Reviewerly/exHarmony huggingface dataset:
GNN at the Edge: Cost-Efficient Graph Neural Network Processing Over Distributed Edge Servers

Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions such as traffic flow forecasting and location-based social recommendation. To bridge this gap, this paper formally studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We build a comprehensive modeling framework that can capture a variety of different cost factors, based on which we formulate a cost-efficient graph layout optimization problem that is proved to be NP-hard. Instead of trivially applying traditional data placement wisdom, we theoretically reveal the structural property of quadratic submodularity implicated in GNN’s unique computing pattern, which motivates our design of an efficient iterative solution exploiting graph cuts. Rigorous analysis shows that it provides parameterized constant approximation ratio, guaranteed convergence, and exact feasibility. To tackle potential graph topological evolution in GNN processing, we further devise an incremental update strategy and an adaptive scheduling algorithm for lightweight dynamic layout optimization. Evaluations with real-world datasets and various GNN benchmarks demonstrate that our approach achieves superior performance over de facto baselines with more than 95.8% cost reduction in a fast convergence speed.
Whole Mammography Diagnosis via Multi-instance Supervised Discriminative Localization and Classification

Precise mammography diagnosis plays a vital role in breast cancer management, especially in identifying malignancy with computer assistance. Due to high resolution, large image size, and small lesion region, it is challenging to localize lesions while classifying the whole mammography, which also renders difficulty for annotating mammography datasets and balancing tumor and normal background regions for training. To fully use local lesion information and macroscopic malignancy information, we propose a two-step mammography classification method based on multi-instance learning. In step one, a multi-task encoder-decoder architecture (mt-ConvNext-Unet) is employed for instance-level lesion localization and lesion type classification. To enhance the ability of feature extraction, we adopt ConvNext as the encoder, and added normalization layer and scSE attention blocks in the decoder to strengthen localization ability of small lesions. A classification branch is used after the encoder to jointly train lesion classification and segmentation. The instance-based outputs are merged into the image-level both for segmentation and classification (SegMap and ClsMap). In step two, a whole mammography classification model is applied for breast-level cancer diagnosis by combining the results of CC and MLO views with EfficientNet. Experimental results on the open dataset show that our method not only accurately classifies breast cancer on mammography but also highlights the suspicious regions.
Robot Imitation Learning From Image-Only Observation Without Real-World Interaction

Learning from observation (LfO) prompts the robot to imitate actions from experts' states via deep reinforcement learning (RL), achieving satisfactory results in simulation environments through hundreds of thousands of robot–environment interactions. While in the real world, constrained by the expensive and potentially dangerous interaction between the real robot and the environment, LfO is still challenging to be popularized. Therefore, reducing the number of interactions in LfO during training in the real world remains a hot research topic. Although significant progress has been made, the interaction is still inevitable. Hence, this article proposes the LION net ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Learning from Image-only Observation net</i> ), which learns action from image-only demonstrations, e.g., a video of a human demonstrating a task, and reduces the number of the robot–environment interaction to zero in the real world. It is expected to be a realistic solution for real-world robot LfO. The LION net comprises two modules: 1) a domain transfer module that bridges the gap between the simulator and the real world and an RL-based control module that utilizes images as input to learning a task. The LION net affords the robot to imitate an action from the image-only human demonstration in the simulator and perform the learned action in the real world without additional training. The proposed method is evaluated on three real-life tasks: 1) pouring water, 2) washing cup, and 3) stacking cube, deployed to a real-world robot, demonstrating state-of-the-art performance.
A Comparison of Multi-Label Text Classification Models in Research Articles Labeled With Sustainable Development Goals

The classification of scientific articles aligned to Sustainable Development Goals is crucial for research institutions and universities when assessing their influence in these areas. Machine learning enables the implementation of massive text data classification tasks. The objective of this study is to apply Natural Language Processing techniques to articles from peer-reviewed journals to facilitate their classification according to the 17 Sustainable Development Goals of the 2030 Agenda. This article compares the performance of multi-label text classification models based on a proposed framework with datasets of different characteristics. Results reveal that a particular combination of a transformation method with a classifier algorithm dominates the performance results.
Learning Bi-Typed Multi-Relational Heterogeneous Graph Via Dual Hierarchical Attention Networks

Bi-typed multi-relational heterogeneous graph (BMHG) is one of the most common graphs in practice, for example, academic networks, e-commerce user behavior graph and enterprise knowledge graph. It is a critical and challenge problem on how to learn the numerical representation for each node to characterize subtle structures. However, most previous studies treat all node relations in BMHG as the same class of relation without distinguishing the different characteristics between the intra-type relations and inter-type relations of the bi-typed nodes, causing the loss of significant structure information. To address this issue, we propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ual <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> ierarchical <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ttention <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etworks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-type and inter-type attention-based encoder under a hierarchical mechanism. Specifically, the former encoder aggregates information from the same type of nodes, while the latter aggregates node representations from its different types of neighbors. Moreover, to sufficiently model node multi-relational information in BMHG, we adopt a newly proposed hierarchical mechanism. By doing so, the proposed dual hierarchical attention operations enable our model to fully capture the complex structures of the bi-typed multi-relational heterogeneous graphs. Experimental results on various tasks against the state-of-the-arts sufficiently confirm the capability of DHAN in learning node representations on the BMHGs.
Continued Fractions Applied to a Family of RSA-like Cryptosystems

Let $$N=pq$$ be the product of two balanced prime numbers p and q. Murru and Saettone presented in 2017 an interesting RSA-like cryptosystem that uses the key equation $$ed \,-\, k (p^2\,+\,p\,+\,1)(q^2\,+\,q\,+\,1) = 1$$ , instead of the classical RSA key equation $$ed - k (p-1)(q-1) = 1$$ . The authors claimed that their scheme is immune to Wiener’s continued fraction attack. Unfortunately, Nitaj et. al. developed exactly such an attack. In this paper, we introduce a family of RSA-like encryption schemes that uses the key equation $$ed \,-\, k [(p^n\,-\,1)(q^n\,-\,1)]/[(p\,-\,1)(q\,-\,1)] = 1$$ , where $$n>1$$ is an integer. Then, we show that regardless of the choice of n, there exists an attack based on continued fractions that recovers the secret exponent.
Dynamic Fusion Nearest Neighbor Machine Translation via Dempster-Shafer Theory

kNN-MT has been recently proposed, uses a token-level k-nearest neighbor approach to retrieve similar sentences, obtaining knowledge guidance from an external memory module, and then combined with the prediction results of the translation model, which greatly improves the accuracy of machine translation. However, kNN-MT uses simple linear interpolation in the fusion of retrieval probability and translation probability, which can not dynamically adjust the fusion ratio according to the matching degree of the retrieved sentences. Moreover, different fusion ratios need to be explored in different translation scenarios, and the translation effect will be affected when the retrieved sentences have a low matching degree or contain noise. In this paper, we propose an approach via Dempster-Shafer theory (DST) to dynamically fuse different probability distributions to suit different scenarios. We demonstrate that our approach is more significantly improved and more robust than the traditional kNN-MT, and we explore the application of kNN-MT in low-resource translation scenarios for the first time.
Particle Detection of Complex Images Based on Convolutional Neural Network

Particle Detection plays important roles in industrial and medical image processing. Non-learning based methods can only deal with the particle images with simple backgrounds and sparse particles. And most neural network based methods can also hardly to deal with overlapping objects and small particles, besides, the huge amount of parameters of these methods also has a great demand for annotated data, which is difficult to get. Therefore, we use bright pixels to represent the spatial position of particles, and artificially divid the sizes of particles into multiple intervals. A feature representation called Light-spots Maps is proposed to replace the bounding box and pixel classifications in our detection networks. Some Light-weight networks based on ResNets is used to transform the particle images into Light-spots maps to detect the particle information. Through experiments, we prove the effectiveness of our method.
Semantic modeling for healthcare applications: an introduction

Healthcare is a sensitive domain for human beings and needs extra care to monitor patients and elderly people. Massive exploitation of associated devices is the primary source for collecting health data during the monitoring of patients. All connected devices such as sensors, actuators, and RFIDs produce challenging data simultaneously and sometimes lack consistency and provide incorrect information. Recently, semantic web technologies have been involved in overcoming these issues and provide a semantic and syntactic representation of data that are interpreted by machines and humans. Ontologies are presented as a backbone of the semantic web and exhibited in semantic models with explicit representation of health data and connected devices. The primary aim of semantic modeling of healthcare data is to facilitate its domain's reusability and generalized representation. In this chapter, a brief introduction of healthcare semantic models has been discussed in general-purpose and domain-specific IoT-based semantic models. It also covers semantic modeling phases such as semantic annotation, semantic linking, the construction process, and semantic representation. Various case studies and examples include all phases.
Novelty Detection of a Rolling Bearing using Long Short-Term Memory Autoencoder

The major challenge in predictive maintenance (PdM) is an insufficiency of failure data used to train the model and the high complexity of industrial plants, where operational conditions changed over time hence the predetermined threshold will change frequently. Novelty detection offers a solution to this problem by detecting anomalies through learning only the normal data. This study implements a novelty detection method as a one-class classification to detect early signs of failure in a rolling bearing using long short-term memory (LSTM) autoencoder. LSTM autoencoder is a deep learning algorithm combining an autoencoder and LSTM network to reconstruct the normal data to learn nonlinear relationships and temporal nature. The model was tested on historical multivariate time-series fault data provided by Case Western Reserve University. The dropout layer is implemented to reduce overfitting by creating an ensemble model of a neural network. The results suggested that the LSTM autoencoder can effectively differentiate between normal and fault patterns of the bearing with up to 92 % accuracy.
A Robo-Pickup Artist Breaking Gender Norms

Come Hither to Me! is feminist robot theater with the objective to break down gender roles and provoke the audience to question accepted norms and stereotypes. Inspired by pick-up artist strategies, the female robotic performer wanders the art gallery and initiates conversation with an audience member using an exaggerated flirtatious comment, followed by a series of compliments, negs (backward compliments), humorous remarks, and both personal and general questions. The conversation design engages the participant in a performative, entertaining interaction with the robot, so she can gain information about the audience member and use it to ultimately ask them out on a date. The objective of the interactions is not only to make the participant laugh and engage but also to make them slightly uncomfortable in order to provoke thoughts and bring attention to one's gendered expectations in normalized sexist societal structures. This critical, yet humorous, robotic performance exemplifies how social justice-oriented design, critical computing, camp aesthetics, and theater staging techniques can be applied to human-robot interaction for social/gender studies, digital performance, and robotic research.
Relationship-based threat modeling

Threat modeling is a common technique for the systematic analysis of system designs to uncover security and privacy threats. Popular threat modeling techniques, however, currently only consider a very localized system context, which hinders the discovery of more complex attack scenarios that involve multiple interactions throughout a system. This may lead to the underestimation of threats that are not harmful by themselves but enable multiple other high-risk threats. Furthermore, current risk assessment approaches require stakeholders to take the system as a whole into account when providing inputs, which is tedious and error-prone.
Governing for Free: Rule Process Effects on Reddit Moderator Motivations

Developing a strong community requires empowered leadership capable of overcoming governance challenges. New online platforms have given users opportunities to practice governance through content moderation roles. The over 2.8 million “subreddit” communities on Reddit are governed by hundreds of thousands of volunteer moderators, many of whom have no training or prior experience in a governing role. While moderators often devote daily time to community maintenance and cope with the emotional effects of hate comments or disturbing content, Reddit provides no compensation for this position. Thus, moderators’ internal motivations and desire to continue filling this role is critical for their community. Drawing upon the relationship between governance procedures and internalized motivation, we investigate how the processes through which subreddit moderators generate community rules increase moderators’ motivation through the meeting of social-psychological needs: Procedural Justice and Self Determination, and Self-Other Merging. Preliminary analysis of survey data from 620 moderators across Reddit shows a correlation between moderators’ administrative behaviors and the social-psychological needs underpinning their motivations. Understanding these relationships will allow us to empower moderators to build engaging and cooperative online communities.
National Registry of Scientific Data Management Plans by IBICT

A Data Management Plan is a document where the main aspects of data management are recorded and sent to funding agencies. In addition, it is part of the data documentation and is filed together with the data in repositories or published by scientific journals. The National Registry of Scientific Data Management Plans (NRSDMP) by IBICT is a tool that seeks assisting researchers on their (meta) data planning and management. It was designed based on the FAIR Principles, which are guidelines to be followed in the search for findable, accessible, interoperable, and reusable data and metadata. The objectives were to: present the NRSDMP developed by IBICT; describe its implementation process; and demonstrate its compliance to the FAIR Principles. The study was designed in a qualitative research of exploratory and descriptive nature with use of the action-research method. The results contribute to the enrichment of theoretical discussions regarding factors that contribute to data management and its documentation, such as the data management plan, articulating the practices applied to the preparation of a FAIR DMP and with instructions that allow making data and metadata FAIR. The NRSDMP also serves as guidance to researchers for reaching FAIRness. In conclusion, the paper presents theoretical and practical contributions to: the development of FAIR compliant tools; as well as the creation of educational content (data literacy) for researchers in need of planning their data management, all in accordance with the international community.
Identification of Toxicity in MultimediaMessages for Controlling Cyberbullying on Social Media by Natural Language Processing

Speaking hatefully is an antisocial conduct. Hate can be expressed on the basis of gender, race, or religion, ethnic, etc. The definition of “hate speech” is ambiguous. The Hate speech is defined by the Council of the European Union as “any types of expression which disseminate, provoke, or defend intolerance as a reason for racial hatred, xenophobia, antisemitism, or other types of hatred, including violent nationalism and ethnocentrism, as well as prejudice and hatred toward migrants, minorities, and those with immigrant backgrounds. The most pressing issue in recent times in social media and online groups is toxicity identification sites for networking. Therefore, it is necessary to create an automatic hazardous identification system to keep people out of and restrict their access to these online settings. The prevalence of hate speech presents significant difficulties for the cyber culture. Users can wish for social media sites and online forums to support anti-hate discourse. Hate speech detection, however, is still a young technology, and system designers must come up with a way to identify unwanted hate speech while upholding the atmosphere of online freedom of speech. No method for detecting excellence has yet been put forth. Identifying the form of communication is hate speech and automatically recognising the hate speech are the two typical obstacles for a hate speech detection task. People must first decide which kinds of speech fall under the category of hate speech before screening it out. The majority of social media platforms define hate speech differently. In this day and age, Internet is necessary, and ethics has to be followed, however several parties spread hate speech deviating on race, ethnicity, and religion. The user’s freedom and anonymity increase the harassment by hate speech. It also adds lack of regulation on social media communication. Cyber bullying and trolling are two kinds of abusive behaviour.
AFA-NET: Adaptive Feature Aggregation Network for Aircraft Fine-Grained Detection in Cloudy Remote Sensing Images

Aircraft is easily covered by clouds in optical remote sensing images. It is a challenge to detect the aircraft and recognize its sub-categories in this situation. However, the methods proposed by the current research are mainly applied to high-quality images, which do not perform well on cloudy images. In this paper, an adaptive feature aggregation network called AFA-Net is proposed to solve this problem. We design a mixed self-attention module that adaptively focuses on the uncovered parts of the aircraft and its neighborhood from space and channel in feature maps. Experiments were done on the Optical Image Aircraft Detection and Recognition Data Set of the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> Tianzhibei Challenge. Compared with the most advanced object detection algorithms, the proposed approach achieves state-of-the-art performance.
Detecting Drift in Deep Learning: A Methodology Primer

Supervised machine learning models are trained on historical data to learn a static mapping between their input and output variables. However, when they are deployed on continuously streamed data, whose nature is likely to change over time (data or concept drift), model performance may suddenly and substantially degrade, forcing practitioners to continuously update the models to reflect the new data distribution. Few methods, however, are available to reliably detect data drift on heterogeneous data types (structured and unstructured), possibly without requiring labeled data at inference time. In this article, we review existing methods for dataset drift detection, discuss their applicability to deep neural networks, and experiment on a practical case study related to semistructured document analysis.
Multi-aspect Sentiment Analysis Using Domain Ontologies

Various aspects or characteristic features of an entity come into interplay to create an underlying fabric upon which sentiments blossom. In multi aspect Sentiment Analysis (SA), potentially related aspects of an entity under review are discussed in a single piece of text such as an online review. In this work, we use domain ontologies for enabling multi-aspect Sentiment Analysis. Since, domain ontologies contain the entire domain knowledge, they assist in enhanced aspect identification and detection of the latent or hidden aspects in a review document. We illustrate our approach by developing a system named Ontology driven Multi Aspect Sentiment Analysis (OMASA) system. We provide hotel reviews as input to this system and identify the panorama of explicitly expressed and latent aspects in a review using hotel domain ontology. After detecting the aspects, we link them with the corresponding opinions to gauge the sentiment pertaining to the aspects extracted. OMASA first computes sentiment scores for every aspect of the hotel. It then evaluates the overall sentiment score. On comparing with the baseline, the experimental results of OMASA show a marked improvement in the aspect level evaluation metrics $$\Delta_{aspect}^2$$ and $$\rho_{aspect}$$ after detecting the hidden aspects. This shows that OMASA has the potential to identify the latent aspects in text thereby improving the quality of SA.