Reviewerly/exHarmony huggingface dataset:
The Role of Geography in Facilitating Learners’ Digital Competence

Access to digital technologies and the Internet is having a decisive impact on all levels of a child’s life: personal, family, social, and in the future, professional. In the educational context of primary schools, both analogue and digital media play a decisive role through various media-didactic (media-educational) functions. On the one hand, digital media and artifacts are used extensively in primary geography lessons. Thus, mainly due to its specific approach, Geography teaching can contribute to reconstructing the complex, digitally shaped reality of life in an interdisciplinary didactic way (learning about media). On the other hand, digital media can support learning and cognitive processes in geography lessons in such a way that includes the development of the understanding of geography as a science process (scientific literacy, e.g., in the form of data acquisition and communication). In this respect, digital media can be seen as “media-didactic added value” (learning with and through media). In addition, digital media are used in geography lessons in several ways. For example, to allow access to things, as a source of information, as a research tool, as a means of documentation, and as a means of communication. This chapter describes the potential of digital technologies and digital media for the further development and innovation of education. It presents ways in which primary geography can and should contribute to facilitating children’s digital competence.
Incremental Unit Networks for Distributed, Symbolic Multimodal Processing and Representation

Incremental dialogue processing has been an important topic in spoken dialogue systems research, but the broader research community that makes use of language interaction (e.g., chatbots, conversational AI, spoken interaction with robots) have not adopted incremental processing despite research showing that humans perceive incremental dialogue as more natural. In this paper, we extend prior work that identifies the requirements for making spoken interaction with a system natural with the goal that our framework will be generalizable to many domains where speech is the primary method of communication. The Incremental Unit framework offers a model of incremental processing that has been extended to be multimodal, temporally aligned, enables real-time information updates, and creates complex network of information as a fine-grained information state. One challenge is that multimodal dialogue systems often have computationally expensive modules, requiring computation to be distributive. Most importantly, when speech is the means of communication, it brings the added expectation that systems understand what they (humans) say, but also that systems understand and respond without delay. In this paper, we build on top of the Incremental Unit framework and make it amenable to a distributive architecture made up of a robot and spoken dialogue system modules. To enable fast communication between the modules and to maintain module state histories, we compared two different implementations of a distributed Incremental Unit architecture. We compare both implementations systematically then with real human users and show that the implementation that uses an external attribute-value database is preferred, but there is some flexibility in which variant to use depending on the circumstances. This work offers the Incremental Unit framework as an architecture for building powerful, complete, and natural dialogue systems, specifically applicable to robots and multimodal systems researchers.
Assessment System for Subjective Well-being of Russian Schoolchildren: a Theoretical and Methodological Rationale

The article considers the meaningful understanding of the category "subjective well-being of Russian schoolchildren" and determines the methodological rationale for developing an assessment system for it. The subjective well-being of Russian students' families is studied through the level of positive/negative emotions and the realization of personal potential, i.e. as satisfaction with such areas of life as family and education. The aim is to describe the methodology for developing an assessment system for subjective well-being of Russian schoolchildren at different stages of education, based on the theoretical analysis of existing meaningful interpretations of the "subjective well-being" category and methods for identifying its level. The task of developing the assessment system for subjective well-being is being solved by identifying and justifying social and psychological factors of family and school environment. Significant factors that affect the children's well-being can include family relationships, spiritual unity, material security, relations with classmates, learning achievements, educational environment. To develop the assessment system we used methods of system analysis, methods of induction and deduction, methods of mathematical analysis, the method of factor analysis and others. The methodology for determining the subjective well-being of Russian schoolchildren includes a criterial framework and assessment tools. Taking into account the identified social and psychological factors of family and school environment, the subjective well-being is studied in criteria of satisfaction/dissatisfaction (positive/negative emotions) and realization of a child's personality potential. The assessment system presents the criteria for identifying the subjective well-being of Russian schoolchildren at different levels of education as two blocks: family and educational well-being. The methodological structuring of the criteria allowed us to develop mathematical formulas which can be used to obtain numerical expressions of both the level of subjective well-being itself and the levels of family and educational well-being.
FPGA Implementation of Masked-AE$HA-2 for Digital Signature Application

Authentication and data integrity are essential features in cryptographic algorithms for generating the digital signature. It will enhance trusted communication in a wireless network. Hash functions are the best choice for generating the digital signatures at the transmitter end and validates the original data at the receiver side in the open public access. These hash functions are prone to advanced attacks such as Side Channel Analysis (SCA), birthday, and pre-image collision. A hybrid Masked AE$HA-2 hash function has been proposed to increase the security strength of digital signature for effective shielding against such attacks. The proposed architecture includes a masked Advanced Encryption Standard (AES-256) followed by the secured hash algorithm (SHA-256) yielding a highly secured Masked AE$HA-2 crypto-style. The masked AES algorithm has the advantage of combining a false key with the real key to hide the original data, thus significantly improving the security. Further, in conventional SHA-2, the message expansion has been replaced with a sliding window protocol to minimize the computational time. Hardware implementation on Virtex-7 device with the help of Xilinx Vivado tool achieves low computational time of 680 ns for generating the masked AE$HA-2 hashed value.
Three-Dimensional Image Inpainting System Using 3D-ED-GAN for Efficient Vision-Based Detection for Rotor Dynamic Balance System

We propose a three-dimensional (3D) image inpainting system using the 3D encoder&#x2013;decoder generative adversarial network (IISU3EDGAN) for providing accurate detection results in vision-based rotor dynamic balancing processes. The proposed IISU3EDGAN system integrates 3D sensors with a deep learning network to reconstruct corrupted rotor images, thereby optimizing the detection parameters. In rotor component detection processes, overexposed images caused by reflections of the metallic rotor shaft affect the accuracy and performance of vision-based inspection systems. Traditional image restoration technologies or inpainting methods are inadequate for solving this problem. By contrast, our proposed system can repair 3D overexposed images of rotors. Compared with traditional image processing methods, the proposed system can adequately manage the complexity of corrupted images. In addition, it can be used to process complex overexposed rotor images and maintain image details. The proposed system can be applied to a wider range of rotor types, and it can be used to optimize the parameters of vision-based rotor detection systems to improve the accuracy of rotor component detection. We conducted experiments and observed that by using 3D sensors and deep learning, the proposed system improved the success rate in the first round of rotor dynamic balancing, reduced the number of rounds required for balancing, and increased the rotor production output. These results thus indicate that the IISU3EDGAN system is applicable and robust and that it can be used to improve the overall efficiency of dynamic balancing on rotor production lines.
Efficient single image haze removal using CLAHE and Dark Channel Prior for Internet of Multimedia Things

The immersive growth of multimedia sensors in conjunction with IoT in smart cities has increased the requirement of efficient multimedia computing. One such example is the degradation of images during bad weather and their efficient processing to help surveillance, driving assistance, etc. Haze removal is an important procedure to avoid ill-condition visibility of the captured images. An optimized haze removal technique helps the local administrative authorities by integrating multimedia sensors with the IoT to improve the quality of life. In this chapter, we analyze the well-known single image dehazing technique – Dark Channel Prior (DCP) in terms of getting better image quality and optimized computational time. The DCP method is stable and a benchmark for dehazing. However, we found that the haze removal effects obtained by the DCP can be further enhanced by preprocessing the underlying image for contrast enhancement. Also, the computational time is improved with respect to the underlying refinements. Considering the dehazing quality and computational efficiency, we analyze the image quality and demonstrate that it can be further improved with a better preprocessing mechanism. The experiments are conducted over a wide variety of haze images from standard datasets and sufficient improvements over the original DCP method are demonstrated.
Social Spammer Detection via Convex Nonnegative Matrix Factorization

With the increasing popularity of social network platforms such as Twitter and Sina Weibo, a lot of malicious users, also known as social spammers, disseminate illegal information to normal users.Several approaches are proposed to detect spammers by training a classifier with optimization methods and mainly using content and social following information.Due to the development of spammers' strategies and the courtesy of some legitimate users, social following information becomes vulnerable to fake by spammers.Meanwhile, the possible social activities and behaviors vary significantly among different users, which leads to a large yet sparse feature space to be modeled by existing approaches.To address issues, in this paper, we propose a new approach named CNMFSD for spammer detection in social networks, which exploits both content information and users interaction relationships in an innovative manner.We have empirically validated the proposed method on a real-world Twitter dataset, and experimental results show that the proposed CNMFSD method improves the detection performance significantly compared with baselines.
Federated Deep Reinforcement Learning-Based Spectrum Access Algorithm With Warranty Contract in Intelligent Transportation Systems

Cognitive radio (CR) provides an effective solution to meet the huge bandwidth requirements in intelligent transportation systems (ITS), which enables secondary users (SUs) to access the idle spectrum of the primary users (PUs). However, the high mobility of users and real-time service requirements result in the additional transmission collisions and interference, which degrades the spectrum access rate and the quality of service (QoS) of users in ITS. This paper proposes a spectrum access algorithm (Feilin) based on federated deep reinforcement learning (FDRL) to improve spectrum access rate, which maximizes the QoS reward function with considering the hybrid benefits of delay, transmission power and utility of SUs. To guarantee the utility of SUs, the warranty contract is designed for SUs to obtain compensation for data transmission failure, which promotes SUs to compete for more spectrum resources. To meet the real-time requirements and improve QoS in ITS, a spectrum access model called FDQN-W is proposed based on federated deep Q-network (DQN), which adopts the asynchronous federated weighted learning algorithm (AFWLA) to share and update the weights of DQN in multiple agents to decrease time cost and accelerate the convergence. Detailed simulation results show that, in the multiuser scenario, compared with the existing methods, the proposed algorithm Feilin increases the spectrum access success rate by 15.1%, and reduces the collision rate with SUs and the collision rate with PUs by 46.4% and 6.8%, respectively.
Astrape: Anonymous Payment Channels with Boring Cryptography

The increasing use of blockchain-based cryptocurrencies like Bitcoin has run into inherent scalability limitations of blockchains. Payment channel networks, or PCNs, promise to greatly increase scalability by conducting the vast majority of transactions outside the blockchain while leveraging it as a final settlement protocol. Unfortunately, first-generation PCNs have significant privacy flaws. In particular, even though transactions are conducted off-chain, anonymity guarantees are very weak. In this work, we present Astrape, a novel PCN construction that achieves strong security and anonymity guarantees with simple, black-box cryptography, given a blockchain with flexible scripting. Existing anonymous PCN constructions often integrate with specific, often custom-designed, cryptographic constructions. But at a slight cost to asymptotic performance, Astrape can use any generic public-key signature scheme and any secure hash function, modeled as a random oracle, to achieve strong anonymity, by using a unique construction reminiscent of onion routing. This allows Astrape to achieve provable security that is “generic” over the computational hardness assumptions of the underlying primitives. Astrape’s simple cryptography also lends itself to more straightforward security proofs compared to existing systems. Furthermore, we evaluate Astrape’s performance, including that of a concrete implementation on the Bitcoin Cash blockchain. We show that despite worse theoretical time complexity compared to state-of-the-art systems that use custom cryptography, Astrape operations on average have a very competitive performance of less than 10 ms of computation and 1 KB of communication on commodity hardware. Astrape explores a new avenue to secure and anonymous PCNs that achieves similar or better performance compared to existing solutions.
Digital Transformation of Higher Education

The digital transformation of society and universities has been accelerating in the COVID-19 pandemic. The digital transformation of education will advance beyond distance and time by utilizing digital technology and develop international and industry-university collaboration. The Japanese Society for Engineering Education initiated a survey and research committee in April 2021. First, the direction of the digital transformation of engineering education will be presented through a multifaceted survey of social needs, information technology, international collaboration, and industry-academia-government collaboration. Secondly, as an advanced model, the realization of graduate and recurrent education in international inter-university and industry-academia collaboration is investigated. This paper is an interim report.
Maturity Concept and Model Review

The management principle is widely known, according to which an activity cannot be managed if it cannot be measured and evaluated for compliance with the established requirements and assessment criteria. In turn, processes cannot be managed if they are not defined, and processes cannot be improved if they are not controlled. These ideas are fully applicable to the information security management processes. The goal of the paper is to introduce the concept of a maturity model, based on a generalization of numerous sources, and to provide a short review of such models. In conclusion, the maturity models for developing a maturity model for network security centers with network security management processes to be assessed are identified for the future work.
The Role of a Chabot Personality in the Attitude of Consumers Towards a Banking Brand

In recent years, it has been seen that brands have opted to implement chatbots as part of their digital customer service channels, especially since they can be hosted on social networks such as Facebook Messenger and WhatsApp. Although the use of this has become widespread, users perceive them as inefficient and of little credibility. The design of the personality of a chatbot arises as an alternative for the brand to generate a more human interaction with the user and show its brand values to generate a positive experience. In addition, a projected growth of the business value of chatbots for brands of 3.9 trillion dollars is estimated by 2022. For this reason, knowing how brands can rely on the correct implementation of the personality of a chatbot creating a positive influence on its users is important in a growing industry. The present research seeks to know how the personality of the chatbot influences the formation of consumer attitudes towards a brand. This exploratory study used a qualitative methodology of phenomenological design with young adults from Lima. Subsequently, it was analyzed through descriptive coding. It was found that the social dynamics, the social role and the physique of the chatbot are the aspects that influence the attitude towards the brand and generate a feeling of trust and closeness between the chatbot and the brand.
Influence of blockchain technology in pharmaceutical industries

Blockchain technology is a decentralized distributed ledger that maintains a list of transactions in a peer-to-peer network in which the security is provided using cryptographic techniques. The history is recorded in every block; these blocks are cryptographically connected and are secured over time. Moreover, this technology has been used for maintaining cryptocurrencies, digital contracts, land ownership, public records, etc., so far. Future applications are likely to include education, intellectual property, supply chain management, medicine, and science. Also, with capital expenditures in blockchain technology expected to hit USD 13.96 billion by 2022, blockchain is a perfect solution for developing a secure drug supply chain where data security and privacy protection are the major concerns. The performance measures such as central dependency, security, smart contract management, encryption, error rate, user privacy, and traceability are considered for the comparison of the proposed technique with existing approaches.
OCR Improves Machine Translation for Low-Resource Languages

We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts.We introduce and make publicly available a novel benchmark, OCR4MT, consisting of real and synthetic data, enriched with noise, for 60 low-resource languages in low resource scripts.We evaluate state-of-the-art OCR systems on our benchmark and analyse most common errors.We show that OCR monolingual data is a valuable resource that can increase performance of Machine Translation models, when used in backtranslation.We then perform an ablation study to investigate how OCR errors impact Machine Translation performance and determine what is the minimum level of OCR quality needed for the monolingual data to be useful for Machine Translation.
The State of the Art Techniques in Recommendation Systems

The fast increment in the measure of information over the internet has made it hard to discover entities that might hold any importance with users. Thus, information has expanded at an unpredicted rate, and the information over-burden issue has turned out to be progressively severe for online users. One answer for this information over-burden issue is the utilization of Recommendation Systems (RSs). Recommendation System is a capable technology that tries to anticipate users’ interests by providing them with the information or services they require without directly enquiring. Recommendation systems are used by most social media services and e-commerce applications to improve user convenience. RS helps to explore options and decrease information overload for online users. This paper presents a complete survey of recommendation systems. This paper forms a platform for researchers in the recommendation system domain and provides collective discussions over various techniques.
Enhancing Context-Aware Reactive Planning for Unexpected Situations of On-Orbit Spacecraft

Spacecraft software is a complicated software system integrating many subsystems of different disciplines. Supporting on-orbit missions needs the knowledge of industrial information integration. To ensure mission success, reactive planning is a critical function in solving dynamic problems during task execution, relying on the knowledge of situation for optimal decision-making. However, it may unable to correctly identify the operational context in unexpected situations. This article solves the unexpected situation problem for reactive planning. It proposes a context-awareness model enhancement framework to identify the triggers of critical situations based on event evolution-based analysis. The framework includes the process of unexpected situation discovery, context identification, and context refinement. With this approach, preventive operational resilience can be achieved. Moreover, a symbiotic computing paradigm and a flexible inference engine are devised for addressing the on-orbit spacecraft-specific challenges. Also, an agent-based reactive system design and a system integration architecture is provided for implementing the proposed approach from both autonomy and information integration perspective. Experiment results show that the proposed approach is effective and efficient to solve the unexpected situation issue. This article offers a crucial insight to context-aware decision-support in space applications.
Recognizing Bot Activity in Collaborative Software Development

Using popular open source projects on GitHub, we provide evidence that bots are regularly among the most active contributors, even though GitHub does not explicitly acknowledge their presence. This poses a problem for techniques that analyze human contributor activity.