of intrusion detection techniques, all types of an intrusion detection system, advantages and disadvantages for each type of IDS, and five datasets that use on the intrusion detection system. Moreover, the authors attempt to present a strong picture of algorithms and datasets that use for IDS in all aspects through their extensive survey.
Neuropsychological testing

Neuropsychological testing is a key diagnostic tool for assessing people with dementia and mild cognitive impairment, but can also help in other neurological conditions such as Parkinson’s disease, stroke, multiple sclerosis, traumatic brain injury and epilepsy. While cognitive screening tests offer gross information, detailed neuropsychological evaluation can provide data on different cognitive domains (visuospatial function, memory, attention, executive function, language and praxis) as well as neuropsychiatric and behavioural features. We should regard neuropsychological testing as an extension of the neurological examination applied to higher order cortical function, since each cognitive domain has an anatomical substrate. Ideally, neurologists should discuss the indications and results of neuropsychological assessment with a clinical neuropsychologist. This paper summarises the rationale, indications, main features, most common tests and pitfalls in neuropsychological evaluation.
Material-independent fabrication of superhydrophobic surfaces by mussel-inspired polydopamine

This study reports methods for general preparation of superhydrophobic surfaces on any type of material surface using mussel-inspired poly(dopamine) (pDA). The use of pDA presents several advantages over conventional superhydrophobic fabrication methods: development of superhydrophobicity in a material-independent manner, enhancement of mechanical stability, decreases in angle hysteresis, and applicability to 3D objects.
WIfI classification: the Society for Vascular Surgery lower extremity threatened limb classification system, a literature review

Abstract The Society for Vascular Surgery has proposed a new classification system for the threatened lower limb, based on the three main factors that have an impact on limb amputation risk: Wound (W), Ischemia (I) and foot Infection (“fI”) - the WIfI classification. The system also covers diabetic patients, previously excluded from the concept of critical limb ischemia because of their complex clinical condition. The classification’s purpose is to provide accurate and early risk stratification for patients with threatened lower limbs; assisting with clinical management, enabling comparison of alternative therapies; and predicting risk of amputation at 1 year and the need for limb revascularization. The objective of this study is to collect together the main points about the WIfI classification that have been discussed in the scientific literature. Most of the studies conducted for validation of this classification system prove its association with factors related to limb salvage, such as amputation rates, amputation-free survival, prediction of reintervention, amputation, and stenosis (RAS) events, and wound healing.
Functional connectivity during spontaneous migraine attacks compared to pain-free periods: a resting-state fMRI study

Motivation: Migraine is a common and debilitating brain disorder, yet, its pathophysiology remains misunderstood. Migraine attacks are characterized by a throbbing headache, often accompanied by altered sensory stimuli processing and cognitive dysfunction. Objective: To investigate migraine mechanisms by evaluating the functional connectivity (FC) in migraineurs during spontaneous migraine attacks, compared to pain-free periods. Methods: Eleven female migraine patients without aura underwent two sessions of resting-state functional magnetic resonance imaging: during a spontaneous attack (ictally) and in a pain-free period (interictally). A data-driven independent component analysis (ICA) was performed in order to identify independent components (ICs) associated with well-established resting-state networks (RSNs). Both whole-brain and region of interest (ROI) FC analyses were performed on the selected ICs to identify differences between the two sessions. ROI-averaged FC was further correlated with patients’ clinical
S EMI - SUPERVISED CLUSTERING FOR OIL PROSPECTIV - ITY

We create a new semi-supervised clustering method, that can be used in a wide range of problems. The clustering is based on a graph Laplacian similarity matrix and has a closed form solution, which enables us to cluster very large unstructured datasets rapidly ( > 10 7 samples). In this work, we apply our method to oil prospectivity, based on seismic surveys and intersecting boreholes. Our clustering method can be extended to a pseudo-labelling deep learning scheme
DEPARTMENT OF INFORMATICS

Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this shortcoming by learning a new class from a few annotated support examples. We introduce, for the ﬁrst time, a novel few-shot framework, for the segmentation of volumetric medical images with only a few annotated slices. Compared to other related works in computer vision, the major challenges are the absence of pre-trained networks and the volumetric nature of medical scans. We address these challenges by proposing a new architecture for few-shot segmentation that incorporates ‘squeeze & excite‘ blocks. Our two-armed architecture consists of a conditioner arm, which processes the annotated support input and generates a task representation. This representation is passed on to the segmenter arm that uses this information to segment the new query image. To facilitate efﬁcient interaction between the conditioner and the segmenter arm, we propose to use ‘channel squeeze & spatial excitation’ blocks – a light-weight computational module – that enables heavy interaction between the both arms with negligible increase in model complexity. This contribution allows us to perform image segmentation without relying on a pre-trained model, which generally is unavailable for medical scans. Furthermore, we propose an efﬁcient strategy for volumetric segmentation by optimally pairing a few slices of the support volume to all the slices of query volume. We perform the experiments for organ segmentation on whole-body contrast-enhanced CT scans from Visceral Dataset. Our proposed model outperforms multiple baselines and existing approaches with respect to the segmentation accuracy by a signiﬁcant margin.
Retrospective Evaluation of Position of Mental Foramen and Its Relationship with Gonial Angle Using Cone Beam Computed Tomography

Introduction: Mental foramen (MF) appearance is quite diverse. Failure to properly diagnose and protect the nerve may result in its damage during medical procedures such as surgery and placement of dental implants. This study evaluated the position of MF and its relationship with gonial angle using cone beam computed tomography (CBCT) images. Methods: 128 gonial angles from 64 CBCT images were measured and divided into low-angle (≤125°) and high-angle (>125°) categories. MF was divided into five types considering its position in cross-sectional profiles: type A (under apex of first premolar); B (under apex of second premolar); C (between first and second premolars); D (anterior to first premolar); and E(posterior to second premolar). The length of mental canal and its buccolingual and superior-inferior position was also evaluated. Independent T-test and Chi-square tests were used with statistical significance set at p Results: The highest frequency of MF position in the high and low-gonial angle categories was type C and the lowest in high-angle and low-angle were type E and D, respectively. The position of the foramen and measurements of the mental canal was not affected by age, gender, or gonial angle (P>0.05). Conclusion: Position of MF was not found to be related to patients’ age, sex, or gonial angles. Since a precise determination of the MF position is difficult due to its different anatomical variations and also because of potential damages during the treatment procedures in this area, radiographic analysis and determining its location among different populations is necessary.
Automata Guided Reinforcement Learning And Skill Composition

—Tasks with complex structures and long horizons pose a challenge for reinforcement learning (RL) agents. Skills learned through RL often generalize poorly across tasks and re-training is usually necessary. We propose a framework that combines temporal logic (TL) with reinforcement learning to address these problems. We also present a technique that constructs new skills from existing ones with no additional exploration. Our method allows for convenient speciﬁcation of complex temporal dependent tasks using logical expressions and automatically generates rewards that align with the overall task goal. We provide theoretical results for our method and evaluate it in both simulation and experiments.
Housing Wealth in Norway, 1993 – 2015∗

We provide a new estimate of household-level housing wealth in Norway between 1993 and 2015 using an ensemble machine learning method on housing transaction data. The new housing wealth measure is an improvement over existing data sources for two reasons. First, the model outperforms previously applied regression models in out-of-sample prediction precision. Second, we extend the sample of estimated housing wealth by including cooperative units, non-id apartments, and cabins.
Experimental Apparent Stern–Geary Coefficients for AZ31B Mg Alloy in Physiological Body Fluids for Accurate Corrosion Rate Determination

The corrosion behavior of AZ31B Mg alloy exposed to Ringer’s, phosphate-buffered saline (PBS), Hank’s, and simulated body fluid (SBF) solutions for 4 days was investigated using electrochemical impedance spectroscopy (EIS), potentiodynamic polarization, weight loss, and surface characterization. Changes in corrosion rates with immersion time determined by weight loss measurements were compared with EIS data to determine the possibility of obtaining quantitative electrochemical information. In addition, changes in the protective properties of the corrosion product layer calculated from the EIS parameters were evaluated as a function of their surface chemical composition as determined by X-ray photoelectron spectroscopy (XPS) and visual observations of the corroded specimen’s surface. Apparent Stern–Geary coefficients for the AZ31B Mg alloy in each test solution were calculated using the relationship between icorr from weight loss measurements and the EIS data (both Rp and Rt). This provided experimental reference B′ values that may be used as a useful tool in independent investigations to improve the accuracy of corrosion rates of AZ31B Mg alloy in simulated body solutions.
On the formation of chondrules

Here are sketches of several projects which are suitable as masters thesis. If they look interesting to you I suggest that you contact me. Whether we can work on a particular project on a particular year depends on my workload and your abilities. The projects are mere sketches, not set in stone. The following ones are available for the year 2016. I can advise at most one masters student in this year.
ReVACNN: Steering Convolutional Neural Network via Real-Time Visual Analytics

Recently, deep learning has become exceptionally popular due to its outstanding performances in various machine learning and artiﬁcial intelligence applications. Convolutional neural network (CNN), a representative model of deep learning, has been successfully applied to solve computationally burdening tasks in various applications like computer vision. Despite its outstanding capability, training a CNN model properly is time-consuming and prone to overﬁtting and/or bad local minima. To address these issues, this study aims at improving the interpretation of the training process and using it for subsequent human intervention, speciﬁcally steering the training process of CNN model in the real time. In this paper, we present ReVACNN, a real-time visual analytic system for CNN, where (1) the overall training process (e.g., the amount of activations and that of changes each ﬁlter/layer has at a particular iteration/epoch) is visualized in network view and (2) the 2D embedding of trained ﬁlters within layers is visualized to show the relationships between ﬁlters as well as layers. In particular, ReVACNN allows users to perform several interactions in real time: (1) skipping the gradient descent update on the sub-part of a CNN model to reduce the subsequent training time and (2) steering ﬁlters interactively in the 2D embedding view to avoid bad local minima. At the end, we present several use cases that demonstrate the beneﬁts users can gain from ReVACNN.