Concept Of Salience Research Articles

Interpreting stakeholder ecosystems through relational stakeholder theory: The case of a highly contested megaproject

AbstractManaging stakeholders' concerns in megaprojects with an inclusive and ethical vision is a current open challenge. To overcome company‐centered stakeholder‐management practices, an ecosystem view, (i.e., one based on the network of relationships among involved stakeholders), should be adopted by designing bottom‐up participatory stakeholder mapping processes that include the stakeholders affected by the construction of a megaproject. This paper presents an analysis of the stakeholder ecosystem of a contested megaproject in Italy (the Turin–Lyon high‐speed rail) aimed at identifying similarities and discrepancies in stakeholder identification and prioritization between the managers' perception and an ecosystem point of view. The study followed a mixed‐method approach. By coding the self‐declared statements from 21 interviews with middle and top managers of the organization in charge of the construction and visualizing the emerging network of actors through a social network analysis and the use of centrality degrees, the most relevant stakeholders are identified. Our findings reveal how the prioritization of stakeholders obtained through the centrality degrees significantly differs from what the managers declare in reference to the concept of salience, highlighting how a bottom‐up stakeholder mapping process—by including the stakeholders themselves in the mapping process—should be designed and reiterated during the whole life of a megaproject in order to adopt an inclusive stakeholder management approach.

Quantifying basic colors' salience from cross‐linguistic corpora

AbstractA corpus‐based quantitative assessment of Berlin and Kay's proposal is presented. We refine the Basic Color Terms hierarchy proposed by Berlin and Kay, through the concept of salience. A cross‐linguistic study with 57 different languages and 136 different linguistic corpora has been conducted. This study uses KonText tool and the corpora included in it. The color labels in different languages have been obtained using a unified methodology from PanLex. We have obtained an individual hierarchy for each of the languages analyzed, as well as a general hierarchy that captures the universal trend. Results show that there is a close relationship between the evolutionary stages in the Berlin and Kay proposal and their frequency in our corpora study, which we could also relate to Zipf's Law. The only color that we certify behaves differently compared to such a proposal is yellow. The main advantage of our approach compared to previous corpora studies is taking into account the anglocentric bias by using a representative typological set of different languages from the world.

Language variation in minority online newspapers: The example of the “Deutsche Allgemeine Zeitung” in Kazakhstan

This article discusses the online edition of the “Deutsche Allgemeine Zeitung” (DAZ, German General Newspaper), an exceptional media product in Kazakhstan. This medium was chosen mainly for its very complex linguistic and cultural configuration. The linguistic forms are German, while the discourse and text traditions suggest a Russian influence due to the long hegemony of Russian as the language of culture and education in Kazakhstan. Additionally, the current sociocultural frame of reference is of genuine Kazakh origin.The central aim of this evidence-based media linguistic analysis was to describe the unique features in the language of this online medium, i.e. the multilingual aspects of language variation and aspects related to language and cultural contact.The methodology is based on a linguistic concept of saliency and used interrater reliability in the linguistic assessment. The study showed that the online version and the print version differ not only on a technical level, but also in their journalistic and linguistic design. The digital version is marked by livelier, more idiomatic, and more colorful texts than the print version. These differences can likely not just be attributed to the greater range of options provided by the new medium, but possibly also to the larger share of contributions by authors from Germany in the online version compared to the print version. In the written practices examined in this article it could be shown that authors from Kazakhstan generally used ‘multiliteracy’ form that also generated emergent forms.

Spatiotemporal cue fusion-based saliency extraction and its application in video compression

Extracting salient regions plays an important role in computer vision tasks, e.g., object detection, recognition and video compression. Previous saliency detection study is mostly conducted on individual frames and tends to extract saliency with spatial cues. The development of various motion feature further extends the saliency concept to the motion saliency from videos. In contrast to image-based saliency extraction, video-based saliency extraction is more challenging due to the complicated distractors, e.g., the background dynamics and shadows. In this paper, we propose a novel saliency extraction method by fusing temporal and spatial cues. In specific, the long-term and short-term variations are comprehensively fused to extract the temporal cue, which is then utilized to establish the background guidance for generating the spatial cue. Herein, the long-term variations and spatial cues jointly highlight the contrast between objects and the background, which can solve the problem caused by shadows. The short-term variations contribute to the removal of background dynamics. Spatiotemporal cues are fully exploited to constrain the saliency extraction across frames. The saliency extraction performance of our method is demonstrated by comparing it to both unsupervised and supervised methods. Moreover, this novel saliency extraction model is applied in the video compression tasks, helping to accelerate the video compression task and achieve a larger PSNR value for the region of interest (ROI).

Machine learning to promote health management through lifestyle changes for hypertension patients

The purpose of this paper is to investigate the use of machine learning models to develop a diagnostic system for hypertension patients so that people can modify their daily lifestyle to manage their condition. We propose this system by adopting the concepts of saliency maps for image data to non-image, lifestyle data with a data perturbation simulation technique. We trained the proposed system on a new lifestyle dataset that we extracted from a survey on Asian sub-population. The proposed system consists of a convolution neural network (CNN) as the diagnostic model, and is combined with simulation techniques to explain the concepts/insights learnt by the CNN. We compared classification performance of the CNN model with other baseline models fitted with other types of hypertension data including neural network, decision tree and other CNN model from literature. The CNN achieved a 68–70% accuracy on training and testing datasets. Comparing with other baseline models, our CNN model provided more consistent performance in terms of accuracy, sensitivity, specificity and area under receiver operating characteristic (ROC) curve. Using the simulations, we learnt that CNN captured not only direct correlation between the variables and the target, but also learnt group-based interactions. Our study reveals that age, gender, diabetes status, body mass index, smoking, occupation and education are some important lifestyle factors affecting hypertension. Avoiding smoking, maintaining a balanced diet to prevent unnecessary weight gaining, regular monitoring of blood sugar level for diabetic care, and stress relief exercise can reduce hypertension risk. • A convolution neural network (CNN) is proposed to predict hypertension. • Data perturbation simulation method is applied to explain concepts learned by CNN. • Both direct and group-based interaction among features are identified as influencers. • Age, gender, diabetes, smoking, occupation and education are some important factors.

Is clean energy contested? Exploring which issues matter to stakeholders in the European Green Deal

The European Green Deal aims at decarbonizing the European Union’s economies by 2050. This implies stepping up policy actions to support both renewable energy and energy efficiency, two domains in which the body of EU law has recently changed. These changes raise the question of stakeholder support for more ambitious policies. To assess such support, we first apply the concept of salience to identify the most important issues for four core European stakeholder groups and follow up with an in-depth investigation. We use a mixed method design, testing external issue salience through a qualitative read-out of 182 stakeholder position papers and 198,128 Twitter messages, and then testing internal salience and readiness to support through analysis of an opinion survey of 20 key European stakeholders. We find stakeholders closely align in demanding binding energy efficiency and renewable energy policies. Strong focus can be expected on energy efficiency targets and measures, less so on the further development of the framework for renewable energies. These findings confirm earlier studies on salience in different policy fields.

Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

The development of computer aided diagnosis (CAD) systems for the task of tumor detection and segmentation in breast ultrasound (BUS) images presents one of the most active research fields. In this paper, a saliency-guided approach for fast and automatic tumor segmentation in BUS images is proposed. We explore the ability of the concept of saliency to emulate radiologist expertise for tumor lesion detection in BUS images. For that, we compute BUS image saliency to generate effective tumor seeds and background seeds. Then, a graph-based interactive image segmentation method is applied automatically using the generated seeds to extract tumor region. For more accurate segmentation, we propose a novel saliency score measure to apply a post-processing step for result refinement. Our results have been compared with other approaches in challenged datasets and demonstrated the effectiveness of our saliency-guided approach for tumor detection and segmentation using different evaluation metrics.

Saliency From Growing Neural Gas: Learning Pre-Attentional Structures for a Flexible Attention System.

Artificial visual attention has been an active research area for over two decades. Especially, the concept of saliency has been implemented in many different ways. Early approaches aimed at closely modeling saliency processing with concepts from biological attention to provide (at least in the long run) general-purpose attention for technical systems. More recent approaches have departed from this agenda, turning to more specific attention-guided tasks, most notably the accurate extraction of salient objects, for which large-scale ground truth datasets make it possible to quantify progress. While the first type of models is troubled by weak performance in these specific tasks, the second type, as we show with a new benchmark, has lost the ability to predict saliency in the original sense, which may be an important factor for future general-purpose attention systems. Here, we describe a new approach using growing neural gas to obtain pre-attentional structures for a scene at an early processing stage. On this basis, traditional saliency concepts can be applied while at the same time they can be linked to mechanisms that make models successful in salient object detection. The model shows high performance at predicting traditional saliency and makes substantial progress toward salient object detection, although it cannot reach the top-level performance of some specialized methods. We discuss the important implications of our findings.

Salient Object Detection via Fuzzy Theory and Object-Level Enhancement

This paper proposes a bottom-up saliency detection method via effective integration of regional saliency measure and object-level information using fuzzy theory. First, we generate an initial saliency map by fusing multiple prior maps. Second, to emphasize the object-level concept of saliency, we further generate many object proposals of the input image. A fuzzy set theory is then applied to measure the objectness score of the object proposals and integrate them into an objectness map. Third, an optimization framework is proposed to effectively fuse various prior saliency cues and object-level information to produce a clean and uniform saliency map as well as to maintain the salient object completeness. Experimental studies in several benchmark datasets confirmed the superiority of the proposed method over state-of-the-art saliency detection methods.

Salient Spin Images : an application to archaeozoology for bones recognition

Archaeozoology or zooarchaeology is the scientific field that aims to rebuild natural and cultural relations between human and animals. To reach that objective, archaeozoologists study faunal remains, such as bones. In this regard, a 3D representation is put into place. This would allow a better understanding of the initial spatial positions of the animals in the pits, and would make it possible to examine in a more rigorous manner the explanatory hypotheses by manipulating the 3D view of the pit and bones. This manipulation invokes the use of computer vision techniques, including 3D object recognition methods. In this work we present a novel approach to recognize 3D bones in occluded and cluttered scenes inspiring from a stat of the art method called spin images. We combine this method with saliency concept to improve the the robustness to occlusions and clutters significantly and also to decrease the complexity of the algorithm.

Image Co-Saliency Detection and Co-Segmentation via Progressive Joint Optimization.

We present a novel computational model for simultaneous image co-saliency detection and co-segmentation that concurrently explores the concepts of saliency and objectness in multiple images. It has been shown that the co-saliency detection via aggregating multiple saliency proposals by diverse visual cues can better highlight the salient objects; however, the optimal proposals are typically region-dependent and the fusion process often leads to blurred results. Co-segmentation can help preserve object boundaries, but it may suffer from complex scenes. To address these issues, we develop a unified method that addresses co-saliency detection and co-segmentation jointly via solving an energy minimization problem over a graph. Our method iteratively carries out the region-wise adaptive saliency map fusion and object segmentation to transfer useful information between the two complementary tasks. Through the optimization iterations, sharp saliency maps are gradually obtained to recover entire salient objects by referring to object segmentation, while these segmentations are progressively improved owing to the better saliency prior. We evaluate our method on four public benchmark data sets while comparing it to the state-of-the-art methods. Extensive experiments demonstrate that our method can provide consistently higher-quality results on both co-saliency detection and co-segmentation.

The Robustness of Echoic Log-Surprise Auditory Saliency Detection

The concept of saliency describes how relevant a stimulus is for humans. This phenomenon has been studied under different perspectives and modalities, such as audio, visual, or both. It has been employed in intelligent systems to interact with their environment in an attempt to emulate or even outperform human behavior in tasks, such as surveillance and alarm systems or even robotics. In this paper, we focus on the aural modality and our goal consists in measuring the robustness of Echoic log-surprise in comparison with a set of auditory saliency techniques when tested on noisy environments for the task of saliency detection. The acoustic saliency methods that we have analyzed include Kalinli’s saliency model, Bayesian log-surprise, and our proposed algorithm, Echoic log-surprise. This last method combines an unsupervised approach based on the Bayesian log-surprise and the biological concept of echoic or auditory sensory memory by means of a statistical fusion scheme, where the use of different distance metrics or statistical divergences, such as Renyi’s or Jensen-Shannon’s among others, are considered. Additionally, for comparison purposes, we have also compared some classical onset detection techniques, such as those based on voice activity detection or energy thresholding. Results show that Echoic log-surprise outperforms the detection capabilities of the rest of the techniques analyzed in this paper under a great variety of noises and signal-to-noise ratios, corroborating its robustness in noisy environments. In particular, our algorithm with the Jensen-Shannon fusion scheme produces the best F-scores. With the aim of better understanding the behavior of Echoic log-surprise, we have also studied the influence of its control parameters, depth and memory, and their influence at different noise levels.

De detectie van saillante taalkenmerken

Abstract Detecting salient features: in defence of a multidimensional approach This paper addresses the question how salience, i.e. the perceptual and cognitive prominence of language features (Kerswill & Williams 2002: 63), can be studied and explained. Within language change studies, the concept of salience is often called upon to explain why certain language features are more prone to change than others, but empirical data to substantiate claims of salience are often lacking or non-convincing. In this paper, an overview is offered of ways in which salience can be studied empirically, building on the insights of recent perceptual dialectological research. It will be argued that a combination of techniques should be strived at, as this allows to combine the strengths of each approach. Subsequently, by offering an overview of existing hypotheses, the question will be addressed how salience can be explained. The formulated insights will be illustrated and tested by means of a multimethodological salience experiment conducted in Flanders. This case study clearly shows (1) the advantage of an approach combining dialect imitation data and several types of interview data and (2) that salience is influenced by a variety of factors, such as the linguistic background of the listener and the context in which the feature occurs.

Ways of (Virtual) Seeing: Applying a Bergerian Lens in a Multi-User Virtual Environment (MUVE)

Ways of (Virtual) Seeing: Applying a Bergerian Lens in a Multi-User Virtual Environment (MUVE)

The Many Saliences of Justice Michael D. Ryan: A Comparative Empirical Analysis of Concepts of Salience as Applied in State Appellate Courts

This Essay began as an effort to honor the work of the late Arizona Supreme Court Justice Michael D. Ryan by examining the impact of his work on Arizona law through the lens of his “most important” opinions. As with all things touched by Justice Ryan it has become something greater than its initial purpose. The Author began this project by surveying three hundred seven Arizona judges about Justice Ryan’s published opinions. The results of this survey were quite surprising. Rather than identifying a common core of important opinions, the survey identified two mutually exclusive sets of important opinions: one for the appellate court and the other for the trial court. In an effort to understand this result, the Author reviewed the salience literature (which identifies measures of case or issue importance) and identified fourteen measures of salience. The application of those measures to Justice Ryan’s published opinions only confirmed the results of the survey because no two measures identified the same set (or even similar sets) of opinions as important. This empirical analysis and review of Justice Ryan’s published opinions demonstrated three things. First, Justice Ryan’s opinions matter to a wide range of people including grandparents, illegal aliens, homeowners, schoolchildren, employees, and convicted felons on death row. Second, the salience literature does not identify a single unitary concept of importance, but rather many different concepts of importance. Finally, the empirical analysis discussed herein unequivocally demonstrates that whether an opinion is important depends upon who you ask.

Salience and Blindness: A Haptic Hike on Gins Mountain

This paper attempts to follow an improbable ridge line between architecture, geography and linguistics, between the optic and haptic ends of the concept of salience, through a reading of Helen Keller Or Arakawa, Madeline Gins’s 1994 essay-cum-joint-biography partly devoted to “salience” approached through the blind figure of Helen Keller (1880-1968). In a chapter titled “Or Mountains Or Lines”, prominent features envisaged from a sighted perception give way, under the condition of blindness, to saddle-points, swivel-areas and moments of stylistic tentativeness when words and/or syntax begins to fail. Salience, revisited through Keller’s apprehension of mountains as vaporous and mobile masses, leads to tentativeness in writing and to what I here call “failience” in discourse: the failure to stabilise discursive referents, the unsettling reversibility of syntactic lines based, for example, on the swivel-point of an “-ing” form. At work in Gins’s writing is the invention of a cathectic, tactile grasp of the world through langage. In order to ease access to a “faulty” perception, Gins invites her readers to transit through the writing of blind cartographer William Prescott, in particular through his “tactile”, linear mapping of the Andes Cordillera, approached as a mobile rhizome rather than as a collection of fixed, “raised” points. Mapping, under conditions of blindness, implies wander lines as much as lines of (thin) air.

The Mountain Sublime of Philip James de Loutherbourg and Joseph Mallord William Turner

Our analysis of the concept of saliency relies on two iconographic representations of Alpine relief. One is from Philip James de Loutherbourg (An Avalanche in the Alps, 1803); the other from Joseph Mallord William Turner (The Fall of an Avalanche in the Grisons, 1810). This comparative study comes with an analysis of the discourse on saliency as Turner had joined a poem to his pictorial work of art. We will thus study the way both artists have looked upon the Alps: as individuals unfamiliar with mountain relief, and depicting a similar natural disaster —an avalanche—, did they merely focus on the inaccessible and dangerous nature of such places?If there are striking analogies between the two canvases, disclosing the existence of several forms and factors of saliency, common to both artists —and directly echoing the aesthetic notion of the Sublime, which emerged in Great-Britain in the 19th century— these visual manifestations of saliency seem to have equivalents in the linguistic field. We thus follow Frederic Landragin when trying to demonstrate that the poem as well as the paintings by De Loutherbourg and Turner hinge upon the same two main mechanisms, based on disruption.

A novel background-weighted histogram scheme based on foreground saliency for mean-shift tracking

Effective appearance models are one critical factor for robust object tracking. In this paper, we introduce foreground feature saliency concept into the background modelling, and put forward a novel foreground saliency-based background-weighted histogram scheme (FSBWH) for target representation and tracking, which exploits salient features from both foreground and background. We think that background and foreground salient features are both crucial for target representation and tracking. Experimental results show that the proposed FSBWH scheme can improve the robustness and performance of tracker significantly especially in complex occlusions and similar background scenes.

Visual Saliency Prediction and Evaluation across Different Perceptual Tasks.

Saliency maps produced by different algorithms are often evaluated by comparing output to fixated image locations appearing in human eye tracking data. There are challenges in evaluation based on fixation data due to bias in the data. Properties of eye movement patterns that are independent of image content may limit the validity of evaluation results, including spatial bias in fixation data. To address this problem, we present modeling and evaluation results for data derived from different perceptual tasks related to the concept of saliency. We also present a novel approach to benchmarking to deal with some of the challenges posed by spatial bias. The results presented establish the value of alternatives to fixation data to drive improvement and development of models. We also demonstrate an approach to approximate the output of alternative perceptual tasks based on computational saliency and/or eye gaze data. As a whole, this work presents novel benchmarking results and methods, establishes a new performance baseline for perceptual tasks that provide an alternative window into visual saliency, and demonstrates the capacity for saliency to serve in approximating human behaviour for one visual task given data from another.

EventMask: A Game-Based Framework for Event-Saliency Identification in Images

The concept of “event” emerged in recent years as a key feature to efficiently index and retrieve media. Several approaches have been proposed to analyze the relationship between events and media, enable event discovery, and perform event-based media tagging, indexing, and retrieval. Despite the outstanding work done in this area, a major problem that remains open is how to infer the link between visual concepts and events. In particular, the possibility of understanding which perceptual elements allow a human recognizing the event depicted by an image would open new directions in event media discovery. In this paper we introduce the concept of event saliency to define the above event-revealing perceptual elements, and we propose an original method to detect it by exploiting crowd knowledge through gamification. We propose an adversarial game with a hidden purpose, where users are engaged in competitive roles: masking photos to prevent competitors recognizing the related event, and discovering events in photos masked by other players. Rules and incentives are defined to minimize cheating and force players to focus on details that really matter. A suitable algorithm composes the masks created by different players on the same media, thus producing a saliency map that, different from the traditional concept of saliency, does not focus on perceptual prominence but rather on event-related semantics of media. A thorough validation on public datasets is presented, and initial experiments to apply event saliency in detection tasks are proposed . Furthermore, an event-saliency dataset is disclosed to allow further research.