Reviewerly/exHarmony huggingface dataset:
Content Clock Graph: Visualizing Teaching Orchestration from Classroom Videos

Visualization for teaching orchestration is a research field of education. Recently, a number of works focus on a partial view such as behaviors or knowledge structure in classroom teaching videos and lack the description of the videos from multiple angles. Therefore, in this paper, we provide a panoramic view of the videos, in which education researchers can intuitively observe teachers' teaching strategies. The panoramic view, namely Content Clock Graph (CCG) is proposed to describe teachers' teaching activities by integrating the data from three dimensions of teaching content, teachers' behaviors and time sequence. In CCG, we take the teaching content as the core of this graph and describe the temporal distribution of behaviors when explaining different content. Besides, this graph has the function of explaining the special teaching phenomena. Experimental results show that we can analyze the organization of teaching content and the temporal distribution of teachers' behaviors from the CCGs of two teachers.
State of Art Survey for Deep Learning Effects on Semantic Web Performance

One of the more significant recent major progress in computer science is the coevolution of deep learning and the Semantic Web. This subject includes research from various perspectives, including using organized information inside the neural network training method or enriching these networks with ontological reasoning mechanisms. By bridging deep learning and the Semantic Web, it is possible to enhance the efficiency of neural networks and open up exciting possibilities in science. This paper presents a comprehensive study of the closest previous researches, which combine the role of Deep Learning and the performance of the Semantic web, which ties together the Semantic Web and deep learning science with their applications. The paper also explains the adoption of an intelligent system in Semantic Deep Learning (SemDeep). As significant results obtained from previous works addressed in this paper, it can be notified that they focussed on real-time detection of phishing websites by HTML Phish. Also, the DnCNN, led by ResNet, achieved the best results, Res-Unit, UNet, and Deeper SRCNN, which recorded 88.5% SSIM, 32.01 percent PSNR 3.90 percent NRMSE.
Embedded Online Machine Learning

The paper presents research on a set of “classical” machine learning algorithms for tiny (microbatch, i.e., batch size equal to or less than 128) embedded online machine learning on ARM processor boards with hard memory limits and a tiny memory footprint while running on a single CPU without multithreading. We propose mathematical improvements to algorithms as well as other programming optimizations. In the presence of evolving data streams, we present an adaptation of the Gradient Boosting Decision Trees (GBDT) learning algorithm for classification tasks, the eXtreme Gradient Boosting (XGBoost, XGB) and the Random Forest (RF) learning algorithms for supervised anomaly detection tasks, and the Extended Isolation Forest (EIF) learning algorithm for unsupervised anomaly detection tasks. In this scenario, as new data is added over time, the connection between the class and the characteristics may shift, resulting in concept drift. The proposed technique generates new members of the ensemble from microbatches and/or batches of data for each algorithm as new data becomes available. The maximum ensemble size is specified, but learning does not stop when it reaches this size because the ensemble is constantly updated with new data to ensure compatibility with the current notion. We tested our technique on real-world data and compared it to the original batch-incremental learning algorithms for data streams. Our implementations gain a speedup in inference up to several times even demonstrating prediction quality improvement by 0.1-0.3 in terms of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${F_{1}}$</tex> measure in some cases.
Introduction to Security Incidents and Response Against Cyber Attacks

This introduction chapter presents the different concepts and context of the book. It explains how the pandemic has led to digital transformation and therefore a dramatic increase in security incidents and cyberattacks. How training to mitigate security incidents and cyberattacks has resulted in more hackers and cybercriminals being trained. To make matters worse, security incidents and cyberattacks are now being armed with technologies like Artificial Intelligence, which can easily outsmart human responses in trying to detect, mitigate, and counter attacks. Along the way, we have learnt a lot and developed ISO27000 standards, NIST 800 61 Incident Handling Guides, as well as local and regional Computer Security Incident Response Teams (CSIRT). A security incident or cyberattack takes place when there is an unauthorized access to an organization computer network. Computer Emergency Response Teams (CERT) and Computer Security Incident Response Teams (CSIRT) are usually responsible for attending to security incidents and cyberattacks at the organizational, national, and regional level. It is important that actual or suspected security incidents be reported as early as possible so that organizations can limit the damage and cost of recovery.
Surreal and futuristic trends in parascientific works

The simultaneous existence of traditional and nontraditional trends in culture inevitably leads them to creative interaction. A case in point are such popular trends in the culture of the 20th and 21st centuries as surrealism, futurism, neo-surrealism, neo-futurism and parascientific creativity. A comparative analysis of these directions has shown that artistic activity in art is often manifested in a creative relationship of traditional and nontraditional cultural trends. It turns out that parascientific creativity, as a nontraditional direction of culture, in its content and plot properties is close to the works of the French surrealists A. Breton, P. Eluard, T. Corbière and Russian futurists V. Mayakovsky and I. Ignatiev, as well as contemporary neo-surrealists and neo-futurists. The author comes to the conclusion that contemporary art, based on parascientific creativity, realizes its spiritual content in culture and brings to life its new creative ideas. Thus, contemporary art is in the process of development and the search for new creative foundations in culture. The postmodern culture also contributes to this trend. It aims at constant change, novelty and originality, thereby influencing contemporary art and bringing it closer to nontraditional cultural trends.
A Study on Migration Scheduling in Distributed Stream Processing Engines

The cost of migrating stateful operators in distributed stream processing has attracted research attention. Reactive migration in response to context changes is the common approach. Other migration scheduling strategies, like proactive migration based on prediction, and delayed migration are nearly neglected.
Pre-classification Supporting Reasoning for Document-level Relation Extraction

The document-level relation extraction task aims to extract relational triples from a document consisting of multiple sentences. Most previous models focus on modeling the dependency between the entities and neglect the reasoning mechanism. Some other models construct paths implicitly between co-sentence entities to find semantic relations. However, they ignore that there are interactions between different triples, especially some triples play an import role in predicting others. In this short research paper, we propose a new two stage framework PCSR(Pre-classification Supporting Reasoning) which captures the interactions between triples and utilizes these information for reasoning. Specifically, we make a pre-classification for each entity pair in the first stage. Then we aggregate the embeddings of predicted triples to enhance entity representation and make a new classification. Since the second classification could find triples missed in the first stage, we take the result as the supplement of the prior one. Experiments on DocRED show that our method achieves an F1 score of 62.11. Compared with the previous state-of-the-art model, our model increase by 0.81 on the test set, which demonstrates the effectiveness of our reasoning mechanism.
Behaviour study of genetic operators for the solid waste collection problem: the municipality of Sidi Bou Said case study

Genetic algorithms are categorised as efficient global search heuristic in solving high computational problems. However, their performance is heavily dependent on the choice of the appropriate operator especially crossover. Moreover, the crossover operator efficiency could considerably be influenced by the problem definition, the fitness function and the instance structure. For the practical demonstration of these phenomena, we tackle the solid waste collection problem. We focus therefore on determining the most promising set of parameters by varying the performance measures. The case of the City of Sidi Bou Said is treated along during our experimental study.
Retrieving Large-scale Product Knowledge by Collaborative Computing of CPUs and GPUs

The knowledge graph is the essential infrastructure of plenty of intelligent Web applications. There are various types of knowledge graphs designed and deployed to make the applications smarter during the past decade. However, the increasing amount of product data brings new challenges to the query and retrieval of products. In this work, we present a product knowledge representation method that converts text into IDs to reduce the data transmission time. In order to decrease the retrieval time, we transform the join operation in the relational database into a matrix operation. We propose a pipeline query optimization strategy under GPUs to speed up the execution of the query. To evaluate the performance, we compare our method with the state-of-the-art RDF engine RDF-3X and gStore on the large-scale product datasets. The experimental results show that our approach can significantly improve the efficiency of the SPARQL query.
Effects of Different Activities on Learning Efficiency of m-Learning Users

This study aims to develop a new learning support system using context-awareness technology in an m-Learning en-vironment. This system is expected to improve learning efficiency more than that of conventional m-Learning. In particular, this paper focuses on the influence of user activity while learning on the learning efficiency of m-Learning as a basic verification. In the experiment, m-Learning was conducted through five types of activities. Using the measurement data obtained during the said activities, this study analyzed learning efficiency in terms of the rate of correct answers in the test of the content learned immediately after learning and three days after. The result indicates that learning while walking improved amount of memorized learning content after three days. In addition, learning efficiency decreased in the case where attention is paid to the surroundings during exercise states, such as walking while using smartphones, compared with walking on a treadmill without needing to pay attention to the surroundings. Given the rating of confidence that the subject answerd, the result suggested that conducting activities during learning may not influence learning efficiency, and that the rate of correct answers and amount of learning during m-Learning may be uncorrelated with learning efficiency.
Explainable Knowledge Reasoning Framework Using Multiple Knowledge Graph Embedding

Knowledge reasoning using knowledge graphs has attracted much attention. However, there is difficulty in integrating various related works to realize complex reasoning with explanation using multiple knowledge graphs. To do this, I propose a reasoning framework which combines multiple knowledge graph embedding techniques with corresponding explainable AI techniques. Experiments using the third knowledge graph reasoning challenge dataset demonstrate the effectiveness of the framework.
A Physical Layer-based Lane Authentication Method

In this study, a scenario is simulated in which a mobile user is set to motion in a straight path and authenticates whether the user is on the correct path in each consecutive position. For this process, channel differences are utilized to propose a lane authentication method. Wireless communication pa-rameters such as the number of receiving antennas and the number of OFDM subcarriers are changed repeatedly for the purpose of finding the optimal parameters. The same approach is applied to machine learning (ML) parameters such as algorithm types, whether or not there is principal component analysis, or the number of folds for k-fold cross-validation. Accuracies and other results obtained from var-ious ML models are presented.
Analysis and Prospects of Using Social Media in the Russian Universities Marketing

The contribution investigates the current situation and opportunities of using social media in the marketing of Russian universities. Based on the analysis of secondary data and the results of ethnographic observation, the authors identified the main benefits from the development of communications and marketing interaction in the field of higher education using social media. Particular attention is paid to indicators for assessing the effectiveness of marketing in social networks of Russian universities based on the assessment of these indicators of three leading universities in Russia. On the basis of the analysis, the prospects and benefits of using social networks as a marketing tool for interaction in Russian universities are formulated.
Recognition of Tire Track Patterns Using SIFT and Template Matching

Recognition of tire track patterns has an important role in both the investigation of crime scenes and the identification of vehicles involved in traffic accidents.Due to the rich texture information they have, texture features are generally used to recognize track images taken from tires.However, recognition of tire tracks taken from crime scenes has not been studied sufficiently.In this study, SIFT-based features and template matching methods were used to recognize tire track/tire track fragment images.In the experiments, fragments taken from clean tracks, dirty tracks and fragments taken from dirty tracks were matched with clean track images, and higher recognition performance was achieved compared to state of art methods.
The impact of Tesla’s bitcoin investment and its plans to accept it as payment method on the evolution of bitcoin

Abstract For the past years, cryptocurrencies have been a hot and controversial topic that has captured the attention of the whole tech world. Even if right now the portfolio of digital assets is considerable in size, the first cryptocurrency, Bitcoin, exploited its pioneer advantage and managed to remain at the center of attention both for the media and investors. During 2020, one of the most chaotic years, Bitcoin boomed in November 2020 almost doubling since the end of 2019. This boom is the result of a combination of factors, such as the fear of missing out translated into a chain reaction of public and private companies to consider Bitcoin a safe reserve asset, a hedging method against inflation, which represents a substitute for traditional hedging instruments, the infrastructure developed around it over the years, and lastly the hype created by influential figures through news and social media platforms. There have been many public figures that exhibited interest in cryptocurrencies through platforms such as Twitter, for instance Elon Musk, Bill Gates, Kanye West, Hugh Laurie, Mike Tyson and Gwyneth Paltrow are just some in a long list of celebrities that backed Bitcoin. This paper aims to analyze the impact that twitter posts have upon the evolution of Bitcoin, coupled with Tesla’s investment and recent statement of introducing Bitcoin as a method of payment in the near future. Our research tries to determine if the news and social media posts, such as tweets have an influence upon Bitcoin’s volatility and fluctuations.
An Optimization of Fuzzy Rough Set Nearest Neighbor Classification Model Using Krill Herd Algorithm for Sentiment Text Analytics

In recent years, social networking sites like Twitter and Facebook are more popular through which emotions and thoughts of its users are shared. Detection and classification of emotions, expressed in social media content, is beneficial for several applications in the domains like e-commerce, politics, social welfare and so on. Several works have been conducted earlier focusing sentiment and emotion analysis. These works primarily concentrated on single-label classification, leaving beside the correlation among different emotions expressed by an individual. In this view, the current research article presents a new sentiment and emotion classification model using Fuzzy Rough Set Nearest Neighbor (FRSNN) with Krill Herd (KH) algorithm i.e., FRSNN-KH. The proposed FRSNN-KH algorithm involves preprocessing, feature extraction and classification. Initially, preprocessing is executed to remove the unwanted words present in the tweet. Next, the features are extracted from the pre-processed tweet by following Bag of Words (BoW) model. Afterwards, FRSNN-based classification process is carried out to segregate the instances under different class labels. Finally, soft computing-based KH algorithm is applied to optimize the rule generation sets by FRSNN model. The presented FRSNN-KH algorithm was experimentally validated using benchmark dataset. The simulation outcome inferred the goodness of the proposed FRSNN-KH algorithm over compared methods under several dimensions.
Pre-training Model Based on Parallel Cross-Modality Fusion Layer

Visual Question Answering (VQA) is a learning task that combines computer vision with natural language processing. In VQA, it is important to understand the alignment between visual concepts and linguistic semantics. In this paper, we proposed a Pre-training Model Based on Parallel Cross-Modality Fusion Layer (P-PCFL) to learn the fine-grained relationship between vision and language. The P-PCFL model is composed of three Encoders: Object Encoder, Language Encoder, and Parallel Cross-Modality Fusion Encoder, with Transformer as the core. We use four different Pre-training missions, namely, Cross-Modality Mask Language Modeling, Cross-Modality Mask Region Modeling, Image-Text Matching, and Image-Text Q&A, to pre-train the P-PCFL model and improve its reasoning and universality, which help to learn the relationship between Intra-modality and Inter-modality. Experimental results on the platform of Visual Question Answering dataset VQA v2.0 show that the Pre-trained P-PCFL model has a good effect after fine-tuning the parameters. In addition, we also conduct ablation experiments and provide some results of Attention visualization to verify the effectiveness of P-PCFL model.
Can We Ever Make a Humanoid Bot that Runs by Itself Without Any Software?

We live in a world where everyone rightly suggests that the brain is not a machine, artificial intelligence is no good, we need natural intelligence, we have to learn from the neuron and other biomaterials, and finally, we have to invent the brain’s functional mystery to revolutionize brain science. However, after saying all these great criticalities, researchers demonstrate only a better fitting method.
Taking two Birds with one k-NN Cache

k-Nearest Neighbors aims at efficiently finding items close to a query in a large collection of objects, and it is used in different applications, from image retrieval to recommendation. These applications achieve high throughput combining two different elements: 1) approximate nearest neighbours searches that reduce the complexity at the cost of providing inexact answers and 2) caches that store the most popular items. In this paper we propose to combine the approximate index for the whole catalog with a more precise index for the items stored in the cache. Our experiments on realistic traces show that this approach is doubly advantageous as it 1) improves the quality of the final answer provided to a query, 2) additionally reduces the service latency.
Hierarchical Graph Sparse Attention Network for Online Action Tracking

Deep neural networks are effectively studied for human action tracking and analysis, which are mostly regarded as classification and annotation for a video. We present a novel hierarchical graph sparse attention model for online action tracking in 2D skeleton. Hierarchical graph networks are designed for human skeleton features and movement of action with dependency between spatial and temporal information. Sparse attention mechanism is respectively incorporated into the hierarchical graph networks with frame-attention in sequence and joints-attention in frames. During video streaming, action labels are online updated with frame graph composed of sparse frames, which are dynamical weighted and updated from consecutive frames with frame-attention. Joints-attention emphasizes main joints features learning for pose estimation with hierarchical graph skeleton. Our networks are trained on UCF dataset and evaluated with qualitative and quantitative analysis. Experiments results show that the proposed networks effectively realize online action tracking and outperforms state-of-the-art methods.
Who can Benefit from Immersive Virtual Reality in Education? Effectiveness of IVR in Teaching using Meta-analysis

Immersive Virtual Reality (IVR) systems offer several learning affordances, that is, characteristics that can elicit learning behaviors promoting learning effects in educational contexts. In particular, the immersive and interactive properties of these systems represent a strong opportunity to support the effective learning effect. A systematic analyze was carried out on the literature published between 2014 and 2020 using meta-analysis. forty-five randomized controlled trials (RCTs) or quasi-experimental studies were identified. We conducted an analysis using the random effects model (REM) to calculate the pooled effect size. The studies were also coded to examine the moderating effects of their characteristics, such as school level, learning effect, subject, control group treatment and learning duration, on the outcome measure. Comprehensive research results show that IVR can support a number of activities and experiences that in turn improve learning and motivate students to fulfill educational goals by eliciting their interest and engagement with the learning materials. The main advantage of IVR seems related to the possibility for users to have first-hand experiences that would not be possible in the real world, simultaneously offering unique opportunities for experiential and situated learning, as well as promoting students&#x0027; motivation and engagement.
FLBID: An Income Distribution Mothed Based on Bargaining for Participants in Federated Learning

Federated Learning, as a distributed machine learning model, can effectively solve the problem of data islands, allowing participants to jointly model on the basis of not sharing data, which can technically break data islands. However, what follows are how to incentivize participants to join the federation and distribute their income fairly. In order to solve these problems, we proposed Federated Learning Bargaining Income Distribution (FLBID) based on Rubinstein Bargaining Model. FLBID can achieve fair income distribution through bargaining between the federation and participants based on their respective contributions. In addition, the "first-mover advantage" in Rubinstein's negotiation model allows participants to actively join the federation. We have proved through experiments that FLBID can distribute income fairly. Further, we have proved that the earlier participants join FL, the more income they can get.