Reviewerly/exHarmony huggingface dataset:
Multimodal sentiment and emotion recognition in hyperbolic space

Prior approaches for multimodal sentiment and emotion recognition (SER) exploit input data representations and neural networks based on the classical Euclidean geometry. Recently, however, the hyperbolic metric proved to be a powerful tool for data mapping, being able to capture the hierarchical structure of the relations among elements in the data. In this paper we propose the use of hyperbolic learning for SER, and show that the inclusion in the neural network of hyperbolic structures mapping the input into the hyperbolic space can improve the quality of the predictions. The benefits brought by the hyperbolic features are evaluated by developing extensions of existing methods following two approaches. From one side, we modified state-of-the-art models by including hyperbolic output layers. From the other, we generated hybrid neural network architectures by combining hyperbolic and Euclidean layers according to different schemes. The proposed hyperbolic models were tested on several classification tasks applied to benchmark multimodal SER datasets. Experiments gave strong evidence that in both simple and complex networks the introduction of a hyperbolic structure results in an improvement of the model accuracy. Specifically, the combined use of hyperbolic and Euclidean layers showed superior performance in almost all the classification tasks.
Data and Channel-Adaptive Sensor Scheduling for Federated Edge Learning via Over-the-Air Gradient Aggregation

Over-the-air gradient aggregation and data-aware scheduling have recently drawn great attention due to the outstanding performance in improving communication efficiency for federated edge learning applications. However, in this case, the estimated gradient suffers from the channel and data distortion induced by channel fading and data-aware scheduling, which introduces significant bias and harms the training performance. To solve these problems, we propose a dynamic data and channel adaptive sensor scheduling and power control algorithm combining a residual feedback mechanism. Instead of discarding the gradients not transmitted to the central server, each sensor keeps track of a local residual to store these gradients. Furthermore, by connecting the model update iterations to a dynamic evolution process, we utilize the Lyapunov drift optimization method to analyze the relationship between the training gain and resource allocation. The derived decentralized optimal solution is adaptive to both the channel state information and data importance to seize good transmission opportunity and important gradients. Theoretical analysis is provided on the convergence of the proposed algorithm in practical training scenarios. Simulation results further illustrate that under the same power cost, the proposed scheme has a much faster convergence rate and lower training loss compared to existing baselines.
Exploring the development trend of internet finance in China: Perspective from club convergence

We first employ β-conditional convergence and log t regression tests based on nonlinear time-varying factor model and club clustering algorithm to analyze the convergence characteristics of the development level of Internet finance in 335 prefecture-level cities in China. The result of log t regression test illustrates that there is no convergence as a whole in the development level of China's Internet finance. However, seven convergence clubs and a divergent group have been formed, and the development level and growth rate of Internet finance among these convergence clubs have shown obvious differences. Moreover, we also employ the Ordered Probit to explore the formation mechanism of the convergence clubs. The results reveal that the regions with a higher level of economic development, traditional financial development, economic openness and Internet development are more inclined to converge in a club with a higher Internet finance development level. Alternatively, the regions that are interfered with more by the government or that have a lower degree of marketization, tend to converge in a club with a lower level. Finally, according to the conclusions, we propose corresponding policy recommendations for promoting the regional coordinated development of China's Internet finance.
Multitarget Tracking With Multiple Models and Visibility: Derivation and Verification on Maritime Radar Data

In this article, we demonstrate how a variation of the joint integrated probabilistic data association with interacting multiple models and a visibility state can be derived as a special case of the Poisson multi-Bernoulli mixture filter with a hybrid state representation and standard approximations. The proposed method is tested on two radar data sets that were recorded during maritime collision-avoidance experiments.
Whose Words Hurt? Contextual Determinants of Offensive Speech

Tracing the boundaries of freedom of expression is a matter of wide societal and academic import-especially, as these boundaries encroach on the politics of inclusion. Yet, the elements that constitute offensive speech and determine its legal status remain poorly defined. In two studies, we examined how lay judges evaluate the offensiveness of apparently descriptive statements. Replicating prior work, we found that non-linguistic features (including speaker intent and outcomes on the audience) modulated the statements' meaning. The speaker's identity-and, in particular, their membership in the target group-independently influenced evaluations of offensive speech among conservatives and progressives alike. When asked to disclose their abstract principles, or jointly evaluate two contrastive cases, participants tended to deny the relevance of identity while primarily endorsing the intent principle. Taken together, our findings confirm that assessments of offensive speech are governed by contextual features, some of which are not introspectively deemed relevant.
A speaker verification backend with robust performance across conditions

In this paper, we address the problem of speaker verification in conditions unseen or unknown during development. A standard method for speaker verification consists of extracting speaker embeddings with a deep neural network and processing them through a backend composed of probabilistic linear discriminant analysis (PLDA) and global logistic regression score calibration. This method is known to result in systems that work poorly on conditions different from those used to train the calibration model. We propose to modify the standard backend, introducing an adaptive calibrator that uses duration and other automatically extracted side-information to adapt to the conditions of the inputs. The backend is trained discriminatively to optimize binary cross-entropy. When trained on a number of diverse datasets that are labeled only with respect to speaker, the proposed backend consistently and, in some cases, dramatically improves calibration, compared to the standard PLDA approach, on a number of held-out datasets, some of which are markedly different from the training data. Discrimination performance is also consistently improved. We show that joint training of the PLDA and the adaptive calibrator is essential -- the same benefits cannot be achieved when freezing PLDA and fine-tuning the calibrator. To our knowledge, the results in this paper are the first evidence in the literature that it is possible to develop a speaker verification system with robust out-of-the-box performance on a large variety of conditions.
Higher School Teacher’s Competences and “Electronic” Pedagogical Culture in the Post-Pandemic World

During the period of the forced remote learning caused by the threat of the coronavirus infection spread, Russian university teachers, as well as their foreign colleagues, had to acquire quickly new skills in the field of information technologies and organization of classes exclusively in electronic mode. However, the changes in the structure of the teachers’ personality were not limited only to informational and methodological competences. The relevance of this research is to test the hypothesis about the emergence of a new type of teacher’s pedagogical culture, characterized by a special awareness of the humanistic and social role of a teacher in the course of e-learning. The aim is to study the content of a new type of pedagogical culture, as well as its structural components performed by the competences, the presence of which was actualized during the pandemic. The theoretical research methods (study and analysis of scientific publications on the teachers’ competences, especially in demand during the pandemic, synthesis, systematization, comparison, etc.) and empirical methods (conversation, questioning, observation) were used in the study. The conducted empirical research, as well as the analysis of domestic and foreign publications show that, despite a short-term nature of remote learning, the transformations in pedagogical culture have the character of a qualitative leap and are global in nature, as they have affected the teachers in all countries. This allows us to declare the emergence of a new type of pedagogical culture, namely the “electronic” pedagogical culture of a higher school teacher, which is a natural result of the development of “basic” culture and is characterized by a change in priorities in the structure and in the content of its constituent blocks of competences. The article compares real changes in the structure of a teacher’s personality with forecasts regarding the “competences of the future”, as well as with the “information pedagogical culture”, which is believed to have arisen in the process of information technologies penetration into the educational process. The scientific novelty of the research consists in substantiating the possibility of transforming the “basic” pedagogical culture of the teacher under the influence of external and internal factors. In theoretical terms, the study expands the scientific understanding of the essence of the teacher’s pedagogical culture, the patterns of its development and transformation, as well as its competence-based structural organization.
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models

Supervised learning models often make systematic errors on rare subsets of the data. When these subsets correspond to explicit labels in the data (e.g., gender, race) such poor performance can be identified straightforwardly. This paper introduces a method for discovering systematic errors that do not correspond to such explicitly labelled subgroups. The key idea is that similar inputs tend to have similar representations in the final hidden layer of a neural network. We leverage this structure by “shining a spotlight” on this representation space to find contiguous regions in which the model performs poorly. We show that the Spotlight surfaces semantically meaningful areas of weakness in a wide variety of existing models spanning computer vision, NLP, and recommender systems, and we verify its performance through quantitative experiments.
Exploiting fermion number in factorized decompositions of the electronic structure Hamiltonian

Achieving an accurate description of fermionic systems typically requires considerably many more orbitals than fermions. Previous resource analyses of quantum chemistry simulation often failed to exploit this low fermionic number information in the implementation of Trotter-based approaches and overestimated the quantum-computer runtime as a result. They also depended on numerical procedures that are computationally too expensive to scale up to large systems of practical interest. Here we propose techniques that solve both problems by using various factorized decompositions of the electronic structure Hamiltonian. We showcase our techniques for the uniform electron gas, finding substantial (over $100\ifmmode\times\else\texttimes\fi{}$) improvements in Trotter error for low-filling fraction and pushing to much higher numbers of orbitals than is possible with existing methods. Finally, we calculate the $T$-count to perform phase estimation on Jellium. In the low-filling regime, we observe improvements in gate complexity of over $10\ifmmode\times\else\texttimes\fi{}$ compared to the best Trotter-based approach reported to date. We also report gate counts competitive with qubitization-based approaches for Wigner-Seitz values of physical interest.
Secure and smart autonomous multi-robot systems for opinion spammer detection

Reviews of social media have become very important reference indicators for people shopping. However, in order to attract more consumers, some bad merchants organize many fraudulent reviewers to conduct malicious reviews aim at misleading consumers, which is called opinion spammer group. Previous studies have shown that there is an implicit community between reviewers In this paper, we try to use community discovery approach in secure and smart autonomous multi-robot systems for opinion spammer detection, and improve the prediction precision. We propose a novel approach, named SPClique, which is based on the Clique Percolation Method (CPM) to detect opinion spammer groups by modeling the review dataset as reviewer projection graph. During the process, we introduce approximate computing to get a valuable smaller reviewer-projection graph and expand the computing power. It first executes the CPM algorithm in the reviewer-projection graph to find all k-clique clusters, and then innovatively describes an opinion spammer group based on k-clique clusters. Second, the group-based spam and individual-based spams indicators are used to measure the suspiciousness of each opinion spammer group. Finally, it outputs the suspect ranking of opinion spammer groups. The prediction precision of our proposed method is better than four advanced comparison methods, and our approach can detect more real opinion spammers in large-scale review datasets.
An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics

Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained results.
Hyperparameter Optimization in a Convolutional Neural Network Using Metaheuristic Algorithms

The hyperparameters of a convolutional neural network always have been important, because the performance of the convolutional neural network depends largely on them. To find manually the optimal value requires a lot of work, experience, and time. Metaheuristic algorithms are approximate general-purpose search and optimization algorithms, they are used to find a good solution into search space. In the book chapter, the hyperparameters of a convolutional neural network are optimized. A metaheuristic algorithm is used to finds the best solution, that is, the best hyperparameter value for higher accuracy of the convolutional neural network (CNN). The results of four metaheuristic algorithms that have the same objective are compared, which are PSO (Particle Swarm Optimization), ABC (Artificial Bee Colony), ALO (Ant Lion Optimization), and BA (Bat Algorithm). Each algorithm uses the same convolutional neural network architecture as well as the same dataset (MNIST database) also each algorithm is run 30 times to perform the corresponding statistics. Finally, the results of the algorithms are shown.
Learning unsupervised node representation from multi-view network

This paper studies the problem of learning node representations for networks with multiple views, which aims to infer robust node representations by simultaneously considering multiple views during the representations learning process. We propose an effective method for this task, named as Multi-View Representation Learning (MVRL). The new method extends the matrix factorization model for node representation learning of multi-view network in unsupervised representation learning scenario, which simultaneously learns a set of view weights to identify the quality of each view, and the network representations as matrix factorization of the weighted combination of multiple views. An efficient optimization method with linear complexity is proposed to solve the new model, and a simple yet efficient method is proposed for fast updating of the new node's vector representation without updating the whole nodes' representation vectors. We have evaluated the performance of our proposed approach on five real-world multi-view network datasets. Experimental results on the node classification task demonstrated the superior performance and efficiency of our proposed method.
An interdisciplinary view of social engineering: A call to action for research

Social engineering is the act of using manipulation and deception to obtain access to confidential information. It is considered one of the leading threats to information security today. The topic is complex and increasing in prevalence among individuals and businesses. This paper explores the subject of social engineering from an interdisciplinary perspective. A literature review from the information technology, psychology, and business disciplines explains the interconnected nature of the topic as well as the necessity to comprehend it from multiple viewpoints. An ethical perspective follows the literature review and analyzes social engineering research from a philosophical and professional viewpoint. A proposed framework provides researchers with a flexible model to use in their studies with an emphasis on a philosophical or practical ethics perspective.
Multi-Dimensional Classification via Decomposed Label Encoding

In multi-dimensional classification (MDC), a number of class variables are assumed in the output space with each of them specifying the class membership w.r.t. one heterogeneous class space. One major challenge in learning from MDC examples lies in the heterogeneity of class spaces, where the modeling outputs from different class spaces are not directly comparable. To tackle this problem, we propose a new strategy named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decomposed label encoding,</i> which enables modeling alignment for MDC in an encoded label space derived from one-versus-one (OvO) decomposition. Specifically, the original MDC output space is transformed into a ternary encoded label space by conducting OvO decomposition w.r.t. each class space. Then, the manifold structure in the feature space is exploited to enrich the labeling information in the encoded label space. Finally, the predictive model is induced by fitting the metric-aligned modeling outputs with enriched labeling information. Extensive experiments over twenty benchmark data sets clearly show the superiority of the proposed MDC strategy against state-of-the-art approaches.
Learning discriminative representations for multi-label image recognition

Multi-label recognition is a fundamental, and yet is a challenging task in computer vision. Recently, deep learning models have achieved great progress towards learning discriminative features from input images. However, conventional approaches are unable to model the inter-class discrepancies among features in multi-label images, since they are designed to work for image-level feature discrimination. In this paper, we propose a unified deep network to learn discriminative features for the multi-label task. Given a multi-label image, the proposed method first disentangles features corresponding to different classes. Then, it discriminates between these classes via increasing the inter-class distance while decreasing the intra-class differences in the output space. By regularizing the whole network with the proposed loss, the performance of applying the well-known ResNet-101 is improved significantly. Extensive experiments have been performed on COCO-2014, VOC2007 and VOC2012 datasets, which demonstrate that the proposed method outperforms state-of-the-art approaches by a significant margin of 3.5% on large-scale COCO dataset. Moreover, analysis of the discriminative feature learning approach shows that it can be plugged into various types of multi-label methods as a general module.
A Collaborative AI-Enabled Pretrained Language Model for AIoT Domain Question Answering

Large-scale knowledge in the Artificial Intelligence of Things (AIoT) field urgently needs effective models to understand human language and automatically answer questions.Pre-trained language models (PLMs) achieve state-of-the-art performance on some question answering (QA) datasets, but few models can answer questions on AIoT domain knowledge.Currently, AIoT domain lacks sufficient QA datasets and largescale pre-training corpora.We propose RoBERTa AIoT to address the problem of the lack of high-quality large-scale labeled AIoT QA datasets.We construct an AIoT corpus to further pretrain RoBERTa and BERT.RoBERTa AIoT and BERT AIoT leverage unsupervised pre-training on a large corpus composed of AIoToriented Wikipedia webpages to learn more domain-specific context and improve performance on the AIoT QA tasks.To finetune and evaluate the model, we construct 3 AIoT QA datasets based on the community QA websites.We evaluate our approach on these datasets and the experimental results demonstrate the significant improvements of our approach.
LayoutGMN: Neural Graph Matching for Structural Layout Similarity

We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize weak labels obtained by pixel-wise Intersection-over-Union (IoUs) to define the triplet loss. Importantly, LayoutGMN is built with a structural bias which can effectively compensate for the lack of structure awareness in IoUs. We demonstrate this on two prominent forms of layouts, viz., floorplans and UI designs, via retrieval experiments on large-scale datasets. In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution. In addition, LayoutGMN is the first deep model to offer both metric learning of structural layout similarity and structural matching between layout elements.