Abstract: Cognitive radio networks can efficiently manage the radio spectrum by utilizing the spectrum holes for secondary users in licensed frequency bands. The energy that is used to detect spectrum holes can be reduced considerably by predicting them. However, collisions can occur either between a primary user and secondary users or among the secondary users themselves. This paper introduces a centralized channel allocation algorithm (CCAA) in a scenario with multiple secondary users to control primary and secondary collisions. The proposed allocation algorithm, which uses a channel state predictor (CSP), provides good performance with fairness among the secondary users while they have minimal interference with the primary user. The simulation results show that the probability of a wrong prediction of an idle channel state in a multi-channel system is less than 0.9%. The channel state prediction saves the sensing energy by 73%, and the utilization of the spectrum can be improved by more than 77%.

Abstract: We present IVOS, an interactive video content search system that allows for object-based search and filtering in video archives. The main idea behind is to use the result of recent object detection models to index all keyframes with a manageable set of object classes, and allow the user to filter by different characteristics, such as object name, object location, relative object size, object color, and combinations for different object classes – e.g., “large person in white on the left, with a red tie”. In addition to that, IVOS can also find segments with a specific number of objects of a particular class (e.g., “many apples” or “two people”) and supports similarity search, based on similar object occurrences.

Abstract: The push for agile pandemic analytic solutions has attained development-stage software modules of applications instead of functioning as full-fledged production-stage applications – i.e., performance, scalability, and energy-related concerns are not optimized for the underlying computing domains. And while the research continues to support the idea that reducing the energy consumption of algorithms improves the lifetime of battery-operated machines, advisable tools in almost any developer setting, an energy analysis report for R-based analytic programs is indeed a valuable suggestion. This article proposes an energy analysis framework for R-programs that enables data analytic developers, including pandemic-related application developers, to analyze the programs. It reveals an energy analysis report for R programs written to predict the new cases of 215 countries using random forest variants. Experiments were carried out at the IoT cloud research lab and the energy efficiency aspects were discussed in the article. In the experiments, ranger-based prediction program consumed 95.8 J.

Abstract: Entrepreneurship Education is an important field of entrepreneurship research and has become a part of many programs of business and engineering schools. Educational games are a powerful tool to create a motivation learning environment. With the goal of investigating digitalization of business games, which are typically played inlarge groups and face to face, we particularly focus on the use case of thebusiness model game called “inspire! build your business”.

Abstract: As a longstanding participating system in the annual Video Browser Showdown (VBS2017-VBS2020) as well as in two iterations of the more recently established Lifelog Search Challenge (LSC2018-LSC2019), diveXplore is developed as a feature-rich Deep Interactive Video Exploration system. After its initial successful employment as a competitive tool at the challenges, its performance, however, declined as new features were introduced increasing its overall complexity. We mainly attribute this to the fact that many additions to the system needed to revolve around the system’s core element – an interactive self-organizing browseable featuremap, which, as an integral component did not accommodate the addition of new features well. Therefore, counteracting said performance decline, the VBS 2021 version constitutes a completely rebuilt version 5.0, implemented from scratch with the aim of greatly reducing the system’s complexity as well as keeping proven useful features in a modular manner.

Abstract: This article proposes an approach to the problem of computational capacities analysis of the computing continuum via theoretical framework of equilibrium phase-transitions and numerical simulations. We introduce the concept of phase transitions in computing continuum and show how this phenomena can be explored in the context of workflow makespan, which we treat as an order parameter. We simulate the behavior of the computational network in the equilibrium regime within the framework of the XY-model defined over complex agent network with Barabasi-Albert topology. More specifically, we define Hamiltonian over complex network topology and sample the resulting spin-orientation distribution with the Metropolis-Hastings technique. The key aspect of the paper is derivation of the bandwidth matrix, as the emergent effect of the “low-level” collective spin interaction. This allows us to study the first order approximation to the makespan of the “high-level” system-wide workflow model in the presence of data-flow anisotropy and phase transitions of the bandwidth matrix controlled by the means of “noise regime” parameter η. For this purpose, we have built a simulation engine in Python 3.6. Simulation results confirm existence of the phase transition, revealing complex transformations in the computational abilities of the agents. Notable feature is that bandwidth distribution undergoes a critical transition from single to multi-mode case. Our simulations generally open new perspectives for reproducible comparative performance analysis of the novel and classic scheduling algorithms.

Abstract: We present our NoShot Video Browser, which has been successfully used at the last Video Browser Showdown competition VBS2020 at the MMM2020. NoShot is given its name due to the fact, that it neither makes use of any kind of shot detection nor utilize the VBS master shots. Instead videos are split into frames with a time distance of one second. The biggest strength of the system lies in its feature “time cache”, which shows results with the best confidence in a range of seconds.

Abstract: Semantic segmentation in surgical videos is a prerequisite for a broad range of applications towards improving surgical outcomes and surgical video analysis. However, semantic segmentation in surgical videos involves many challenges. In particular, in cataract surgery, various features of the relevant objects such as blunt edges, color and context variation, reflection, transparency, and motion blur pose a challenge for semantic segmentation. In this paper, we propose a novel convolutional module termed as ReCal module, which can calibrate the feature maps by employing region intra-and-inter-dependencies and channel-region cross-dependencies. This calibration strategy can effectively enhance semantic representation by correlating different representations of the same semantic label, considering a multi-angle local view centering around each pixel. Thus the proposed module can deal with distant visual characteristics of unique objects as well as cross-similarities in the visual characteristics of different objects. Moreover, we propose a novel network architecture based on the proposed module termed as ReCal-Net. Experimental results confirm the superiority of ReCal-Net compared to rival state-of-the-art approaches for all relevant objects in cataract surgery. Moreover, ablation studies reveal the effectiveness of the ReCal module in boosting semantic segmentation accuracy.

Abstract: A critical complication after cataract surgery is the dislocation of the lens implant leading to vision deterioration and eye trauma. In order to reduce the risk of this complication, it is vital to discover the risk factors during the surgery. However, studying the relationship between lens dislocation and its suspicious risk factors using numerous videos is a time-extensive procedure. Hence, the surgeons demand an automatic approach to enable a larger-scale and, accordingly, more reliable study. In this paper, we propose a novel framework as the major step towards lens irregularity detection. In particular, we propose (I) an end-to-end recurrent neural network to recognize the lens-implantation phase and (II) a novel semantic segmentation network to segment the lens and pupil after the implantation phase. The phase recognition results reveal the effectiveness of the proposed surgical phase recognition approach. Moreover, the segmentation results confirm the proposed segmentation network’s effectiveness compared to state-of-the-art rival approaches.

Abstract: A typical life cycle of an online game is reflected in its usage patterns. A game first builds a user base, then reaches an absolute peak, to then being played by a minimum number of dedicated fans at the end of its life. Apart from this development, extraordinary internal and external events can be observed as changes in usage in games, especially multiplayer and massive multiplayer ones. For the usage of video games, the COVID-19 pandemic has impacted usage as it had on the game business itself. However, research lacks data to investigate these relations further. Usage statistics of games are rarely accessible for researchers. In this paper, we relate usage statistics to viewership and popularity of a game using available data sources like online statistics or activity on Twitch.tv. In a first study, data from the online role-playing game (MMORPG) Eternal Lands is analyzed. Eternal Lands is a free, multiplayer, online game that was created already in 2002. The usage patterns show day/night cycles of players in the prime time of the time zones where most players are located and increased playing activity on weekends. A general trend over time shows a slowly diminishing user base over the years since its introduction. In April 2020, a significant rise in user activities can be observed, attributed to lockdowns in many countries due to the COVID-19 pandemic. This can be attributed to regular players investing more time playing the game during the lockdown and to new or recurring players, who have not played the game intensively before, were looking for a distraction during the lockdown. In a second study, we focus on complementary viewer statistics on the popular game streaming platform Twitch.tv. We can observe that the COVID-19 pandemic impacted the playing time, as mentioned earlier. We relate usage data to viewership and streaming statistics of popular games. With the example of Eternal Lands, being a game that never went viral, we discuss the possibility of approximating a game's popularity through game streaming and viewership.

Abstract: HTTP Adaptive Streaming (HAS) enables high quality stream-ing of video contents. In HAS, videos are divided into short intervalscalled segments, and each segment is encoded at various quality/bitratesto adapt to the available bandwidth. Multiple encodings of the same con-tent imposes high cost for video content providers. To reduce the time-complexity of encoding multiple representations, state-of-the-art methods typically encode the highest quality representation first and reusethe information gathered during its encoding to accelerate the encodingof the remaining representations. As encoding the highest quality rep-resentation requires the highest time-complexity compared to the lowerquality representations, it would be a bottleneck in parallel encoding scenarios and the overall time-complexity will be limited to the time-complexity of the highest quality representation. In this paper and toaddress this problem, we consider all representations from the highestto the lowest quality representation as a potential, single reference toaccelerate the encoding of the other, dependent representations. We for-mulate a set of encoding modes and assess their performance in terms ofBD-Rate and time-complexity, using both VMAF and PSNR as objec-tive metrics. Experimental results show that encoding a middle qualityrepresentation as a reference, can significantly reduce the maximum en-coding complexity and hence it is an efficient way of encoding multiplerepresentations in parallel. Based on this fact, a fast multirate encodingmethod is proposed which utilizes depth and prediction mode of a middle quality representation to accelerate the encoding of the dependentrepresentations.

Abstract: News is the most important and sensitive piece of information which affects the society nowadays. In the current scenario, there are two ways to propagate news all over the world; first one is the traditional way, i.e., newspaper and second is electronic media like social media websites. Electronic media is the most popular medium these days because it helps to propagate news to huge audience in few seconds. Besides these benefits of electronic media, it has one disadvantage also, i.e., “spreading the Fake News”. Fake news is the most common problem these days. Even big companies like Twitter, Facebook, etc. are facing fake news problems. Several researchers are working in these big companies to solve this problem. Fake news can be defined as the news story that is not true. In some specific words, we can say that news is fake if any news agency declares a piece of news deliberately written as false and it is also verifiably as false. This paper focuses on some key characteristics of fake news and how it is affecting the society nowadays. It also includes various key viewpoints which are useful to categorize whether the news is fake or not. At last, this paper discussed some key challenges and future directions that help in increasing accuracy in detection of fake news on the basis of P2C2 (Propagation, Pattern, Comprehension & Credibility) approach having two phases: Detection and Verification. This paper helps readers in two ways (i) Newcomer can easily get the basic knowledge and impact of fake news; (ii) They can get knowledge of different perspectives of fake news which are helpful in the detection process.

Abstract: The ARTICONF project funded by the European Horizon 2020 program addresses issues of trust, time-criticality and democratisation for a new generation of federated infrastructure, to full the privacy, robustness, and autonomy related promises critical in proprietary social media platforms. It aims to: (1) simplify the creation of open and agile social media ecosystem with trusted participation using a two stage permissioned blockchain; (2) automatically detect interest groups and communities using graph anonymization techniques for decentralised and tokenized decision-making and reasoning; (3) elastically autoscale time-critical social media applications through an adaptive orchestrated Cloud edge-based infrastructure meeting application runtime requirements; and (4) enhance monetary inclusion in collaborative models through cognition and knowledge supply chains. We summarize the initial envisaged architecture of the ARTICONF ecosystem, the industrial pilot use cases for validating it, and the planned innovations compared to related other European research projects.

Abstract: With the rapidly increasing popularity of social media applications, decentralized control and ownership is taking more attention topreserve user's privacy. However, the lack of central control in the decentralized social network poses new issues of collaborative decision makingand trust to this permission-less environment. To tackle these problemsand ful ll the requirements of social media services, there is a need forintelligent mechanisms integrated to the decentralized social media thatconsider trust in various aspects according to the requirement of services. In this paper, we describe an adaptive microservice-based designcapable of nding relevant communities and accurate decision makingby extracting semantic information and applying role-stage model whilepreserving anonymity. We apply this information along with exploitingPareto solutions to estimate the trust in accordance with the quality ofservice and various con icting parameters, such as accuracy, timeliness,and latency.

Abstract: Community-based question answering (CQA) is an online-based crowdsourcing service that enables users to share and exchange information in the field of natural language processing. A major challenge of CQA service is to determine the high-quality answer with respect to the given question. The existing methods perform semantic matches between a single pair of a question and its relevant answer. In this paper, a Spatio-Temporal bidirectional Long Short-Term Memory (ST-BiLSTM) method is proposed to predict the semantic representation between the question–answer and answer–answer. ST-BiLSTM has two LSTM network instead of one LSTM network (i.e., forward and backward LSTM). The forward LSTM controls the spatial relationship and backward LSTM for examining the temporal interactions for accurate answer prediction. Hence, it captures both the past and future context by using two networks for accurate answer prediction based on the user query. Initially, preprocessing is carried out by name-entity recognition (NER), dependency parsing, tokenization, part of speech (POS) tagging, lemmatization, stemming, syntactic parsing, and stop word removal techniques to filter out the useless information. Then, a par2vec is applied to transform the distributed representation of question and answer into a fixed vector representation. Next, ST-BiLSTM cell learns the semantic relationship between question–answer and answer–answer to determine the relevant answer set for the given user question. The experiment performed on SemEval 2016 and Baidu Zhidao datasets shows that our proposed method outperforms than other state-of-the-art approaches.

Abstract: With the emerging Internet of Things (IoT), distributed systems enter a new era. While pervasive and ubiquitous computing already became reality with the use of the cloud, IoT networks present new challenges because the ever growing number of IoT devices increases the latency of transferring data to central cloud data centers. Edge and fog computing represent practical solutions to counter the huge communication needs between IoT devices and the cloud. Considering the complexity and heterogeneity of edge and fog computing, however, resource provisioning remains the Achilles heel of efficiency for IoT applications. According to the importance of operating-system virtualization (so-called containerization), we propose an application-aware container scheduler that helps to orchestrate dynamic heterogeneous resources of edge and fog architectures. By considering available computational capacity, the proximity of computational resources to data producers and consumers, and the dynamic system status, our proposed scheduling mechanism selects the most adequate host to achieve the minimum response time for a given IoT service. We show how a hybrid use of containers and serverless microservices improves the performance of running IoT applications in fog-edge clouds and lowers usage fees. Moreover, our approach outperforms the scheduling mechanisms of Docker Swarm.

Abstract: In crisis situations like riots, earthquakes, storms, etc. information plays a central role in the process of organizing interventions and decision making. Due to their increasing use during crises, social media (SM) represents a valuable source of information that could help obtain a full picture of people needs and concerns. In this chapter, we highlight the importance of SM networks in crisis management (CM) to show how information is propagated through. The chapter also summarizes the current state of research related to information propagation in SM networks during crises. In particular three classes of information propagation research categories are identified: network analysis and community detection, role and topic-oriented information propagation, and infrastructure-oriented information propagation. The chapter describes an analysis framework that deals with structural information propagation for crisis management purposes. Structural propagation is about broadcasting specific information obtained from social media networks to targeted sinks/receivers/hubs like emergency agencies, police department, fire department, etc. Specifically, the framework aims to identify the discussion topics, known as sub-events , related to a crisis (event) from SM contents. A brief description of techniques used to detect topics and the way those topics can be used in structural information propagation are presented.

Abstract: This chapter introduces the concept of Sensory Experience which aims to define the Quality of Experience (QoE) going beyond audio-visual content. In particular, we show how to utilize sensory effects such as ambient light, scent, wind, or vibration as additional dimensions contributing to the quality of the user experience. Therefore, we utilize a standardized representation format for sensory effects that are attached to traditional multimedia resources such as audio, video, and image contents. Sensory effects are rendered on special devices (e.g., fans, lights, motion chair, scent emitter) in synchronization with the traditional multimedia resources and shall stimulate also other senses than hearing and seeing with the intention to increase the Quality of Experience (QoE), in this context referred to as Sensory Experience.

Abstract: Multimedia (especially video) services constitute a dominant and ever increasing portion of the global Internet traffic, while they are expected to also play a major role in the Future Internet scene. In order to address this reality in the networking domain, a promising perspective is to gradually shift from the current, service-unaware, best-effort nature of IP networks into a network logic which is service-aware – and, in specific, media-aware. This chapter discusses how media-awareness can be introduced in the networking domain in a way which is both feasible and scalable, leveraging at the same time state-of-the-art technologies in video representations, such as Scalable Video Coding (SVC) and Dynamic Adaptive Streaming over HTTP (DASH).

Abstract: Scalable Coherent Interface (SCI) is the specification (standardized by ISO/IEC and the IEEE) of a high-speed, flexible, scalable, point-to-point-based interconnect technology that was implemented in various ways to couple multiple processing nodes. SCI supports both the message-passing and shared-memory communication models, the latter in either the cache-coherent or non-coherent variants. SCI can be deployed as a system area network for compute clusters, as a memory interconnect for large-scale, cache-coherent, distributed-shared-memory multiprocessors, or as an I/O subsystem interconnect.