Reviewerly/exHarmony huggingface dataset:
An Image Watermarking based on Multi-level Authentication for Quick Response Code

This research presented a digital watermarking scheme using multi-level authentication for protecting QR code images in order to provide security and authenticity. This research focuses on the improved digital watermarking scheme for QR code security that can protect the confidentiality of the information stored in QR code images from the public. Information modification, malicious attack, and copyright violation may occur due to weak security and disclosure pattern of QR code. Digital watermarking can be a solution to reduce QR code imitation and increase QR code security and authenticity. The objectives of this research are to provide QR code image authentication and security, tamper localization, and recovery scheme on QR code images. This research proposed digital watermarking for QR code images based on multi-level authentication with Least Significant Bit (LSB) and SHA-256 hash function. The embedding and extracting watermark utilized region of Interest (ROI) and Region of Non-Interest (RONI) in the spatial domain for improving the depth and width of QR code application in the anti-counterfeiting field. The experiments tested the reversibility and robustness of the proposed scheme after a tempered watermarked QR code image. The experimental results show that the proposed scheme provides multi-level security, withstands tampered attacks and it provided high imperceptibility of QR code image.
Stigmergy-Based Collision-Avoidance Algorithm for Self-Organising Swarms

Grasso, Paolo Innocente, Mauro SebastiánReal-time multi-agent collision-avoidance algorithms comprise a key enabling technology for the practical use of self-organising swarms of drones. This paper proposes a decentralised reciprocal collision-avoidance algorithm, which is based on stigmergy and scalable. The algorithm is computationally inexpensive, based on the gradient of the locally measured dynamic cumulative signal strength field which results from the signals emitted by the swarm. The signal strength acts as a repulsor on each drone, which then tends to steer away from the noisiest regions (cluttered environment), thus avoiding collisions. The magnitudes of these repulsive forces can be tuned to control the relative importance assigned to collision avoidance with respect to the other phenomena affecting the agent’s dynamics. We carried out numerical experiments on a self-organising swarm of drones aimed at fighting wildfires autonomously. As expected, it has been found that the collision rate can be reduced either by decreasing the cruise speed of the agents and/or by increasing the sampling frequency of the global signal strength field. A convenient by-product of the proposed collision-avoidance algorithm is that it helps maintain diversity in the swarm, thus enhancing exploration.
Using Artificial Intelligence for Obtaining Vehicle Occupancy Using Security Cameras

Controlling vehicle occupancy is very useful for detecting overcrowding situations in public transportation. In a pandemic, it becomes essential to prevent disease spread by dynamically adapting the route plan and traffic scheduling and allowing passengers to choose vehicles that carry fewer passengers in real-time. However, given the size and complexity of the system, this task is not trivial. In this paper, a classification method for passenger occupancy analysis is proposed. The classifier is based on a Convolutional Neural Network (CNN) model. As input to the proposed model, images from Closed-Circuit Television (CCTV) cameras, already present in the vehicles for security reasons, were used. The addressed problem is especially challenging due to the non-standardization of the equipment, poor quality of the images, and cameras’ variable positioning inside the vehicle. Besides, as there is no pre-existing image dataset for training, one must deal with a small number of labeled images. Two datasets are created, varying on the assessed quality score. Three CNN architectures were evaluated on the datasets, to validate the feasibility of the proposed method. Experiments on the created public transportation dataset show that the proposal of this work achieves promising results.
Linear-Time Minimal Cograph Editing

We present an algorithm for computing a minimal editing of an arbitrary graph G into a cograph, i.e. a set of edits (additions and deletions of edges) that turns G into a cograph and that is minimal for inclusion. Our algorithm runs in linear time in the size of the input graph, that is \(O(n+m)\) time where n and m are the number of vertices and the number of edges of G, respectively.
Multimodal Corpus Analysis of Autoblog 2020: Lecture Videos in Machine Learning

This paper introduces a lecture video corpus, Autoblog 2020. With the increase of online learning in universities, there is a demand for a systematic toolchain development for lecture video processing. However, the existing lecture video corpus does not satisfy the requirement for such tasks, and lecture transcription and analyses are relatively unexplored areas in speech and natural language research. Autoblog 2020 Corpus is developed towards the end goal of free video-to-blog post conversion software that supports making video presentations more accessible. It will include automatic editing of disfluencies, automatic speech recognition (ASR), and spoken term extraction so that researchers can process and share their contents more efficiently. In this paper, we present a description of the corpus, linguistic analyses and preliminary experiment results regarding ASR, keyword extraction, and segmentation. The results will be used in future work to develop a video-to-blog post conversion.
Workshop on Recommender Systems in Fashion and Retail

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this domain, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. Moreover, the research interest on this area is increasing, demonstrated by the success of the first and second editions of the fashionXrecsys Workshop in 2019-2020. The third edition of the workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce.
MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.
Modern Cause and Effect Model by Factors of Root Cause for Accident Prevention in Small to Medium Sized Enterprises

Factors related to root causes can cause commonly occurring accidents such as falls, slips, and jammed injuries. An important means of reducing the frequency of occupational accidents in small- to medium-sized enterprises (SMSEs) of South Korea is to perform intensity analysis of the root cause factors for accident prevention in the cause and effect model like decision models, epidemiological models, system models, human factors models, LCU (life change unit) models, and the domino theory. Especially intensity analysis in a robot system and smart technology as Industry 4.0 is very important in order to minimize the occupational accidents and fatal accident because of the complexity of accident factors.We have developed the modern cause and effect model that includes factors of root cause through statistical testing to minimize commonly occurring accidents and fatal accidents in SMSEs of South Korea and systematically proposed educational policies for accident prevention.As a result, the consciousness factors among factors of root cause such as unconsciousness, disregard, ignorance, recklessness, and misjudgment had strong relationships with occupational accidents in South Korean SMSEs.We conclude that the educational policies necessary for minimizing these consciousness factors include continuous training procedures followed by periodic hands-on experience, along with perceptual and cognitive education related to occupational health and safety.
The Term “Library Service” in the Conceptual Framework of Library Science

The article considers the contradiction in concepts connected with key content of the library activity. Some fundamental documents use the term “library service”, others use “information and library service”, “library and information service” terms. The different content is also given to these concepts. The inconsistency is increased by the fact that since Soviet times the library services have often been understood as library activity in general. There is given the brief review of primarily positions on the conceptual framework of library service: library activity, work with readers, guidance of reading, library service, library and information service, information and library service. In violation of the Federal Law № 78-FZ “On Librarianship”, the term “library and information service” is enshrined in the names of textbooks, GOST R 7.0.103—2018 “Library and information service. Terms and definitions” and GOST R 7.0.104—2019 “Library and information services of scientific library. Types, forms and modes of delivery”. While library science is striving hard to integrate itself into information science, information science itself is far from recognising library and bibliographic science as an integral part of it. “Library service” is considered both as specific and general library science concept. The definition of “work” in relation to library science is given: it is the function of library staff to create values, provide benefits or meet the informational, cultural, educational needs of readers. The author proposes to intensify the concept of “library readers study”, to develop thoroughly its theory and methodology. The paper provides additional arguments in favour of the concept of “library readers study” in addition to the well-known ones. It is argued that in addition to revealing the links between the readers’ contingent and the library staff, it is necessary to reveal the links of the “readers’ contingent” subsystem with other subsystems of the first and second circuits of the library as a system.
Online Social Network Post Classification: A Multiclass approach

This paper presents the results of automating the process of social network posts’ two-level hierarchical (ensemble) classification. The research aims to build a second-level model that allows to classify three main classes into subclasses using methods of multi-class classification (SVM, naive Bayesian classifier, random forest and multilayer perceptron). This work’s theoretical and practical significance is determined by the fact that the resulting model will partially automate the process of assessing the severity of users’ psychological characteristics on their text posts in social networks, as well as create the potential to refine the estimates of the protection of users from social engineering attacks, and the development of recommendation systems offering measures to improve users’ protection. The novelty of the work comes from the complementing of the previously developed two-level classification of social network posts with a second-level model based on known multiclass classification’s methods.
Analyzing Particularities of Sensor Datasets for Supporting Data Understanding and Preparation

Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data). This work analyzes several aspects of the data generated by different sensor types to understand particularities in the data, linking them with existing data mining methodologies. Using data from different sources, this work analyzes how the type of sensor used and its measurement units have an important impact in basic statistics such as variance and mean, because of the statistical distributions of the datasets. The work also analyzes the behavior of outliers, how to detect them, and how they affect the equivalence of sensors, as equivalence is used in many solutions for identifying anomalies. Based on the previous results, the article presents guidance on how to deal with data coming from sensors, in order to understand the characteristics of sensor datasets, and proposes a parallelized implementation. Finally, the article shows that the proposed decision-making processes work well with a new type of sensor and that parallelizing with several cores enables calculations to be executed up to four times faster.
Digital Watermarking for Anonymized Data With Low Information Loss

At present, massive amounts of data are utilized for artificial intelligence technologies such as machine learning and deep learning. However, these data must be utilized carefully while preserving data privacy. Data anonymization is a technique enabling both data mining and privacy protection, preventing the identification of individuals by generalizing the data to include multiple records with the same values. In this study, we consider a data-publishing infrastructure for personal data sharing. The infrastructure anonymizes data prior to publishing it to users for privacy protection; however, the problem of unauthorized republishing by malicious users must be considered. To address this issue, we studied digital watermarking methods that correlate data users with anonymized data. Our previous method embedded information indicating the original user to detect illegally republished data. However, this method did not focus on information loss. This study proposes another digital watermarking method for anonymized data that achieves low information loss. The proposed method replaces values in tuples to embed information. To reduce the information loss caused by the embedding, the proposed method selects replacement values from the candidates whose meanings are similar to the original. We propose the use of vector-conversion tables to select replacement values. The proposed method also extends the maximum length of the embedded bit string by embedding multiple bits into a single tuple. Moreover, we measured the tolerance to distortion attacks to evaluate the efficacy of the proposed method. The proposed method is non-blind, i.e., data prior to digital watermarking is required to perform extraction.
Co-Trained Random Vector Functional Link Network

In this paper, we propose ensemble of random vector functional link network known as co-trained random vector functional link network (coRVFL). Random vector functional link network solves the optimization problem via closed form solution and hence avoids the problems of slow convergence and local minima problems. The proposed coRVFL trains two RVFL models jointly such that each RVFL model is constructed with different feature projection matrix and hence, shows better generalization performance. We use randomly projected features and sparse-l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> . norm autoencoder based features to train the proposed coRVFL model. Experimental results show that the proposed coRVFL is performing better in comparison with the baseline models. Furthermore, statistical analysis reveals that the proposed coRVFL model performs statistically better than the baseline approaches.
General meta-learning paradigm based on prior-models, meta-model, meta-algorithm, and few-shot-base-model

As the studies on Machine Intelligence continue, new methods emerge with the purpose of improving computational learning, especially in deep-learning to overcome the need of high amount of data used to solve tasks. Therefore, paradigms like Meta Learning for few-shot learning intents to work out this situation. Nevertheless, current meta-learning methods address this situation using a single model trained in different levels of learning: meta-level and base-level. Instead, a new methodology to implement meta-learning for few-shot learning is proposed in this paper. This methodology is based on the concepts of modularity and reuse of optimized and previously implemented models and it is divided into elements of the process of learning. The elements are prior-models, meta-model, few-shot-base-model and a meta-algorithm. The proposed meta-algorithm generates a distribution over trained meta-models. Then, the best meta-model whose meta-tasks are more similar to the new tasks that the few-shot-base-model will solve is chosen. Also, a new concept of meta-few-shot sampling is proposed in order to evaluate how many meta-tasks are used in the meta-training stage. The experiments with 5-way 5-shot configuration show a competitive result compared with state-of-the-art methods. The proposed method achieves 97.56±1.57% and 83.33±8.33 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in the CUB-200-2011 dataset, using the EfficientNetB7 and the DenseNet121 as prior-models, respectively; and, 99.56±0.63% (EfficientNetB7) and 94.89±4.28% (DenseNet121) in the Mini-ImageNet dataset.
AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

As an essential step in the restoration of Terracotta Warriors, the results of fragments classification will directly affect the performance of fragments matching and splicing. However, most of the existing methods are based on traditional technology and have low accuracy in classification. A practical and effective classification method for fragments is an urgent need. In this case, an attention-based multi-scale neural network named AMS-Net is proposed to extract significant geometric and semantic features. AMS-Net is a hierarchical structure consisting of a multi-scale set abstraction block (MS-BLOCK) and a fully connected (FC) layer. MS-BLOCK consists of a local-global layer (LGLayer) and an improved multi-layer perceptron (IMLP). With a multi-scale strategy, LGLayer can parallel extract the local and global features from different scales. IMLP can concatenate the high-level and low-level features for classification tasks. Extensive experiments on the public data set (ModelNet40/10) and the real-world Terracotta Warrior fragments data set are conducted. The accuracy results with normal can achieve 93.52% and 96.22%, respectively. For real-world data sets, the accuracy is best among the existing methods. The robustness and effectiveness of the performance on the task of 3D point cloud classification are also investigated. It proves that the proposed end-to-end learning network is more effective and suitable for the classification of the Terracotta Warrior fragments.
Attention-Based Graph Neural Network for News Recommendation

News recommendation aims to alleviate the big explosion of news information and helps users find their interesting news. Existing news recommendation models model users' historical click news as users' interests. Although they have achieved acceptable recommendation accuracy, they suffer from severe data sparse problems because of the limited news clicked by users. Further, the user's historical click sequence information has different effects on the user's interest, and simply combining them can not reflect this difference. Therefore, we propose an attention-based graph neural network news recommendation model. In our model, muti-channel convolutional neural network is used to generate news representations, and recurrent neural network is used to extract the news sequence information that users clicked on. Users, news, and topics are modeled as three types of nodes in a heterogeneous graph, and their relationships are modeled as edges. Graph neural network is used to effectively extract the structural information from heterogeneous graph, and helps to solve the problem of sparse data. Taking into account the different effects of different information on recommendation results, we use the attention mechanism to fuse this information distinctively. Extensive experiments conducted on the real online news datasets show that our model is superior to advanced deep learning-based recommendation methods.
Beating White-Box Defenses with Black-Box Attacks

Deep learning has achieved great results in the last decade, however, it is sensitive to so called adversarial attacks - small perturbations of the input that cause the network to classify incorrectly. In the last years a number of attacks and defenses against these attacks were described. Most of the defenses however focus on defending against gradient-based attacks. In this paper, we describe an evolutionary attack and show that the adversarial examples produced by the attack have different features than those from gradient-based attacks. We also show that these features mean that one of the state-of-the-art defenses fails to detect such attacks.
Toward Domain Adaptation for small data sets

Domain Adaptation (DA), i.e. reusing Machine Learning (ML) pre-trained models across related domains, is increasingly favored, especially in applications domains where training data is scarce. However, the adaptation process still requires data and time to adjust the model to the new domain. In this work, we propose a data selection mechanism to minimize the data required from the new domain when adapting a pre-trained ML model. Our target is to achieve short retraining time, minimize new domain data usage, and maximize transferred knowledge, lowering the overall cost of adapting pre-trained models. The result of our experiment indicates achieving a fast accuracy improvement in adapting the pre-trained model using our approach, comparing the regular one with minimum data usage.
Aspect-Based Sentiment Classification with Reinforcement Learning and Local Understanding

Aspect-based sentiment analysis is a fine-grained classification task in natural language processing. In this paper, we propose a new framework with reinforcement learning agent to assess the importance of words in the sentence for sentiment analysis and mask out insignificant ones. Our method emphasizes on local linguistic understanding and extracts aspect-agnostic background information as well as aspect-relevant information. Experiments on three common datasets show that the proposed method is effective and achieves substantial performance improvements over comparison models.