Reviewerly/exHarmony huggingface dataset:
Remembering winter was coming

Today’s popular tv series tend to develop continuous, complex plots spanning several seasons, but are often viewed in controlled and discontinuous conditions. Consequently, most viewers need to be re-immersed in the story before watching a new season. Although discussions with friends and family can help, we observe that most viewers make extensive use of summaries to re-engage with the plot. Automatic generation of video summaries of tv series’ complex stories requires, first, modeling the dynamics of the plot and, second, extracting relevant sequences. In this paper, we tackle plot modeling by considering the social network of interactions between the characters involved in the narrative: substantial, durable changes in a major character’s social environment suggest a new development relevant for the summary. Once identified, these major stages in each character’s storyline can be used as a basis for completing the summary with related sequences. Our algorithm combines such social network analysis with filmmaking grammar to automatically generate character-oriented video summaries of tv series from partially annotated data. We carry out evaluation with a user study in a real-world scenario: a large sample of viewers were asked to rank video summaries centered on five characters of the popular tv series Game of Thrones, a few weeks before the new, sixth season was released. Our results reveal the ability of character-oriented summaries to re-engage viewers in television series and confirm the contributions of modeling the plot content and exploiting stylistic patterns to identify salient sequences.
FlashRegex

Regular expressions (regexes) are widely used in different fields of computer science such as programming languages, string processing and databases. However, existing tools for synthesizing or repairing regexes were not designed to be resilient to Regex Denial of Service (ReDoS) attacks. Specifically, if a regex has super-linear (SL) worst-case complexity, an attacker could provide carefully-crafted inputs to launch ReDoS attacks. Therefore, in this paper, we propose a programming-by-example framework, FlashRegex, for generating anti-ReDoS regexes by either synthesizing or repairing from given examples. It is the first framework that integrates regex synthesis and repair with the awareness of ReDoS-vulnerabilities. We present novel algorithms to deduce anti-ReDoS regexes by reducing the ambiguity of these regexes and by using Boolean Satisfiability (SAT) or Neighborhood Search (NS) techniques. We evaluate FlashRegex with five related state-of-the-art tools. The evaluation results show that our work can effectively and efficiently generate anti-ReDoS regexes from given examples, and also reveal that existing synthesis and repair tools have neglected ReDoS-vulnerabilities of regexes. Specifically, the existing synthesis and repair tools generated up to 394 ReDoS-vulnerable regex within few seconds to more than one hour, while FlashRegex generated no SL regex within around five seconds. Furthermore, the evaluation results on ReDoS-vulnerable regex repair also show that FlashRegex has better capability than existing repair tools and even human experts, achieving 4 more ReDoS-invulnerable regex after repair without trimming and resorting, highlighting the usefulness of FlashRegex in terms of the generality, automation and user-friendliness.
The Zoom-bie Student and the Lecturer

As part of the Special Section: Technology &amp; Pandemic, this article exam­ines the experience of teaching and learning via Zoom. I examine how technologies mediate the learning process with the postphenomenological notions of embodiment and hermeneutic relations. This section serves as a basis for understanding the trans­formation of that process into online learning. The next section is named “the Zoom-bie”—a combination of the words Zoom and zombie. The figure of the Zoom-bie provides me a way to critically review the new practices experienced in the spring semester of 2020. After analyzing the variations of the learning process with a fresh look at embodiment and hermeneutic relations, the last section titled “the digital classroom” examines this transformation from an alternative point-of-view, that of the classroom as a technology-saturated background.
Token Economics in Real Life: Cryptocurrency and Incentives Design for Insolar's Blockchain Network

The study of setting up cryptocurrency incentive mechanisms and operationalizing governance is called token economics. Given the US$250 billion market cap for cryptocurrencies, there is compelling need to investigate it. In this article, we present facets of the token engineering process for a Swiss blockchain startup.
Permissioned vs. Permissionless Blockchain: How and Why There Is Only One Right Choice

The blockchain structure can be utilized in an efficient way to verify and ensure the integrity of the data and prevent historical transactions from tampering, such that if one block is altered, then the rest of next blocks are no longer valid. However, it is possible to re-calculate all the next blocks in a closed and permissioned network, such that all altered blocks will become valid again. In other words, only chaining blocks to each other, based on the hash of the previous one, cannot ensure the integrity and the security of the data and protect transactions against tampering. In this paper, we motivate why permissionless blockchain in an open network should be considered as the only acceptable type of blockchain and show that permissioned blockchain and closed network makes chaining transactions (i.e. blockchain) as an unhelpful structure. Of course, this never means that we ignore the existing problems in permissionless blockchains (such as, scalability of the network and transaction throughput), but also, we argue that permissioned blockchain cannot be an acceptable solution for those problems, where chaining transactions will no longer be meaningful. In other words, in a closed network, we no longer need to chain transactions. Although, this is a central issue and has been addressed in recent years, we are trying to bring new elements and an enriched point of view.
Fake It to Make It

Exploratory prototyping techniques are critical to devising new robot forms, actions, and behaviors, and to eliciting human responses to designed interactive features, early in the design process. In this opinion piece, we establish the contribution of exploratory prototyping to the field of human-robot interaction, arguing research engaged in design exploration-rather than controlled experimentation-should be focused on flexibility rather than specificity, possibility rather than replicability, and design insights as incubated subjectively through the designer rather than dispassionately proven by statistical analysis. We draw on literature in HCI for examples of published design explorations in academic venues, and to suggest how analogous contributions can be valued and evaluated by the HRI community. Lastly, we present and examine case studies of three design methods we have used in our own design work: physical prototyping with human-in-the-loop control, video prototyping, and virtual simulations.
Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears

In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears.The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC).We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model.We applied transfer learning to increase the initial feature sets and reduced the training time to train our model.We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset.The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten.Additional visualization and analysis using the Gradientweighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models.This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network

Brain tumor segmentation plays an essential role in medical image analysis. In recent studies, deep convolution neural networks (DCNNs) are extremely powerful to tackle tumor segmentation tasks. We propose in this paper a novel training method that enhances the segmentation results by adding an additional classification branch to the network. The whole network was trained end-to-end on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. On the BraTS’s test set, it achieved an average Dice score of $$80.57\%$$ , $$85.67\%$$ and $$82.00\%$$ , as well as Hausdorff distances $$(95\%)$$ of 14.22, 7.36 and 23.27, respectively for the enhancing tumor, the whole tumor and the tumor core.
Social and Economic Contribution of 5G and Blockchain With Green Computing: Taxonomy, Challenges, and Opportunities

In recent years Fifth Generation (5G) technology is the most recent advancement in a wireless communication network.There is the advent of using the 5G with diverse data structures.The Blockchain (BC) has become an approving adoption for decentralized, peer-to-peer, distributed transparent ledger systems with a diverse data structure.The use of 5G with BC is an emerging trend in communication technology.The elasticity of 5G with BC enables many applications to reciprocity information molds it a fast, transparent, consequential, and safe for transportation of data in this smart era.Green computing (GC) is presently the intense optimistic tactic for the integration of smart technology in a diverse and distributed world of power consumption.This Systematic Mapping Study (SMS) has been analyzed by cautiously elected publications between 2016 and 2020 in well-putative venus.This study analyzed the advanced research on power consumption solutions for BC-based 5G communication, Moreover, a taxonomy of 5G based on green BC and GC in various areas is presented.Furthermore, Green energy renewable communication (GERC) problems are being observed in this research by integrating three discrete technologies such as 5G with green BC and GC also along with smart systems.Lastly, the research gaps had been bestowed to render future directions for the researchers in 5G with green BC and GC as the solution for rechargeable data packets.
Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid

Traditional breast cancer image classification methods require manual extraction of features from medical images, which not only require professional medical knowledge, but also have problems such as time-consuming and labor-intensive and difficulty in extracting high-quality features. Therefore, the paper proposes a computer-based feature fusion Convolutional neural network breast cancer image classification and detection method. The paper pre-trains two convolutional neural networks with different structures, and then uses the convolutional neural network to automatically extract the characteristics of features, fuse the features extracted from the two structures, and finally use the classifier classifies the fused features. The experimental results show that the accuracy of this method in the classification of breast cancer image data sets is 89%, and the classification accuracy of breast cancer images is significantly improved compared with traditional methods.
Gaussian soft control-based quantum fan-out gate in ground-state manifolds of neutral atoms

We propose a reliable scheme for one-step synthesizing of a quantum fan-out gate in a system of neutral atoms. By introducing the off-resonant driving fields with Gaussian temporal modulation, the dynamics of the system is strictly restricted to the ground-state subspace on the basis of unconventional Rydberg pumping, which exhibits more robustness than the constant driving method against the fluctuation of system parameters, such as operating time and environment noise. As a direct application of this quantum fan-out gate, we discuss in detail the preparation of multipartite Greenberger–Horne–Zeilinger (GHZ) state for neutral atoms. The result shows that a high fidelity better than 99% can be obtained within the state-of-the-art experiments.
Modeling discretionary lane-changing decisions using an improved fuzzy cognitive map with association rule mining

The discretionary lane-changing process consists of two phases. The first phase is decision making on lane-changing, and the second phase is the execution of this decision. The first phase has a complex structure that is affected by many parameters. In this phase, some parameters are present that affect lane-changing directly, while some other indirect parameters motivate drivers to perform lane-changing. This study focuses on discovering the parameters that prompt drivers to change lanes. The parameters determined as a result of the interviews with the drivers were examined in the field study. Then, the impact of the parameters for lane-changing were discovered using association rule mining and the proposed Significant Association Features Extractor (SigAFE) algorithm. Fuzzy Cognitive Map (FCM) discretionary lane-changing decision models were developed using the impact values that were discovered using the SigAFE algorithm. The performances of the models were revealed with the actual data of the field study.
An Efficient Intelligent Power Detection Method for Photovoltaic System

Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.
A Novel Tool for Immersive Authoring of Experiential Learning in Virtual Reality

Training in VR can reduce risks and costs while allowing frequent and diversified experiential learning activities. We present a novel VR immersive authoring tool for experiential learning courses with industrial machinery. A trainer can create a course from scratch, defining all its components (structure, models, tools, and settings). The actions which trainees should perform can be specified by demonstration. After completing the course, trainees' actions will be matched against the trainer's.
A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars

Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of devices to a centralized server, it its own implementation challenges such as robustness, centralized server security, communication resources constraints, and privacy leakage due to the capability of a malicious aggregation server to infer sensitive information of end-devices. To address the aforementioned limitations, a dispersed federated learning (DFL) framework for autonomous driving cars is proposed to offer robust, communication resource-efficient, and privacy-aware learning. A mixed-integer non-linear programming (MINLP) optimization problem is formulated to jointly minimize the loss in federated learning model accuracy due to packet errors and transmission latency. Due to the NP-hard and non-convex nature of the formulated MINLP problem, we propose the Block Successive Upper-bound Minimization (BSUM) based solution. Furthermore, the performance comparison of the proposed scheme with three baseline schemes has been carried out. Extensive numerical results are provided to show the validity of the proposed BSUM-based scheme.
Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models

Recently Inverted Beta-Liouville mixture models have emerged as an efficient paradigm for proportional positive vectors modeling and unsupervised learning. However, little attention has been devoted to investigate these generative models within discriminative classifiers. Our aim here is to reveal the structure of non-Gaussian data by generating new probabilistic SVM kernels from inverted-Beta Liouville mixture models. The inverted Beta-Liouville has a more general covariance structure and a smaller number of parameters than the inverted Dirichlet and generalized inverted Dirichlet, respectively, which makes it more practical and useful. A principled Bayesian learning algorithm is developed to accurately estimate the model's parameters. To cope with the problem of selecting the optimal number of components, we further propose a nonparametric Bayesian learning algorithm based on an extended infinite mixture model which may have better modelling and clustering capabilities than the finite model for some applications. Finally, the resulting generative model is exploited to build several efficient probabilistic SVM kernels in order to enhance the expected clustering and modeling performance. Through a number of experimental evaluations involving visual scenes classification, text categorization and texture images discrimination, we prove the merits of the proposed work.
Adversarial laws of large numbers and optimal regret in online classification

Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed sub-population is well-estimated by its frequency in the sample. We study laws of large numbers in sampling processes that can affect the environment they are acting upon and interact with it. Specifically, we consider the sequential sampling model proposed by Ben-Eliezer and Yogev (2020), and characterize the classes which admit a uniform law of large numbers in this model: these are exactly the classes that are online learnable. Our characterization may be interpreted as an online analogue to the equivalence between learnability and uniform convergence in statistical (PAC) learning. The sample-complexity bounds we obtain are tight for many parameter regimes, and as an application, we determine the optimal regret bounds in online learning, stated in terms of Littlestone's dimension, thus resolving the main open question from Ben-David, Pál, and Shalev-Shwartz (2009), which was also posed by Rakhlin, Sridharan, and Tewari (2015).
Music classroom assistant teaching system based on intelligent speech recognition

In order to improve the effect of music classroom teaching and the degree of informatization, this paper builds a music classroom auxiliary teaching system with the support of intelligent speech recognition technology, and conducts in-depth research on the audio classification and segmentation technology of music teaching classrooms. Moreover, this paper uses support vector machines to divide audio into five types: mute, background sound, song music, speech, and noisy speech. At the same time, this paper also proposes a smoothing method based on the classification result sequence to obtain audio segmentation points. In addition, this paper constructs a system model based on the actual needs of music classroom teaching, and performs vocal feature recognition with the support of intelligent speech recognition. Finally, this paper verifies and analyzes the performance of the system constructed in this paper through experimental research. The research results show that the intelligent music classroom auxiliary teaching system constructed in this paper has a certain effect.
Fundamental Design Principles for Reinforcement Learning Algorithms

Along with the sharp increase in visibility of the field, the rate at which new reinforcement learning algorithms are being proposed is at a new peak. While the surge in activity is creating excitement and opportunities, there is a gap in understanding of two basic principles that these algorithms need to satisfy for any successful application. One has to do with guarantees for convergence, and the other concerns the convergence rate. The vast majority of reinforcement learning algorithms belong to a class of learning algorithms known as stochastic approximation (SA). The objective here is to review the foundations of reinforcement learning algorithm design based on recent and ancient results from SA. In particular, it was established in [Borkar and Meyn, 2000] that both stability and convergence of these algorithms are guaranteed by analyzing the stability of two associated ODEs. Moreover, if the linearized ODE passes a simple eigenvalue test, then an optimal rate of convergence is guaranteed. This chapter contains a survey of these concepts, along with a survey of the new class of Zap reinforcement learning algorithms introduced by the authors. These algorithms can achieve convergence almost universally, while also guaranteeing optimal rate of convergence.
Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technologies, such as Machine Learning (ML), as well as the skills to implement this knowledge to solve real-life problems. Therefore, both students and professors require teaching and learning tools that facilitate the introduction to such topics. Consequently, this study looks forward to contributing to the development of those tools by introducing the basic theory behind three machine learning classifying algorithms: K-Nearest-Neighbor (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron; as well as discussing the diverse advantages and disadvantages of each method. Moreover, it is proposed to analyze how these methods work on different conditions through their implementation over a test bench. Thus, in addition to the description of each algorithm, we discuss their application to solving three different binary classification problems using three different datasets, as well as comparing their performances in these specific case studies. The findings of this study can be used by teachers to provide students the basic knowledge of KNN, LDA, and perceptron algorithms, and, at the same time, it can be used as a guide to learn how to apply them to solve real-life problems that are not limited to the presented datasets.