Paging Algorithms Research Articles

Immersive Multimedia Art Design Based on Deep Learning Intelligent VR Technology

The rapid development of computer science has led to the rapid development of new media technology. Among them, VR technology is sought after by the vast market for its multidimensional experience and immersive model. VR technology plays an important role in today’s art exhibition and presentation. However, in the process of transforming 2D images into 3D models, traditional VR technology has some problems, such as long loading time, low model decision-making efficiency, and chaotic operation process, which often lead to visual interference when viewers watch art. Based on this background and the interactive page algorithm based on traditional VR technology, this paper studies and improves the visual interference in immersive multimedia art design by using task technology perception matching model (TTF). The results show that the improved task technology perception matching model reduces the visual and auditory interference by 10% in the process of virtual reality art display and viewing, and the model layout is more reasonable, and the utilization rate is higher.

How to Tame "Your" Algorithm

As our social worlds increasingly shift online, many of the technologies people encounter are mediated by algorithms. Algorithms have become deeply embedded into people's online lives, often working to tailor and personalize their routine encounters with the world. How does one domesticate, or make one's own, an algorithmic system? One of the goals as people adopt new technologies is to weave them into their everyday routines, establishing a pattern of use in order to make that technology their own. In this paper we focus on people's experiences domesticating the short-form video sharing application, TikTok. Through an interview study with 16 LGBTQ+ TikTok users, we explore how people's routine experiences with TikTok's For You Page algorithm influence and inform their domestication process. We first highlight people's motivations for adopting TikTok and the challenges they encounter in this initial acquisition phase of domestication. After adopting the platform, we discuss the challenges people experience across the final three phases of domestication: objectification, incorporation, and conversion. We find that though they enjoy TikTok, our participants feel that they are never fully able to domesticate TikTok. As they are never able to fully control their digital selves, and thus integrate it into their routine lives, TikTok is in constant misalignment with their personal moral economy. We discuss the implications of domesticating algorithmic systems, examining the questions of whose values shape the moral economy created by and through people's uses of algorithmic systems, and the impact of nostalgia on the domestication process.

Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance

Algorithms in online platforms interact with users' identities in different ways. However, little is known about how users understand the interplay between identity and algorithmic processes on these platforms, and if and how such understandings shape their behavior on these platforms in return. Through semi-structured interviews with 15 US-based TikTok users, we detail users' algorithmic folk theories of the For You Page algorithm in relation to two inter-connected identity types: person and social identity. Participants identified potential harms that can accompany algorithms' tailoring content to their person identities. Further, they believed the algorithm actively suppresses content related to marginalized social identities based on race and ethnicity, body size and physical appearance, ability status, class status, LGBTQ identity, and political and social justice group affiliation. We propose a new algorithmic folk theory of social feeds-The Identity Strainer Theory-to describe when users believe an algorithm filters out and suppresses certain social identities. In developing this theory, we introduce the concept of algorithmic privilege as held by users positioned to benefit from algorithms on the basis of their identities. We further propose the concept of algorithmic representational harm to refer to the harm users experience when they lack algorithmic privilege and are subjected to algorithmic symbolic annihilation. Additionally, we describe how participants changed their behaviors to shape their algorithmic identities to align with how they understood themselves, as well as to resist the suppression of marginalized social identities and lack of algorithmic privilege via individual actions, collective actions, and altering their performances. We theorize our findings to detail the ways the platform's algorithm and its users co-produce knowledge of identity on the platform. We argue the relationship between users' algorithmic folk theories and identity are consequential for social media platforms, as it impacts users' experiences, behaviors, sense of belonging, and perceived ability to be seen, heard, and feel valued by others as mediated through algorithmic systems.

For You, or For"You"?

Online communities provide spaces for people who are vulnerable and underserved to seek support and build community, such as LGBTQ+ people. Today, some online community spaces are mediated by algorithms. Scholarship has found that algorithms have become deeply embedded in the systems that mediate our routine engagements with the world. Yet, little is known about how these systems impact those who are most vulnerable in society. In this paper, we focus on people's everyday experiences with one algorithmic system, the short video sharing application TikTok. TikTok recently received press that it was suppressing and oppressing the identities of its growing LGBTQ+ user population through algorithmic and human moderation of LGBTQ+ creators and content related to LGBTQ+ identity. Through an interview study with 16 LGBTQ+ TikTok users, we explore people's everyday engagements and encounters with the platform. We find that TikTok's For You Page algorithm constructs contradictory identity spaces that at once support LGBTQ+ identity work and reaffirm LGBTQ+ identity, while also transgressing and violating the identities of individual users. We also find that people are developing self-organized practices in response to these transgressions and violations. We discuss the implications of algorithmic systems on people's identity work, and introduce the concept of algorithmic exclusion, and explore how people are building resilience following moments of algorithmic exclusion.

Metabolomic profiling identifies novel associations with Electrolyte and Acid-Base Homeostatic patterns

Electrolytes have a crucial role in maintaining health and their serum levels are homeostatically maintained within a narrow range by multiple pathways involving the kidneys. Here we use metabolomics profiling (592 fasting serum metabolites) to identify molecular markers and pathways associated with serum electrolyte levels in two independent population-based cohorts. We included 1523 adults from TwinsUK not on blood pressure-lowering therapy and without renal impairment to look for metabolites associated with chloride, sodium, potassium and bicarbonate by running linear mixed models adjusting for covariates and multiple comparisons. For each electrolyte, we further performed pathway enrichment analysis (PAGE algorithm). Results were replicated in an independent cohort. Chloride, potassium, bicarbonate and sodium associated with 10, 58, 36 and 17 metabolites respectively (each P < 2.1 × 10−5), mainly lipids. Of all the electrolytes, serum potassium showed the most significant associations with individual fatty acid metabolites and specific enrichment of fatty acid pathways. In contrast, serum sodium and bicarbonate showed associations predominantly with amino-acid related species. In the first study to examine systematically associations between serum electrolytes and small circulating molecules, we identified novel metabolites and metabolic pathways associated with serum electrolyte levels. The role of these metabolic pathways on electrolyte homeostasis merits further studies.

Mobility pattern of individual user in dynamic mobile phone network using call data record

The pervasiveness of cellular telephones, along with rapid advancement in cellular technology, wireless networks and mobile applications, has made them increasingly effective in our daily lives. Individual human activity with respect to travel behaviour is still in a less explored stage. In the past few decades, travel patterns and human activity have become more complex and exhibit a high degree of variations in space and time. In this study, how and in what extent Call Data Records (CDRs) from mobile phones provide the underlying activities of anonymised customers when a user initiates or receives a voice call or message is investigated in detail. Thus, this paper attempts to discover personal human mobility patterns where location, duration and days are the essential attributes in the interpretation of results. Profile-based paging algorithm has been proposed based on the individual human travel pattern. The finding suggests that the proposed algorithm is three times more efficient than conventional paging (blanket paging) and two times more effective than various other intelligent paging algorithms. Further, a substantial saving of about 35% in bandwidth is also obtained from the validation results.

Il museo virtuale della Valle del Calore

The Virtual Museum of the Upper Calore Valley is a website which allows visitors to travel in time and space through and have access to various information on monuments, towns, culture, history, wine and food of the Hirpinian territory. By accessing six fictional videos on characters drawn from local history, users can also experience a historical overview, from the Longobard invasion up to the Unification of Italy, through the troubled periods of the Kingdom of Naples. The project works by open source software for video editing, GIS elaboration, and image processing. The browsing platform is based on the earliest release of the Aton framework created by CNR ITABC for browsing large-scale geographical and architectural data, with advanced features for graphic rendering, stereoscopic view, online representation of complex geometry and resolution through powerful paging algorithms. Aton is compatible with every modern HTML5 multimedia standard and is a powerful tool for historical storytelling.

The MT Stack: Paging Algorithm and Performance in a Distributed Virtual Memory System

&#x0D; &#x0D; &#x0D; Advances in parallel computation are of central importance to Artificial Intelligence due to the significant amount of time and space their pro- grams require. Functional languages have been identified as providing a clear and concise way of programming parallel machines for artificial intelligence tasks. The problems of exporting, creating, and manipulating processes have been thoroughly studied in relation to the paralleliza- tion of functional languages, but none of the necessary support structures needed for the ab- straction, like a distributed memory, have been properly designed. In order to design and im- plement parallel functional languages efficiently, we propose the development of an all-software based distributed virtual memory system de- signed specifically for the memory demands of a functional language. In this paper, we review the MT architecture and briefly survey the related literature that lead to its development. We then present empirical results obtained from observ- ing the paging behavior of the MT stack. Our empirical results suggest that LRU is superior to FIFO as a page replacement policy for MT stack pages. We present a proof that LRU is an opti- ?Partially supported by the Seton Hall University Re- search Council. †Partially supported by NSF grant CDA-9114481. ‡Partially supported by NSF grant HRD-9703600. mal page replacement policy. Based on this proof the MT stack page replacement policy was de- veloped and implemented. We outline the paging algorithm and present an argument of partial cor- rectness. The MT stack page replacement policy is superior to LRU, because it does not incur the expensive time penalties associated with imple- menting LRU in software.&#x0D; &#x0D; &#x0D;

Use of a novel electronic maternal surveillance system to generate automated alerts on the labor and delivery unit

BackgroundMaternal early warning systems reduce maternal morbidity. We developed an electronic maternal surveillance system capable of visually summarizing the labor and delivery census and identifying changes in clinical status. Automatic page alerts to clinical providers, using an algorithm developed at our institution, were incorporated in an effort to improve early detection of maternal morbidity. We report the frequency of pages generated by the system. To our knowledge, this is the first time such a system has been used in peripartum care.MethodsAlert criteria were developed after review of maternal early warning systems, including the Maternal Early Warning Criteria (MEWC). Careful consideration was given to the frequency of pages generated by the surveillance system. MEWC notification criteria were liberalized and a paging algorithm was created that triggered paging alerts to first responders (nurses) and then managing services due to the assumption that paging all clinicians for each vital sign triggering MEWC would generate an inordinate number of pages. For preliminary analysis, to determine the effect of our automated paging algorithm on alerting frequency, the paging frequency of this system was compared to the frequency of vital signs meeting the Maternal Early Warning Criteria (MEWC). This retrospective analysis was limited to a sample of 34 patient rooms uniquely capable of storing every vital sign reported by the bedside monitor.ResultsOver a 91-day period, from April 1 to July 1, 2017, surveillance was conducted from 64 monitored beds, and the obstetrics service received one automated page every 2.3 h. The most common triggers for alerts were for hypertension and tachycardia. For the subset of 34 patient rooms uniquely capable of real-time recording, one vital sign met the MEWC every 9.6 to 10.3 min. Anecdotally, the system was well-received.ConclusionsThis novel electronic maternal surveillance system is designed to reduce cognitive bias and improve timely clinical recognition of maternal deterioration. The automated paging algorithm developed for this software dramatically reduces paging frequency compared to paging for isolated vital sign abnormalities alone. Long-term, prospective studies will be required to determine its impact on patient outcomes.

On the Separation and Equivalence of Paging Strategies and Other Online Algorithms

We introduce a new technique for the analysis of online algorithms, namely bijective analysis, that is based on pair-wise comparison of the costs incurred by the algorithms. Under this framework, an algorithm A is no worse than an algorithm B if there is a bijection $$\pi $$ defined over all request sequences of a given size such that the cost of A on $$\sigma $$ is no more than the cost of B on $$B(\pi (\sigma ))$$ . We also study a relaxation of bijective analysis, termed average analysis, in which we compare two algorithms based on their corresponding average costs over request sequences of a given size. We apply these new techniques in the context of two fundamental online problems, namely paging and list update. For paging, we show that any two lazy online algorithms are equivalent under bijective analysis. This result demonstrates that, without further assumptions on characteristics of request sequences, it is unlikely, or even undesirable, to separate online paging algorithms based on their performance. However, once we restrict the set of request sequences to those exhibiting locality of reference, and in particular using a model of locality due to Albers et al. (J Comput Syst Sci 70(2):145–175, 2005), we demonstrate that Least-Recently-Used (LRU) is the unique optimal strategy according to average analysis. This is, to our knowledge, the first deterministic model to provide full theoretical backing to the empirical observation that LRU is preferable in practice. Concerning list update, we obtain similar conclusions, in terms of the bijective comparison of any two online algorithms, and in terms of the superiority (albeit not necessarily unique) of the Move-To-Front (MTF) heuristic in the presence of locality of reference.

Discovering Student E-Learning Preferred Navigation Paths Using Selection Page and Time Preference Algorithm

In this paper, the student navigation paths and student or visitor interested page is identified. Student navigation interest pattern mining contains both the frequently navigation path based on webpage memory size and session length .Relatively comparing access proportion of viewing time and selective page size, preference can be used for mining student learning pattern instead of interested subject. In order to identify Preferred Navigation Paths, an efficient algorithm for Visitor Access Matrix (VAM) by the page to page transition probabilities statistics of all visitor behaviors is introduced in this paper. Second, we propose an efficient algorithm for Selection and Time Preference (SATP) to identify the preference of web pages by viewing time. Third, the user interested page would calculate by both memory size and session. In this way we proposed the Preference of page content size and session identifier algorithm. The performance of the proposed algorithms is evaluated and the algorithms can determine preferred navigation path efficiently. The experimental results show the accuracy and scalability of the algorithms. This approach may be helpful in E-learning, E-business, such as web personalization and website designer

On the Smoothness of Paging Algorithms

We study the smoothness of paging algorithms. How much can the number of page faults increase due to a perturbation of the request sequence? We call a paging algorithm smooth if the maximal increase in page faults is proportional to the number of changes in the request sequence. We also introduce quantitative smoothness notions that measure the smoothness of an algorithm. We derive lower and upper bounds on the smoothness of deterministic and randomized demand-paging and competitive algorithms. Among strongly-competitive deterministic algorithms LRU matches the lower bound, while FIFO matches the upper bound. Well-known randomized algorithms like Partition, Equitable, or Mark are shown not to be smooth. We introduce two new randomized algorithms, called Smoothed-LRU and LRU-Random. Smoothed- LRU allows to sacrifice competitiveness for smoothness, where the trade-off is controlled by a parameter. LRU-Random is at least as competitive as any deterministic algorithm while smoother.

Efficient Proximity Search with Query Logs

information retrieval technology there are various techniques for fetching data from resources. And that technique also contains various issues. Information retrieval techniques require advanced manipulating schemes which improves keyword search. There are many techniques have been proposed but results get down when large amount data interrupted. In this paper, have tendency to achieve efficient time and space complexities by integrating proximity information. This system improves the performance by using previous searching results. All the previous system consist basic solutions for extracting results and ranking them. Query logs consists the last searching results and use that results for next search. Fuzzy keyword search truly enhance the system usability. Existing system in databases requires to write complete keyword for searching but by using auto-complete scheme it is easy to type less and find more. In this system proper demand paging algorithm is used for finding previous results.

Improved lung recruitment and diaphragm mobility with an in-line speaking valve in tracheostomised mechanically ventilated patients – An observational study

Introduction: Critically ill children are at an increased risk of malnutrition. Variability in feeding practices has been highlighted as a barrier to optimising nutrition during Paediatric Intensive Care Unit (PICU) admission. Introduction of a feeding algorithmhas been shown to improve nutritional outcomes in this population. Studyobjective: To develop and implement a feeding algorithm for patients in a tertiary 22 bed PICU. Methods: The study was performed in three phases: Phase 1: A survey of nursing attitudes and perceived practices to enteral feeding. Phase 2: A three week prospective nutrition audit to evaluate the adequacy of energy and protein intake compared to estimated requirements. Phase 3: A multidisciplinary working group formed to devise, pilot and implement a feeding algorithm. Results: Phase 1: Demonstrated considerable variability and specific barriers to feeding. These included delayed medical approval to initiate feeds and multiple fasting for procedures and possible extubations. Phase 2: The mean time to feed initiation was 22h (range 0–92h). Within 72h of admission, patients achieved 66% of estimated energy requirements (EER); and 36% of estimated protein requirements (EPR), with only 4 (15%) reaching their EER; none reached EPR. Phase 3: A multidisciplinary team (dietetics, medical and nursing) reviewed the results of phases 1 and 2 and developed a single page algorithm. This was piloted in April 2014 and the final algorithm initiated in May 2014. Conclusion:Barriers to nutrition in a tertiary children’s hospital PICUwere identified by survey and audit tools. A feeding algorithm was developed by a multidisciplinary team utilising these results. A one page feeding algorithm was initiated in May 2014.

Engineering Efficient Paging Algorithms

In the field of online algorithms, paging is a well-studied problem. LRU is a simple paging algorithm that incurs few cache misses and supports efficient implementations. Algorithms outperforming LRU in terms of cache misses exist but are in general more complex and thus not automatically better, since their increased runtime might annihilate the gains in cache misses. In this article, we focus on efficient implementations for the O n OPT class described in Moruz and Negoescu [2012], particularly on an algorithm in this class, denoted RDM , that was shown to typically incur fewer misses than LRU. We provide experimental evidence on a wide range of cache traces showing that our implementation of RDM is competitive to LRU with respect to runtime. In a scenario incurring realistic time penalties for cache misses, we show that our implementation consistently outperforms LRU, even if the runtime of LRU is set to zero.

Relative interval analysis of paging algorithms on access graphs

Access graphs, which have been used previously in connection to competitive analysis and relative worst order analysis to model locality of reference in paging, are considered in connection with relative interval analysis. The algorithms LRU, FIFO, FWF, and FAR are compared using the path, star, and cycle access graphs. In this model, some of the results obtained are not surprising. However, although LRU is found to be strictly better than FIFO on paths, it has worse performance on stars, cycles, and complete graphs, in this model. We solve an open question from Dorrigiv et al. (2009) [13], obtaining tight bounds on the relationship between LRU and FIFO with relative interval analysis.

IMPLEMENTATION OF LRU ALGORITHM FOR LOCATION UPDATE IN WIRELESS M2M COMMUNICATION

Wireless M2M communication is about enabling the flow of data between machine and machine and ultimately between machine and people. For communication it is necessary to trace the Machine–Type-Communication (MTC) device successfully so novel energy and memory efficient location update scheme for wireless M2M communication is studied which is more efficient for a MTC device to perform location updates only when new nodes are added into the network to avoid call dropping ratio. Flexible and distributed Wireless M2M communication is robust and rapidly deployable/reconfigurable, so they are highly appealing for a lot critical applications, like deep space communication, disaster relief, battlefield communication, outdoor mining, car that react in real time to prevent accidents etc. This paper presents different paging algorithms that are studied on the basis of certain parameters such as endto-end delay, compression rate, data transmission energy and call-mobility-ratio. Least Recent Used (LRU) algorithm is implemented for optimally pruning the nodes from the network for efficient location update which is considered as the best paging algorithm.

Elastic paging

We study a generalization of the classic paging problem where memory capacity can vary over time - a property of many modern computing realities, from cloud computing to multi-core and energy-optimized processors. We show that good performance in the "classic" case provides no performance guarantees when memory capacity fluctuates: roughly speaking, moving from static to dynamic capacity can mean the difference between optimality within a factor 2 in space, time and energy, and suboptimality by an arbitrarily large factor. Surprisingly, several classic paging algorithms still perform remarkably well, maintaining that factor 2 optimality even if faced with adversarial capacity fluctuations - without taking those fluctuations into explicit account!

OnlineMin: A Fast Strongly Competitive Randomized Paging Algorithm

In the field of online algorithms paging is one of the most studied problems. For randomized paging algorithms a tight bound of H k on the competitive ratio has been known for decades, yet existing algorithms matching this bound have high running times. We present a new randomized paging algorithm OnlineMin that has optimal competitiveness and allows fast implementations. In fact, if k pages fit in internal memory the best previous solution required O(k 2) time per request and O(k) space. We present two implementations of OnlineMin which use O(k) space, but only O(logk) worst case time and O(logk/loglogk) worst case time per page request respectively.

WriteShield: A Pseudo Thin Client for Prevention of Information Leakage

While thin-client systems are diffusing as an effective security method in enterprises and organizations, there is a new approach called pseudo thin-client system. In this system, local disks of clients are write-protected and user data is forced to save on the central file server to realize the same security effect of conventional thin-client systems. Since it takes purely the software-based simple approach, it does not require the hardware enhancement of network and servers to reduce the installation cost. However there are several problems such as no write control to external media, memory depletion possibility, and lower security because of the exceptional write permission to the system processes. In this paper, we propose WriteShield, a pseudo thin-client system which solves these issues. In this system, the local disks are write-protected with volume filter driver and it has a virtual cache mechanism to extend the memory cache size for the write protection. This paper presents design and implementation details of WriteShield. Besides we describe the security analysis and simulation evaluation of paging algorithms for virtual cache mechanism and measure the disk I/O performance to verify its feasibility in the actual environment.