Reviewerly/exHarmony huggingface dataset:
Systematic Review on Digital Competence in the Spanish Context

The widespread use of information and communication technologies has accelerated profound changes in society. This has prompted education systems to adapt to these changes. These responses focus on digital competence. The general objective of the study is to analyse the evolution of digital competence in the Spanish context. This study has followed a bibliometric methodology. An analysis of 286 documents extracted from Web of Science has been performed. The results show that only two lines are observed that stand out from the rest. These are the cases of “digital competence-ICT-students” and “teacher-education_educational-technology_teacher-digital competence.” This indicates that over time the research focus has shifted to the use of technological resources in students and teachers. It can be concluded that the subject of study focused on digital competence in the Spanish field is being established from 2018 to the present, with its lines of research focusing on pedagogical teaching methods, technological resources and on the agents involved in training processes. This refers to the skills required by students and teachers for them to be able to successfully conduct training processes using technology. This study has been funded by the project Socio-economic impact of COVID-19 on Andalusian university students. Reference: CV20-01,248.
Artificial Intelligence and Intellectual Property Law: From Diagnosis to Action

The use of "artificial intelligence" systems becomes ever more widespread and far-reaching. Technological and economic concepts for an AI-based future are about to be implemented. It is, hence, time for the intellectual property system to develop answers to the challenges brought about by AI. Against this background, Zurich University’s Center for Intellectual Property and Competition Law (CIPCO) has initiated a joint research project on AI/IP with the Swiss Intellectual Property Institute (IPI). A first stage of this project has evaluated the state of the legal and economic discourse. These insights form the basis for policy recommendations on how the intellectual property system ought to be adapted to AI-related developments. The present paper describes – as draft work in progress – the project setup and summarizes its results gained so far. In doing so, it addresses key AI/IP issues, including business models of AI innovation leaders, inventorship/creatorship of AI systems de lege lata and de lege ferenda, the DABUS litigation, the discussion on whether new types of IP rights are necessary to protect AI inventions, the allocation of entitlements and liability regarding such innovations, AI-related revisions in the guidelines of important patent and trademark offices, the use such offices make of AI tools, the need for new protection carve-outs (e.g. to foster text and data mining), as well as AI’s potential raising the bar-effect.
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation

Previous attempts to build effective semantic parsers for Wizard-of-Oz (WOZ) conversations suffer from the difficulty in acquiring a high-quality, manually annotated training set.Approaches based only on dialogue synthesis are insufficient, as dialogues generated from state-machine based models are poor approximations of real-life conversations.Furthermore, previously proposed dialogue state representations are ambiguous and lack the precision necessary for building an effective agent.This paper proposes a new dialogue representation and a sample-efficient methodology that can predict precise dialogue states in WOZ conversations.We extended the ThingTalk representation to capture all information an agent needs to respond properly.Our training strategy is sample-efficient: we combine (1) fewshot data sparsely sampling the full dialogue space and (2) synthesized data covering a subset space of dialogues generated by a succinct state-based dialogue model.The completeness of the extended ThingTalk language is demonstrated with a fully operational agent, which is also used in training data synthesis.We demonstrate the effectiveness of our methodology on MultiWOZ 3.0, a reannotation of the MultiWOZ 2.1 dataset in ThingTalk.ThingTalk can represent 98% of the test turns, while the simulator can emulate 85% of the validation set.We train a contextual semantic parser using our strategy, and obtain 79% turn-by-turn exact match accuracy on the reannotated test set. 1
Shape-Biased Ellipse Detection Network With Auxiliary Task

Ellipse shapes taken by a projection lens encode rich semantic information, and ellipse detection is an important and convenient means to obtain spatial semantics from images. However, accurate and robust detectors for real-world images remain challenging. In this article, a shape-biased ellipse detection network with an auxiliary task is proposed to fulfill this requirement, which can learn and characterize low-level features more effectively. Our multitask-based architecture simultaneously performs elliptical bounding box detection, edge prediction, and parameter regression. First, since ellipses are represented as a set of arcs, we enhance the contact between low-level features and high-level features to better retain structural information. Second, ellipse edge prediction as an auxiliary task drives the backbone biased to ellipse shapes rather than others and makes more efficient use of limited datasets. In addition, by modeling the ellipse as a 2-D Gaussian distribution, Wasserstein distance is used as the loss function to minimize the distance between two Gaussian distributions. Experiments on real-world datasets show that the proposed EllipseNet achieves better performance than state-of-the-art methods. Extensive ablation studies validate the effectiveness of the proposed backbone module and auxiliary branch.
Client-Side Hashing for Efficient Typo-Tolerant Password Checkers

Credential leaks still happen with regular frequency, and show evidence that, despite decades of warnings, password hashing is still not correctly implemented in practice. The common practice today, inherited from previous but obsolete constraints, is to transmit the password in cleartext to the server, where it is hashed and stored. This allows some usability improvements, such as typo-tolerant password checkers — which can correct up to 32% of typos, with no negative impact on security — formally introduced by Chatterjee et al. in 2016, but used in some preliminary forms since 2012. This article investigates the advantages and drawbacks of the alternative of hashing client-side, and shows that it is present today exclusively on Chinese websites. It introduces an alternative typo-correction framework based on client-side hashing, which corrects up to 57% of typos without affecting user experience, at no computational cost to the server. Finally, it proposes some potential ways to improve the industry standards by enforcing accountability on password security.
Bin Reservation Based on IVPO Reversible Data Hiding Using Smoothness Mode Selection Strategy

Pixel value ordering (PVO) is a popular reversible data hiding (RDH) technology. The embedding strategy of the PVO method is to divide the image into blocks of the same size, sort the pixel values in each blocks, and embed the secret data to the maximum and minimum pixel values when embeddable conditions are satisfied. The embedding capacity of the PVO method will change due to the complexity of the block. If the pixels in the block is too different from each other, the embedding capacity of the PVO method will be limited. Therefore, in view of this limitation of the PVO method, this research proposes the smoothness mode selection (SMS) strategy. The SMS strategy determines the image shape according to the complexity of the pixels within image blocks, and then uses the bin reservation based on improved PVO RDH method to embed the secret data in the picture. The embedding strategy of this method enables each block of four pixels to carry at most four bits, so it has a significant higher embedding capacity. The experimental results show that the embedding capacity of our proposed method is higher than other PVO-based RDH schemes, and it also maintains a certain good image quality.
GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer

Interpreting NLP models is fundamental for their development as it can shed light on hidden properties and unexpected behaviors.However, while transformer architectures exploit contextual information to enhance their predictive capabilities, most of the available methods to explain such predictions only provide importance scores at the word level.This work addresses the lack of feature attribution approaches that also take into account the sentence structure.We extend the SHAP framework by proposing GrammarSHAP-a modelagnostic explainer leveraging the sentence's constituency parsing to generate hierarchical importance scores.
Traffic Density Determination Using Advanced Computer Vision

Estimation of traffic density is a fundamental component of an automated traffic evaluation and monitoring system. It estimation can be used in several traffic monitoring and management applications—from identification of congested traffic flow to macroscopic traffic management in any environment. In this paper, we evaluated MobileNet-SSD and faster R-CNN to evaluate parameterized traffic density in any given traffic image. SSD is capable of handling multiple shapes, sizes, and view angles of objects. MobileNet-SSD, an optimized and a faster algorithm for mobiles, is a cross-trained model from SSD to MobileNet architecture. Faster R-CNN adds a region proposal network (RPN) to generate region proposals instead of the selective search algorithm, which adds a significant processing time to R-CC-based models. The RPN uses anchor boxes for object detection. Producing RPN in the network is faster and better optimized to the data. In this study, we pick a specific use case for the application of MobileNet-SSD and faster R-CNN framework. The advantages and shortcomings of the MobileNet-SSD framework and faster R-CNN were analyzed using feeds from multiple traffic cameras and publicly available labeled MIO-TCD dataset. Multiple models of fasterR-CNN were evaluated on both TensorFlow 1.x and TensorFlow 2.x. Based on the evaluation and a compromise between speed and accuracy, it was finally decided to use faster R-CNN Inception V2 trained on the COCO dataset as the backbone and it was trained on a combination of MIO-TCD localization dataset and manually labeled dataset captured from live camera feeds. The model uses TensorFlow 1.x as the framework.
Colorization of Grayscale Images Using Convolutional Neural Network and Siamese Network

Image colorization implies adding colors to a grayscale image such that the colorized image is perceptually meaningful and visually appealing. The key challenge to this problem is that there is no unique solution to the colorization problem (e.g., leaves may be colored in green, yellow and brown). This problem has attracted much attention in the research community, and the way it is tackled by researchers has been evolving over time. In this paper, image colorization using a Siamese network in combination with a convolutional neural network is proposed. The novelty of this work is that a custom loss function is developed using a Siamese network for training a colorization neural network. The proposed method is more or less at par with existing algorithms.
Challenges and Trends on Post-Quantum Cryptography

The classical encryption code is facing challenges due to no copy protection mechanism. The attempt to explore diverse challenges in classical encryption code, such as prime factorization of a large number, is now possible with sub-polynomial time. As a result, classical cryptographic algorithms are real challenges and threads. Hence, post-quantum security or encryption codes in communication are now prime focus. In this chapter, the first challenges are discussed, and finally, several quantum cryptographic encryption algorithms are discussed, namely BB84, quantum teleportation, quantum cheque, and post-quantum cryptographic development.
Textual Sentimental Classification using Convolution Neural Network Algorithm

Sentiment analysis on classification of text data is one of the emerging tasks in natural language processing compared to others. In the main, there is a need for big dig for meaningful information from the data present on the internet through sentiment analysis. Deep learning has already been successful in keeping up with sentiment analysis tasks utilizing deep learning models. Nowadays, client happiness is the most crucial factor for every mature firm. As a result, many businesses live and promote their products and services on social media platforms, where they eventually receive evaluations and feedback from their customers. Studying each word and phrase one at a time is time-consuming. Thus, evaluating the sentiment of all texts provides organizations with an overview of how positive and negative users are on a given issue. In this study, we investigated word embedding architecture utilizing the convolutional neural network algorithm or approach (CNN). We created a convolutional neural network architecture using a continuous loop of the tensor flow technique, along with word2vec and evaluated the outcome by using the measures accuracy, precision, recall, and f1-scoring. It reaches an accuracy of more than 92 percent, significantly boosting text categorization accuracy.
Building a Modern Crisis Management System: Organizational Behavioral Analysis

All governments in the world attach great importance to crisis response. To prevent and minimize damages to people’s lives and properties, normal government administration and basic social order caused by a public emergency, all countries have put in place measures and policies to deal with crises related to public emergencies. These measures fall mainly into two categories. The first is laws that stipulate government duties and power in crisis response, setting the rule of law as the basic principle to follow in dealing with emergencies. The second is an effective national crisis alert and fast response mechanism, which minimizes the losses to people’s lives and properties and damages to normal social order caused by a public emergency. In an era of globalization, in particular, with rapid IT development, governments should pay special attention to media relations at times of crisis and try to be their allies. In the meantime, given the extensive damages caused by crises to public security, it is also extremely important to establish and improve a comprehensive crisis response network across the whole society to enhance the overall crisis response capability. Based on the analysis above, this chapter, from the perspective of organizational behavior, focuses on four topics concerning the building of a modern crisis management system: government function during a crisis, the role of the media, crisis response network, and legal principles. It then goes on to point out aspects that the government should pay special attention to during a crisis or emergency.
ScamBlk: A Voice Recognition-Based Natural Language Processing Approach for the Detection of Telecommunication Fraud

Telecom fraud has consistently caused extreme economic losses to telecom clients all around the world for years. Scammers use a wide variety of sophisticated techniques, making them indistinguishable from a harmless caller to access the victims’ details or money. A voice recognition-based natural language processing-infused machine learning approach, ScamBlk, is proposed which uses real-world call content derived from the call audio. The audio from the call is used as input and is transcribed into the textual form which is further pre-processed and fed to the machine learning model. The machine learning model uses an ensemble approach deploying the bagging of a sequential model long-term short-term memory network and a linear model of support vector machine to classify the scam phrases in the phone conversation. The machine learning model is trained using a custom dataset with scam phrases obtained from various online sources. The ensemble model is found to be superior to other machine learning approaches obtaining an accuracy of 97.08%, allowing us to conclude that the ScamBlk approach is efficacious in detecting potential scam calls.
Intelligent Obstacle Avoidance Controller for QBot2

This paper describes the design process and the steps involved in the implementation of a fuzzy logic-based controller for autonomous navigation of an educational robot called Qbot2. The differential-driven robot is equipped with three ultrasonic sensors (SRF-04) mounted on the front bumper and DC motors with built-in encoders and current sensors. The controller takes the inputs from three ultrasonic sensors and generates the speed commands to avoid any obstacle in its path. PCB is designed to process all the data from the sensors and direct the signal to the motors via motor driver circuits. The low-level implementation of the hurdle avoidance controller is implemented using an inexpensive Arduino UNO in real-time. The controller performance is validated through an experimental run. The designed platform can be used to implement various fuzzy inference systems in real-time and hence can be utilized as laboratory work for testing soft computing algorithms on mobile robotics.
A Hybrid Multi-waypoints Path Planning System for Robots with Minimum Turning Radius Constraint Using GA-B-Spline and Dubins Interpolation

Planning a reasonable path for underactuated robots with a minimum turning radius is essential, especially for high-speed Unmanned surface vehicles (USVs) or Autonomous underwater vehicles (AUVs) with poor mobility. This paper will propose a path planning algorithm that creates a B-spline curve interpolating multi-waypoints, optimized with a genetic algorithm (GA) under constraints of the vehicle’s turning radius and heading conditions in complex obstacle-filled environments. Then, this algorithm combines with the LOS guidance and sliding mode controller (SMC) to become complete guidance, navigation, and control system (GNC) for a USV model. This system is compared to a different GNC system with Dubins path planning to prove that our method generates a sub-optimal path and increases path following and WPs tracking performance in complex environments. Finally, this paper proposes an intelligent hybrid path planning system from those two mentioned above to take advantage of each. It is proved that this proposed algorithm can be applied to nonholonomic robots with minimum turning radius to create a path with high generality, suitable for practical applications and form a solid foundation for experiments in the near future.
A Hybrid Model for Chinese Confusable Words Distinguishing in Proofreading

Distinguishing the confusable words is an essential task for Chinese teaching and publications proofreading. Existing studies have made progress in analyzing confusable words in the lexical semantic view. However, few effective automated methods are applied to solve this problem. This paper proposes a hybrid model to distinguish the confusable words in proofreading. Through ensemble learning, the model combines contextual and non-contextual features of the input sentence. Experimental results on two test sets demonstrate that the hybrid model achieves superior performance against other baseline methods including solely BERT.
Explainable fuzzy cluster-based regression algorithm with gradient descent learning

We propose an algorithm for n-dimensional regression problems with continuous variables. Its main property is explainability, which we identify as the ability to understand the algorithm’s decisions from a human perspective. This has been achieved thanks to the simplicity of the architecture, the lack of hidden layers (as opposed to deep neural networks used for this same task) and the linguistic nature of its fuzzy inference system. First, the algorithm divides the joint input-output space into clusters that are subsequently approximated using linear functions. Then, we fit a Cauchy membership function to each cluster, therefore identifying them as fuzzy sets. The prediction of each linear regression is merged using a Takagi-Sugeno-Kang approach to generate the prediction of the model. Finally, the parameters of the algorithm (those from the linear functions and Cauchy membership functions) are fine-tuned using Gradient Descent optimization. In order to validate this algorithm, we considered three different scenarios: The first two are simple one-input and two-input problems with artificial data, which allow visual inspection of the results. In the third scenario we use real data for the prediction of the power generated by a Combined Cycle Power Plant. The results obtained in this last problem (3.513 RMSE and 2.649 MAE) outperform the state of the art (3.787 RMSE and 2.818 MAE).
Uncertain graph generating approach based on differential privacy for preserving link relationship of social networks

With the widespread use of social networks in our daily life, the personal privacy in social networks has become a growing concern.To prevent the link relationship of social networks from disclosing users' sensitive information when the social networks data is released, an uncertain graph approach based on differential privacy is introduced, which can resist attacks based on background knowledge and possesses better data utility.In this approach, we propose modification of edges based on random response (MERR) algorithm and injection of uncertainty based on k-edges-differential privacy (IUDP) algorithm.The MERR algorithm can modify the edge of original graph according to random response mechanism, while the IUDP algorithm injects uncertainty to generate an uncertain graph.For evaluating our approach, the expectation of editing distance between two graphs is adapted to measure the level of privacy preserving.In addition, our approach is conducted in different datasets and compared with other approaches.The experimental results indicate that this approach achieves differential privacy and has better data utility.
BLOCKCHAIN TECHNOLOGIES IN SMART CITIES

This paper aims to show that smart cities are an alternative for the process of sustainable political, economic, social and environmental transformation. Surveys of smart city rankings and indexes were carried out to verify which cities are standing out in certain dimensions. It was found that blockchain technology enables very good results in the articulation of the autopoietic, self-regulating and sustainable smart city. In collecting data on the degree of applicability of the blockchain, carried out with market experts in the use of this technology, it was identified that the application of the blockchain has greater emphasis on the brain and less emphasis on the Locomotor System of cities.