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The purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.

To promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.

The experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.

Hashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.

One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.

Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.

The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.

TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

The purpose of this paper is to effectively deal with querying of classification with membership.

The authors propose a scheme comprising a layer of Bloom filter for membership checking and a second layer based on neural network for dealing with the classification requirement.

Not only could false positives be dramatically decreased, but also classification could be achieved with the proposed scheme.

The experimental data were randomly generated instead of real‐world ones.

It is difficult to implement this scheme in a real‐world environment, such as the internet. Second, the neural network requires time to converge to a satisfactory level.

Internet ethic might be compromised by hackers once they find a way around the filtering mechanism.

The neural network was moditified and utilized for the first time to be suitable for our purpose. Second, the two‐layer design shows effectiveness.

Micro-video platforms have gained attention in recent years and have also become an important new channel for merchants to advertise their products. Since little research has studied micro-video advertising, this paper aims to fill the research gap by exploring the determinants of micro-video advertising clicks. We form a micro-video advertising click prediction model and demonstrate the effectiveness of the multimodal information extracted from the advertisement producers, commodities being sold and micro-video contents in the prediction task.

A multimodal analysis framework was conducted based on real-world micro-video advertisement datasets. To better capture the relations between different modalities, we adopt a cooperative learning model to predict the advertising clicks.

The experimental results show that the features extracted from different data sources can improve the prediction performance. Furthermore, the combination of different modal features (visual, acoustic, textual and numerical) is also worth studying. Compared to classical baseline models, the proposed cooperative learning model significantly outperforms the prediction results, which demonstrates that the relations between modalities are also important in advertising micro-video generation.

To the best of our knowledge, this is the first study analysing micro-video advertising effects. With the help of our advertising click prediction model, advertisement producers (merchants or their partners) can benefit from generating more effective micro-video advertisements. Furthermore, micro-video platforms can apply our prediction results to optimise their advertisement allocation algorithm and better manage network traffic. This research can be of great help for more effective development of the micro-video advertisement industry.

Sponsoring an employee for an MBA costs a company a great deal—in time as well as money. But now, employees can earn their degrees without leaving the office.

The purpose of this paper is to present a novel online video-based approach to supervision for statutory caseworkers. Caseworkers recorded a video of their meetings with their clients and sent the video to their supervisor. The supervisor selected clips in the video. They held an online meeting where they reviewed the clips, and the supervisor gave feedback and they reflected together. The caseworker then used what they had learnt in their future practice. The caseworker then recorded a new meeting, and the supervision cycle restarted.

In total, 11 statutory caseworkers from three municipalities in the Copenhagen area participated in semi-structured qualitative interviews. The interviews focused on the professional learning and challenges caseworkers faced in relation to participating in the supervision process.

The caseworkers reported that they used the method to assess their own practice in a more realistic way as the use of video gave a more accurate image than merely recalling what had occurred. They reflected about and developed their relationship with clients, their conversational style and use of communication techniques, skills in relation to running meetings, and skills in relation to eliciting the young person’s perspective. The caseworkers were anxious when they received their first feedback from supervisors, but this diminished. The focus on supporting clients in their personal development challenged caseworkers who identified as having an administrative rather than interventional role. Some found the online meeting technology difficult to master.

This study presents and explores the use of a novel approach to statutory casework supervision.

This chapter is about online hate speech propagated via platforms operated by social media companies (SMCs). It examines the options open to states in forcing SMCs to take responsibility for the hateful content that appears on their sites. It examines the technological and legal context for imposing legal obligations on SMCs, and analyses initiatives in Germany, the United Kingdom, the European Union and elsewhere. It argues that while SMCs can play a role in controlling online hate speech, there are limitations to what they can achieve.

This paper aims to build on the latest advances in cognitive computing techniques to systematically illustrate how unstructured data from users can offer significant value for co-innovation.

The paper adopts a general overview approach to understand how unstructured data from users can be analyzed with cognitive computing techniques for innovation. The paper links the computerized techniques with marketing innovation problems with an integrated framework using dynamic capabilities and complexity theory.

The paper identifies a suite of methodologies for facilitating company co-innovation via engaging with customers and external data with cognitive computing technologies. It helps to expand marketing researchers and practitioners’ understanding of using unstructured data.

This paper provides a conceptual framework that divides co-innovation process into three stages, ideas generation, ideas integration and ideas evaluation, and maps cognitive computing methodologies and technologies to each stage. This paper makes the theoretical contributions by developing propositions from both customer and firm perspectives.

This paper can be used for companies to engage consumers and external data for co-innovation activities by strategically select appropriate cognitive computing techniques to analyze unstructured data for better insights.

Given the lack of systematic discussion regarding what is possible from using cognitive computing to analyze unstructured data for co-innovation. This paper makes first attempt to summarize how unstructured data can be analyzed with cognitive computing techniques. This paper also integrates complexity theory to the framework from a novel perspective.

Most people are probably aware of malware, but they may not be aware of malware in what may be its most dangerous form, i.e. causing physical harm, even death, to individuals. This paper aims to document how software can cause malicious harm to individuals by attacking modern systems that appear to be neglected and under-researched.

This paper will review some of the most significant areas of concern with respect to end of days malware, i.e. malware that has a dangerous intent. The areas included are automobiles, medical devices and air traffic control systems.

The potential harmful effects of malware are often not well known by consumers and businesses around the world. These issues are not limited to just financial harm. Lives can actually be in danger. Underestimating the importance of cybersecurity and understanding the dangers that are associated with advancing technology are global issues that will continue unless there is enough awareness to force businesses and governments to address these issues. It is critical that safeguards are established.

While many papers have been written about malware and the implications of having malicious software infect a computer or a network, little attention has been paid to “end of days” malware. With advancing technology, malware now has the ability to cause serious injury or death to individuals who have minimal or no knowledge of the potential consequences of, for example, driving in an automobile, wearing or having an internal medical device or flying on an airplane. It is up to businesses and governments to address these issues.

A huge volume of published research articles is available on social media which evolves because of the rapid scientific advances and this paper aims to investigate the research structure of social media.

This study employs an integrated topic modeling and text mining-based approach on 30381 Scopus index titles, abstracts, and keywords published between 2006 and 2021. It combines analytical analysis of top-cited reviews with topic modeling as means of semantic validation. The output sequences of the dynamic model are further analyzed using the statistical techniques that facilitate the extraction of topic clusters, communities, and potential inter-topic research directions.

This paper brings into vision the research structure of social media in terms of topics, temporal topic evolutions, topic trends, emerging, fading, and consistent topics of this domain. It also traces various shifts in topic themes. The hot research topics are the application of the machine or deep learning towards social media in general, alcohol consumption in different regions and its impact, Social engagement and media platforms. Moreover, the consistent topics in both models include food management in disaster, health study of diverse age groups, and emerging topics include drug violence, analysis of social media news for misinformation, and problems of Internet addiction.

This study extends the existing topic modeling-based studies that analyze the social media literature from a specific disciplinary viewpoint. It focuses on semantic validations of topic-modeling output and correlations among the topics and also provides a two-stage cluster analysis of the topics.