Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and Secure Anonymous Authentication With Location Privacy for IoT-Based WBANs

Internet-of-Things (IoT)-based wireless body area networks (WBANs) play an important role in modern medical systems for patient-health monitoring. WBANs have the capability to collect real-time biological information from the patients’ body using intelligent sensors and then send the collected information to the remote doctors or medical experts using the Internet. In recent years, numerous anonymous authentication schemes were proposed to provide security in WBANs. However, many of these schemes are not computationally efficient during anonymous authentication. Moreover, the previous schemes did not provide location privacy for both doctors and patients. In order to overcome these limitations, in this article, we propose an efficient and secure anonymous authentication framework with location privacy preservation for IoT-based WBANs. The comprehensive analysis section shows that the proposed scheme overcomes the security weaknesses in the existing schemes and also provides low computation cost during anonymous authentication.
Arabic sign language recognition based on HOG descriptor

We present in this paper a new approach for Arabic sign language (ArSL) alphabet recognition using hand gesture analysis. This analysis consists in extracting a histogram of oriented gradient (HOG) features from a hand image and then using them to generate an SVM Models. Which will be used to recognize the ArSL alphabet in real-time from hand gesture using a Microsoft Kinect camera. Our approach involves three steps: (i) Hand detection and localization using a Microsoft Kinect camera, (ii) hand segmentation and (iii) feature extraction using Arabic alphabet recognition. One each input image first obtained by using a depth sensor, we apply our method based on hand anatomy to segment hand and eliminate all the errors pixels. This approach is invariant to scale, to rotation and to translation of the hand. Some experimental results show the effectiveness of our new approach. Experiment revealed that the proposed ArSL system is able to recognize the ArSL with an accuracy of 90.12%.
Abstraction of Sound Signal Characteristics Based on Wavelet Packet Decomposition

In this paper, we present a method of feature extraction base on wavelet packet decomposition and introduce the theory of wavelet packet decomposition and use it in the course of feature extraction in hedgehopping acoustic objectives identification system Experimenting by several typical hedgehopping acoustic objectives, we validate it′s validity
Applying the ADDIE Instructional Design Model to Multimedia Rich Project-based Learning Experiences in the Korean Classroom

The purpose of this study was to apply the ADDIE instructional design model to develop multimedia rich project-based learning methods for effective instruction in a Korean mechanical engineering high school. This study was conducted as action research based on a high school situation. The study included 40 participants in a class purposively selected from 52 classes at 2080 student high school. Data were collected through observations, surveys and artifacts. Results indicated the multimedia rich project-based learning allowed students to take part in learning activities and there was close cooperation with and among group members to create better products. Also, the flexibility in the project-based learning environment allowed the participants to make decisions about their abilities, resources, and plans. Recommendations and implications for teacher educators as well as in-service and pre-service teachers are also presented.
Determination of Antioxidant and Phenolic Content of Edible Plants

Today, there is a growing interest in nutritional awareness and a selective preference towards a healthy and balanced diet. A number of varieties of vegetables are good sources of essential components in human nutrition, providing vitamins, minerals, and fibre in general. Furthermore, chemical substances specific for given vegetables are responsible for various effects in the living organisms. For instance, natural antioxidants are the commonly known components of vegetables, neutralizing free radicals in the human body. The aim of the research was to evaluate antioxidant activity and phenolic content in leaves of three common Urtica dioca L. , Rumex acetosella L. , and Chenopodium album L. The antioxidan activity of these plants varied from 117.99 to 1273.83 μMTE/g. Urtica dioica L. had the highest antioxidan activity (1273.83 μMTE/g) followed by Rumex scutatus L. (225.09 μMTE/g) while the lowest value was observed in Chenopodium album L. (117.99 μMTE/g). The highest phenolic compound was determined in Urtica dioica L. (0.811 mMGAE/g), and the lowest phenolic compound was found in Rumex scutatus L. (0.481 mMGAE/g). The results obtained in this study indicate that wild Urtica dioca L. , Rumex acetosella L. , and Chenopodium album L. could be an important dietary source because of its good antioxidant and phenolic content properties.
Esterase-Cleavable 2D Assemblies of Magnetic Iron Oxide Nanocubes: Exploiting Enzymatic Polymer Disassembling To Improve Magnetic Hyperthermia Heat Losses

Here, we report a nanoplatform based on iron oxide nanocubes (IONCs) coated with a bioresorbable polymer that, upon exposure to lytic enzymes, can be disassembled increasing the heat performances in comparison with the initial clusters. We have developed two-dimensional (2D) clusters by exploiting benchmark IONCs as heat mediators for magnetic hyperthermia and a polyhydroxyalkanoate (PHA) copolymer, a biodegradable polymer produced by bacteria that can be digested by intracellular esterase enzymes. The comparison of magnetic heat performance of the 2D assemblies with 3D centrosymmetrical assemblies or single IONCs emphasizes the benefit of the 2D assembly. Moreover, the heat losses of 2D assemblies dispersed in water are better than the 3D assemblies but worse than for single nanocubes. On the other hand, when the 2D magnetic beads (2D-MNBs) are incubated with the esterase enzyme at a physiological temperature, their magnetic heat performances began to progressively increase. After 2 h of incubation, specific absorption rate values of the 2D assembly double the ones of individually coated nanocubes. Such an increase can be mainly correlated to the splitting of the 2D-MNBs into smaller size clusters with a chain-like configuration containing few nanocubes. Moreover, 2D-MNBs exhibited nonvariable heat performances even after intentionally inducing their aggregation. Magnetophoresis measurements indicate a comparable response of 3D and 2D clusters to external magnets (0.3 T) that is by far faster than that of single nanocubes. This feature is crucial for a physical accumulation of magnetic materials in the presence of magnetic field gradients. This system is the first example of a nanoplatform that, upon exposure to lytic enzymes, such as those present in a tumor environment, can be disassembled from the initial 2D-MNB organization to chain-like assemblies with clear improvement of the heat magnetic losses resulting in better heat dissipation performances. The potential application of 2D nanoassemblies based on the cleavable PHAs for preserving their magnetic losses inside cells will benefit hyperthermia therapies mediated by magnetic nanoparticles under alternating magnetic fields.
Advanced Quantum Based Neural Network Classifier and Its Application for Objectionable Web Content Filtering

In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website’s contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content.
Ongoing problems with special and general relativity, and solutions

It will be shown that a certain configuration of the Sagnac experiment disproves Einstein’s theory of relativity. Consequently, there is no need for four-dimensional space-time. Euclidean geometry also applies when describing relativistic phenomena. This makes the visualization and mathematical treatment of those physical processes much easier. In addition, it explains physical phenomena which are not presently understood, particularly in the field of astronomy and cosmology.
High Performance Frequent Subgraph Mining on Transactional Datasets

Graph data mining has been a crucial as well as inevitable area of research. Large amounts of graph data are produced in many areas, such as Bioinformatics, Cheminformatics, Social Networks, and Web etc. Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities. Frequent subgraph mining is one such area where the task is to find overly recurring patterns/subgraphs. To tackle this problem, many main memory-based methods were proposed, which proved to be inefficient as the data size grew exponentially over time. In the past few years several research groups have attempted to handle the frequent subgraph mining (FSM) problem in multiple ways. Many authors have tried to achieve better performance using Graphic Processing Units (GPUs) which has multi-fold improvement over in-memory while dealing with large datasets. Later, Google's MapReduce model with the Hadoop framework proved to be a major breakthrough in high performance large batch processing. Although MapReduce came with many benefits, its disk I/O and non-iterative style model could not help much for FSM domain since subgraph mining process is an iterative approach. In recent years, Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability. This is a right fit solution for iterative style of programming as well. In this work, we cover how high-performance computing has helped in improving the performance tremendously in the transactional directed and undirected aspect of graphs and performance comparisons of various FSM techniques are done based on experimental results. INDEX WORDS: Frequent Subgraphs, Isomorphism, Spark, Scala, Hadoop MapReduce, DB4O HIGH PERFORMANCE FREQUENT SUBGRAPH MINING ON TRANSACTIONAL DATASETS
An Efficient Fog as-a-Power-Economy-Sharing Service

Cheap energy from the utility can be stored during off-peak time in batteries and utilized during on-peak time. However, energy storage more than future demand can degrade the system. The sharing of surplus stored energy within the community can reduce the energy consumption cost. A system of fetching surplus energy from storage systems of smart homes is indispensable to share within the community. In this paper, Fog-as-a-Power-Economy-Sharing service is proposed, which interconnects the distributed batteries based power and forms the unified energy storage system for a community. In a community, capacities of batteries storage, investment and power demand of every smart home are different. However, participants of the service share their battery based stored energy for social welfare following the no-profit-no-loss rule to form a unified energy storage system. When energy exhausted smart homes request for energy, the fog decides either power is supplied from the unified energy storage system or from the utility to reduce global energy cost for the community. In the paper, global cost (energy cost and cost of fogs’ services) for twelve communities is analyzed, which is payable by smart homes in the communities. The results validate the significance of the proposed time efficient Fog-as-a-Power-Economy-Sharing service, which reduces global power cost and computing cost for each community.
Low voltage & controlled switching of MoS2-GO resistive layers based ReRAM for non-volatile memory applications

Emerging information technology and data deluge foster the unprecedented demands of higher chip density, clocking speed, data storage and lower power dissipation for on-chip non-volatile memories (NVMs). Here, two types of metal-insulator-metal (MIM) based NVM structures were fabricated and demonstrated involving controlled functionalization of molybdenum disulfide (MoS2) and graphene oxide (GO) nanocomposite as a resistive switching layer. The first type of device constitutes Aluminum (Al) top and bottom electrode resulting in the Al/MoS2-GO/Al structure. While the second type of device uses Al top electrode and Indium Tin Oxide (ITO) bottom electrode resulting in Al/MoS2-GO/ITO. The current-voltage (I–V) characteristics for fabricated Al/MoS2-GO/Al and Al/MoS2-GO/ITO MIM structures exhibited considerable ION/IOFF ratio of ∼102 (SET and RESET state at 0.5 V and −0.4 V) and ∼101 (SET and RESET state at 0.3 V and −1 V), respectively. The I–V characteristics for Al/MoS2-GO/Al MIM structure showed low voltage switching, substantial memory retention ∼104 s and endurance for up to 25 cycles. The low voltage and controlled switching operation for Al/MoS2-GO/Al MIM structures may be attributed to the presence of a large number of oxygen vacancies, defects in MoS2-GO, promoting enhanced charge hopping via interfacial oxide at MoS2-GO/Al interface as compared to MoS2-GO/ITO.
Comparative assessment of artificial sweeteners toxicity via express biotest

Various food additives are being produced and consumed by population in greater and greater quantities and risks of probable toxic effects exerted by them are growing as well. These additives frequently occur in various combinations in food products and the environment, they can be consumed for a long period of time and produce hazardous mutagenic and carcinogenic effects. Therefore, it is extremely vital to assess combined impacts exerted by food additives so that their safety would be proven. There are certain advantages related to vegetative test-systems and cytogenetic analysis procedures for biological tests data when it comes to screening for toxic and mutagenic effects produced by chemicals. Allium-test which applies Allium cera bulb onion roots as a test-object is quite distinctive. When compared with other tests that employ animals and various cell cultures, this test turns out to be less complicated and costly and more sensitive as well. Our research goal was to examine influences exerted by such artificial sweeteners as aspartame and sucralose on living weight gain and mitotic anomalies frequency in apical meristem cells in Allium cera bulb onion roots. We also assessed a synergy effect caused by combined exposure to both these chemicals. We detected that aspartame caused a significant decrease in root living weight against the control while there were no toxic effects caused by sucralose. Maximum toxicity was detected when a test-system was exposed to both artificial sweeteners together and it was considered to result from the above mentioned synergy effect. Chromosome aberrations frequency in test samples differed insignificantly from the control but we also detected authentic changes in chromosome anomalies spectrum in root meristem cells. Disorders in chromosome disjunction and anomalies in the mitotic apparatus were the most frequently registered ones.
Leveraging Kernel Security Mechanisms to Improve Container Security: a Survey

Containerization is a lightweight virtualization technique reducing virtualization overhead and deployment latency compared to full VM; its popularity is quickly increasing. However, due to kernel sharing, containers provide less isolation than full VM. Thus, a compromised container may break out of its isolated context and gain root access to the host server. This is a huge concern, especially in multi-tenant cloud environments where we can find running on a single server containers serving very different purposes, such as banking microservices, compute nodes or honeypots. Thus, containers with specific security needs should be able to tune their own security level. Because OS-level defense approaches inherited from time-sharing OS generally requires administrator rights and aim to protect the entire system, they are not fully suitable to protect usermode containers. Research recently made several contributions to deliver enhanced security to containers from host OS level to (partially) solve these challenges. In this survey, we propose a new taxonomy on container defense at the infrastructure level with a particular focus on the virtualization boundary, where interactions between kernel and containers take place. We then classify the most promising defense frameworks into these categories.
Epileptic Seizure Detection System Based on Multi-Domain Feature and Spike Feature of EEG

Epilepsy is caused by sudden abnormal discharges of neurons in the brain. This paper constructs an automatic seizure detection system, which combines the predicting result of multi-domain feature with the predicting result of spike rate feature to detect the occurrence of epileptic seizures. After segmenting EEG data into 5[Formula: see text]s with 80% overlap epochs, the paper extracts time domain features, frequency domain features and hurst exponents (HE) from each epoch and these features are reduced by linear discriminant analysis (LDA) to be input parameters of the random forest (RF) classifier, which provides classification of the EEG epochs concerning the existence of seizures. In parallel, the paper extracts spikes from EEG data with morphological filter and calculates the spike rate to determine whether there is seizure. Then the results obtained by these two methods are merged as the final detection result. The paper shows that the accuracy (AC), sensitivity (SE), specificity (SP) and the false positive ratio based on event (FPRE) obtained by hybrid method are 98.94%, 76.60%, 98.99% and 2.43 times/h, respectively. Finally, the paper applies the seizure detection method to do seizure warning and recording to help the family member to take care of the patients and the doctor to adjust the antiepileptic drugs (AEDs).
Is Your Application Gender Biased

The process of design is underpinned by a solid methodology that comprises stages of research, creation and validation of the ideas in order to ensure that a product or a service will meet the users’ needs. However, in the realm of online dating applications, women reported to have a frustrating experience, marked by sexist remarks and offensive behaviour. When design fails to deliver a good experience and to meet women’s needs, one question rises: are applications biased in the beginning of the design process? This research proposes a method to investigate biases in the design of mobile experiences.
Pose Estimation for Omni-directional Cameras using Sinusoid Fitting

We propose a novel pose estimation method for geometric vision of omni-directional cameras. On the basis of the regularity of the pixel movement after camera pose changes, we formulate and prove the sinusoidal relationship between pixels movement and camera motion. We use the improved Fourier-Mellin invariant (iFMI) algorithm to find the motion of pixels, which was shown to be more accurate and robust than the feature-based methods. While iFMI works only on pin-hole model images and estimates 4 parameters (x, y, yaw, scaling), our method works on panoramic images and estimates the full 6 DoF3D transform, up to an unknown scale factor. For that we fit the motion of the pixels in the panoramic images, as determined by iFMI, to two sinusoidal functions. The offsets, amplitudes and phase-shifts of the two functions then represent the 3D rotation and translation of the camera between the two images. We perform experiments for 3D rotation, which show that our algorithm outperforms the feature-based methods in accuracy and robustness. We leave the more complex 3D translation experiments for future work.
Evaluating the Perceived Persuasiveness Questionnaire by Applying a Closed Card Sorting Task

This research aims to contribute to the validation of the Perceived Persuasiveness Questionnaire (PPQ) by investigating whether the items of the PPQ match the original constructs. The study includes 35 participants, mostly German university students, who conducted a closed card sort by sorting the PPQ items with the nine constructs of the PPQ. The researcher analysed the data in form of a qualitative analysis, based on two types of frequency tables of all the sorted items and conducted a hierarchical cluster analysis using SPSS. Most of the PPQ items were found to match the constructs they are intended to measure, except for some items which turned out to match an alternative construct more than the original construct or seemed to match two constructs. The cluster analysis resulted in a formation of five constructs of which two were similar to the original constructs. Furthermore, one new construct was found containing items of four different original constructs. Contrasting results with previous research underline the need for further research to contribute to the validation of the PPQ. A final version of the questionnaire could help to facilitate the use of persuasive elements used in eHealth technologies during the development and after implementation.
Advances in Compression of Power Quality Signals

The emerging technology of smart grids relies heavily on monitoring the distribution networks for disturbances using, for example, the devices installed for measuring power quality signals. Obviously, efficient compression of these electrical signals is of paramount importance to allow fast transmission, remote analysis and automation of response to disturbances, as well as archival storage. While prior approaches to compress electrical signal disturbances employed standard effective components such as wavelet transforms, non-uniform quantizers, and entropy coding, the overall system design was largely ad-hoc in the sense that it did not directly account for or adapt to data statistics. Instead, we propose to jointly design all system modules, including transforms, quantizers and entropy coders, within a genetic algorithm-based optimization framework, while accounting for the variation in statistics across different disturbances, within a two-step “classify then compress” procedure. Specifically, we jointly design the family of wavelets to be employed, the non-uniform quantizer structure, and probability tables for the entropy coder, to optimize the rate-distortion trade-off. Experimental results for 8 classes of commonly occurring power quality disturbances, which were synthetically generated to ensure rich and comprehensive training and test sets, validate the effectiveness of the proposed approach with significant performance gains over prior techniques.