Obsolete Information Research Articles

Visual event boundaries trigger forgetting despite active maintenance in visual working memory.

The contents of visual perception are inherently dynamic-just as we experience objects in space, so too events in time. The boundaries between these events have downstream consequences. For example, memory for incidentally encountered items is impaired when walking through a doorway, perhaps because event boundaries serve as cues to clear obsolete information from previous events. Although this kind of "memory flushing" can be adaptive, work on visual working memory (VWM) has focused on the opposite function of active maintenance in the face of distraction. How do these two cognitive operations interact? In this study, observers watched animations in which they walked through three-dimensionally rendered rooms with picture frames on the walls. Within the frames, observers either saw images that they had to remember ("encoding") or recalled images they had seen in the immediately preceding frame ("test"). Half of the time, a doorway was crossed during the delay between encoding and test. Across experiments, there was a consistent memory decrement for the first image encoded in the doorway compared to the no-doorway condition while equating time elapsed, distance traveled, and distractibility of the doorway. This decrement despite top-down VWM efforts highlights the power of event boundaries to structure what and when we forget.

Recursive Identification of Time-Varying Hammerstein Systems With Matrix Forgetting

The real-time estimation of the time-varying Hammerstein system by using a noniterative learning schema is considered and extended to incorporate a matrix forgetting factor. The estimation is cast in a Variational-Bayes framework to best emulate the original posterior distribution of the parameters within the set of distributions with feasible moments. The recursive concept we propose approximates the exact posterior comprising undistorted information about the estimated parameters. In many practical settings, the incomplete model of parameter variations is compensated by forgetting of obsolete information. As a rule, the forgetting operation is initiated by the inclusion of an appropriate prediction alternative into the time update. It is shown that the careful formulation of the prediction alternative, which relies on Bayesian conditioning, results in partial forgetting. The paper inspects two options with respect to the order of the conditioning in the posterior, which proves vital in the successful localization of the source of inconsistency in the data-generating process. The geometric mean of the discussed alternatives then modifies recursive learning through the matrix forgetting factor. We adopt the decision-making approach to revisit the posterior uncertainty by dynamically allocating the probability to each of the prediction alternatives to be combined.

Numeric and morphological studies of the African lion (Panthera leo leo) pectoral limb

BackgroundThe Importance of the appendicular skeleton in the conformation, shape and physiology of wild animals especially carnivores for adaptation and survival cannot be overemphasized, as limited and obsolete information on the bones of the forelimb necessitated this study. Matured adult African lions (Male and female) that weighed 173 kg and 112 kg were obtained at different times after post mortem examinations of their carcasses. Bone preparation was achieved via cold water maceration after proper skin and muscle removal to a barest minimum.ResultsThe acromion process of the scapula consisted of a ventral hamate and caudal suprahamate processes. The Clavicle was absent in this species. The musculospiral groove of the humerus was more or less absent while its supracondyloid foramen and crest were positioned above the medial condyle and lateral condyle respectively. The radio-ulna presented a twisted appearance with the radius slightly curved thereby creating an extensive interosseous space that spanned its entire length. The seven (7) carpal bones were uniquely arranged in two rows while the 5 metacarpals anchored 5 digits with 3 phalanges except the first with 2 phalanges each. Two sesamoid bones were located on the ventral surface of each Metacarpophalangeal joint. None was seen on its dorsal surface. The 3rd phalanx had a unique appearance with a crescent plate projecting from the ventral cavity. The average total number of bones constituting the pectoral limb was 80.ConclusionNumerical information and detailed anatomical features of the pectoral limb bones of the African lion (Panthera leo leo) have added some valuable literature to science. This further serves as a baseline data for future scientific exposition on this species.

A Comparative Analysis of the Soviet Military City Plans of Tel Aviv, Israel and Copenhagen, Denmark (1985)

ABSTRACT This paper presents a comparative analysis of the Soviet military city plans of Tel Aviv (Israel) and Copenhagen (Denmark), that both date from 1985. It presents a methodological framework to examine and compare the quality and completeness of thematic information included in the plans, particularly the strategically important objects (which are identified, numbered and colour coded according to their function). Our results indicate that the Soviet city plans do not include all military sites located in the areas they cover. In addition, several sites are erroneously included in the lists of strategically important objects and the lists also include obsolete information on sites that were no longer in military use. Ultimately, our analysis found no substantial difference in thematic accuracy between the two plans.

Using Domain Adaptation for Incremental SVM Classification of Drift Data

A common assumption in machine learning is that training data is complete, and the data distribution is fixed. However, in many practical applications, this assumption does not hold. Incremental learning was proposed to compensate for this problem. Common approaches include retraining models and incremental learning to compensate for the shortage of training data. Retraining models is time-consuming and computationally expensive, while incremental learning can save time and computational costs. However, the concept drift may affect the performance. Two crucial issues should be considered to address concept drift in incremental learning: gaining new knowledge without forgetting previously acquired knowledge and forgetting obsolete information without corrupting valid information. This paper proposes an incremental support vector machine learning approach with domain adaptation, considering both crucial issues. Firstly, a small amount of new data is used to fine-tune the previous model to generate a model that is sensitive to the new data but retains the previous data information by transferring parameters. Secondly, an ensemble and model selection mechanism based on Bayesian theory is proposed to keep the valid information. The computational experiments indicate that the performance of the proposed model improved as new data was acquired. In addition, the influence of the degree of data drift on the algorithm is also explored. A gain in performance on four out of five industrial datasets and four synthetic datasets has been demonstrated over the support vector machine and incremental support vector machine algorithms.

Data obsolescence detection in the light of newly acquired valid observations

The information describing the conditions of a system or a person is constantly evolving and may become obsolete and contradict other information. A database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. In this paper, we propose a novel approach for dealing with the information obsolescence problem. Our approach aims to detect, in real-time, contradictions between observations and then identify the obsolete ones, given a representation model. Since we work within an uncertain environment characterized by the lack of information, we choose to use a Bayesian network as our representation model and propose a new approximate concept, $\epsilon$-Contradiction. The new concept is parameterised by a confidence level of having a contradiction in a set of observations. We propose a polynomial-time algorithm for detecting obsolete information. We show that the resulting obsolete information is better represented by an AND-OR tree than a simple set of observations. Finally, we demonstrate the effectiveness of our approach on a real elderly fall-prevention database and showcase how this tree can be used to give reliable recommendations to doctors. Our experiments give systematically and substantially very good results.

Exploring factors that mitigate the continued influence of misinformation

BackgroundThe term “continued influence effect” (CIE) refers to the phenomenon that discredited and obsolete information continues to affect behavior and beliefs. The practical relevance of this work is particularly apparent as we confront fake news everyday. Thus, an important question becomes, how can we mitigate the continued influence of misinformation? Decades of research have identified several factors that contribute to the CIE reduction, but few have reported successful elimination. Across three studies, we evaluated the relative contribution of three factors (i.e., targeting the misinformation, providing an alternative explanation, and relative importance of the misinformation content) to the reduction of the CIE.ResultsAcross three studies and two different CIE measures, we found that alternative provision consistently resulted in CIE reduction. Furthermore, under certain conditions, the combination of alternative inclusion and direct targeting of misinformation in the correction statement resulted in successful elimination of the CIE, such that individuals who encountered that type of correction behaved similarly to baseline participants who never encountered the (mis)information. In contrast, under one CIE measure, participants who received correction statements that failed to include those elements referenced the (mis)information as frequently as baseline participants who never encountered a correction. Finally, we delineated several component processes involved in misinformation outdating and found that the extent of outdating success varied as a function of the causality of misinformation.ConclusionsThe damaging effects of fake news are undeniable, and the negative consequences are exacerbated in the digital age. Our results contribute to our understanding of how fake news persists and how we may begin to mitigate their effects.

COVID-19 Dashboard for the Lisbon Region - an advisory tool for policy

Issue/problemAt the time of arrival of the COVID-19 pandemic to Portugal, the Public Health Department of Lisbon and Tagus Valley Regional Health Administration was confronted with its obsolete information system, which could not provide the information that Public Health Authorities needed to bring the pandemic under control. In order to tackle this issue, our goal was to aggregate all the relevant epidemiological data for the region in an accessible and user-friendly dashboard.MethodsStarting from March 2020 we created a dashboard, which compiled and analyzed COVID-19 data, such as: confirmed and active cases, deaths, hospitalizations (including ICU beds), high/low risk contacts, mandatory confinements, tests, and clusters. This dashboard had anonymized data and was updated daily as well as constantly upgraded by taking into consideration new incoming data and feedback provided by users, serving its purpose as an advisory tool for policy.ResultsThe dashboard was available to Local, Regional, and National Public Health Authorities and made possible to monitor trends and predict epidemiological changes that, prior to its existence, were dispersed in several datasets. Most importantly, it allowed for a better allocation of human resources for contact tracing and case management activities, to easily identify basic social needs for the most vulnerable citizens. It also provided for policy measures adjusted to the smallest administrative division in Portugal, the parish, that ultimately allowed for a better epidemiological control in Lisbon Metropolitan Area in 2020.LessonsThe regional dashboard is far from perfect, but it highlights the constant need for robust information and epidemiological surveillance systems, serving both National, Regional and Local Public Health Authorities. It appeared out of necessity during a pandemic and it proved once more the importance of data analytics for guiding public health action based on evidence.Key messages Data analytics can serve both as an advisory tool for policy as well as to guide public health interventions.The COVID-19 Dashboard for the Lisbon Region aggregates relevant epidemiological data that helped Public Health authorities better understand and control the Pandemic in the region.

W11. GENOME-WIDE ANALYSIS OF THE ANXIETY DISORDER SPECTRUM

The information describing a person is constantly evolving and may become obsolete and contradict other information. A personal database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. This study focuses on proposing a novel approach for dealing with the information obsolescence problem in the elderly-fall prevention context. Our approach aims to continuously monitor elderly information in order to detect the change in the behavior of an elderly person and to prevent him from falls. It consists of detecting contradictions between newly acquired information about a single elderly and what we already know about that person, then identifying among his observations those that have become obsolete and need to be updated. We propose a new approximate concept,£-Contradiction, which represents the confidence level of having a contradiction in a set of observations when a causal Bayesian network is our representation model. We propose a polynomial-time algorithm for detecting obsolete information and show that the resulting obsolete observations are given as an explanation AND-OR tree.

TH42. APPLICATION OF REFERENCE-BASED CELL TYPE ESTIMATION IN PLACENTAL TISSUE AND COMPARISON WITH A REFERENCE-FREE APPROACH FOR DNA METHYLATION STUDIES

The information describing a person is constantly evolving and may become obsolete and contradict other information. A personal database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. This study focuses on proposing a novel approach for dealing with the information obsolescence problem in the elderly-fall prevention context. Our approach aims to continuously monitor elderly information in order to detect the change in the behavior of an elderly person and to prevent him from falls. It consists of detecting contradictions between newly acquired information about a single elderly and what we already know about that person, then identifying among his observations those that have become obsolete and need to be updated. We propose a new approximate concept,£-Contradiction, which represents the confidence level of having a contradiction in a set of observations when a causal Bayesian network is our representation model. We propose a polynomial-time algorithm for detecting obsolete information and show that the resulting obsolete observations are given as an explanation AND-OR tree.

A knowledge based system for the management of a time stamped uncertain observation set with application on preserving mobility

The aim of this study is to maintain up-to-date information about the current state of elderly people that are medically followed for risks of fall. Our proposal consists of an individual information database management system that can provide information on-demand on various variables. Such a system has to deal with several sources of uncertainty: lack of information, evolving information and reliability of the information sources. We consider that the features of the person may evolve with time causing uncertainty due to obsolete information. Our context includes new information received bit by bit, with no possibility to collect all required information at once. This paper establishes a first proposal to manage a set of uncertain observations, in order to reduce erroneous and obsolete information while keeping the benefit of previously collected information. We propose an architecture of the system based on a probabilistic knowledge model about the characteristics of interest, a set of decay functions that help to evaluate the confidence degree in previous observations, and a reasoning module to manage new observations, maintain the compatibility and the quality of the observation set. We detail the algorithms of the reasoning module, and the algorithm to update the confidence degree of the observations.

Recency and quality-based ranking question in CQAs: A Stack Overflow case study

Recency ranking, in Community-based Question Answering (CQA), would refer to put recent answers in a list’s top positions. To be recent is not related to how new is the date of creation or editing of a given answer, but how current is the content of the answer. A good ranking should also consider the answers’ quality since a current but no quality answer may be useless. Similarly, a high-quality answer, presenting adequate text and references with obsolete information, may be valueless. Combining these two issues (recency and quality) is crucial as users usually hope for current solutions and need to have fast/easy access (top items in the ranking) to the best answers to solve their problems quickly. The CQAs usually provide voting mechanisms so that the users can indicate the best quality answers. However, this method is not concerned with the recency of the answers besides being a slow and subjective process, which does not keep up with new content’s dynamism. Therefore, we propose an automatic approach that, besides the quality, also considers the answer’s recency to generating the ranking. We have used textual and non-textual features that indicate the answers’ quality and recency, extracted from the users’ answers in the CQA environment as a whole. In our approach, quality is used to classify the answers between good and poor, using a threshold value, generating two sets of answers: high quality and low quality. Then, both sets are sorted into recency order. Finally, these sets are concatenated, giving rise to the final ranking, so that the best and most current answers are in the top positions. To verify our proposal’s effectiveness, we have performed a case study in Stack Overflow CQA with a set of experiments, using different combinations of characteristics and different learning to rank Stack Overflow. Then, our main contributions are: (1) an approach to ranking answers of a questions dataset on the recency and quality of an answer; (2) a thorough evaluation of 9 learning to rank algorithms, showing that Coordinate Ascent and LambdaMart have the best performance in this task; (3) a feature analysis, which has shown that features related to the age of the response contributed to improving the ranking performance taking recency and quality into account. Furthermore, as far as we know, it is the first work that considers the recency of an answer in this task.

Probabilistic causal model for the detection of obsolete personal information to prevent falls in the elderly

The information describing a person is constantly evolving and may become obsolete and contradict other information. A personal database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. This study focuses on proposing a novel approach for dealing with the information obsolescence problem in the elderly-fall prevention context. Our approach aims to continuously monitor elderly information in order to detect the change in the behavior of an elderly person and to prevent him from falls. It consists of detecting contradictions between newly acquired information about a single elderly and what we already know about that person, then identifying among his observations those that have become obsolete and need to be updated. We propose a new approximate concept,£-Contradiction, which represents the confidence level of having a contradiction in a set of observations when a causal Bayesian network is our representation model. We propose a polynomial-time algorithm for detecting obsolete information and show that the resulting obsolete observations are given as an explanation AND-OR tree.

The Growth of Follower Networks on Social Media Platforms for Humanitarian Operations

Information is a perishable resource that is costly to produce and distribute to stakeholders. To ensure the rapid and broad diffusion of this resource, humanitarian organizations (HOs) have become increasingly intent on building larger follower networks on social media platforms. Our study examines the growth of these networks, particularly in response to the sharing of social media content originally produced by the HOs during periods of normalcy and emergency. To analyze this phenomenon, we formulate a structural model grounded in theoretical work on strategic network formation. Our model evaluates the probability of individual users deciding to follow a HO in response to content sharing as a function of their utility and costs of doing so. We estimate the model using a unique Twitter dataset from a natural experiment involving a major earthquake. We find that content sharing more effectively contributes to the expansion of HOs’ follower networks after the earthquake. Our results also indicate that content sharing stimulates the formation of follower links to HOs from users who are at the highest risk of receiving obsolete information. Moreover, strategically engaging with users to share and diffuse content can yield greater follower link formation activity. Based on these findings, we derive operational implications for HOs that aim to increase the size of their follower networks on social media platforms.

Fighting disaster misinformation in Latin America: the #19S Mexican earthquake case study

Social media platforms have been extensively used during natural disasters. However, most prior work has lacked focus on studying their usage during disasters in the Global South, where Internet access and social media utilization differs from developing countries. In this paper, we study how social media was used in the aftermath of the 7.1-magnitude earthquake that hit Mexico on September 19 of 2017 (known as the #19S earthquake). We conduct an analysis of how participants utilized social media platforms in the #19S aftermath. Our research extends investigations of crisis informatics by: 1) examining how participants used different social media platforms in the aftermath of a natural disaster in a Global South country; 2) uncovering how individuals developed their own processes to verify news reports using an on-the-ground citizen approach; 3) revealing how people developed their own mechanisms to deal with outdated information. For this, we surveyed 356 people. Additionally, we analyze one month of activity from: Facebook (12,606 posts), Twitter (2,909,109 tweets), Slack (28,782 messages), and GitHub (2,602 commits). This work offers a multi-platform view on user behavior to coordinate relief efforts, reduce the spread of misinformation and deal with obsolete information which seems to have been essential to help in the coordination and efficiency of relief efforts. Finally, based on our findings, we make recommendations for technology design to improve the effectiveness of social media use during crisis response efforts and mitigate the spread of misinformation across social media platforms.

Retention of learned predator recognition in embryonic and juvenile rainbow trout

AbstractMinimizing predation risk, especially for young or naïve individuals, can be achieved by learning to recognize predators. Embryonic learning may optimize survival by allowing for the earliest possible response to predation threats posthatch. However, predatory threats often change over an individual’s lifetime, and using old information can be detrimental if it becomes outdated. Adaptive forgetting allows an individual to discount obsolete information in decision-making and instead emphasize newer, more relevant information when responding to predation threats. Little is known about the extent to which young individuals can learn and forget information about predation threats. Here we demonstrate that rainbow trout 1) are capable of learning from both conspecific and heterospecific alarm cues as embryos, newly hatched larvae, and free-swimming larvae, 2) exhibit adaptive forgetting of predator information at all stages, and 3) display dynamic adaptive forgetting based on the ontogeny of learning. Specifically, fish that learned information as embryos retained the information for longer periods than those that learned the same information as newly hatched alevins.

Web of cybersecurity: Linking, locating, and discovering structured cybersecurity information

SummaryCybersecurity is one of the main concerns of many organizations today, and accessibility to cybersecurity information in a timely manner is crucial to maintaining cybersecurity. Various repositories of cybersecurity‐related information are publicly available on the Internet. However, users are unaware of many of them, and it is impractical for them to keep track of all of them. Cybersecurity information stored in these repositories must be able to be located and accessed by the parties who need it. To address this issue, this paper proposes a mechanism of linking, locating, and discovering various cybersecurity information to improve its accessibility in a timely manner. This mechanism allows us to locate various cybersecurity information having different schemata by generating metadata with which a list of cybersecurity information is managed. The information structure incorporated in this mechanism is unique, and it makes our mechanism flexible and extensible. The structure consists of categories and formats that are linked to each other. The mechanism can propagate information updates to minimize the risk of obsolete information. This paper also introduces a prototype of the mechanism to demonstrate its feasibility, and it analyzes the mechanism's extensibility, scalability, and information credibility. Through this study, we aim to improve the accessibility of cybersecurity information on the Internet and facilitate information sharing beyond organizational borders, with the eventual goal of creating a web of cybersecurity.

Regularized Extended Estimation With Stabilized Exponential Forgetting

This technical note concerns the problem of variable regularized estimation of time-varying nonlinear systems from the Bayesian viewpoint. The questions of how to impose the posterior information being variably regularized and how to forget this information are carefully discussed. The estimator design adopts the strategy of the iterated Kalman filter but differs in that, instead of the separated moments of the linearized system, only the augmented covariance matrix is updated. To suppress obsolete information, a decision problem involving the Kullback-Leibler divergence is solved. The decision provides the best combination of a pair of time-evolution model hypotheses in terms of the geometric mean. As a result, exponential forgetting with the adaptively tuned factor is inserted into the estimation process. The regularization of the investigated statistics is induced through the processing of externally supplied information. The presented estimator allows for absolute discarding or, conversely, retention of external information produced in terms of the Normal-Wishart distribution.

Forgetting techniques for stream-based matrix factorization in recommender systems

Forgetting is often considered a malfunction of intelligent agents; however, in a changing world forgetting has an essential advantage. It provides means of adaptation to changes by removing effects of obsolete (not necessarily old) information from models. This also applies to intelligent systems, such as recommender systems, which learn users’ preferences and predict future items of interest. In this work, we present unsupervised forgetting techniques that make recommender systems adapt to changes of users’ preferences over time. We propose eleven techniques that select obsolete information and three algorithms that enforce the forgetting in different ways. In our evaluation on real-world datasets, we show that forgetting obsolete information significantly improves predictive power of recommender systems.

The Merger of Technology & Talent

This article discusses various challenges faced by the utilities industry in retaining knowledge and important institutional awareness as trained engineers are closing their retirement age. Utilities need to find a way to hold onto critical knowledge and important institutional awareness of these long-time employees while letting go of the obsolete information that they also hold. However, through various technological innovations, the industry is trying to take full advantage of the Internet of Things (IoT) and the wired and wireless world. Advanced Meter Infrastructure—the so-called Smart Grid—and the suite of devices known as the IoT will play a key role in the transition to a new workforce, and change the types of workers utilities employ. This latest technology will require new skill sets to construct, operate, maintain, and decommission. Digital technology, combined with and contained in such devices as cameras, strain gauges, accelerometers, microphones, and the like, can replace the senses of workers. Software coupled with computing power can improve the speed and accuracy of the engineering calculations or better analyze risk. Robots or unmanned vehicles can carry tools and sensors where it is dangerous or uneconomical to send workers.