Reviewerly/exHarmony huggingface dataset:
Crowdsensing-Enabled Service Design for Floating Students During the COVID-19 Pandemic

This pictorial presents the concept and preliminary design of “Tecnico Go!”, an application conceived to support college students during the COVID-19 pandemic. The project applied HCI and user-centred design methods to understand students’ needs, pains, and desires during the pandemic period. The authors collected, unpacked, and reflected on user-centred data, synthesised personas and scenarios via the research through design approach. Synthesis of results points to the concept of “floating students” as a model embracing flexibility in visiting the campus safely and using the facilities when needed. Problem-oriented student journeys were identified and then used to ideate solution-oriented scenarios and a service blueprint to illuminate the continued development of the “Tecnico Go!” mobile application. This pictorial illustrates this process from data collection through customer journeys, user-centred blueprint and wireframes.
Research Data Curation in Visualization : Position Paper

Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.
VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection

Visual Relationship Detection (VRD) impels a computer vision model to see beyond an individual object instance and understand how different objects in a scene are related. The traditional way of VRD is first to detect objects in an image and then separately predict the relationship between the detected object instances. Such a disjoint approach is prone to predict redundant relationship tags (i.e., predicate) between the same object pair with similar semantic meaning, or incorrect ones that have a similar meaning to the ground truth but are semantically incorrect. To remedy this, we propose to jointly train a VRD model with visual object features and semantic relationship features. To this end, we propose VReBERT, a BERT-like transformer model for Visual Relationship Detection with a multi-stage training strategy to jointly process visual and semantic features. We show that our simple BERT-like model is able to outperform the state-of-the-art VRD models in predicate prediction. Furthermore, we show that by using the pre-trained VReBERT model, our model pushes the state-of-the-art zero-shot predicate prediction by a significant margin (+8.49 R@50 and +8.99 R@100).
Unsupervised Representation Learning Method In Sensor Based Human Activity Recognition

Deep learning methods contribute to improve the estimation accuracy in human activity recognition (HAR) using sensor data. In general, the dataset used in HAR consists of accelerometer data and activity labels. Because of the widespread use of mobile devices, large amount of accelerometer sensor data without activity labels can be easily collected. The problem of annotation needs a large amount of time-consuming cost and human labor to annotate a activity labels to recorded sensor data. Therefore, we need a method to make deep learning models acquire feature representations from accelerometer data without activity labels in HAR. In this study, based on the unsupervised representation learning method proposed in image recognition, we proposed a new unsupervised representation learning method which combines segment discrimination (SD), autoencoder (AE) and feature independent softmax (FIS). Our experimental results showed that our proposed method outperformed the conventional method in fine-tuning accuracy in HAR.
Overview of Machine Learning Algorithms

Knowledge is an invaluable resource for almost all entities, be they firms, organizations, communities, or individuals. Knowledge needs to be captured, processed, and analyzed. Well-defined knowledge can be represented in an accurate manner, such as a mathematical formula or a certain set of rules [1]. Knowledge can also be modeled, where a model permits us to explain reality, classify objects, and predict a value (or if an event will occur) knowing its relationship to other known values. If our knowledge is not complete, then we can approximate reality by learning from previous experiences and predicting an outcome with a certain likelihood of accuracy. Alongside the representation of knowledge, we need to store on a computer a reasoning method, i.e., an algorithm (a series of steps to be followed) to process this knowledge to arrive at an outcome/output (e.g., a decision, classification, or diagnosis).
Blockchain-Based Decentralized Digital Content Management and Sharing System

With the explosion of the big data era, storing and sharing data has become a necessity. However, sharing and using misleading data can lead to serious consequences. Therefore, it is necessary to develop safe frameworks and protocols for storing and sharing data among parties on the Internet. Unfortunately, the current systems struggle against the typical challenges to maintain confidence, integrity, and privacy. This paper proposes a blockchain network in a decentralized storage system that supports the management and shares digital content preserving its integrity and privacy. The system allows users to access authenticated data and develops protocols to protect the privacy of data owners. The detailed analysis and proof show that the proposed system is considered a promising solution for sharing and storing data in the big data era.
Algorithmic Advances for the Adjacency Spectral Embedding

The Random Dot Product Graph (RDPG) is a popular generative graph model for relational data. RDPGs postulate there exist latent positions for each node, and specifies the edge formation probabilities via the inner product of the corresponding latent vectors. The embedding task of estimating these latent positions from observed graphs is usually posed as a non-convex matrix factorization problem. The workhorse Adjacency Spectral Embedding offers an approximate solution obtained via the eigendecomposition of the adjacency matrix, which enjoys solid statistical guarantees but can be computationally intensive and is formally solving a surrogate problem. In this paper, we bring to bear recent non-convex optimization advances and demonstrate their impact to RDPG inference. We develop first-order gradient descent methods to better solve the original optimization problem, and to accommodate broader network embedding applications in an organic way. The effectiveness of the resulting graph representation learning framework is demonstrated on both synthetic and real data. We show the algorithms are scalable, robust to missing network data, and can track the latent positions over time when the graphs are acquired in a streaming fashion.
A study on the methodology of Software Project Management used by students whether they are using an Agile or Waterfall methodology

There are many methodologies any developer can rely on in software development. These methodologies have been developed continuously due to growth in the requirements of the required programs and work environment. Each method, whether traditional project management or Agile project management, has strengths and weaknesses. For example, the difficulty of choosing the appropriate methodology that matches the quality of the requirements that we need to develop, or the team's focus on a method that they always rely on in their work style, can lead to a failure in the development process. One of the most popular methodologies today is Agile project management. It works in various fields and is not limited to software development only because of its strength to focus on delivering the product in the fastest time and at a lower cost. Instead, it is considered the pioneer in its various methods for managing software projects. From this point of view, we will compare and analysis the data collected from two colleges in Tripoli, Libya, in the field of Informatics. To find out the approved methodology the students used in their projects, the reasons for their selection, and the difficulties they encountered while following the chosen method.
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning

Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process. However, the problem of class imbalance is rarely discussed, despite its presence in several ZSL datasets. In this paper, we propose a Neural Network model that learns a latent feature embedding and a Gaussian Process (GP) regression model that predicts latent feature prototypes of unseen classes. A calibrated classifier is then constructed for ZSL and Generalized ZSL tasks. Our Neural Network model is trained efficiently with a simple training strategy that mitigates the impact of class-imbalanced training data. The model has an average training time of 5 minutes and can achieve state-of-the-art (SOTA) performance on imbalanced ZSL benchmark datasets like AWA2, AWA1 and APY, while having relatively good performance on the SUN and CUB datasets.
Analysis of Data Center Development Problems in the Era of Digital Transformation

The main problems of the development of telecom operators and cloud service providers are analyzed. In response to modern challenges, such as the unprecedented growth in the use of digital services in the world, the Internet of Things, which fosters the emergence of big data and the introduction of machine learning, telecom operators and service providers are faced with the task of improving their own IT infrastructure to quickly respond to the growing needs of society. In addition, the large-scale digital transformation announced by the government of the Russian Federation, focused on the implementation of domestic solutions, creates new challenges for data centers. The main cycle of works on the management of computing resources of cloud data centers is proposed, which are based on a set of models and methods for the optimal allocation of computing resources.
Optimized Power Control for Over-the-Air Federated Averaging With Data Privacy Guarantee

This paper investigates the privacy-preserving power control problem for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">over-the-air federated edge learning</i> (Air-FEEL), which features “one-shot” aggregation and significantly reduced communication latency. In Air-FEEL, the inevitable random perturbation encountered in the aggregation process due to the corruption by channel fading and noise poses a fundamental trade-off between the privacy and accuracy, as larger perturbation may harm the accuracy but benefit the privacy, and vice versa. Therefore, power control in Air-FEEL, as the main approach to regulate the said random perturbation, needs to be judiciously designed for balancing the accuracy-privacy trade-off. This, however, is a largely uncharted area. To bridge the research gap, we first analyze the convergence behavior (in terms of the optimality gap) and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differential privacy</i> (DP) performance of Air-FEEL with respect to the power control policy at different iterations. Then, to achieve the maximized training accuracy under given DP guarantee requirement, we minimize the optimality gap by jointly optimizing the power control at edge devices and the denoising factors at the edge server, subject to a set of power constraints at individual edge devices and the DP constraint. Experimental results validate that the proposed power control policy for Air-FEEL achieves a better privacy-accuracy trade-off compared with the benchmarks.
Deep Reinforcement Learning for Robotic Control with Multi-Fidelity Models

Deep reinforcement learning (DRL) can be used for the development of robotic controllers. Complicated kinematic relationships can be learned by a DRL agent, which will result in a control policy that takes actions based on an observed state. However, a DRL agent typically goes through much trial and error before beginning to take appropriate actions. Therefore, it is often useful to leverage simulated robotic manipulators before performing any training or testing on actual hardware. There are several options for such simulation, ranging from simple kinematic models to more complex models seeking to accurately simulate the effects of gravity, inertia, and friction. The latter models can provide excellent representations of a robotic plant, but typically with a noticeably increased computational expense. Reducing the expense of simulating the robotic plant (while still maintaining a reasonable degree of accuracy) can accelerate an already expensive DRL training loop. In this work, we present a methodology for using a lower-fidelity model (based on Denavit-Hartenberg parameters) to initialize the training of a DRL agent for control of a Sawyer robotic arm. We show that the trained DRL policy can then be fine-tuned in a higher-fidelity simulation provided by the robot's manufacturer. We demonstrate the accuracy of the fully trained policy by transferring it to the actual hardware, demonstrating the power of DRL to learn complicated robotic tasks entirely in simulation. Finally, we benchmark the time required to train a policy using each level of fidelity.
How Train–Test Leakage Affects Zero-Shot Retrieval

Neural retrieval models are often trained on (subsets of) the millions of queries of the MS MARCO/ORCAS datasets and then tested on the 250 Robust04 queries or other TREC benchmarks with often only 50 queries. In such setups, many of the few test queries can be very similar to queries from the huge training data—in fact, 69% of the Robust04 queries have near-duplicates in MS MARCO/ORCAS. We investigate the impact of this unintended train–test leakage by training neural retrieval models on combinations of a fixed number of MS MARCO/ORCAS queries, which are very similar to actual test queries, and an increasing number of other queries. We find that leakage can improve effectiveness and even change the ranking of systems. However, these effects diminish the smaller, and thus more realistic, the extent of leakage is in all training instances.
E-voting System using Public Blockchain

Our paper aims at developing an online e-voting system using blockchain. It proposes the use of block chain to build an efficient and secure system for conducting elections online while using anonymity. The voter accounts are mapped to public keys of accounts on the blockchain randomly. When a vote is cast it is written to the blockchain and recorded. It cannot be modified as the blockchain is immutable. This system would ensure anonymity as there is no voter data stored on the blockchain. Since the mapping of the user accounts to the public key is not known, the votes cannot be traced to the voter. Upon completion of the election the blockchain can be made available to the public in order to verify their votes individually by providing them access to public keys. This ensures fair elections which can be conducted at any level.
Distributed Estimation With Iterative Inverse Covariance Intersection

For distributed estimation in sensor network systems, intractable correlation among local estimates has a great impact on the accuracy and consistency of the fused estimate. An inconsistent or over conservative estimate would lead to degradation of estimation performance or even estimation divergence. In this brief, a distributed iterative diffusion estimation algorithm based on inverse covariance intersection (ICI) technique is proposed to address the unknown correlation and yield an estimate with uncertainty close to the true mean square error. The proposed algorithm is referred to as iterative ICI (It-ICI), which leverages the iteration of fusions that is only executed locally. Every sensor node only combines estimates from its neighbors. By iterations, the local information can be propagated to the entire sensor network and the accuracy can be incrementally improved. The fused estimate is proved to be unbiased with bounded covariance provided that the local filters’ estimates are unbiased and bounded. The performance of the proposed distributed estimation algorithm is illustrated by a collaborative target tracking problem in terms of enhanced tracking accuracy and consistency.
Benchmarking 360° Saliency Models by General-Purpose Metrics

How to effectively evaluate a model's capability to predict the visual attention of observers in 360° scenes gains interest along with the advancement of saliency prediction modeling of omnidirectional images (ODIs). So far, many general-purpose metrics from 2D saliency literature have been adopted to evaluate the 360° saliency models. However, whether they are still effective when being adopted to evaluate the 360° saliency models has not been explored. In this paper, we testify several standard saliency evaluation metrics on the 360° saliency models and comprehensively analyze their behaviors in the omnidirectional scenario. We find that 1) most metrics under-penalize false positives; 2) existing 360° datasets involve severe equator bias that few metrics can effectively penalize. We hope this case study can provide a guideline for benchmarking 360° image/video saliency models.
Exploiting Robust Memory Features for Unsupervised Reidentification

Unsupervised re-identification (ReID) is a task that does not use labels for classification and recognition. It is fundamentally challenging due to the need to retrieve target objects across different perspectives, and coupled with the absence of ID labels for supervision. However, the most severe aspect is that the appearance features of vehicles can be shifted under different viewpoints, which may cause an imbalance in inter-class and intra-class variation. In this work, we propose a fully unsupervised re-identification method applied to the person and vehicle domain. In particular, different from previous methods of cluster pseudo-labelling, we compress the feature dimensions used to generate the labels and improve the quality of the pseudo-label. Then, in the update memory feature module, we introduce the idea of partitioning to construct algorithms for dynamically finding inter-class and intra-class boundaries to improve the robustness of the model. To demonstrate the effectiveness of the proposed method, we conduct experiments on one vehicle dataset (VeRi-776) and one person datasets (MSMT17). Experimental results demonstrate that our method is effective in enhancing the performance of the ReID task, and the proposed method achieves the state-of-the-art performance. The code has been made available at https://github.com/ljwwwiop/unsupervised_reid .
Out-of-Distribution Identification: Let Detector Tell Which I Am Not Sure

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in most practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to detection uncertainty. In this work, the Feature structured OOD-IDentification (FOOD-ID) model is proposed to reduce the uncertainty of detection results by identifying the OOD instances. Instead of outputting each detection result directly, FOOD-ID uses a likelihood-based measuring mechanism to identify whether the feature satisfies the corresponding class distribution and outputs the OOD results separately. Specifically, the clustering-oriented feature structuration is firstly developed using class-specified prototypes and Attractive-Repulsive loss for more discriminative feature representation and more compact distribution. With the structured features space, the density distribution of all training categories is estimated based on a class-conditional normalizing flow, which is then used for the OOD identification in the test stage. The proposed FOOD-ID can be easily applied to various object detectors including anchor-based frameworks and anchor-free frameworks. Extensive experiments on the PASCAL VOC-IO dataset and an industrial defect dataset demonstrate that FOOD-ID achieves satisfactory OOD identification performance, with which the certainty of detection results is improved significantly.
Multi-View Partial Multi-Label Learning via Graph-Fusion-Based Label Enhancement

Multi-view partial multi-label learning (MVPML) aims to learn a multi-label predictive model from the training examples, each of which is presented by multiple feature vectors while associated with a set of candidate labels where only a subset is correct. Generally, existing techniques work simply by identifying the ground-truth label via aggregating the features from all views to train a final classifier, but ignore the cause of the incorrect labels in the candidate label sets, i.e., the diverse property of the representation from different views leads to the incorrect labels which form the candidate labels alone with the essential supervision. In this paper, a novel MVPML approach is proposed to learn the predictive model and the incorrect-labeling model jointly by incorporating the graph-fusion-based topological structure of the feature space. Specifically, the latent label distribution and the incorrect labels are identified simultaneously in a unified framework under the supervision of candidate labels. In addition, a common topological structure of the feature space from all views is learned via the graph fusion for further capturing the latent label distribution. Experimental results on the real-world datasets clearly validate the effectiveness of the proposed approach for solving multi-view partial multi-label learning problems.
Split-word Architecture in Recurrent Neural Networks POS-Tagging

We analyze Recurrent Neural Network (RNN) architectures to handle the problem of Part-of-Speech (POS) Tagging. When linguistic rules are inserted ad-hoc into the decision algorithm, there is a difficulty in understanding the role of prior information and learning. The real potential of recurrent networks is demonstrated in this paper on the Italian language in a purely data-driven approach, where we can reach the state-of-the-art on the UD_Italian-ISTD (Italian Stanford Dependency Treebank) dataset in comparison to TINT. We propose a methodology for splitting words that are mapped to embedding spaces and fed to forward-backward networks.