Reviewerly/exHarmony huggingface dataset:
STEM technology-based model helps create an educational environment for developing students' technical and creative thinking

For successful technology adaptation today, individuals need not so much acquired experience and knowledge as certain personality traits in the form of skills, competencies, and abilities for collaborative problem solving, as well as achievement motivation and self-development. The purpose of this study was to develop and test a model for the formation of personality traits associated with the development of technical and creative thinking. The study was conducted using the modeling method and a psychodiagnostic approach based on the characteristics of creative thinking. An experimental study was conducted with a sample of 120 students from Plekhanov Russian College of Economics. The age range of the respondents was from 19 to 21 years. The results showed 1) the characteristics and dynamics of students' value systems and creative thinking, 2) a developed program for the development of intrinsic motivation, 3) a model for designing a pedagogical environment for students' engineering and creative thinking in education STEM; 4) testing the developed programs and models. The results also showed that there is a statistically significant relationship between the development of students' intrinsic motivation and the reorientation from normative-limited to creative-free thinking. Considering the results of this study, it was concluded that the model developed by the authors helped to shape and develop students' engineering and creative thinking. Implications for further research and teaching are drawn.
A decentralized blockchain-based key management protocol for heterogeneous and dynamic IoT devices

Secure communication is one of the main challenges that are slowing down the development of the Internet of Things (IoT). Key Management (KM) is particularly a difficult security issue, mainly because of the lack of resources of the IoT devices. Most of the existing solutions do not consider the heterogeneous and dynamic nature of the IoT. They do not regard the difference in capability of its components and impose equal loads on them. Moreover, they store keys in device memories before deployment, which makes adding devices difficult afterwards. We propose a novel decentralized blockchain-based KM protocol for the IoT. Our solution balances the loads between nodes according to their capabilities. We prove that this makes it efficient and scalable. Furthermore, our solution securely rekey the network upon a change. To decentralize the KM, we use the blockchain technology and smart contracts. We show that the system continues to operate when an entity fails and that the compromise of an entity does not jeopardize the whole network. We also prove that our solution fulfills the IoT requirements in terms of security and performance. Finally, we propose an implementation on IoT platforms to validate our theoretical analysis and simulation results.
Polarized Graph Neural Networks

Despite the recent success of Message-passing Graph Neural Networks (MP-GNNs), the strong inductive bias of homophily limits their ability to generalize to heterophilic graphs and leads to the over-smoothing problem. Most existing works attempt to mitigate this issue in the spirit of emphasizing the contribution from similar neighbors and reducing those from dissimilar ones when performing aggregation, where the dissimilarities are utilized passively and their positive effects are ignored, leading to suboptimal performances. Inspired by the idea of attitude polarization in social psychology, that people tend to be more extreme when exposed to an opposite opinion, we propose Polarized Graph Neural Network (Polar-GNN). Specifically, pairwise similarities and dissimilarities of nodes are firstly modeled with node features and topological structure information. And specially, we assign negative weights for those dissimilar ones. Then nodes aggregate the messages on a hyper-sphere through a polarization operation, which effectively exploits both similarities and dissimilarities. Furthermore, we theoretically demonstrate the validity of the proposed operation. Lastly, an elaborately designed loss function is introduced for the hyper-spherical embedding space. Extensive experiments on real-world datasets verify the effectiveness of our model.
The effects of touchless interaction on usability and sense of presence in a virtual environment

Abstract For software applications with a significant level of user involvement, the traditional concept of usability has evolved into the more complex idea of user experience, which also covers emotional, cognitive or physical responses. In virtual reality, user experience also depends on the user perception related to some peculiarities of immersive environments, where also the devices employed for user interaction play a determinant role. This has led to the design of the Presence Questionnaire (PQ) for the evaluation of the effectiveness of virtual environments. This work analyzes the effects of two different interaction modalities on usability and sense of presence: in particular, the Myo armband, a gesture-based device for touchless interaction, is compared with the Vive handheld controller bundled with the HTC Vive headset. A total of 84 subjects were recruited to test the virtual environment and asked them to fill in a questionnaire obtained by combining the Usability Metric for User eXperience (UMUX) questionnaire, the System Usability Scale (SUS) and the presence questionnaire (PQ), which was specifically designed for virtual environments. A comparison between the scores obtained for the two interaction modalities revealed which questionnaire items are significantly influenced by the input interface and deduce some insights about the consequences on human factors.
Data Poisoning in Sequential and Parallel Federated Learning

Federated Machine Learning has recently become a prominent approach to leverage data that is distributed across different clients, without the need to centralize data. Models are trained locally, and only model parameters are shared and aggregated into a global model. Federated learning can increase privacy of sensitive data, as the data itself is never shared, and benefit from the distributed setting by utilizing computational resources of the clients. Adversarial Machine Learning attacks machine learning systems in respect to their confidentiality, integrity or availability. Recent research has shown that many forms of machine learning are susceptible to these types of attacks. Besides its advantages, federated learning opens new attack surfaces due to its distributed nature, which amplifies concerns of adversarial attacks. In this paper, we evaluate data poisoning attacks in federated settings. By altering certain training inputs that are used in the training phase with a specific pattern, an adversary may later trigger malicious behavior in the prediction phase. We show on datasets for traffic sign and face recognition that federated learning is effective on a similar level as centralized learning, but is indeed vulnerable to data poisoning attacks. We test both a parallel as well as a sequential (incremental cyclic) federated learning, and perform an in-depth analysis on several hyper-parameters of the adversaries.
Privacy-Preserving Fair Learning of Support Vector Machine with Homomorphic Encryption

Fair learning has received a lot of attention in recent years since machine learning models can be unfair in automated decision-making systems with respect to sensitive attributes such as gender, race, etc. However, to mitigate the discrimination on the sensitive attributes and train a fair model, most fair learning methods have required to get access to the sensitive attributes in training or validation phases. In this study, we propose a privacy-preserving training algorithm for a fair support vector machine classifier based on Homomorphic Encryption (HE), where the privacy of both sensitive information and model secrecy can be preserved. The expensive computational costs of HE can be significantly improved by protecting only the sensitive information, introducing refined formulation and low-rank approximation using shared eigenvectors. Through experiments on the synthetic and real-world data, we demonstrate the effectiveness of our algorithm in terms of accuracy and fairness and show that our method significantly outperforms other privacy-preserving solutions in terms of better trade-offs between accuracy and fairness. To the best of our knowledge, our algorithm is the first privacy-preserving fair learning algorithm using HE.
Graph Neural Networks Beyond Compromise Between Attribute and Topology

Although existing Graph Neural Networks (GNNs) based on message passing achieve state-of-the-art, the over-smoothing issue, node similarity distortion issue and dissatisfactory link prediction performance can’t be ignored. This paper summarizes these issues as the interference between topology and attribute for the first time. By leveraging the recently proposed optimization perspective of GNNs, this interference is analyzed and ascribed to that the learned representation in GNNs essentially compromises between the topology and node attribute. To alleviate the interference, this paper attempts to break this compromise by proposing a novel objective function, which fits node attribute and topology with different representations and introduces mutual exclusion constraints to reduce the redundancy in both representations. The mutual exclusion employs the statistical dependence, which regards the representations from topology and attribute as the observations of two random variables, and is implemented with Hilbert-Schmidt Independence Criterion. Derived from the novel objective function, a novel GNN, i.e., Graph Neural Network Beyond Compromise (GNN-BC), is proposed to iteratively updates the representations of topology and attribute by simultaneously capturing semantic information and removing the common information, and the final representation is the concatenation of them. The performance improvements on node classification and link prediction demonstrate the superiority of GNN-BC on relieving the interference.
CycleNER: An Unsupervised Training Approach for Named Entity Recognition

Named Entity Recognition (NER) is a crucial natural language understanding task for many down-stream tasks such as question answering and retrieval. Despite significant progress in developing NER models for multiple languages and domains, scaling to emerging and/or low-resource domains still remains challenging, due to the costly nature of acquiring training data. We propose CycleNER, an unsupervised approach based on cycle-consistency training that uses two functions: (i) sentence-to-entity – S2E and (ii) entity-to-sentence – E2S, to carry out the NER task. CycleNER does not require annotations but a set of sentences with no entity labels and another independent set of entity examples. Through cycle-consistency training, the output from one function is used as input for the other (e.g. S2E → E2S) to align the representation spaces of both functions and therefore enable unsupervised training. Evaluation on several domains comparing CycleNER against supervised and unsupervised competitors shows that CycleNER achieves highly competitive performance with only a few thousand input sentences. We demonstrate competitive performance against supervised models, achieving 73% of supervised performance without any annotations on CoNLL03, while significantly outperforming unsupervised approaches.
Learning Recommenders for Implicit Feedback with Importance Resampling

Recommendation is prevalently studied for implicit feedback recently, but it seriously suffers from the lack of negative samples, which has a significant impact on the training of recommendation models. Existing negative sampling is based on the static or adaptive probability distributions. Sampling from the adaptive probability receives more attention, since it tends to generate more hard examples, to make recommender training faster to converge. However, item sampling becomes much more time-consuming particularly for complex recommendation models. In this paper, we propose an Adaptive Sampling method based on Importance Resampling (AdaSIR for short), which is not only almost equally efficient and accurate for any recommender models, but also can robustly accommodate arbitrary proposal distributions. More concretely, AdaSIR maintains a contextualized sample pool of fixed-size with importance resampling, from which items are only uniformly sampled. Such a simple sampling method can be proved to provide approximately accurate adaptive sampling under some conditions. The sample pool plays two extra important roles in (1) reusing historical hard samples with certain probabilities; (2) estimating the rank of positive samples for weighting, such that recommender training can concentrate more on difficult positive samples. Extensive empirical experiments demonstrate that AdaSIR outperforms state-of-the-art methods in terms of sampling efficiency and effectiveness.
Federated SPARQL Query Processing over Heterogeneous Linked Data Fragments

Linked Data Fragments (LDFs) are Web interfaces that enable querying knowledge graphs on the Web. These interfaces, such as SPARQL endpoints or Triple Pattern Fragment servers, differ in the SPARQL expressions they can evaluate and the metadata they provide. So far, federated query processing has focused on federations with a single type of LDF interface, typically SPARQL endpoints. In this work, we address the challenges of SPARQL query processing over federations with heterogeneous LDF interfaces. To this end, we propose an interface-aware framework and illustrate its applicability with a prototypical approach. The results over the FedBench benchmark show a substantial improvement in performance by devising this interface-aware approach that exploits the capabilities of heterogeneous interfaces in federations.
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations

Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize. This paper proposes an intelligent N management system using deep reinforcement learning (RL) and crop simulations with Decision Support System for Agrotechnology Transfer (DSSAT). We first formulate the N management problem as an RL problem. We then train management policies with deep Q-network and soft actor-critic algorithms, and the Gym-DSSAT interface that allows for daily interactions between the simulated crop environment and RL agents. According to the experiments on the maize crop in both Iowa and Florida in the US, our RL-trained policies outperform previous empirical methods by achieving higher or similar yield while using less fertilizers.
Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction

Dense retrieval has shown promising results in many information retrieval (IR) related tasks, whose foundation is high-quality text representation learning for effective search. Some recent studies have shown that autoencoder-based language models are able to boost the dense retrieval performance using a weak decoder. However, we argue that 1) it is not discriminative to decode all the input texts and, 2) even a weak decoder has the bypass effect on the encoder. Therefore, in this work, we introduce a novel contrastive span prediction task to pre-train the encoder alone, but still retain the bottleneck ability of the autoencoder. % Therefore, in this work, we propose to drop out the decoder and introduce a novel contrastive span prediction task to pre-train the encoder alone. The key idea is to force the encoder to generate the text representation close to its own random spans while far away from others using a group-wise contrastive loss. In this way, we can 1) learn discriminative text representations efficiently with the group-wise contrastive learning over spans and, 2) avoid the bypass effect of the decoder thoroughly. Comprehensive experiments over publicly available retrieval benchmark datasets show that our approach can outperform existing pre-training methods for dense retrieval significantly.
Radical Futures: Supporting Community-Led Design Engagements through an Afrofuturist Speculative Design Toolkit

When considering the democratic intentions of co-design, designers and design researchers must evaluate the impact of power imbalances embedded in common design and research dynamics. This holds particularly true in work with and for marginalized communities, who are frequently excluded in design processes. To address this issue, we examine how existing design tools and methods are used to support communities in processes of community building or reimagining, considering the influence of race and identity. This paper describes our findings from 27 interviews with community design practitioners conducted to evaluate the Building Utopia toolkit, which employs an Afrofuturist lens for speculative design processes. Our research findings support the importance of design tools that prompt conversations on race in design, and tensions between the desire for imaginative design practice and the immediacy of social issues, particularly when designing with Black and brown communities.
Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale

The layout of a mobile screen is a critical data source for UI design research and semantic understanding of the screen. However, UI layouts in existing datasets are often noisy, have mismatches with their visual representation, or consists of generic or app-specific types that are difficult to analyze and model. In this paper, we propose the CLAY pipeline that uses a deep learning approach for denoising UI layouts, allowing us to automatically improve existing mobile UI layout datasets at scale. Our pipeline takes both the screenshot and the raw UI layout, and annotates the raw layout by removing incorrect nodes and assigning a semantically meaningful type to each node. To experiment with our data-cleaning pipeline, we create the CLAY dataset of 59,555 human-annotated screen layouts, based on screenshots and raw layouts from Rico, a public mobile UI corpus. Our deep models achieve high accuracy with F1 scores of 82.7% for detecting layout objects that do not have a valid visual representation and 85.9% for recognizing object types, which significantly outperforms a heuristic baseline. Our work lays a foundation for creating large-scale high quality UI layout datasets for data-driven mobile UI research and reduces the need of manual labeling efforts that are prohibitively expensive.
An Exceedingly Simple Consistency Regularization Method For Semi-Supervised Medical Image Segmentation

The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. In this paper, we introduce a novel regularization strategy involving interpolation-based mixing for semi-supervised medical image segmentation. The proposed method is a new consistency regularization strategy that encourages segmentation of interpolation of two unlabelled data to be consistent with the interpolation of segmentation maps of those data. This method represents a specific type of data-adaptive regularization paradigm which aids to minimize the overfitting of labelled data un-der high confidence values. The proposed method is advantageous over adversarial and generative models as it requires no additional computation. Upon evaluation on two publicly available MRI datasets: ACDC and MMWHS, experimental results demonstrate the superiority of the proposed method in comparison to existing semi-supervised models. Code is available at: https://github.com/hritam-98/ICT-MedSeg
Topology-Aware Learning for Volumetric Cerebrovascular Segmentation

This paper presents a topology-aware learning strategy for volumetric segmentation of intracranial cerebrovascular structures. We propose a multi-task deep CNN along with a topology-aware loss function for this purpose. Along with the main task (i.e. segmentation), we train the model to learn two related auxiliary tasks viz. learning the distance transform for the voxels on the surface of the vascular tree and learning the vessel centerline. This provides additional regularization and allows the encoder to learn higher-level intermediate representations to boost the performance of the main task. We compare the proposed method with six state-of-the-art deep learning-based 3D vessel segmentation methods, by using a public Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) dataset. Experimental results demonstrate that the proposed method has the best performance in this particular context.
Diff in the Loop: Supporting Data Comparison in Exploratory Data Analysis

Data science is characterized by evolution: since data science is exploratory, results evolve from moment to moment; since it can be collaborative, results evolve as the work changes hands. While existing tools help data scientists track changes in code, they provide less support for understanding the iterative changes that the code produces in the data. We explore the idea of visualizing differences in datasets as a core feature of exploratory data analysis, a concept we call Diff in the Loop (DITL). We evaluated DITL in a user study with 16 professional data scientists and found it helped them understand the implications of their actions when manipulating data. We summarize these findings and discuss how the approach can be generalized to different data science workflows.
Research on Modulation Signal Recognition Based on CLDNN Network

Modulated signal recognition and classification occupies an important position in electronic information warfare, intelligent wireless communication, and fast modulation and demodulation. To address the shortcomings of existing recognition methods, such as high manual involvement, few recognition types, and a low recognition rate under a low signal-to-noise ratio, we propose an attention mechanism short-link convolution long short-term memory deep neural networks (ASCLDNN) recognition model. The network is optimized for modulated signal recognition and incorporates an attention mechanism to achieve higher accuracy by adding weights to important signals. The experimental results show that ASCLDNN can recognize 11 signal modulations with high accuracy at a low signal-to-noise ratio and no confusion for specific signals.
Multi-Task Learning with Context-Oriented Self-Attention for Breast Ultrasound Image Classification and Segmentation

Breast cancer is a great threat to women’s health. Automatic analysis of Breast UltraSound (BUS) images can help radiologists make more accurate and efficient diagnoses of breast cancer. We propose a Multi-Task Learning Network with Context-Oriented Self-Attention (MTL-COSA) module to automatically and simultaneously segment tumors and classify them as benign or malignant. The COSA module incorporates prior medical knowledge to guide the network to learn contextual relationships for better feature representations in BUS images. Extensive cross-validation experiments are conducted on two public datasets to evaluate the performance of MTL-COSA and several state-of-the-art methods. MTL-COSA achieves the best classification results and second-best segmentation results compared with deep learning-based methods (5 classification methods and 3 segmentation methods).