Validation of computational prediction of horizontal gene transfer events—XenoCluster

In this paper, we assess the biological accuracy and relevance of XenoCluster results compared to a reported set of known xenologs in yeast (Hall et al. Eukaryot. Cell 4(6):1102–1115, 2005). We were able to assign a high-priority rank to all reported yeast xenologs for which we had sufficient genomic sequence data.     

Picture or it didn’t happen: catch the truth for events

Multimedia Tools and Applications - Pictures spreading on the Internet are essential for the authenticity of events. Each day, huge amounts of data are published on social media, and many of them...     

Leveraging enterprise knowledge graph to infer web events’ influences via self-supervised learning

Knowledge graph (KG) techniques have achieved successful results in many tasks, especially in semantic web and natural language processing domains. In recent years, representation learning on KG has been successfully applied to e-business applications, such as event-driven automatic investment strategies. However, there is still limited research about learning events’ influence on KG for modern quantitative investment. In this paper, we propose a novel event influence learning framework to predict stock market trends, called ST-Trend, leveraging enterprise knowledge graph to represent company correlation relationships, for mining the deep background knowledge of web events, with three self-supervised learning tasks. In particular, we devise two jointly self-supervised tasks to identify the relations between web events and companies. The first task is for generating ground-truth event-company correlation labels based on the enterprise knowledge graph. The second task is used to train how to identify the correlated companies of an event based on the generated correlation labels, with the encoding of web events, company features, and technical sequential data. We then design the prediction network to infer an event’s influence on stock price trends of the identified correlated companies based on the enterprise KG. Finally, we perform extensive experiments on a massive real-life dataset to validate the effectiveness of our proposed framework, and the experimental results demonstrate its superior performance in predicting stock market trends via considering events’ influences with the enterprise knowledge graph.     

Chance discovery in medicine — Detection of rare risky events in chronic diseases —

One of the most important characteristics of chance discovery is that it focuses on the specific events or patterns in which the essential nature of an applied domain is implicitly included. The understanding and forecasting of such patterns and events will have a significant impact on decision making in the applied domain. This paper discusses the meaning of chance discovery from the viewpoint of medicine. Since chance discovery in medicine can be viewed as the way to find a suitable occasion for some critical actions or to check the dangerous possibilities, called rare risky events, detection and interpretation of rare but important events are ones of the components that supports chance discovery. According to this observation, several approaches for detecting rare events were introduced and evaluated by a small dataset on neurological diseases. Experimental results show that a set of events which include rare risky events can be detected by the introduced detection method, though interpretation by domain experts is required for selection of such events. Shusaku Tsumoto, Ph.D.: He graduated from Osaka University, School of Medicine in 1989. After residents of neurology in Chiba University Hospital, he was involved in developing hospital information system in Chiba University Hospital. He moved to Tokyo Medical University in 1993 and started his research on rough sets and data mining in medicine. He received his Ph.D (Computer Science) from Tokyo Institute of Technology in 1997, and is now a Professor at Department of Medical Informatics, Shimane Medical University. His interests include approximate reasoning, data mining, fuzzy sets, knowledge acquisition, mathematical theory of data mining, and rough sets (alphabetical order).     

Remembrance of cases past: who remembers what, when confronting critical flight events?

Recent "naturalistic" theories of decision making emphasize the role of stored prior experiences or cases as a guide to current action. However, there is little empirical evidence on the role that case-based remindings play in real-life decision making. The present study utilized a Web-based survey to collect data about the role of prior cases in pilot decision making about critical flight events. Results showed that more than half of the 1081 pilots who responded could provide details about utilizing a previous case in responding to a critical flight event. These events were most likely to involve weather or equipment failure. The cases were found to be useful in situational assessment rather than option evaluation. The use of cases increased with age and experience. Data obtained from a concurrent conventional survey showed broadly similar results. The implications of these results are that case-based remindings play an important role in expert pilot decision making and that a training system that incorporates case-based learning would be a potentially useful means of improving pilot decision making. Actual or potential applications of this research include the development of case-based training systems to enhance flight training.     

SEDRET—an intelligent system for the diagnosis and prediction of events in power plants

Artificial Intelligence applications in large-scale industry, such as fossil power plants, require the ability to manage uncertainty and time. In this paper, we present an intelligent system to assist an operator of a power plant. This system, called SEDRET, is based on a novel knowledge representation of uncertainty and time, called Temporal Nodes Bayesian Networks (TNBN), a type of Probabilistic Temporal Network. A set of temporal nodes and a set of edge define a TNBN, each temporal node is defined by a value of a variable and a time interval associate to the change of variable value. A TNBN generates a formal and systematic structure for modeling the temporal evolution of a process under uncertainty. The inference mechanism is based on probabilistic reasoning. A TNBN can be used to recognize events and state variables with respect to current plant conditions and predict the future propagation of disturbances. SEDRET was validated with the diagnosis and prediction of events in a steam generator with a power plant training simulator. The results performed in this work indicate that SEDRET can potentially improve plant availability through early diagnosis and prediction of disturbances that could lead to plant shutdown.     

Eliciting children’s recall of events: how do computers compare with humans?

This study investigated the usefulness of an interactive computer program in eliciting children's reports about an event. Fifty-four 5–6- and fifty-nine 7–8-year old children participated in an event with their regular class teacher which involved several activities and a mildly negative secret. Four days and again 14 days later, the children were interviewed individually by computer (alone) or by a human interviewer. The computer program incorporated animation and audio whereby an animated figure asked the questions and the children were required to provide a verbal response. The accuracy and detail of the children’s reports was similar across the interview conditions. The children were more willing to review their answers with the computer than the adult interviewer. However, responses to the computer were less consistent across the interviews, and the children were less willing to disclose the secret in the second interview to the computer compared with the human interviewer. Overall, the computer revealed little benefit in eliciting children’s recall of the event over the standard face-to-face interview.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.     

What happens where during disasters? A Workflow for the multifaceted characterization of crisis events based on Twitter data

Twitter data are a valuable source of information for rescue and helping activities in case of natural disasters and technical accidents. Several methods for disaster‐ and event‐related tweet filtering and classification are available to analyse social media streams. Rather than processing single tweets, taking into account space and time is likely to reveal even more insights regarding local event dynamics and impacts on population and environment. This study focuses on the design and evaluation of a generic workflow for Twitter data analysis that leverages that additional information to characterize crisis events more comprehensively. The workflow covers data acquisition, analysis and visualization, and aims at the provision of a multifaceted and detailed picture of events that happen in affected areas. This is approached by utilizing agile and flexible analysis methods providing different and complementary views on the data. Utilizing state‐of‐the‐art deep learning and clustering methods, we are interested in the question, whether our workflow is suitable to reconstruct and picture the course of events during major natural disasters from Twitter data. Experimental results obtained with a data set acquired during hurricane Florence in September 2018 demonstrate the effectiveness of the applied methods but also indicate further interesting research questions and directions.     

Changing the role of the air traffic controller: how will free flight affect memory for spatial events?

At present, air traffic controllers (ATCOs) exercise strict control over routing authority for aircraft movement in airspace. The onset of a free flight environment, however, may well result in a dramatic change to airspace jurisdictions, with aircraft movements for the large part being governed by aircrew, not ATCOs. The present study examined the impact of such changes on spatial memory for recent and non-recent locations of aircraft represented on a visual display. The experiment contrasted present conditions, in which permission for manoeuvres is granted by ATCOs, with potential free flight conditions, in which aircrew undertake deviations without explicit approval from ATCOs. Results indicated that the ATCO role adopted by participants impacted differently on short-term and long-term spatial representations of aircraft manoeuvres. Although informing participants of impending deviations has beneficial effects on spatial representations in the short term, long-term representations of spatial events are affected deleteriously by the presentation of subsequent information pertaining to other aircraft. This study suggests strongly that recognition of the perceptual and cognitive consequences of changing to a free flight environment is crucial if air safety is not to be jeopardized.     

To log,or not to log: using heuristics to identify mandatory log events – a controlled experiment

Context

User activity logs should capture evidence to help answer who, what, when, where, why, and how a security or privacy breach occurred. However, software engineers often implement logging mechanisms that inadequately record mandatory log events (MLEs), user activities that must be logged to enable forensics.

Goal

The objective of this study is to support security analysts in performing forensic analysis by evaluating the use of a heuristics-driven method for identifying mandatory log events.

Method

We conducted a controlled experiment with 103 computer science students enrolled in a graduate-level software security course. All subjects were first asked to identify MLEs described in a set of requirements statements during the pre-period task. In the post-period task, subjects were randomly assigned statements from one type of software artifact (traditional requirements, use-case-based requirements, or user manual), one readability score (simple or complex), and one method (standards-, resource-, or heuristics-driven). We evaluated subject performance using three metrics: statement classification correctness (values from 0 to 1), MLE identification correctness (values from 0 to 1), and response time (seconds). We test the effect of the three factors on the three metrics using generalized linear models.

Results

Classification correctness for statements that did not contain MLEs increased 0.31 from pre- to post-period task. MLE identification correctness was inconsistent across treatment groups. For simple user manual statements, MLE identification correctness decreased 0.17 and 0.12 for the standards- and heuristics-driven methods, respectively. For simple traditional requirements statements, MLE identification correctness increased 0.16 and 0.17 for the standards- and heuristics-driven methods, respectively. Average response time decreased 41.7 s from the pre- to post-period task.

Conclusion

We expected the performance of subjects using the heuristics-driven method to improve from pre- to post-task and to consistently demonstrate higher MLE identification correctness than the standards-driven and resource-driven methods across domains and readability levels. However, neither method consistently helped subjects more correctly identify MLEs at a statistically significant level. Our results indicate additional training and enforcement may be necessary to ensure subjects understand and consistently apply the assigned methods for identifying MLEs.

     

Interannual variability of convective activity over the tropical Indian Ocean during the El Niño/La Niña events

Convection over the tropical Indian Ocean is important to the global and regional climate. This study presents the monthly climatology of convection, inferred from the outgoing longwave radiation (OLR), over the tropical Indian Ocean. We also examine the impact of El Niño/La Niña events on the convection pattern and how variations in convection over the domain influence the spatial rainfall distribution over India. We used 35 recent years (1974–2008) of satellite-derived OLR over the area, the occurrence of El Niño/La Niña events and high resolution grid point rainfall data over India. The most prominent feature of the annual cycle of OLR over the domain is the movements of convection from south-east to north and north-west during the winter to the summer monsoon season. This feature represents the movement of the inter-tropical convergence zone (ITCZ). The climatology of OLR during the winter months (December–February) over the domain is characterized by high subsidence over central India with a decrease of OLR values towards the north and south. Moderate convection is also seen over the Himalayan Range and the south-east Indian Ocean. In contrast, during the summer (June–September) the OLR pattern indicates deep convection along the monsoon trough and over central India, with subsidence over the extreme north-west desert region. The annual march of convection over the Arabian Sea and Bay of Bengal sector shows that the Arabian Sea has a limited role, compared to the Bay of Bengal, in the annual cycle of the convection over the tropical Indian Ocean. The composite OLR anomalies for the El Niño cases during the summer monsoon season show suppressed convection over all of India and moderate convection over the central equatorial Indian Ocean and over the northern part of the Bay of Bengal. Meanwhile in La Niña events the OLR pattern is nearly opposite to the El Niño case, with deep convection over entire Indian region and adjoining seas and subsidence over the northern Bay of Bengal and extreme north-west region. The spatial variability of the 1°?×?1° summer monsoon rainfall data over India is also examined during El Niño/La Niña events. The results show that rainfall of the summer monsoon season over the southern peninsular of India and some parts of central India are badly affected during El Niño cases, while the region lying along the monsoon trough and the west coast of India have received good amounts of rainfall. This spatial seasonal summer monsoon rainfall distribution pattern seems to average out the influence of El Niño events on total summer monsoon rainfall over India. It seems that, in El Niño events, the convection pattern over the Bay of Bengal remains unaffected during summer monsoon months and thus this region plays an important role in giving good summer monsoon rainfall over the northern part of India, which dilutes the influence of El Niño on seasonal scale summer monsoon rainfall over India. These results are also confirmed by using a monthly bias-corrected OLR dataset.     

Comparative study of the 1982–1983 and 1997–1998 El Niño events over different types of vegetation in South America

This work presents results which help to understand the behaviour of the Normalized Difference Vegetation Index (NDVI) anomalies over the South America continent during the two strongest El Niño events of the last century (1982–1983 and 1997–1998). The data used are parts of a long-term series (July 1981 to December 1999) of calibrated NDVI data derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) datasets. Special emphasis has been given to the analysis of the response of the major Brazilian vegetation types. This paper introduces an approach that enhances NDVI anomalies relative to the long-term climatology of the region. We find a negative NDVI anomaly for most of the region during the 1982–1983 event, whereas for the 1997–1998 event positive NDVI anomalies were observed over most regions. Only the ‘Nordeste’ region showed a similar vegetation response for both events. We identify three possible factors that may play a role in the different NDVI responses to the two El Niño events. Firstly, poor intercalibration of sensors may account for some, but not all of the differences. Secondly the response of the vegetation may depend upon the climate conditions prior to the El Niño events. Thirdly, the difference in the onset date and the duration of the mature phase of the two El Niño events, associated with very different Atlantic surface temperatures are shown to have dynamical consequences which may impact upon the vegetation.