THE IMPLEMENTATION OF ONLINE LEARNING POLICY DURING THE COVID-19 PANDEMIC AT DHARMAWANGSA UNIVERSITY

This study aims to determine (1) to describe the online learning policy at Dharmawangsa University, (2) Describe and analyze the Implementation of Online Learning Policies During the COVID-19 Pandemic Period at Dharmawangsa University., (3) Describe whether there are any obstacles during the online learning process (4) Describe what inputs can be taken from student complaints about the online learning process at Dharmawangsa University. Data collection techniques in research using interview techniques and documentation. The data analysis technique used in this research is qualitative analysis through data reduction, data display, and conclusion drawing. The results of this study indicate that (1) Dharmawangsasudang University followed the instructions of the Ministry of Education and Culture SE No. 5 of 2020 and SE of Higher Education No. 1 of 2020 and was following these instructions. (2) Lecturers and teaching staff at Dharmawangasa University also know the Circular. However, it is not comprehensive (3) The implementation of online learning policies during the COVID-19 pandemic at Dharmawangsa University, most of the lecturers answered less effectively, especially when taking lessons where the courses required doing practicum or practice so that they could not run under learning outcomes. (4 ) There are significant obstacles when the online learning process is carried out, including uneven network connections in the student's residential area and insufficient internet quota in carrying out the online learning. (5) As for input from complaints submitted by students during the learning policy online during the pandemic, including uneven network connections and the lack of quotas owned by students.

Identifying a Whole-brain Connectome-based Model in Drug-naïve Parkinson’s Disease for Predicting Motor Impairment

Identifying a whole-brain connectome-based predictive model in drug-naïve patients with Parkinson’s disease and verifying its predictions on drug-managed patients would be useful in determining the intrinsic functional underpinnings of motor impairment and establishing general brain-behavior associations. In this study, we constructed a predictive model from the resting-state functional data of 47 drug-naïve patients by using a connectome-based approach. This model was subsequently validated in 115 drug-managed patients. The severity of motor impairment was assessed by calculating Unified Parkinson’s Disease Rating Scale Part III scores. The predictive performance of model was evaluated using the correlation coefficient (rtrue) between predicted and observed scores. As a result, a connectome-based model for predicting individual motor impairment in drug-naïve patients was identified with significant performance (rtrue = 0.845, p < 0.001, ppermu = 0.002). Two patterns of connection were identified according to correlations between connection strength and the severity of motor impairment. The negative motor-impairment-related network contained more within-network connections in the motor, visual-related, and default mode networks, whereas the positive motor-impairment-related network was constructed mostly with between-network connections coupling the motor-visual, motor-limbic, and motor-basal ganglia networks. Finally, this predictive model constructed around drug-naïve patients was confirmed with significant predictive efficacy on drug-managed patients (r = 0.209, p = 0.025), suggesting a generalizability in Parkinson’s disease patients under long-term drug influence. In conclusion, this study identified a whole-brain connectome-based model that could predict the severity of motor impairment in Parkinson’s patients and furthers our understanding of the functional underpinnings of the disease.

EFEKTIVITAS PEMBELAJARAN DARING MATA PELAJARAN BIOLOGI SELAMA PANDEMI COVID-19 DI SMA IT AL HUDA WONOGIRI

This study aims to describe (1) the effectiveness of implementing online learning for Biology subjects using googlemeet during the Covid-19 pandemic at SMAIT Al Huda Wonogiri and (2) the obstacles faced. This research was conducted at SMAIT Al Huda Wonogiri using a descriptive analytic approach. The data from this study were obtained through a survey distributed to students through the Whatsapps application in the form of a google form. The results obtained that online learning at SMAIT Al Huda Wonogiri is quite effective where students feel happy with online learning for Biology subjects, teachers and students interact well, teaching materials presented by teachers are of sufficient quality, students are easy to follow online learning, students It is easy for students to ask questions, and learning materials can be delivered well by the teacher. However, there are still obstacles during learning, including signal interference or internet service which causes the teacher's voice to be less clear to students or frequent network connections, limitations of gadgets and quotas. ABSTRAKPenelitian ini bertujuan untuk mendeskripsikan (1) efektivitas pelaksanaan pembelajaran daring mata pelajaran Biologi menggunakan googlemeet selama masa pandemi Covid-19 di SMAIT Al Huda Wonogiri dan (2) kendala yang dihadapi. Penelitian ini dilakukan di SMAIT Al Huda Wonogiri menggunakan pendekatan deskriptif analitik. Data dari penelitian ini diperoleh melalui survei yang disebarkan kepada peserta didik melalui aplikasi Whatsapps dalam bentuk googleform. Hasil penelitian yang diperoleh bahwa pembelajaran daring di SMAIT Al Huda Wonogiri cukup efektif dimana peserta didik merasa senang dengan pembelajaran daring mata pelajaran Biologi, guru dan peserta didik berinteraksi dengan baik, bahan ajar yang disajikan guru cukup berkualitas, peserta didik mudah mengikuti pembelajaran daring, peserta didik mudah dalam mengajukan pertanyaan, dan materi pembelajaran dapat tersampaikan dengan baik oleh guru. Meskipun demikian, kendala selama pembelajaran tetap ada diantaranya gangguan sinyal atau layanan internet yang menyebabkan suara guru yang kurang jelas terdengar oleh peserta didik atau sering putusnya koneksi jaringan, keterbatasan perangkat gawai dan kuota.

A New Hybrid Online and Offline Multi-Factor Cross-Domain Authentication Method for IoT Applications in the Automotive Industry

Connected vehicles have emerged as the latest revolution in the automotive industry, utilizing the advent of the Internet of Things (IoT). However, most IoT-connected cars mechanisms currently depend on available network services and need continuous network connections to allow users to connect to their vehicles. Nevertheless, the connectivity availability shortcoming in remote or rural areas with no network coverage makes vehicle sharing or any IoT-connected device problematic and undesirable. Furthermore, IoT-connected cars are vulnerable to various passive and active attacks (e.g., replay attacks, MiTM attacks, impersonation attacks, and offline guessing attacks). Adversaries could all use these attacks to disrupt networks posing a threat to the entire automotive industry. Therefore, to overcome this issue, we propose a hybrid online and offline multi-factor authentication cross-domain authentication method for a connected car-sharing environment based on the user’s smartphone. The proposed scheme lets users book a vehicle using the online booking phase based on the secured and trusted Kerberos workflow. Furthermore, an offline authentication phase uses the OTP algorithm to authenticate registered users even if the connectivity services are unavailable. The proposed scheme uses the AES-ECC algorithm to provide secure communication and efficient key management. The formal SOV logic verification was used to demonstrate the security of the proposed scheme. Furthermore, the AVISPA tool has been used to check that the proposed scheme is secured against passive and active attacks. Compared to the previous works, the scheme requires less computation due to the lightweight cryptographic algorithms utilized. Finally, the results showed that the proposed system provides seamless, secure, and efficient authentication operation for the automotive industry, specifically car-sharing systems, making the proposed system suitable for applications in limited and intermittent network connections.

Federated learning over WiFi: Should we use TCP or UDP?

Federated learning is a distributed learning paradigm where a centralized model is trained on data distributed over a large number of clients, each with unreliable and relatively slow network connections. The client connections typically have limited bandwidth available to them when using networks such as 2G, 3G, or WiFi. As a result, communication often becomes a bottleneck. Currently, the communication between the clients and server is mostly based on TCP protocol. In this paper, we explore using the UDP protocol for the communication between the clients and server. In particular, we develop UDP-based algorithms for gradient aggregation-based federated learning and model aggregation-based federated learning. We propose methods to construct model updates in case of packet loss with the UDP protocol. We present a scalable framework for practical federated learning. We conduct experiments over WiFi and observe that the UDP-based protocols can lead to faster convergence than the TCP-based protocol -- especially in bad networks. Code available at the repository: \url{https://github.com/vineeths96/Federated-Learning}.

Highly accurate and efficient two phase-intrusion detection system (TP-IDS) using distributed processing of HADOOP and machine learning techniques

Network security and data security are the biggest concerns now a days. Every organization decides their future business process based on the past and day to day transactional data. This data may consist of consumer’s confidential data, which needs to be kept secure. Also, the network connections when established with the external communication devices or entities, a care should be taken to authenticate these and block the unwanted access. This consists of identification of the malicious connection nodes and identification of normal connection nodes. For that, we use a continuous monitoring of the network input traffic to recognize the malicious connection request called as intrusion and this type of monitoring system is called as an Intrusion detection system (IDS). IDS helps us to protect our network and data from insecure and malicious network connections. Many such systems exists in the real time scenario, but they have critical issues of performance like accuracy and efficiency. These issues are addressed as a part of this research work of IDS using machine learning techniques and HDFS. The TP-IDS is designed in two phases for increasing accuracy. In phase I of TP-IDS, Support Vector Machine (SVM) and k Nearest Neighbor (kNN) are used. In phase II of TP-IDS, Decision Tree (DT) and Naïve Bayes (NB) are used, where phase II is the validation phase of the system for increasing accuracy. Also, both the phases are having Hadoop distributed file system underlying data storage and processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.

Physician Network Connections Associated With Faster De-Adoption of Dronedarone for Permanent Atrial Fibrillation

Background: Physicians’ professional networks are an important source of new medical information and have been shown to influence the adoption of new treatments, but it is unknown how physician networks impact the de-adoption of harmful practices. Methods: We analyzed changes in physicians’ use of dronedarone after the PALLAS trial (Palbociclib Collaborative Adjuvant Study; November 2011) showed that dronedarone increased the risk of death from cardiovascular events among patients with permanent atrial fibrillation. Deidentified administrative claims from the OptumLabs Data Warehouse were combined with physicians’ demographic information from the Doximity database and publicly available data on physicians’ patient-sharing relationships compiled by the Centers for Medicare and Medicaid Services. We used a linear probability model with an interrupted linear time trend specification to model the impact of the PALLAS trial on physicians’ dronedarone usage between 2009 and 2014. Results: Before the PALLAS trial, the use of dronedarone was increasing by 0.22 percentage points per quarter (95% CI, 0.19–0.25) in our Medicare Advantage sample (N=343 429 patient-quarter observations) and 0.63 percentage points per quarter (95% CI, 0.52–0.75) in our commercially insured sample (N=44 402 patient-quarter observations). After the PALLAS trial and subsequent United States Food and Drug Administration black box warning, physicians in the Medicare Advantage sample with an above-median number of network connections to other physicians decreased their quarterly usage of dronedarone by 0.12 percentage points more per quarter (95% CI, −0.20 to −0.04; P =0.031) than physicians with equal to or below the median number of network connections. Similar patterns existed in the commercially insured sample ( P =0.0318). Conclusions: After controlling for a wide range of patient, physician, and geographic characteristics, physicians with a greater number of network connections were faster de-adopters of dronedarone for patients with permanent atrial fibrillation after the PALLAS trial and subsequent United States Food and Drug Administration black box warning detailed the harmfulness of dronedarone for these patients. Policies for improving physicians’ responsiveness to new medical information should consider utilizing the influence of these important professional network relationships.

Identifying a Whole-brain Connectome-based Model in Drug-naïve Parkinson’s Disease for Predicting Motor Impairment

Background The functional alternation of distinct brain networks contribute to motor impairment in Parkinson’s disease (PD) remains unclear. Identifying a whole-brain connectome-based predictive model (CPM) in drug-naïve patients and verifying its predictability among drug-managed patients would be helpful to detect generalizable brain-behavior association and reflect intrinsic functional underpinning of motor impairment. Methods Resting-state functional data of 47 drug-naïve patients were enrolled to construct a predictive model by using the CPM approach, which was subsequently validated in 115 drug-managed patients. The severity of motor impairment was assessed by calculating Unified Parkinson’s Disease Rating Scale part III (UPDRS III) scores. Predictive performance was evaluated with the correlation coefficient(rtrue) and the mean squared error (MSE) between observed and predicted scores. Results A CPM for predicting individual motor impairment in drug-naïve PD was identified with significant performance (rtrue=0.845, p < 0.001, MSE = 137.57). Two connection patterns were recognized according to the correlation coefficients between the connections’ strength and motor impairment severity. The negative motor-impairment-related network contained more within-network connections in the motor, visual-related, and default mode networks, while the positive motor-impairment-related network was constructed mostly with between-network connections coupled motor-visual, motor-limbic, and motor-basal ganglia networks. The predictability of constructed model was further confirmed in drug-managed patients (r = 0.209, p = 0.025, MSE = 182.96), suggesting generalizability in PD patients with lasting dopaminergic medication influence. Conclusions This study identified a whole-brain connectome-based model that could predict the severity of motor impairment for PD. The connection patterns generated from the model reflected that functional segregation of motor, visual-related, and default mode networks play an important role in PD motor impairment, and higher connections coupling motor and non-motor regions might demonstrate a compensatory mechanism to overcome motor impairment.