On the Aggression Diffusion Modeling and Minimization in Twitter

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.

Role of circulating molecules in age-related cardiovascular and metabolic disorders

Studies analyzing heterochronic parabiosis mice models showed that molecules in the blood of young mice rejuvenate aged mice. Therefore, blood-based therapies have become one of the therapeutic approaches to be considered for age-related diseases. Blood includes numerous biologically active molecules such as proteins, metabolites, hormones, miRNAs, etc. and accumulating evidence indicates some of these change their concentration with chronological aging or age-related disorders. The level of some circulating molecules showed a negative or positive correlation with all-cause mortality, cardiovascular events, or metabolic disorders. Through analyses of clinical/translation/basic research, some molecules were focused on as therapeutic targets. One approach is the supplementation of circulating anti-aging molecules. Favorable results in preclinical studies let some molecules to be tested in humans. These showed beneficial or neutral results, and some were inconsistent. Studies with rodents and humans indicate circulating molecules can be recognized as biomarkers or therapeutic targets mediating their pro-aging or anti-aging effects. Characterization of these molecules with aging, testing their biological effects, and finding mimetics of young systemic milieu continue to be an interesting and important research topic to be explored.

Laser Fabrication of Titanium Alloy-Based Photothermal Responsive Slippery Surface

In recent years, biomimetic materials inspired from natural organisms have attracted great attention due to their promising functionalities and cutting-edge applications, emerging as an important research topic. For example, how to reduce the reflectivity of the solid surface and increase the absorption of the substrate surface is essential for developing light response smart surface. Suitable solutions to this issue can be found in natural creatures; however, it is technologically challenging. In this work, inspired from butterfly wings, we proposed a laser processing technology to prepare micro nanostructured titanium alloy surfaces with anti-reflection properties. The reflectivity is significantly suppressed, and thus, the light absorption is improved. Consequently, the anti-reflection titanium alloy surface can be further employed as a photothermal substrate for developing light-responsive slippery surface. The sliding behavior of liquid droplets on the smart slippery surface can be well controlled via light irradiation. This method facilitates the preparation of low-reflection and high-absorption metallic surfaces towards bionic applications.

Influential Indicators and Measurements of Mediating and Moderating Roles on SME Performance

Overcoming the failure of SMEs has been an important research topic. The critical research finding is that it has verified the essential elements of performance improvement. We presented a solution to the research question, "Is there a causal relationship between the effect on SMEs' success on capacity and business performance?". We analyzed whether the competence of SMEs had a mediating effect between success variables and performance. Secondary effects were empirically studied by converting independent variables to Higher-Order Component (HOC). The second-order variable of management influenced financial, non-financial, and technical performance, and the second-order variable of technology affected technical performance. As a result of introducing demographic variables as a controlling variable for performance, gender, and year of establishment showed a moderating effect on technical and non-financial performance. We expect to contribute to practical application to SME CEOs and government policymakers, support organizations, academia, and industry.

3D reconstruction of human body model based on incremental Motion recovery structure

3D reconstruction of human body model is a very important research topic in 3D reconstruction and also a challenging research direction in engineering field. In this paper, the whole pipeline flow of 3D reconstruction of human body model based on incremental motion recovery structure is proposed. Use mobile phone to collect images from different angles and screen them; Secondly, feature extraction and matching under SIFT operator, sparse reconstruction of incremental motion recovery structure, dense reconstruction based on depth map and other processes are carried out. Poisson surface reconstruction is finally carried out to achieve model reconstruction. Experiments show that the effect subject of the reconstructed model is clear.

TAML: A Traffic-aware Multi-task Learning Model for Estimating Travel Time

Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize estimated traffic condition (by explicit features such as average traffic speed) for auxiliary supervision of travel time estimation, they fail to model their mutual influence and result in inaccuracy accordingly. To this end, in this article, we propose an improved traffic-aware model, called TAML, which adopts a multi-task learning network to integrate a travel time estimator and a traffic estimator in a shared space and improves the accuracy of estimation by enhanced representation of traffic condition, such that more meaningful implicit features are fully captured. In TAML, multi-task learning is further applied for travel time estimation in multi-granularities (including road segment, sub-path, and entire path). The multiple loss functions are combined by considering the homoscedastic uncertainty of each task. Extensive experiments on two real trajectory datasets demonstrate the effectiveness of our proposed methods.

Toward a Family Science Perspective on Executive Compensation in Family Firms: A Review and Research Agenda

In family firms, the family often plays a central role in the strategic decisions of the business. However, until recently, research has primarily focused on exploring the role that business factors play in firm decision-making, with less attention given to the role of the family system. This article reviews the research on executive compensation in family firms to understand whether and how the family system has been considered within this work. Guided by the application of family science theories, we provide a framework to explain why it is important to incorporate the family system in the future study of executive compensation in family firms. We conclude by discussing a research agenda outlining how elements of the family system can be integrated into future executive compensation research to inspire scholars to think differently about this important research topic.

The Rise of Political Influencers— Perspectives on a Trend Towards Meaningful Content

Despite an increasing information overflow in the era of digital communication, influencers manage to draw the attention of their followers with an authentic and casual appearance. Reaching large audiences on social media, they can be considered as digital opinion leaders. In the past, they predominantly appeared as experts for topics like fashion, sports, or gaming and used their status to cooperate with brands for marketing purposes. However, since recently influencers also turn towards more meaningful and political content. In this article, we share our perspective on the rise of political influencers using examples of sustainability and related topics covered on Instagram. By applying a qualitative observational approach, we illustrate how influencers make political communication look easy, while at the same time seamlessly integrating product promotions in their social media feeds. In this context, we discuss positive aspects of political influencers like contributions to education and political engagement, but also negative aspects such as the potential amplification of radical political ideology or conspiracy theories. We conclude by highlighting political influencers as an important research topic for conceptual and empirical studies in the future.

Predatory Journals: How Far Can They Go? An In-Depth Look at the Outliers = Grabežljivi časopisi: koliko daleko mogu ići? Temeljita analiza vrijednosti

For over a decade, predatory publishers, journals and conferences have continuously menaced the research community, preying on its resources, and diminishing the general trust in science, becoming an important research topic. Previous studies have focused on identifying their characteristics, in order to increase the academic awareness and help researchers not becoming a prey. At the same time, predatory publishers diversified their strategies; the academic community developed disparate reactions, which determined more and diverse predatory strategies, aimed at luring and deceiving the scientists. While the process is still ongoing, the present research is aimed at exposing the most extreme predation strategies, in an effort to make the line separating honest and predatory journals more traceable. The analysis of relevant samples focuses on the language issue, based on the hypothesis according to which the predatory publishers are located in countries where English is rarely spoken. The findings, including inventing English names, advertisements making no sense for the Western world, lack of quality control and a poor graphic language, confirm the hypothesis, and are also able to stand at the core of possible guidelines for exposing predatory publishers based on specific features of their calls. = Više od deset godina, grabežljivi izdavači, časopisi i konferencije kontinuirano su ugrožavali istraživačku zajednicu, loveći njene resurse i umanjujući opće povjerenje u nauku, postajući važna istraživačka tema. Prethodne studije bile su usredotočene na identificiranje njihovih karakteristika, kako bi se povećala akademska svijest i pomoglo istraživačima da ne postanu plijen. Istodobno, grabežljivi izdavači diverzificirali su svoje strategije; akademska zajednica razvila je različite reakcije, koje su odredile više i raznovrsnije predatorske strategije, usmjerene na mamljenje i obmanjivanje naučnika. Iako je postupak još uvijek u toku, ovo je istraživanje usmjereno na izlaganje najekstremnijih strategija grabežljivosti, nastojeći da linija koja razdvaja poštene i grabežljive časopise postane sljedivija. Analiza relevantnih studija slučaja usredotočena je na jezičko pitanje, zasnovano na hipotezi prema kojoj se izdavači nalaze u zemljama u kojima se engleski jezik rijetko govori. Nalazi, uključujući izmišljanje engleskih imena, reklame koje nemaju smisla za zapadni svijet, nedostatak kontrole kvaliteta i loš grafički jezik, potvrđuju hipotezu i takođe mogu stajati u srži mogućih smjernica za izlaganje predatorskih izdavača na osnovu specifičnih karakteristika njihovih poziva.

Annotation and Classification of Graphs of Property Values Reported in Material Science Literature

Building a system for extracting information from the scientific literature is an important research topic in the field of inorganic materials science. However, conventional extraction systems have a limitation in that they do not extract characteristic values from nontextual components, such as charts, diagrams, and tables, which provide key information in many scientific documents. Although there have been several studies on identifying the characteristic values of graphs in the literature, there is no general method that classifies graphs according to the property conditions of the values in the field of materials science. Therefore, in this study, we focus on graphs that are figures representing graphically numerical data, such as a bar graph and line graph, as the first step toward developing a framework for extracting material property information from such noncontextual components. We propose deep-learning-based classification models for identifying the types of graph properties, such as temperature and time, by combining graph images, text in graphs, and captions in neural networks. To train and evaluate the models, we construct a material graph dataset with different types of material properties from a large collection of data from journals in the field of materials science. By using cloud sourcing, we annotate 16,668 images. Our experimental results demonstrate that the best model can achieve high performance with a microaveraged F-score of 0.961.