On-line Problem Research Articles

STUDENTS’ PERCEPTION OF SPADA UWGM AS LEARNING MANAGEMENT SYSTEM (LMS) IN WIDYA GAMA MAHAKAM SAMARINDA UNIVERSITY

Lecturers and students must be prepared to have technical knowledge and skills about easy applications and features that can accommodate online learning needs. It has been notified based on the researcher in pre research with the students. During the learning activities, the students find particular problems in learning online through SPADA as the LMS. However, they should know the system characteristic and functions to use the system effectively. Therefore, based on the background above, the researcher examined EFL students’ perceptions of the SPADA as a learning management system. The objectives of this research study are to find out what are students’ perception of SPADA UWGM as Learning Management System (LMS) in Widya Gama Mahakam Samarinda University. This study used qualitative research with case study approaches. The result showed that perception of using SPADA as the learning management system in the fifth semester of Class a English Education Department in Widya Gama Mahakam Samarinda University divided into three indicators of perception that are acceptance, understanding, and evaluation. Furthermore, from the student’s perception of acceptance the students stated that online learning can be said to be an effective learning resource when supported by adequate infrastructure such as good networks and the SPADA as the Learning Management System but the students still have difficulties in acceptance the material with SPADA as the Learning Management System

An Improved Kalman Filter Based on Long Short-Memory Recurrent Neural Network for Nonlinear Radar Target Tracking

The target tracking of nonlinear maneuvering radar in dense clutter environments is still an important but difficult problem to be solved effectively. Traditional solutions often rely on motion models and prior distributions. This paper presents a novel improved architecture of Kalman filter based on a recursive neural network, which combines the sequence learning of recurrent neural networks with the precise prediction of Kalman filter in an end-to-end manner. We employ three LSTM networks to model nonlinear motion equation, motion noise, and measurement noise, respectively, and learn their long-term dependence from a large amount of training data. They are then applied to the prediction and update process of Kalman filter to calculate the estimated target state. Our approach is able to address the tracking problem of nonlinear maneuvering radar target online end-to-end and does not require the motion models and prior distributions. Experimental results show that our method is more effective and faster than the traditional methods and more accurate than the method with LSTM network alone.

Learning Tahfiz with Talaqqi Method Using Whatsapp Application

All levels of education in Indonesia during the pandemic were carried out online, including one of the Tahfidz Darul Huffaz Houses which was carried out online. Technology plays an important role in educational problems that occur during the pandemic, because technology can help the learning process remotely. However, in reality there are obstacles encountered when running the online Thafidz program, such as the lack of student motivation to participate in post-online Tahfidz learning activities. The method used in this study uses qualitative methods to describe how the description of the implementation of tahfidz Al-Qur'an learning with the online talaqqi method using the WhatsApp application, data collection is taken from interviews and field observations by observing real phenomena. The purpose of this study is to find out how Tahfidz learning is done online using the WhatsApp application, then to find out the supporting factors in Tahfidz learning with the Talaqqi method using the WhatsApp application, knowing the inhibiting factors in Tahfidz learning using the Talaqqi method through the WhatsApp application, knowing what efforts what a teacher does in dealing with problems in learning Tahfidz online with the Talaqqi method through the WhatsApp application. Based on the results of the study, it can be seen that learning Tahfidz using the WhatsApp application has had a good impact, it is very possible in the Tahfidz learning process, because by using WhatsApp Groups it has become a liaison between students and teachers to carry out Tahfidz learning normally, and allows for the memorization of Al-Quran to run smoothly. Qur'an, and muraja'ah normally.

Measuring the digital divide among people with severe mental ill health using the essential digital skills framework.

Amid the vast digitalisation of health and other services during the pandemic, people with no digital skills are at risk of digital exclusion. This risk might not abate by the end of the pandemic. This article seeks to understand whether people with severe mental ill health (SMI) have the necessary digital skills to adapt to these changes and avoid digital exclusion. Two hundred and forty-nine adults with SMI across England completed a survey online or offline. They provided information on their digital skills based on the Essential Digital Skills (EDS) framework, sociodemographic information, and digital access. This is the first time that the EDS is benchmarked in people with SMI. 42.2% had no Foundation Skills, and 46.2% lacked skills for daily life (lacking Foundation or Life Skills). 23.0% of those working lacked skills for professional life (lacking Foundation or Work Skills). The most commonly missing skills were handling passwords and using the device settings (Foundation Skills) and online problem solving (Skills for Life). People were interested in learning more about approximately half of the skills they did not have. People were more likely to lack Foundation Skills if they were older, not in employment, had a psychosis-spectrum disorder, or had no Internet access at home. A significant portion of people with SMI lacked Foundation Skills in this objective and benchmarked survey. This points to a high risk for digital exclusion and the need for focused policy and tailored health sector support to ensure people retain access to key services and develop digital skills and confidence. To our knowledge, this is the first time this has been described using the EDS framework. Services, including the National Health Service (NHS), need to be aware of and mitigate the risks.

Strong Convexity of Feasible Sets in Off-line and Online Optimization

It is known that the curvature of the feasible set in convex optimization allows for algorithms with better convergence rates, and there is renewed interest in this topic for both off-line and online problems. In this paper, leveraging results on geometry and convex analysis, we further our understanding of the role of curvature in optimization: We first show the equivalence of two notions of curvature, namely, strong convexity and gauge bodies, proving a conjecture of Abernethy et al. As a consequence, this shows that the Frank–Wolfe–type method of Wang and Abernethy has accelerated convergence rate [Formula: see text] over strongly convex feasible sets without additional assumptions on the (convex) objective function. In online linear optimization, we identify two main properties that help explaining why/when follow the leader (FTL) has only logarithmic regret over strongly convex sets. This allows one to directly recover and slightly extend a recent result of Huang et al., and to show that FTL has logarithmic regret over strongly convex sets whenever the gain vectors are nonnegative. We provide an efficient procedure for approximating convex bodies by strongly convex ones while smoothly trading off approximation error and curvature. This allows one to extend the improved algorithms over strongly convex sets to general convex sets. As a concrete application, we extend results on online linear optimization with hints to general convex sets. Funding: This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grant Bolsa de Produtividade em Pesquisa #4310516/2017-0] Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001) Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (Jovem Cientista do Nosso Estado), Bolsa de Produtividade em Pesquisa #312751/2021-4 from CNPq, FAPERJ [Grant “Jovem Cientista do Nosso Estado”].

Caching with Time Windows and Delays

We consider two generalizations of the classical weighted paging problem that incorporate the notion of delayed service of page requests. The first is the (weighted) paging with time windows (\sf PageTW) problem, which is like the classical weighted paging problem except that each page request only needs to be served before a given deadline. This problem arises in many practical applications of online caching, such as the “deadline” I/O scheduler in the Linux kernel and video-on-demand streaming. The second, and more general, problem is the (weighted) paging with delay (\sf PageD) problem, where the delay in serving a page request results in a penalty being added to the objective. This problem generalizes the caching problem to allow delayed service, a line of work that has recently gained traction in online algorithms (e.g., [Y. Emek, S. Kutten, and R. Wattenhofer, Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, 2016, pp. 333--344; Y. Azar et al., Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017, pp. 551--563; Y. Azar and N. Touitou, Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019, pp. 60--71]). We give $O(\log k\log n)$-competitive algorithms for both the \sf PageTW and \sf PageD problems on $n$ pages with a cache of size $k$. This significantly improves on the previous best bounds of $O(k)$ for both problems [Y. Azar et al., Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017, pp. 551--563]. We also consider the offline \sf PageTW and \sf PageD problems, for which we give $O(1)$-approximation algorithms and prove APX-hardness. These are the first results for the offline problems; even NP-hardness was not known before our work. At the heart of our algorithms is a novel “hitting-set” LP relaxation of the \sf PageTW problem that overcomes the $\Omega(k)$ integrality gap of the natural LP for the problem. To the best of our knowledge, this is the first example of an LP-based algorithm for an online problem with delays/deadlines.

STUDENT PROBLEMS IN ONLINE LEARNING: SOLUTIONS TO KEEP EDUCATION GOING ON

The spread of the Covid-19 virus that has swept across the world has changed various aspects of human life, one of which is the change of learning process that commonly occurs face to face in the classroom become online learning. Online learning is chosen as the solution to keep teaching and learning during this pandemic and this is done by various levels of education starting from primary school, middle school, high school, to college levels. The existence of cases of learning difficulties experienced by students during the Covid-19 pandemic made students do not understand the subject matter delivered by the teacher. This study aimed to find out some solutions of students' problem in the online learning process. Participants in this study were 25 English education students from a private university in Bandar Lampung. The researcher distributed a close ended questionnaire of 9 questions in the form of Google to collect data, and used qualitative data to illustrate the results of the questionnaire in data findings. The results of the study show that there were some problems faced by students in online learning included (1) students difficult to understanding materials, (2) students encounter technical difficulties, (3) limited internet quota, (4) poor communication, and (5) learning becomes less effective. The solutions that researcher has found to overcome the problems are namely (1) teachers or lecturers must implement face to face class twice a week, (2) teachers should use platforms with simpler operations in online learning, (3) teachers or lecturers should provide a material that is interactive, dynamic and attractive and (4) the government must extend the quota assistance for students.

Online reinforcement learning for the shape morphing adaptive control of 4D printed shape memory polymer

Combining 3D printing and smart materials, 4D printing technologies enable the printed actuators to further change their shapes or other properties after prototyping. However, the shape morphing of 4D printed actuators suffers from poor controllability and low precision. One of the main challenges is that the 4D printed actuators are hard to be modeled and it is difficult to develop an appropriate controller for them. In this study, various popular reinforcement learning (RL) methods are applied to address the problem of online and adaptive model-free control of 4D printed shape memory polymer (SMP). Their training efficiencies are compared and an adaptive LQR controller based on Q learning is developed to realize efficient online learning. The RL controller achieves precise and quick shape control within 2−−3 learning episodes and is adaptive to the changing properties of SMP. The RL controller performance is then compared with a model-based LQR controller and shows high control precision and excellent adaptability to the varying control plant.

Overloaded, uninformed, losted…– information problems of an individual as a consequence of global infodemic

Today, the Internet is a key mechanism for distributing both reliable information and disinformation. On an unprecedented scale we are confronted with the problem of spreading false information online. Their overabundance and rate of growth has led to the emergence and continued growth of the phenomenon of infodemia. Living in a world of excess false information has serious consequences, especially for emotionally immature and cognitively adolescent users of digital space. In this context, the didactic activities of social institutions (including schools) to ensure and strengthen the information competence of children and adolescents become extremely important. The formation of the right attitudes and skills for receiving and using information is becoming one of the important elements of the process of media education of students today.

The Trade-Offs between Optimality and Feasibility in Online Routing with Dedicated Path Protection in Elastic Optical Networks.

The article discusses an online problem of routing and spectrum allocation with dedicated path protection in elastic optical networks. We propose three novel algorithms to solve this problem. The first of them is the minimum-cost–maximum-flow heuristic algorithm, which calculates the solution assuming that the spectrum units on the working and dedicated backup path are the same. Such an assumption, on the one hand, increases the bandwidth blocking probability; however, on the other hand, it enables a simple, cheap and fast way to connect customers to the network during the implementation phase of elastic optical networks. The next two algorithms, which determine the exact solutions, are based on the branch and bound method. The first calculates the working and dedicated backup paths with the minimum total occupied bandwidth, called the total cost, while the second calculates the paths with the minimum total length. These algorithms enable the performance evaluation of the proposed heuristic algorithm and provide the answer as to what should be optimized, the total cost or the total length of paths, in order to minimize the bandwidth blocking probability. Extensive simulation research has shown that the proposed heuristic algorithm can be used in elastic optical networks, but with a small network load. Moreover, it is shown that the optimization of the total cost of paths provides a slightly lower blocking probability than the optimization of the total length of paths.

Resource allocation algorithm of an indoor OFDMA VLC system.

In this work, a resource management algorithm is proposed for an indoor visible light communication (VLC) access network using an orthogonal frequency division multiple access (OFDMA) scheme. With OFDMA, adaptive modulation is adopted to improve the frequency spectrum efficiency of this VLC system. Specifically, the long-term time-averaged optimization problem is transformed into a series of single timeslot online problems via a Lyapunov optimization technique. Then, each user is provided with a separate queue to maintain the random arrival data by a VLC access point, and a subcarrier allocation solution is subsequently presented to maximize the transmission rate. Numerical simulation shows that compared with the best channel state information (BC) algorithm and round robin (RR) algorithm, the proposed algorithm can not only ensure the transmission rate but also greatly improve system stability. In particular, for mean arrival rate B=7Kbits/timeslot, the system stability by the proposed algorithm improves by about 53.36% and 61.36% in comparison with BC and RR algorithms, respectively. This work will benefit the design and development of indoor OFDMA VLC systems.

Online Distributed Optimization With Nonconvex Objective Functions: Sublinearity of First-Order Optimality Condition-Based Regret

In this article, the problem of online distributed optimization with a set constraint is solved by employing a network of agents. Each agent only has access to a local objective function and set constraint, and can only communicate with its neighbors via a digraph, which is not necessarily balanced. Moreover, agents do not have prior knowledge of their future objective functions. Different from existing works on online distributed optimization, we consider the scenario, where objective functions at each time step are nonconvex. To handle this challenge, we propose an online distributed algorithm based on the consensus algorithm and the mirror descent algorithm. Of particular interest is that regrets involving first-order optimality condition are used to measure the performance of the proposed algorithm. Under mild assumptions on the communication graph and objective functions, we prove that regrets grow sublinearly. Finally, a simulation example is worked out to demonstrate the effectiveness of our theoretical results.

Temporary Transitions to Online Problem-based Learning: Advice for Tutors and Learners

We provide a narrative review of the crucial elements for online Problem Based Learning (PBL) and a reflective overview of factors to consider when temporarily moving to online tutorials, forming a practical guide for educators in the health professions and beyond. We give general set-up advice based on the literature and our own recent experience (tutor and learner observational feedback, departmental meeting notes, newly-developed written resources and performance reports) of transitioning between temporary online PBL and face-to-face PBL but note that the majority of this advice translates easily to many types of virtual, interactive tutorial. We also include contextual evidence and theories from existing literature, with a focus on online PBL facilitation, learning and quality assurance. Despite widespread implementation of online teaching, there remain unanswered questions about whether deep learning occurs. The focus of this reflective paper is to better align online PBL practice with the principles of contextual, active, collaborative and self-directed learning and learning issues to be pursued.

Early Detection of Mental Disorders and Psychosocial Problems in Students Taking Online Learning during COVID-19 Pandemic

The online problems impact from online learning faced by students if not handled immediately will lead to the impact of mental disorders and psychosocial problems that are bigger and more serious. The aim of this study is to conducted early detection of mental disorders and psychosocial problems in nursing students who run online clinical practices during the COVID-19 pandemic at Tribhuwana Tunggadewi University, Malang. This research uses descriptive research with a cross-sectional study approach. That is, data between the independent and dependent variables collected at the same time. The population in this study were all students at Tribhuwana Tunggadewi University Malang, class of 2016, as many as 276 people. Sampling in this study is the total sampling technique is the overall sampling technique. This research used Chi square analytics to test the hypothesis. Result of this study shows that gender is significantly associated with psychological problems (p = 0.038), while gender has no relationship with the use of psychoactive use (p = 0.789), and PTSD (p = 0.551). It is concluded that mental disorders and psychosocial problems (i.e., psychological problem, psychoactive use, PTSD and unidentified psychologicaly) were prevalent among nursing students who run online clinical practice at Tribhuwana Tunggadewi. Students’ gender are associated with psychological problems, while has no associated with psychoactive use and PTSD. This study recommend that faculty authority, health professionals, and other professional related to mental health should play an important role for the prevention and earlier recognition of and intervention for psychosocial problems in nursing students who run online clinical practice during the COVID-19 pandemic.

On-line three-dimensional packing problems: A review of off-line and on-line solution approaches

Three-Dimensional Packing Problems (3D-PPs) can be applied to effectively reduce logistics costs in various areas, such as airline cargo management and warehouse management. In general, 3D-PP studies can be divided into two different streams: those tackling the off-line problem, where full knowledge about items is available beforehand; and those tackling the on-line (real-time) problem, where items arrive one by one and should be packed immediately without having full prior knowledge about them. During the past decades, off-line and on-line 3D-PPs have been studied in the literature with various constraints and solution approaches. However, and despite the numerous practical applications of on-line problems in real-world situations, most of the literature to date has focused on off-line problems and is quite sparse when it comes to on-line solution methods. In this regard, and despite the different nature of on-line and off-line problems, some approaches can be applied in both environments. Hence, we conducted an in-depth and updated literature review to identify and structure various constraints and solution methods employed by researchers in off-line and on-line 3D-PPs. Building on this, by bringing together the two separate streams of the literature, we identified several off-line approaches that can be adopted in on-line environments. Additionally, we addressed relevant research gaps and ways to bridge them in the future, which can help to develop this research field.

Problems in teaching and learning English online during the Covid-19 pandemic

The coronaviruses (Covid-19) pandemic has spread rapidly worldwide, including the education sector. The policy toward the study from home during the Covid-19 outbreak caused a new problem in the teaching and learning system. The objectives of the research were to find out and describe the problem faced by the teachers in conducting online learning during the Covid-19 pandemic. This is a qualitative method approach. The research subjects are three English teachers. The data was gathered through observation and interviews from three English teachers at SMA Negeri 4 Banda Aceh. Some of the problems emerged during online learning, nine problems. The arisen of teaching problems were: internet access, disability in accessing technology, difficulty in explaining the materials, flexibility in controlling and managing students' activities, indiscipline, time-consuming, lack students' motivation, students absent in online class, and the last parents support system. The findings showed that there are two solutions to overcome the problems by teachers, namely, applying online chat of WhatsApp and keeping the interaction with the students' parents to support the student activities and explain the lesson plans while online learning teaching and learning process.

Cervical cancer diagnosis based on modified uniform local ternary patterns and feed forward multilayer network optimized by genetic algorithm

Cervical cancer is one of the most common types of cancer for women. Early and accurate diagnosis can save the patient's life. Pap smear testing is nowadays commonly used to diagnose cervical cancer. The type, structure and size of the cervical cells in pap smears images are major factors which are used by specialist doctors to diagnosis abnormality. Various image processing-based approaches have been proposed to acquire pap smear images and diagnose cervical cancer in pap smears images. Accuracy is usually the primary objective in evaluating the performance of these systems. In this paper, a two-stage method for pap smear image classification is presented. The aim of the first stage is to extract texture information of the cytoplasm and nucleolus jointly. For this purpose, the pap smear image is first segmented using the appropriate threshold. Then, a texture descriptor is proposed titled modified uniform local ternary patterns (MULTP), to describe the local textural features. Secondly, an optimized multi-layer feed-forward neural network is used to classify the pap smear images. The proposed deep neural network is optimized using genetic algorithm in terms of number of hidden layers and hidden nodes. In this respect, an innovative chromosome representation and cross-over process is proposed to handle these parameters. The performance of the proposed method is evaluated on the Herlev database and compared with many other efficient methods in this scope under the same validation conditions. The results show that the detection accuracy of the proposed method is higher than the compared methods. Insensitivity to image rotation is one of the major advantages of the proposed method. Results show that the proposed method has the capability to be used in online problems because of low run time. The proposed texture descriptor, MULTP is a general operator which can be used in many computer vision problems to describe texture properties of image. Also, the proposed optimization algorithm can be used in deep-networks to improve performance.

Adaptive Bin Packing with Overflow

Motivated by bursty bandwidth allocation and by the allocation of virtual machines to servers in the cloud, we consider the online problem of packing items with random sizes into unit-capacity bins. Items arrive sequentially, but on arrival, an item’s actual size is unknown; only its probabilistic information is available to the decision maker. Without knowing this size, the decision maker must irrevocably pack the item into an available bin or place it in a new bin. Once packed in a bin, the decision maker observes the item’s actual size, and overflowing the bin is a possibility. An overflow incurs a large penalty cost, and the corresponding bin is unusable for the rest of the process. In practical terms, this overflow models delayed services, failure of servers, and/or loss of end-user goodwill. The objective is to minimize the total expected cost given by the sum of the number of opened bins and the overflow penalty cost. We present an online algorithm with expected cost at most a constant factor times the cost incurred by the optimal packing policy when item sizes are drawn from an independent and identically distributed (i.i.d.) sequence of unknown length. We give a similar result when item size distributions are exponential with arbitrary rates. We also study the offline model, where distributions are known in advance but must be packed sequentially. We construct a soft-capacity polynomial-time approximation scheme for this problem and show that the complexity of computing the optimal offline cost is [Formula: see text]-hard. Finally, we provide an empirical study of our online algorithm’s performance.

Randomized Online Computation with High Probability Guarantees

We study the relationship between the competitive ratio and the tail distribution of randomized online problems. To this end, we identify a broad class of online problems for which the existence of a randomized online algorithm with constant expected competitive ratio r implies the existence of a randomized online algorithm that has a competitive ratio of (1+varepsilon )rwith high probability, measured with respect to the optimal profit or cost, respectively. The class of problems includes some of the well-studied online problems such as paging, k-server, and metrical task systems on finite metric spaces.

Measuring the effectiveness of online problem solving for improving academic performance in a probability course

Research on impact in student achievement of online homework systems compared to traditional methods is ambivalent. Methodological issues in the study design, besides of technological diversity, can account for this uncertainty. Hypothesis This study aims to estimate the effect size of homework practice with exercises automatically provided by the ‘e-status’ platform, in students from five Engineering programs. Instead of comparing students using the platform with others not using it, we distributed the subject topics into two blocks, and created nine probability problems for each block. After that, the students were randomly assigned to one block and could solve the related exercises through e-status. Teachers and evaluators were masked to the assignation. Five weeks after the assignment, all students answered a written test with questions regarding all topics. The study outcome was the difference between both blocks’ scores obtained from the test. The two groups comprised 163 and 166 students. Of these, 103 and 107 respectively attended the test, while the remainder were imputed with 0. Those assigned to the first block obtained an average outcome of −1.85, while the average in the second block was −3.29 (95% confidence interval of difference, −2.46 to −0.43). During the period in which they had access to the platform before the test, the average total time spent solving problems was less than three hours. Our findings provide evidence that a small amount of active online work can positively impact on student performance.