Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Algorithm using Chao-Scattering of Partial Homomorphic Encryption

Abstract A big challenge in privacy-preserving is found when trying is to share data while protecting the information of personally identifiable, such as phone numbers, email addresses, and biometric. The same challenges have appeared when keeping sensitive data remotely in complete privacy, without recognizing them from any unauthorized persons. In such cases, Homomorphic encryption is the highlight and considered the most suitable solution. The goal of this paper is to keep the privacy of the data stored in a database system using a new Chao-modification to the partial homomorphic encryption algorithm. It uses the chaotic system to scatter the symbols of plain data before direct them to the encryption algorithm. This will be appending an additional level of security to partial homomorphic encryption algorithms that helps them to be more resisting attacking.
Face Recognition – Extensive Survey and Recommendations

Nowadays, the digital environment is a fast-growing and potential realm of the world. Human verification and identification can be done online. Face recognition is the competitive method and best biometric modality for human identification and recognition in comparison to voice, iris, thumb, ear, hand, and retina scans. This is a potential emerging area that required sophisticated research in both academics and industry to think of a few powerful face detection strategies making it quite possible in computer vision. Also, it’s a very challenging research area because of unconstrained environments. Though most of the existing research has provided promising solutions, some of the algorithms find it difficult to yield results under different unconstrained conditions such as lighting, expression, illuminate, pose variation, low resolution, and occlusion. This paper provides a detailed review of the past as well as current research techniques and highlights the drawbacks. Especially the model, pattern, manual, and automated feature extraction techniques have been reviewed extensively and their drawbacks are highlighted. Additionally, the performances of face recognition on the standard datasets are analyzed. Finally, recommendations are provided to overcome the existing problem faced during the time of face recognition, which will help to improve the research in the future.
Uncertainty Quantification in Image Segmentation Using the Ambrosio–Tortorelli Approximation of the Mumford–Shah Energy

Abstract The quantification of uncertainties in image segmentation based on the Mumford–Shah model is studied. The aim is to address the error propagation of noise and other error types in the original image to the restoration result and especially the reconstructed edges (sharp image contrasts). Analytically, we rely on the Ambrosio–Tortorelli approximation and discuss the existence of measurable selections of its solutions as well as sampling-based methods and the limitations of other popular methods. Numerical examples illustrate the theoretical findings.
On the potential of normalized TSP features for automated algorithm selection

Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.
Enhancing Personalization by Integrating Top-Down and Bottom-Up Approaches to Learner Modeling

Learner models are representations of the learner’s knowledge, skills and other attributes used by Adaptive Instructional Systems (AISs) to personalize their interactions with the learners (e.g., by implementing adaptive feedback, and recommending tasks/activities). Top-down and bottom-up approaches to learner modeling provide various affordances and challenges in terms of the need for interpretable learner models, the amount of data available, the complexity of the model, and the amount of human effort needed to implement and validate learner models. Research shows that hybrid approaches involving both top-down and bottom-up approaches are needed to effectively deal with the challenges of learner modeling in AISs. This paper describes several learner modeling approaches for integrating top-down and bottom-up approaches to gather additional evidence for supporting assessment claims and implementing personalization approaches. We elaborate on several learner modeling issues, including (a) evidence identification and aggregation in assessment systems; (b) making sense of process data aimed at supporting assessment claims related to learner cognition; and (c) approaches for improving interpretability and explainability of student models with some implications for validity and fairness of AISs.
Academic Article Recommendation by Considering the Research Field Trajectory

Academic resources discovery has been an open and challenging problem. How to find the needed articles in a large number of research papers has always been a time-consuming task, especially for scholars or students who are new to the field. With the advancement of information technology and the evolution of application, the research field has its own evolutionary process. If beginners can master the evolution and the technology of the research field, they will get a deep understanding of the current target areas. Therefore, in this paper, we adopt the concept of research field trajectory to develop a paper recommendation method which is useful for beginners. The goal is to systematically provide research papers, so that researchers who are new to the field can quickly find academic research papers related to their own research, and form a solid research foundation. Furthermore, it also can recommend to researchers the emerging research papers in this field. To show the benefit of our approach, a set of experiments is performed. The experimental results show that the proposed method is effective in solving this problem.
ANNs for Automatic Speech Recognition—A Survey

The rapid and widespread industrial growth increases the need for industrial automation. Automatic speech recognition (ASR) is an important aspect that has been incorporated in the industrial automation by using the artificial intelligence. In the last two decades, artificial neural network (ANN) has attracted a significant research attention toward speech recognition due to its ability to deal with the complex problem. This paper provides a recent survey on the utilization of artificial neural network (ANN) for various speech recognition applications. The survey focuses on the ANN architecture, methodology, hyperparameters, database, and performance evaluation.
Security Analysis of Transaction Authorization Methods for Next Generation Electronic Payment Services

Real-world and ubiquitous human-computer interactions require payment processes that have to be instant, convenient and interoperable. However, these functional requirements are in opposition to one of the most significant non-functional requirement: security of the payment process. A number of various attacks on confidentiality of payment or payer data, integrity, authenticity and non-repudiation of payment transaction, as well as on availability of the payment service are reported. Next generation electronic payment services utilize wide range of payment authorization methods. Security analysis of authorization methods described in this paper includes four sequential phases. The first one is identification of relevant authorization methods related to payment authorization in various scenarios. The second one is identification of classes of vulnerabilities and threats that are, or potentially can be, related to transaction authorization processes. The third phase comprises analysis of risks resulting from possible impact of the threats on the authorization methods. The fourth phase covers identification of all types of countermeasures that can be applied against risks identified in the previous phase. The result of presented work can be useful in a number of risk analysis scenarios. Especially in those, where security of composed system is analyzed, which means a system that supports a number of assets, electronic payments methods, and countermeasures or security controls in various scenarios when they are simultaneously used and interact with each other.
Sign Language Recognition Using Convolutional Neural Networks

The lack of familiarity with sign language among non-users and the absence of a universal method for communication poses major problems for the deaf community. The digitization of communication channels can greatly benefit society. Previous attempts to develop sign language recognition (SLR) systems have posed disadvantages of low feasibility, portability, and accuracy which hamper widespread use. In this paper, a vision-based sign language recognition system using convolutional neural networks (CNN) is proposed that can efficiently and accurately recognize the American Sign Language (ASL) alphabet. The proposed method is implemented using the Keras library on Python. It is easy to implement and has attained high accuracies of over 99% with low bias and variance. The implementation has been validated on the ASL MNIST dataset and its performance is established through simulation studies.
Literature Review of Blockchain Technology in Space Industry: Challenges and Applications

In Earth Observation (EO) applications, the new trend moves toward using small satellites architectures like constellations and swarms. These architectures are low-cost, simple, and have relatively short development time. The Blockchain proved high efficiency in terrestrial systems due to its reliability, security, and latency reduction. In mobile satellite communication system, Blockchain represents a revolution in how to validate data integrity and create secure prioritized data exchange. In this paper, we present a survey about the adoption of Blockchain in space industry and its applications. This study presents challenges, suggested solutions and open research points in building satellite systems based on blockchain.
Design of Children’s Entertainment and Education Products Based on AR Technology

In the Plan for New-generation Artificial Intelligence Development promulgated by the state in recent years, the introduction of artificial intelligence and immersive teaching into smart education has been put forward, aiming to construct the online education model with an XR integration and to build interactive learning environment through the energizing of 5G. This project discusses about how to design education products integrating virtual and real factors so as to effectively improve the situationality and experience of children in the learning of music theory, mobilize children to learn through visual sense, kinesthesis, hearing and other senses with the help of AR recognition technology, thus presenting abstract concepts and theories to the children in a more intuitive way, thus improving their learning efficiency. Finally, the researchers designed and developed a set of game products for music learning combining software and hardware, and a usability test of the prototype was conducted so as to carry out the iterated design. The design in this project addresses the problems of boredom and passive learning seen in children learning music theory in the traditional way, combines obscure knowledge of music theory with interactive games, thus promoting children learners’ understanding of music theory. The way of immersive learning with an integration of virtual and real factors, interactive means and perception methods have improved the learning efficiency and creativity of the learners, optimized the design and realization process of the interaction and feedback mechanism, and brought about game-changing changes to educational ideas and teaching approaches.
A Data Sharing Scheme Based on Blockchain System and Attribute-Based Encryption

Blockchain systems offer a decentralized, immutable and transparent architecture that can give the ownership and control of data back to users, empower trusted and accountable data sharing. However, there still exist different scalability, security and potential privacy issues in blockchain networks, such as on-chain data privacy, data origin authentication, or compliance with privacy regulations (e.g. GDPR). To address these challenges, we propose a privacy-preserving and user-controlled architecture for data sharing based on blockchain system and Ciphertext-Policy Attribute-Based Encryption (CP-ABE), called ThemisABE. The proposed scheme has properties including one-to-many data encrypting and fined-grained access control. In the ThemisABE, any party can be an authority and the algorithm of CP-ABE generates the secret key corresponding to attributes for data user through any chosen set of nodes in blockchain system. Moreover, in order to realize accountability of CP-ABE algorithm and avoid performance bottleneck incurred by smart contracts, a kind of data structure of transactions is designed to record in the ledger, following an approaches to off-chain computations. The results of security analysis and experimental simulation show that the proposed scheme can solves the problems of privacy security and localization of data sharing. At the same time, the data sharing scheme has better performance and could be applied in some scenarios, such as eHealth, eGovernment or cross-border paperless trade.
Blockchain based Data Sharing for User Experience Driven Slice SLA Guarantee

In 5G network, NWDAF (network data analysis function) carries out intelligent modeling based on user experience data and network parameters to support SLA (Service Level Agreement) guarantee. In order to ensure the generalization and long-term applicability of the AI (artificial intelligence) model, network slicing data sharing among multiple parties is needed to help build effective and comprehensive modeling data collection and improve the quality of AI modeling and SLA guarantee with advantage of lower network cost. Based on this, the paper proposes a solution of slice data sharing within the alliance based on blockchain for user experience-driven slice SLA guarantee. The network slicing related data required for NWDAF modeling is provided to and maintained in the blockchain by alliance members. This matches the relationship between 5G stakeholders and helps to improve the authenticity, integrity, privacy and quality of shared data, so as to support NWDAF to train AI model efficiently and accurately, and then improve SLA guarantee level of network slice. The paper also carries out experimental design and optimization direction analysis.
Modeling Team Dynamics for the Characterization and Prediction of Delays in User Stories

In agile software development, proper team structures and effort estimates are crucial to ensure the on-time delivery of software projects. Delivery performance can vary due to the influence of changes in teams, resulting in team dynamics that remain largely unexplored. In this paper, we explore the effects of various aspects of teamwork on delays in software deliveries. We conducted a case study at ING and analyzed historical log data from 765,200 user stories and 571 teams to identify team factors characterizing delayed user stories. Based on these factors, we built models to predict the likelihood and duration of delays in user stories. The evaluation results show that the use of team-related features leads to a significant improvement in the predictions of delay, achieving on average 74%-82% precision, 78%-86% recall and 76%-84% F-measure. Moreover, our results show that team-related features can help improve the prediction of delay likelihood, while delay duration can be explained exclusively using them. Finally, training on recent user stories using a sliding window setting improves the predictive performance; our predictive models perform significantly better for teams that have been stable. Overall, our results indicate that planning in agile development settings can be significantly improved by incorporating team-related information and incremental learning methods into analysis/predictive models.
Blockchain based Big Data Platform of City Brain

Blockchain technology, with its distributed ledger, decentralization, high security, no tampering and other features, helps to solve the problems of data source confirmation, traceability and authorized data sharing. The effective utilization of data to achieve automatic governance and trusted decision-making of the city is known as "City Brain". The city brain is the key in smart city development, while the key to build a city brain is data resources. Aiming at the challenges existing in the construction of big data platform, this paper proposes a new solution based on blockchain, establishes the entity model and data model, analyzes the business model, designs the blockchain data platform framework and the cloud-blockchain integrated operation mode, and at last discusses the issues concerned in the application.
Efficiently computing runs on a trie

A maximal repetition, or run, in a string, is a maximal periodic substring whose smallest period is at most half the length of the substring. In this paper, we consider runs that correspond to a path on a trie, or in other words, on a rooted edge-labeled tree where each edge is labeled with a single symbol, and the endpoints of the path must be a descendant/ancestor of the other. For a trie with n edges, we show that the number of runs is less than n. We also show an asymptotic lower bound on the maximum density of runs in tries: limn→∞⁡ρT(n)/n>0.9932348 where ρT(n) is the maximum number of runs in a trie with n edges. Furthermore, we also show an O(nlog⁡log⁡n) time and O(n) space algorithm for finding all runs.
Explainable Noisy Label Flipping for Multi-Label Fashion Image Classification

In online shopping applications, the daily insertion of new products requires an overwhelming annotation effort. Usually done by humans, it comes at a huge cost and yet generates high rates of noisy/missing labels that seriously hinder the effectiveness of CNNs in multi-label classification. We propose SELF-ML, a classification framework that exploits the relation between visual attributes and appearance together with the "low-rank" nature of the feature space. It learns a sparse reconstruction of image features as a convex combination of very few images - a basis - that are correctly annotated. Building on this representation, SELF-ML has a module that relabels noisy annotations from the derived combination of the clean data. Due to such structured reconstruction, SELF-ML gives an explanation of its label-flipping decisions. Experiments on a real-world shopping dataset show that SELF-ML significantly increases the number of correct labels even with few clean annotations.
Anonymous node location based on clustering

Abstract With the rapid development of network technology, the network’s scale is increasing and becoming more complex. It is imperative to understand the structure of the network for network management and network security. Therefore, the construction of the routing topology has become a top priority. Routing topology analysis is already a relatively mature technology, but anonymous nodes’ positioning has not received enough attention. Nowadays, the primary solutions to anonymous nodes are graph analysis and tomographic tree construction. Based on these two methods, this paper proposes a rough location of anonymous nodes based on the method of three-level clustering. They are the K-means clustering of data before anonymous identification, hierarchical clustering of feature values in anonymous identification, and central point calculation based on clustering ideas during anonymous positioning. Experiments have proved that performing the first-level K-means clustering before anonymous node identification can effectively improve anonymous positioning accuracy. And the three-layer clustering idea proposed in this paper can roughly get the location of anonymous nodes based on reducing the complexity of the original method.
Stigmergic Multi-Agent Systems in the Semantic Web of Things

Intelligent, autonomous agents are still not available in the Semantic Web at large scale today. Also the fields of Semantic Web and Multi-Agent Systems are not working together very closely although they could profit much from each other. Existing approaches merely use the Web as a transport layer and are not properly aligned to the architectural style of the Web. The Internet of Things, which would be very useful for agents to act upon Things in the real world, on the other side is very fragmented and not easily inter-operable. The Web of Things has emerged as an approach to use the (Semantic) Web as an application layer for Things. It is still unclear however, how agents on the WoT should look like. We propose that the Semantic Web is a suitable integration layer, both for agents and Things. We investigate how a Multi-Agent System in the Semantic Web of Things can be build by utilizing simple reflex agents and the communication paradigm of stigmergy. We map Things and artifacts to Web resources that are managed by a Web server and provide affordances to agents through hypermedia.
Generating Hypothetical Events for Abductive Inference

Abductive reasoning starts from some observations and aims at finding the most plausible explanation for these observations.To perform abduction, humans often make use of temporal and causal inferences, and knowledge about how some hypothetical situation can result in different outcomes.This work offers the first study of how such knowledge impacts the Abductive αNLI task -which consists in choosing the more likely explanation for given observations.We train a specialized language model LM I that is tasked to generate what could happen next from a hypothetical scenario that evolves from a given event.We then propose a multi-task model MT L to solve the αNLI task, which predicts a plausible explanation by a) considering different possible events emerging from candidate hypothesesevents generated by LM I -and b) selecting the one that is most similar to the observed outcome.We show that our MT L model improves over prior vanilla pre-trained LMs finetuned on αNLI.Our manual evaluation and analysis suggest that learning about possible next events from different hypothetical scenarios supports abductive inference.O 1 : Dotty was being very grumpy.O 2 : She felt much better