Reviewerly/exHarmony huggingface dataset:
FedV

Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties to keep their data private and only model updates are shared. Most existing approaches have focused on horizontal FL, while many real scenarios follow a vertically-partitioned FL setup, where a complete feature set is formed only when all the datasets from the parties are combined, and the labels are only available to a single party. Privacy-preserving vertical FL is challenging because complete sets of labels and features are not owned by one entity. Existing approaches for vertical FL require multiple peer-to-peer communications among parties, leading to lengthy training times, and are restricted to (approximated) linear models and just two parties. To close this gap, we propose FedV, a framework for secure gradient computation in vertical settings for several widely used ML models such as linear models, logistic regression, and support vector machines. FedV removes the need for peer-to-peer communication among parties by using functional encryption schemes and works for larger and changing sets of parties. We empirically demonstrate the applicability for multiple ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer to the comparable state-of-the-art approaches.
Cartan Subalgebra Approach to Efficient Measurements of Quantum Observables

An arbitrary operator corresponding to a physical observable cannot be measured in a single measurement on currently available quantum hardware. To obtain the expectation value of the observable, one needs to partition its operator to measurable fragments. However, the observable and its fragments generally do not share any eigenstates, and thus the number of measurements needed to obtain the expectation value of the observable can grow rapidly even when the wavefunction prepared is close to an eigenstate of the observable. We provide a unified Lie algebraic framework for developing efficient measurement schemes for quantum observables, it is based on two elements: 1) embedding the observable operator in a Lie algebra and 2) transforming Lie algebra elements into those of a Cartan sub-algebra (CSA) using unitary operators. The CSA plays the central role because all its elements are mutually commutative and thus can be measured simultaneously. We illustrate the framework on measuring expectation values of Hamiltonians appearing in the Variational Quantum Eigensolver approach to quantum chemistry. The CSA approach puts many recently proposed methods for the measurement optimization within a single framework, and allows one not only to reduce the number of measurable fragments but also the total number of measurements.
A meta-analysis of the effect of virtual reality technology use in education

With the rapid development of information technologies, virtual reality (VR) has caught much attention in education. However, scanty studies have been committed to a meta-analysis of its effect in education in terms of various components. This study, through a meta-analysis, systematically reviews the effect of VR technologies on educational outcomes in terms of various components. It concludes that VR technologies generally exert a strong and positive influence on educational outcomes despite some negative findings in their effect on anxiety, cognition, creativeness, gender differences, learning attitudes, learner satisfaction, and engagement. Future research could expand the research by including more publications and examine the effect of VR technologies on educational outcomes through interdisciplinary cooperation.HighlightsThe use of VR could significantly improve overall learning outcomes at the .05 level in education;The use of VR could significantly improve learning achievements at the .05 level;A proper VR-based educational design may be conducive to positive results.
Factors affecting the adoption of blockchain technology in innovative Italian companies: an extended TAM approach

Purpose The present research aims to identify the determinants for users' behavioral adoption of Blockchain, exploring the relationships among these variables and investigating whether the proposed model can provide a more comprehensive manner to understand the adoption of Blockchain technology. Design/methodology/approach This study adopts the Technology Acceptance Model (TAM) approach and extends it with external constructs: “reduced cost” and “efficiency and security”. This paper used a quantitative and exploratory approach through the collection and analysis of data from a total of 108 Italian innovative SME. We have used the Partial Least Squares Structural Equation modeling (PLS-SEM) approach using SmartPLS for model evaluation. Findings The results show that “efficiency and security” is an important driver of firms' decision-making process to adopt Blockchain. Moreover, the results show that perceived usefulness is a strong predictor of the intention to use Blockchain in business processes. Originality/value This research advances the literature on technology adoption in business processes, focusing on a particular technology: Blockchain. The field has been strengthened by investigating the determinants of technology adoption, adding new perspectives; both reduced cost and efficiency, and security.
Autonomous Thermal Vision Robotic System for Victims Recognition in Search and Rescue Missions

Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to the application and development of new and improved neural networks to vision models together with modern optical sensors that incorporate thermal cameras, capable of capturing data in post-disaster environments (PDE) with rustic conditions (low luminosity, suspended particles, obstructive materials). Due to the high risk posed by PDE because of the potential collapse of structures, electrical hazards, gas leakage, etc., primary intervention tasks such as victim identification are carried out by robotic teams, provided with specific sensors such as thermal, RGB cameras, and laser. The application of Convolutional Neural Networks (CNN) to computer vision is a breakthrough for detection algorithms. Conventional methods for victim identification in these environments use RGB image processing or trained dogs, but detection with RGB images is inefficient in the absence of light or presence of debris; on the other hand, developments with thermal images are limited to the field of surveillance. This paper's main contribution focuses on implementing a novel automatic method based on thermal image processing and CNN for victim identification in PDE, using a Robotic System that uses a quadruped robot for data capture and transmission to the central station. The robot's automatic data processing and control have been carried out through Robot Operating System (ROS). Several tests have been carried out in different environments to validate the proposed method, recreating PDE with varying conditions of light, from which the datasets have been generated for the training of three neural network models (Fast R-CNN, SSD, and YOLO). The method's efficiency has been tested against another method based on CNN and RGB images for the same task showing greater effectiveness in PDE main results show that the proposed method has an efficiency greater than 90%.
GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily

Graph Neural Networks (GNNs) are widely used on a variety of graph-based machine learning tasks. For node-level tasks, GNNs have strong power to model the homophily property of graphs (i.e., connected nodes are more similar), while their ability to capture heterophily property is often doubtful. This is partially caused by the design of the feature transformation with the same kernel for the nodes in the same hop and the followed aggregation operator. One kernel cannot model the similarity and the dissimilarity (i.e., the positive and negative correlation) between node features simultaneously even though we use attention mechanisms like Graph Attention Network (GAT), since the weight calculated by attention is always a positive value. In this paper, we propose a novel GNN model based on a bi-kernel feature transformation and a selection gate. Two kernels capture homophily and heterophily information respectively, and the gate is introduced to select which kernel we should use for the given node pairs. We conduct extensive experiments on various datasets with different homophily-heterophily properties. The experimental results show consistent and significant improvements against state-of-the-art GNN methods.
Cryptocurrencies and CBDC: The Route Ahead

Abstract This paper aims to provide an outline of the dynamic landscape of cryptocurrencies and central bank digital currencies (CBDC) so as to comment about the role and prospect of both in the future. We highlight the main drivers of the ongoing digital currency wave from a socio‐economic as well as historical perspective. From an investment standpoint, we evaluate the merits of placing a cryptocurrency within a diversified portfolio and analyse other factors to be taken into account when it comes to asset allocation considerations. We also explore environmental, social, and governance (ESG) implications of the introduction of such digital currencies. Finally, we comment on the current status of national CBDC projects and what it would mean for the digital currency universe when the official CBDC roll‐outs begin in a few years.
A Systematic Literature Review of Blockchain Technology for Smart Villages

According to the United Nations, Sustainable Development Goals are framed for improving rural health, hunger, poverty issues, environmental conditions, and illiteracy globally. With the upcoming technology, there have been many advances in the lifestyle of people all around the world. Comparatively, more emphasis has been given to the development of urban areas than rural. The sustainable development of a country depends on the growth of its rural areas. Countless technological and theoretical models, projects, and frameworks have been proposed and implemented to help overcome sundry issues and challenges faced by rural people in quotidian life. New technological methods are deemed to be the future of livability, therefore; a technologically advanced solution for sustainable rural development is called for. Blockchain Technology is the next step for innovation and development and it has far many applications in sustainable rural development that are yet to be discovered. The objective of this paper is to explicitly review research conducted in rural development to fill the undone work in the future with better research ideas, to make rural areas a livable and advanced place while also maintaining their integrity leading to sustainable development. To conduct such a review, a systematic research methodology is applied following regulations in the conduction of standardized but explorative analysis. Within the timeline of 2010-2021, 112 papers are carefully selected to perform the systematic review. This review will provide a comprehensible as well as concise research compendium for all applications proposed, implemented, and possible in the future to realize the concept of smart villages for the development of rural areas using blockchain technology.
An emotional classification method of Chinese short comment text based on ELECTRA

Chinese short comment texts have the characteristics of feature sparseness, interlacing, irregularity, etc., which makes it difficult to fully grasp the overall emotional tendency of users. In response to such problem, the text proposes a new method based on ELECTRA and hybrid neural network. This method can more accurately capture the emotional features of the text, improve the classification effect, enhance the evaluation feedback mechanism, and facilitate user decision-making. First, in the embedding layer, ELECTRA model is used to replace BERT model, which can avoid the inconsistency of the mask training and fine-tuning process of the traditional pre-training model. Then, in the training layer, the self-attention mechanism and the BiLSTM are selected to obtain the fine-grained semantic representation information of the review text more comprehensively. Finally, in the output layer, the softmax classifier classifies the input corpus according to the sentiment characteristics of the Chinese short text. The experimental results show that the proposed model has an efficiently improvement in accuracy and there are some discoveries about the training effect of the pre-training model on text sentiment analysis tasks.
Attend to the Difference: Cross-Modality Person Re-Identification via Contrastive Correlation

The problem of cross-modality person re-identification has been receiving increasing attention recently, due to its practical significance. Motivated by the fact that human usually attend to the difference when they compare two similar objects, we propose a dual-path cross-modality feature learning framework which preserves intrinsic spatial structures and attends to the difference of input cross-modality image pairs. Our framework is composed by two main components: a Dual-path Spatial-structure-preserving Common Space Network (DSCSN) and a Contrastive Correlation Network (CCN). The former embeds cross-modality images into a common 3D tensor space without losing spatial structures, while the latter extracts contrastive features by dynamically comparing input image pairs. Note that the representations generated for the input RGB and Infrared images are mutually dependant to each other. We conduct extensive experiments on two public available RGB-IR ReID datasets, SYSU-MM01 and RegDB, and our proposed method outperforms state-of-the-art algorithms by a large margin with both full and simplified evaluation modes.
Federated intelligence of anomaly detection agent in IoTMD-enabled Diabetes Management Control System

Implantable internet of things medical devices (IoTMD) has drawn a disruptive transformation in the healthcare domain. It has improved the services of healthcare providers in delivering patient care. Even better, it has supported individuals that are suffering from a chronic condition in self-managing their condition. Patients diagnosed with diabetes are the leading beneficiaries of IoTMD, assisting them in maintaining their sugar level at a normal range. However, the security defense of these systems against potential cyberthreats is currently underdeveloped. Such threats must not be ignored as they can put the lives of the patients in danger. Thus, this paper proposes deep learning (DL)-based anomaly detection system composed of estimation and classification models applied to a subdomain in healthcare systems referred to as Diabetes Management Control System (DMCS). The estimation model was used to forecast the glucose level of patients at each evaluation time step, whereas the classification model is intended to detect anomalous data points. This paper implements convolutional neural network (CNN) and multilayer perceptron (MLP) algorithms for comparison purposes. Moreover, considering that the dataset contains sensitive physiological information of the patients, this paper implements the independent learning (IL) and federated learning (FL) methods to maintain user data privacy. Furthermore, the post-quantization compression technique was also applied to convert models into their lightweight version, overcoming the computationally demanding processes of DL. Based on the experiment results, the FL method showed a higher recall rate (> = 98.69%) than the IL method (< = 97.87%). Additionally, the FL-aided CNN-based anomaly detection system performs better than the MLP-based approach. The former achieved an average recall rate of 99.24%, whereas the latter only attained 98.69%. Without loss of recall value, the inference latency of the models was tremendously reduced from more than 300 ms to less than two milliseconds when the original models were converted to their lightweight version.
Empowering Edge Intelligence by Air-Ground Integrated Federated Learning

Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies intelligence over the whole network from the core to the edge, including end devices. Nevertheless, fulfilling this vision, particularly the intelligence at the edge, is extremely challenging due to the limited resources of edge devices as well as the ubiquitous coverage envisioned by 6G. To empower edge intelligence, in this article, we propose a framework called air-ground integrated federated learning (AGIFL), which organically integrates air-ground integrated networks and federated learning (FL). In AGIFL, leveraging the flexible on-demand 3D deployment of aerial nodes such as unmanned aerial vehicles (UAVs), all the nodes can collaboratively train an effective learning model by FL. We also conduct a case study to evaluate the effect of two different deployment schemes of UAVs on learning and network performance. Last but not least, we highlight several technical challenges and future research directions in AGIFL.
On Pursuit of Designing Multi-modal Transformer for Video Grounding

Video grounding aims to localize the temporal segment corresponding to a sentence query from an untrimmed video.Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression.2) Bottomup model: It directly predicts frame-wise probabilities of the referential segment boundaries.However, all these methods are not end-to-end, i.e., they always rely on some time-consuming post-processing steps to refine predictions.To this end, we reformulate video grounding as a set prediction task and propose a novel end-toend multi-modal Transformer model, dubbed as GTR.Specifically, GTR has two encoders for video and language encoding, and a crossmodal decoder for grounding prediction.To facilitate the end-to-end training, we use a Cubic Embedding layer to transform the raw videos into a set of visual tokens.To better fuse these two modalities in the decoder, we design a new Multi-head Cross-Modal Attention.The whole GTR is optimized via a Many-to-One matching loss.Furthermore, we conduct comprehensive studies to investigate different model design choices.Extensive results on three benchmarks have validated the superiority of GTR.All three typical GTR variants achieve recordbreaking performance on all datasets and metrics, with several times faster inference speed.Our project is available at GTR.
Multilingual Offensive Language Identification for Low-resource Languages

Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g., hate speech, cyberbullying, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this article, we take advantage of available English datasets by applying cross-lingual contextual word embeddings and transfer learning to make predictions in low-resource languages. We project predictions on comparable data in Arabic, Bengali, Danish, Greek, Hindi, Spanish, and Turkish. We report results of 0.8415 F1 macro for Bengali in TRAC-2 shared task [23], 0.8532 F1 macro for Danish and 0.8701 F1 macro for Greek in OffensEval 2020 [58], 0.8568 F1 macro for Hindi in HASOC 2019 shared task [27], and 0.7513 F1 macro for Spanish in in SemEval-2019 Task 5 (HatEval) [7], showing that our approach compares favorably to the best systems submitted to recent shared tasks on these three languages. Additionally, we report competitive performance on Arabic and Turkish using the training and development sets of OffensEval 2020 shared task. The results for all languages confirm the robustness of cross-lingual contextual embeddings and transfer learning for this task.
A Two-Stream Neural Network for Pose-Based Hand Gesture Recognition

Pose-based hand gesture recognition has been widely studied in the recent years. Compared with full body action recognition, hand gesture involves joints that are more spatially closely distributed with stronger collaboration. This nature requires a different approach from action recognition to capturing the complex spatial features. Many gesture categories, such as “Grab” and “Pinch,” have very similar motion or temporal patterns posing a challenge on temporal processing. To address these challenges, this article proposes a two-stream neural network with one stream being a self-attention-based graph convolutional network (SAGCN) extracting the short-term temporal information and hierarchical spatial information, and the other being a residual-connection-enhanced bidirectional independently recurrent neural network (IndRNN) for extracting long-term temporal information. The SAGCN has a dynamic self-attention mechanism to adaptively exploit the relationships of all hand joints in addition to the fixed topology and local feature extraction in the GCNs. The proposed method effectively takes advantage of the GCN and IndRNN to capture the temporal-spatial information. The widely used Dynamic Hand Gesture dataset (two evaluation protocols) and First-Person Hand Action dataset are used to validate its effectiveness and our method achieves state-of-the-art performance with 96.31%, 94.05%, and 90.26%, respectively, in terms of recognition accuracy.
Sponge Examples: Energy-Latency Attacks on Neural Networks

The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While such devices enable us to train large-scale neural networks in datacenters and deploy them on edge devices, their designers' focus so far is on average-case performance. In this work, we introduce a novel threat vector against neural networks whose energy consumption or decision latency are critical. We show how adversaries can exploit carefully-crafted sponge examples, which are inputs designed to maximise energy consumption and latency, to drive machine learning (ML) systems towards their worst-case performance. Sponge examples are, to our knowledge, the first denial-of-service attack against the ML components of such systems. We mount two variants of our sponge attack on a wide range of state-of-the-art neural network models, and find that language models are surprisingly vulnerable. Sponge examples frequently increase both latency and energy consumption of these models by a factor of 30×. Extensive experiments show that our new attack is effective across different hardware platforms (CPU, GPU and an ASIC simulator) on a wide range of different language tasks. On vision tasks, we show that sponge examples can be produced and a latency degradation observed, but the effect is less pronounced. To demonstrate the effectiveness of sponge examples in the real world, we mount an attack against Microsoft Azure's translator and show an increase of response time from 1ms to 6s (6000×). We conclude by proposing a defense strategy: shifting the analysis of energy consumption in hardware from an average-case to a worst-case perspective.