Miracle-dz/UniFAR-FTUs huggingface dataset:
A provably secure biometrics and ECC‐based authentication and key agreement scheme for WSNs

Wireless sensor networks (WSNs) underpin many applications of the Internet of Things (IoT), ranging from smart cities to unmanned surveillance and others. Efficient user authentication in WSNs, particularly in settings with diverse IoT device configurations and specifications (eg, resource‐constrained IoT devices) and difficult physical conditions (eg, physical disaster area and adversarial environment such as battlefields), remains challenging, both in research and in practice. Here, we put forth a user anonymous authentication scheme, relying on both biometrics and elliptic curve cryptography, to establish desired security features like forward and backward secrecy. We then make use of the Random‐or‐Real (RoR) model to prove the security of our scheme. We have implemented the proposed scheme in an environment compatible with WSNs. We show after conducting the comparison of the proposed scheme with some recent and related existent schemes that it satisfies various essential and desirable security attributes of a WSN environment. We conclude that the proposed scheme is suitable for the WSN scenario demanding high security.
Optimization of Automated Guided Vehicles (AGV) Fleet Size With Incorporation of Battery Management

OPTIMIZATION OF AUTOMATED GUIDED VEHICLES (AGV) FLEET SIZE WITH INCORPORATION OF BATTERY MANAGEMENT Ahmed Hamdy Old Dominion University, 2019 Director: Dr. Ghaith Rabadi An important aspect in manufacturing automation is material handling. To facilitate material handling, automated transport systems are implemented and employed. The AGV (automated guided vehicle) has become widely used for internal and external transport of materials. A critical aspect in the use of AGVs is determining the number of vehicles required for the system to meet the material handling requirements. Several models and simulations have been applied to determine the fleet size. Most of these models and simulations do not incorporate the battery usage of the vehicles and the effect it can have on the throughput and the number of AGVs required for the system. The goal of this research is to develop a simulation model to determine the optimized number of AGVs that is capable of increasing throughput while meeting the material handling requirements of the system. This model incorporates the battery management aspect and issues, which are usually omitted in AGV research. This includes the charging options and strategies, the number and location of charging stations, maintenance, and extended charging. The analysis entails studying various scenarios by applying different charging options and strategies and changing different parameters to achieve improved throughput and an optimized AGV fleet size. The results clearly show that battery management can have a significant effect on the average throughput and the AGV usage. It is important that the battery management of the AGVs is addressed adequately to run an AGV system efficiently.
Sentiment Classification of Current Public Opinion on BREXIT: Naïve Bayes Classifier Model vs Python’s TextBlob Approach

Sentiment Analysis is playing a crucial role in technological world due to tremendous growth in the field of social media. The motivation regarding sentiment analysis comes from the fact that social media platforms like twitter provide a great platform which is used by general public to express their opinions about a product or an event. Such opinions provide an opportunity to researchers to work on data mining based on public reviews and opinion and provide critical insights helpful for organizations in better decision making. This paper discusses comparison in performances of Naive Bayes Classifier Model and Python’s TextBlob library by carrying out sentiment classification on current public opinion on BREXIT. In order to achieve the objectives, natural language processing, concepts including regular expression library and count vectorization have been used. Also, Natural Language Toolkit library along with TextBlob library are used to clean the data and provide polarity score to the tweets respectively. Naive Bayes Classification algorithm is then introduced into the model after training it on Sentiment140 Twitter dataset to provide an accuracy comparison to that of the TextBlob. Therefore, useful insights are produced considering visualizations obtained from Tableau. Moreover, the results of this research provide an insight about public opinions of different countries about BREXIT. Also, the results will help British and Irish governments to formulate their foreign policies and internal policies in order to maintain their relationship with their major business friendly countries.
Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders

Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations. We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence. Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph. Our model is arc-factored and therefore parsing and learning are both tractable. Experiments show our model achieves significant and consistent improvement over the supervised baseline.
Microfoundations and Recursive Analysis: A Mixed-Methods Framework for Language-Based Research, Computational Methods, and Theory Development

This chapter argues that the primary reason for the underdeveloped state of microfoundations research in language and communications studies is methodological. It asserts that the long-standing methodological division between micro and macro analyses (traditionally small-scale and large-scale, respectively) has led to their continued theoretical separation. The paper draws from Giddens’ theory of structuration and newly developed computational methods to outline an alternative, mixed-methods framework for discourse analysis that the author calls Recursive Analysis (RA). The author demonstrates the application of the RA framework through a case study of the electric vehicle industry that aligns small-scale and large-scale textual analysis to generate theoretical insights.
Self-Adaptive Scaling for Learnable Residual Structure

Residual has been widely applied to build deep neural networks with enhanced feature propagation and improved accuracy. In the literature, multiple variants of residual structure are proposed. However, most of them are manually designed for particular tasks and datasets and the combination of existing residual structures has not been well studied. In this work, we propose the Self-Adaptive Scaling (SAS) approach that automatically learns the design of residual structure from data. The proposed approach makes the best of various residual structures, resulting in a general architecture covering several existing ones. In this manner, we construct a learnable residual structure which can be easily integrated into a wide range of residual-based models. We evaluate our approach on various tasks concerning different modalities, including machine translation (IWSLT-2015 EN-VI and WMT-2014 EN-DE, EN-FR), image classification (CIFAR-10 and CIFAR-100), and image captioning (MSCOCO). Empirical results show that the proposed approach consistently improves the residual-based models and exhibits desirable generalization ability. In particular, by incorporating the proposed approach to the Transformer model, we establish new state-of-the-arts on the IWSLT-2015 EN-VI low-resource machine translation dataset.
Optimizing location and size of distributed generators based on the metaheuristic algorithms for solving network reconfiguration problem

This paper demonstrates the method of optimizing the location and size of the distributed generators with the aim of reducing active power loss by changing the state of the existing tie and sectionalizing switches on the distribution network. A new algorithm is proposed to solve the distribution network reconfiguration problem based on the Runner-root Algorithm (RRA). In the first stage, the Runner-root algorithm is applied to solve the reconfiguration problem, and the next stage of optimizing the new distribution network configuration takes into account the influence of the location and the size of the distributed generators. The calculation results show that the proposed method can simultaneously solve the problem of optimizing the location and size of distributed generators when reconfiguring the distribution network. The results of the comparison with other studies in the same distribution network system 69 nodes showed that the proposed method is highly effective and can be used for reference in the process of optimizing the operating mode of a distribution network.
EVALUATION OF THE EFFECTIVENESS OF ANTENATAL PREPARATION FOR CHILDBIRTH COURSES ON THE HEALTH OF THE MOTHER AND THE NEWBORN

Since lack of awareness, fear of unknown and also lack of prenatal preparation in pregnant women increase complication of the pregnancy, labor and consequently the medical intervention, this investigation was carried out in order to assess the effectiveness of the preparation classes for pregnancy courses on decrease of complications and medical intervention in pregnant women who referred to Hedayat hospital between May 2000 and March 2001. A clinical experimental study (Sequential Randomized control trial) was carried out on 200 pregnant women expecting their first child, under 35 years of age, with gestational age of about 20 weeks and absence of hypertension, diabetes, multifetal pregnancy, smoking, habit and threatened abortion symptoms in their history. They were randomly divided to experimental & control group. Mothers in exprimental group were trained both theoretically and practically in 8 sessions, while those in control group received routine care only. Mother’s emotional and physical health during pregnancy and the collected data from labor and neonates health situations were recorded and analysed and then compared between the 2 groups. The collected data showed that back pain, pelvic pain and tiredness during pregnancy were less in trained women and their daily activities were more in comparison with non-trained women. There was not any significant difference in the quality of sleep, marriage and social relationship between the two groups. Women who attended antenatal classes had an increase in the percentage of fullterm delivery(7%), whereas the others had a higher risk of cesarean sections (16%) Moreover, duration of active labor and the second stage of it was shorter in trained women. Statistically, there were significant differences between the criteria mentioned above (P<0.04, P<0.04, P<0.002, P<0.004). The rate of prescription of oxytocin, antispasmodic drugs and sedative and also birth weight and apgar in neonates were the same in the two groups. The findings show that an increase in awareness and preparations during pregnancy help mothers to pass this important period of their life with more ease and enjoyment and less medical interventions. Hence, arranging preparation for pregnancy courses and more studies on methods of preparations for childbirth used in other countries is recommended.
A Comprehensive Survey on Vertical Handover Security Attacks during Execution Phase

The Mobile Users' demand (MUs) in getting Always Best Connected (ABC) is on the increase by using different Radio Access Technologies (RATs): Wi-Fi, GSM (2G), UMTS (3G), LTE (4G) and 5G. To fulfill the ABC, three phases of Vertical Handover (VHO) are required: Initiation, Decision and Execution. The importance of the security factor particularly arises during the execution phase where the mobility management protocols are taken place: Mobile IPv4 (MIPv4) and Mobile IPv6 (MIPv6). This paper therefore surveys the current research initiatives on VHO security attacks during execution phase in order to precisely identify the active security challenges.
Student perceptions of their involvement in formative assessment feedback practices: “I can do it myself”

Student voice makes a valuable contribution in the effort to evaluate the impact of Assessment for Learning (AfL) as it is the student learning that AfL practices seek to enhance. The authors report on a practitioner-led qualitative study involving 11 students’ perceptions of the impact of student-generated feedback practices. This study was conducted in a senior high-school English classroom in New Zealand and involved teacher-led development of students’ ability to evaluate and give feedback on essay writing. A partnership model of teaching and learning was an underpinning pedagogic principle in a teaching unit focusing on essay writing where a multidimensional criterion rubric was used, model answers were marked, and students were led to create feedback on their own and peers’ essays. Students reported that they valued their involvement in the assessment process and described benefits of increased engagement and the enhancement of their self-regulatory skills, as well as a deepened understanding of essay writing. Through the use of student-generated feedback, students were enabled to become partners with their teacher and share power and responsibility for classroom assessment.
MALONDIALDEHYDE LEVELS IN PATIENTS WITH CHRONIC PERIODONTITIS

Periodontitis is a chronic condition leading to the destruction of the periodontium. A case control study was carried out in 30 subjects with chronic periodontitis aged 30-65 years (group II) and age matched with 30 control subjects (group I). Salivary and serum malondialdehyde, which is a marker of lipid peroxidation, was estimated in the cases and controls. Salivary MDA was elevated (p<0.001) in patients with chronic periodontitis there was no change in serum MDA levels when compared with normal controls. Increased levels in MDA may be closely associated with periodontal disease and salivary estimation may provide advantage in pathogenesis of the periodontal disease.
Minimal Invasive Surgical Release of the Median Nerve at the Wrist: A Study about 36 patients

Surgical treatment of carpal tunnel syndrome (CTS) has evolved those recent years. Endoscopic and minimal invasive techniques have grown considerably, to limit the incidence of cutaneous complications associated with conventional open surgery, reducing hospitalization time and allow rapid functional recovery of the hand. Purpose: The purpose of this work is to analyze in our practical conditions, the results of surgical treatment of CTS with a minimal invasive technique using a transverse palmar incision made in the wrist crease. Patients and methods: This was a prospective study of 36 patients who underwent surgical treatment by minimal invasive CTS technique. The series consisted of 24 women and 12 men, whose average age was 43 years. 47.22% of patients were hand workers. All patients had a CTS confirmed by electromyography. The intervention took place under loco regional anesthesia with axillary block. All patients have been operated by the same surgeon. For each patient, the same surgical procedure has been applied. After a transverse incision of about 1cm in the crease of the wrist, it was directed anterograde section of flexor retinaculum (RDF) using a special curved bistoury. A simple bandage was made and no immobilization has been made. Results: In the immediate post-operative follow up, local hematoma was found in 04 patients. Locally, the evolution of the scar was favorable in all patients. At 30th day, 34 patients had more functional impairment, or pain in the scar, and had recovered more than 50% grip strength. They could use all their hand for daily activities. At three months, 83% of patients had the same grip strength on both sides. Discussion and conclusion: the interest of this minimal invasive technique is based on the quality of short term postoperative follow up that are more comfortable for the patient, allowing faster functional recovery of the hand, without being more iatrogenic.
THE SOCIAL FUNCTION OF PAIN CATASTROPHIZING IN CHILDREN AND ADOLESCENTS

This study investigated the value of pain catastrophizing in explaining pain, disability and somatic complaints, beyond negative affectivity. Two cross-sectional studies, one in a sample of school children (n = 193) and a second in a clinical sample of children with recurrent or chronic pain (n = 43), were conducted. In both studies, measures of pain catastrophizing and negative affectivity were examined for their ability to explain pain, disability, and somatic complaints. In both studies, pain catastrophizing significantly accounted for the variance of pain, disability and somatic complaints, beyond the effects of age, sex, and negative affectivity. Furthermore, pain catastrophizing significantly mediated the relationship between negative affectivity and somatic complaints in both studies, and between negative affectivity and functional disability Study 1. Results suggest the importance of assessing for pain catastrophizing in children. Pain catastrophizing is further discussed in terms of communicating distress to significant others. 1 Vervoort, T. , Goubert, L. , Eccleston, C. , Bijttebier, P., & Crombez, G. (2006). Catastrophic thinking about pain is independently associated with pain severity, disability, and somatic complaints in school children and children with chronic pain. Journal of Pediatric Psychology, 31, 674-683.
Diseases Classification for Tea Plant Using Concatenated Convolution Neural Network

Plant diseases can cause a significant decrease in tea crop production. Early disease detection can help to minimize the loss. For tea plants, experts can identify the diseases by visual inspection on the leaves. However, providing experts to deal with disease identification may be very costly. The machine learning technology can be implemented to provide automatic plant disease detection. Currently, deep learning is state-of-the-art for object identification in computer vision. In this study, the researchers propose the Convolutional Neural Network (CNN) for tea disease detections. The researchers focus on the implementation of concatenated CNN, namely GoogleNet, Xception, and Inception-ResNet-v2, for this task. About 4727 images of tea leaves are collected, comprising of three types of diseases that commonly occur in Indonesia and a healthy class. The experimental results confirm the effectiveness of concatenated CNN for tea disease detections. The accuracy of 89.64% is achieved.
Hematoxylin and Eosin (H&E) Stained Liver Portal Area Segmentation Using Multi-Scale Receptive Field Convolutional Neural Network

Portal area segmentation is an important step in the quantitative histological analysis process for hepatitis grading. However, portal areas often appear of different sizes and appearances due to the variations of surrounding components such as the ductule, bile duct, artery, and portal vein. The slim fibrosis expanding from the portal area further increases challenges of the portal area segmentation. A Multi-scale Receptive Field Convolutional Neural Network (MRF-CNN) for the segmentation of the liver portal areas in hematoxylin and eosin (H&E) stained whole slide images (WSIs) is proposed in this paper. The MRF-CNN adopts the atrous spatial pyramid pooling (ASPP) with multiple atrous rates and symmetric encoder-decoder with feature concatenation architecture. The atrous rates in ASPP are devised of receptive fields to extract features of meaningful tissue components in parallel in portal areas. Along with the MRF-CNN, a small object sensitive loss function is also proposed to have the network focus on small portal areas and slim fibrosis. The results show that the proposed model achieves Intersection over Union (IOU) of 0.87 and sensitivity of 0.92. Compared to recent segmentation researches such as Fully Convolutional Network (FCN), U-Net and SegNet, the proposed network achieves an overall the best IOU and sensitivity performance. Experimental results also show that the designed ASPP block benefits in feature extraction, and the ability of identifying small objects in proposed small object sensitive loss has a significant improvement of the segmentation result comparing to the original cross entropy loss.
Comparison of immature porcine and human bone mineral density about the knee: implications for graft pullout studies

Background:Orthopaedic hardware pull-out studies frequently utilize various animal models to assess axial and multiplanar loading of bone. Biomechanical models and techniques for testing are common; however, bone quality measures frequently are not provided in published studies. Bone mineral density (BMD) and bone mineral content (BMC) are used for assessment of bone quality. The authors sought to determine whether immature porcine knees would be an appropriate model for human graft and hardware pull-out studies. Our hypothesis was that immature cadaver porcine knees (ICPK) would be less dense than those of healthy adult human distal femurs and proximal tibias and thus an inadequate model for human graft and hardware pull-out studies. Methods:This was an in vivo and in vitro comparative laboratory study of BMD and BMC between two human knee groups and ICPK. Eleven ICPK 3 mo in age (body mass 25.0±3.3 kg) were used. Human Group 1 consisted of 18 collegiate athletes with a mean age of 19.2±0.86 yr (body mass 76.1±14.4 kg). Human group 2 included 10 people with a mean age of 34.4±9.3 yr (body mass 79.3±5.9 kg). BMD and BMC were measured using a Lunar 10655/Prodigy DEXA Scanner and software. ANOVA tested differences among the three groups, and post hoc testing assessed differences between the groups. Results:Differences in BMD were shown among the three groups for distal femur (ICPK-0.91±0.11 g/cm2; HG1-1.17±0.34 g/cm2; HG2-1.2±0.32 g/cm2 [P=0.037]), and proximal tibia (ICPK-0.94±0.12 g/cm2; HG1-1.41±0.22 g/cm2; HG2- 1.26±0.17 g/cm2 [P=0.001]). Statistical differences were found among the three groups for femoral BMC (ICPK-3.51±1.25 g; HG1-7.5±3.06 g; HG 2-11.59±5.40 g [P=0.001]) and tibial BMC (ICPK-4.58±2.00 g; HG1-9.64±3.92 g; HG2-13.07±5.37 g [P=0.001]). Conclusions:BMD and BMC in the distal femur and proximal tibia of an immature pig are significantly less than that of an adult human. Testing of graft pull-out using an immature porcine model involving the bones about the knee may not be accurately extrapolated to humans. We believe our work illuminates the need for bone quality assessment and documentation with BMD and BMC when performing or comparing pull-out studies.
An Intelligent System for Predicting a User Access to a Web Based E-Learning System Using Web Mining

In this Internet era, with ever-increasing interactions among participants, the size of the data is increasing so rapidly such that the information available to us in the near future is going to be unpredictable. Modeling and visualizing such data are one of the challenging tasks in the data analytics field. Therefore, business intelligence is the way in which a company can use data to improve business and operational efficiency whereas data analytics involves improving ways of making intelligence out of that data before acting on it. Thus, the proposed work focuses on prevailing challenges in data analytics and its application on social media like Facebook, Twitter, blogs, e-commerce, e-service and so on. Among all of the possible interactions, e-commerce, e-education, and e-services have been identified as important domains for analytics techniques. So, it focuses on machine learning technique in improving practice and research in such e-X domains. Empirical analysis is done to show the performance of proposed system using real-time datasets.