MUMAP: Modified Ultralightweight Mutual Authentication protocol for RFID enabled IoT networks

Flawed authentication protocols led to the need for a secured protocol for radio frequency identification (RFID) techniques. In this paper, an authentication protocol named Modified ultralightweight mutual authentication protocol (MUMAP) has been proposed and cryptanalysed by Juel-Weis challenge. The proposed protocol aimed to reduce memory requirements in the authentication process for low-cost RFID tags with limited resources. Lightweight operations like XOR and Left Rotation, are used to circumvent the flaws made in the other protocols. The proposed protocol has three-phase of authentication. Security analysis of the proposed protocol proves its resistivity against attacks like desynchronization, disclosure, tracking, and replay attack. On the other hand, performance analysis indicates that it is an effective protocol to use in low-cost RFID tags. Juel-Weis challenge verifies the proposed protocol where it shows insusceptibility against modular operations

Action recognition using Kinematics Posture Feature on 3D skeleton joint locations

Action recognition is a very widely explored research area in computer vision and related fields. We propose Kinematics Posture Feature (KPF) extraction from 3D joint positions based on skeleton data for improving the performance of action recognition. In this approach, we consider the skeleton 3D joints as kinematics sensors. We propose Linear Joint Position Feature (LJPF) and Angular Joint Position Feature (AJPF) based on 3D linear joint positions and angles between bone segments. We then combine these two kinematics features for each video frame for each action to create the KPF feature sets. These feature sets encode the variation of motion in the temporal domain as if each body joint represents kinematics position and orientation sensors. In the next stage, we process the extracted KPF feature descriptor by using a low pass filter, and segment them by using sliding windows with optimized length. This concept resembles the approach of processing kinematics sensor data. From the segmented windows, we compute the Position-based Statistical Feature (PSF). These features consist of temporal domain statistical features (e.g., mean, standard deviation, variance, etc.). These statistical features encode the variation of postures (i.e., joint positions and angles) across the video frames. For performing classification, we explore Support Vector Machine (Linear), RNN, CNNRNN, and ConvRNN model. The proposed PSF feature sets demonstrate prominent performance in both statistical machine learning- and deep learning-based models. For evaluation, we explore five benchmark datasets namely UTKinect-Action3D, Kinect Activity Recognition Dataset (KARD), MSR 3D Action Pairs, Florence 3D, and Office Activity Dataset (OAD). To prevent overfitting, we consider the leave-one-subject-out framework as the experimental setup and perform 10-fold cross-validation. Our approach outperforms several existing methods in these benchmark datasets and achieves very promising classification performance

Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-of-the-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions

Magnetic and orbital correlations in multiferroic CaMn7_7O12_{12} probed by x-ray resonant elastic scattering

The quadruple perovskite CaMn$_7$O$_{12}$ is a topical multiferroic, in which the hierarchy of electronic correlations driving structural distortions, modulated magnetism, and orbital order is not well known and may vary with temperature. x-ray resonant elastic scattering (XRES) provides a momentum-resolved tool to study these phenomena, even in very small single crystals, with valuable information encoded in its polarization- and energy-dependence. We present an application of this technique to CaMn$_7$O$_{12}$. By polarization analysis, it is possible to distinguish superstructure reflections associated with magnetic order and orbital order. Given the high momentum resolution, we resolve a previously unknown splitting of an orbital order superstructure peak, associated with a distinct \textit{locked-in} phase at low temperatures. A second set of orbital order superstructure peaks can then be interpreted as a second-harmonic orbital signal. Surprisingly, the intensities of the first- and second-harmonic orbital signal show disparate temperature and polarization dependence. This orbital re-ordering may be driven by an exchange mechanism, that becomes dominant over the Jahn-Teller instability at low temperature.Comment: 6 pages, 4 figures and 1 supplementary with 3 figure

The impact of childhood atopic dermatitis on quality of life of the paediatric population

BackgroundAtopic Dermatitis (AD) is a chronic skin condition characterized by pruritis which presents with xerosis, lichenification, and the eruption of eczematous lesions.AimsTo measure the quality of life in the paediatric population with atopic dermatitis at King Abdulaziz Medical City in Jeddah, Saudi Arabia.Methods The assessment tool utilized was the Infants' Dermatitis Quality of Life Index (IDQOL) questionnaire which is validated and available in Arabic. The sample size is 80 participants. Demographics, history of atopy, current treatment, and the percentage of body involved were described as frequencies. Chi-square test was performed to determine if there was a significant difference between gender, age group and the presence of other atopic disease in comparison to percentages of body involved. The analysis of the questionnaire’s items was done by One-way ANOVA to determine where significant impact on quality of life was present.Results There was a significant difference in overall IDQOL score between patient who had asthma with AD and those who did not (p=0.016). Significantly, the higher the percentage of body area affected by AD, the higher IDQOL score (p < 0.0001). No significant difference was identified for the chi-square test. Among questionnaire’s items sleep disturbance was affected the most among patients in relation to increase in distribution of disease along the body (p < 0.0001).ConclusionThe study concluded that the IDQoL among paediatric population with Atopic Dermatitis was significantly impaired, and it showed that the disease severity was proportionally related to the impairment of patients’ quality of life. Therefore, we highly recommend further studies in the same field to be able to generalize the results in the Saudi paediatric population

Design, Synthesis, and Testing of a Molecular Truck for Colonic Delivery of 5-Aminosalicylic Acid

A molecular scaffold bearing eight terminal alkyne groups was synthesized from sucrose. Eight copies of an azide-terminated, azo-linked precursor to 5-aminosalicylic acid were attached to the scaffold via copper(I)-catalyzed azide–alkyne cycloaddition. The resulting compound was evaluated in a DSS model of colitis in BALB/c mice against sulfasalazine as a control. Two independent studies verified that the novel pro-drug, administered in a dose calculated to result in an equimolar 5-ASA yield, outperformed sulfasalazine in terms of protection from mucosal inflammation and T cell activation. A separate study established that 5-ASA appeared in feces produced 24–48 h following administration of the pro-drug. Thus, a new, orally administered pro-drug form of 5-aminosalicylic acid has been developed and successfully demonstrated

Fortalecimiento de gestiones a través del Centro de Información de Actividades Porcinas (CIAP) para el desarrollo sustentable de pequeños y medianos productores porcinos familiares de la zona de influencia de la Facultad de Ciencias Agrarias de la Universidad Nacional de Rosario

Fortalecimiento de gestiones a través del Centro de Información de Actividades Porcinas (CIAP) para el desarrollo sustentable de pequeños y medianos productores porcinos familiares de la zona de influencia de la Facultad de Ciencias Agrarias de la Universidad Nacional de RosarioFil: Silva, Patricia. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentin