Reviewerly/exHarmony huggingface dataset:
Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.
Code Prediction by Feeding Trees to Transformers

Code prediction, more specifically autocomplete, has become an essential feature in modern IDEs. Autocomplete is more effective when the desired next token is at (or close to) the top of the list of potential completions offered by the IDE at cursor position. This is where the strength of the underlying machine learning system that produces a ranked order of potential completions comes into play. We advance the state-of-the-art in the accuracy of code prediction (next token prediction) used in autocomplete systems. Our work uses Transformers as the base neural architecture. We show that by making the Transformer architecture aware of the syntactic structure of code, we increase the margin by which a Transformer-based system outperforms previous systems. With this, it outperforms the accuracy of several state-of-the-art next token prediction systems by margins ranging from 14% to 18%. We present in the paper several ways of communicating the code structure to the Transformer, which is fundamentally built for processing sequence data. We provide a comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on Facebook internal Python corpus. Our code and data preparation pipeline will be available in open source.
LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search

Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployments in resource-constrained applications. In this work, we present LightTrack, which uses neural architecture search (NAS) to design more lightweight and efficient object trackers. Comprehensive experiments show that our LightTrack is effective. It can find trackers that achieve superior performance compared to handcrafted SOTA trackers, such as SiamRPN++ [30] and Ocean [56], while using much fewer model Flops and parameters. Moreover, when deployed on resource-constrained mobile chipsets, the discovered trackers run much faster. For example, on Snapdragon 845 Adreno GPU, LightTrack runs 12× faster than Ocean, while using 13× fewer parameters and 38× fewer Flops. Such improvements might narrow the gap between academic models and industrial deployments in object tracking task. LightTrack is released at here.
Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection

Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects. In this paper, we propose a convex-hull feature adaptation (CFA) approach for configuring convolutional features in accordance with oriented and densely packed object layouts. CFA is rooted in convex-hull feature representation, which defines a set of dynamically predicted feature points guided by the convex intersection over union (CIoU) to bound the extent of objects. CFA pursues optimal feature assignment by constructing convex-hull sets and dynamically splitting positive or negative convex-hulls. By simultaneously considering overlapping convex-hulls and objects and penalizing convex-hulls shared by multiple objects, CFA alleviates spatial feature aliasing towards optimal feature adaptation. Experiments on DOTA and SKU110K-R datasets show that CFA significantly outperforms the baseline approach, achieving new state-of-the-art detection performance. Code is available at github.com/SDL-GuoZonghao/BeyondBoundingBox.
Domain Adaptation with Auxiliary Target Domain-Oriented Classifier

Domain adaptation (DA) aims to transfer knowledge from a label-rich but heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and attracts considerable attention. Different from previous methods focusing on learning domain-invariant feature representations, some recent methods present generic semi-supervised learning (SSL) techniques and directly apply them to DA tasks, even achieving competitive performance. One of the most popular SSL techniques is pseudo-labeling that assigns pseudo labels for each unlabeled data via the classifier trained by labeled data. However, it ignores the distribution shift in DA problems and is inevitably biased to source data. To address this issue, we propose a new pseudo-labeling framework called Auxiliary Target Domain-Oriented Classifier (ATDOC). ATDOC alleviates the classifier bias by introducing an auxiliary classifier for target data only, to improve the quality of pseudo labels. Specifically, we employ the memory mechanism and develop two types of nonparametric classifiers, i.e. the nearest centroid classifier and neighborhood aggregation, without introducing any additional network parameters. Despite its simplicity in a pseudo classification objective, ATDOC with neighborhood aggregation significantly outperforms domain alignment techniques and prior SSL techniques on a large variety of DA benchmarks and even scare-labeled SSL tasks.
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large images are usually decomposed into small sub-images in practical usages. Based on this processing, we found that different image regions have different restoration difficulties and can be processed by networks with different capacities. Intuitively, smooth areas are easier to super-solve than complex textures. To utilize this property, we can adopt appropriate SR networks to process different sub-images after the decomposition. On this basis, we propose a new solution pipeline – ClassSR that combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the subimages into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions. We further introduce a new classification method with two losses – Class-Loss and Average-Loss to produce the classification results. After joint training, a majority of sub-images will pass through smaller networks, thus the computational cost can be significantly reduced. Experiments show that our ClassSR can help most existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN) save up to 50% FLOPs on DIV8K datasets. This general framework can also be applied in other low-level vision tasks.
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future is still far from resolved. The key to predict future facts is to thoroughly understand the historical facts. A TKG is actually a sequence of KGs corresponding to different timestamps, where all concurrent facts in each KG exhibit structural dependencies and temporally adjacent facts carry informative sequential patterns. To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently. Specifically, for the evolution unit, a relation-aware GCN is leveraged to capture the structural dependencies within the KG at each timestamp. In order to capture the sequential patterns of all facts in parallel, the historical KG sequence is modeled auto-regressively by the gate recurrent components. Moreover, the static properties of entities, such as entity types, are also incorporated via a static graph constraint component to obtain better entity representations. Fact prediction at future timestamps can then be realized based on the evolutional entity and relation representations. Extensive experiments demonstrate that the RE-GCN model obtains substantial performance and efficiency improvement for the temporal reasoning tasks on six benchmark datasets. Especially, it achieves up to 11.46% improvement in MRR for entity prediction with up to 82 times speedup compared to the state-of-the-art baseline.
DRRS-BC: Decentralized Routing Registration System Based on Blockchain

The border gateway protocol (BGP) has become the indispensible infrastructure of the Internet as a typical inter-domain routing protocol. However, it is vulnerable to misconfigurations and malicious attacks since BGP does not provide enough authentication mechanism to the route advertisement. As a result, it has brought about many security incidents with huge economic losses. Exiting solutions to the routing security problem such as S-BGP, So-BGP, Ps-BGP, and RPKI, are based on the Public Key Infrastructure and face a high security risk from the centralized structure. In this paper, we propose the decentralized blockchain-based route registration framework-decentralized route registration system based on blockchain (DRRS-BC). In DRRS-BC, we produce a global transaction ledge by the information of address prefixes and autonomous system numbers between multiple organizations and ASs, which is maintained by all blockchain nodes and further used for authentication. By applying blockchain, DRRS-BC perfectly solves the problems of identity authentication, behavior authentication as well as the promotion and deployment problem rather than depending on the authentication center. Moreover, it resists to prefix and subprefix hijacking attacks and meets the performance and security requirements of route registration.
Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT

The Artificial Intelligence Internet of Things (AIoT) is an emerging concept aiming to perceive, understand and connect the ‘intelligent things’ to make the intercommunication of various networks and systems more efficient. A key step in achieving this goal is to carry out high-precision data analysis at the edge and cloud level. Clustering and dimensionality reduction in AIoT can facilitate efficient data management, storage, computing, and transmission of various data-driven AIoT applications. For high-efficiency data clustering and dimensionality reduction, this paper develops a prior-dependent graph (PDG) construction method to model and discover the complex relations of data. With the proper utilization and incorporation of data priors, i.e., (a) element local sparsity; (b) pair-wise symmetry; (c) multi-instance manifold smoothness; and (d) matrix low-rankness, the obtained graph has the characteristics of local sparsity, symmetry, low-rank, and can well reveal the complex multi-instance proximity among data points. The developed PDG model is then applied for two typical data analysis tasks, i.e., unsupervised data clustering and dimensionality reduction. Experimental results on multiple benchmark databases verify that, compared with some existing graph learning models, the PDG model can achieve substantial performance, which can be deployed in edge computing modules to provide efficient solutions for massive data management and applications in AIoT.
The internet of things security: A survey encompassing unexplored areas and new insights

The cosmic evolution of the Internet of things (IoTs) in par with its realization in all spheres of life undertakings, mandates continuous research pursuits in IoT and its associated components. While the rapid evolution of IoTs has facilitated monumental opportunities for humanity, it has also acted as a catalyst precipitating diverse security issues. Cybercrimes have been on the rise as criminals and hackers continue to take advantage of IoT's security loopholes and vulnerabilities. The enormity of the attacks has not only been damaging to the quality of life, but it poses a disservice and an unquantifiable risk to human safety. Thus, a timely and comprehensive review, analysis and investigation of the security of IoTs is crucial. Through a systematic literature review of over 200 articles, we set out the latest findings and trends to provide new insights into the security of IoTs, taking cognizant of its social, economic, technical and legal implications, which will be beneficial to researchers, manufacturers, individuals, organizations, and governments. Although many studies reviewing the state of IoT exist in the literature, no studies shape the area of its security well. Hence, there is currently no study that provides an in-depth survey of the emerging security concerns of IoT from diverse perspectives and in tandem with the current condition of the global world today. Compared to other related reviews on the security of IoTs, this survey encompasses much more technical angles to the security of IoT. It begins with the review of the concept of IoTs, the assessments of its industrial development trends, revolutionary paradigms and updated security of the IoT. Key challenges in the security of blockchain technology, a recent spike in distributed denial of service (DDoS) attacks due to the COVID-19 pandemic are sensitive areas that have remained untouched by previous review works. Additionally, politics and security of electoral votes, forensic issues in the IoT era and much more are some of the new depths missing in the literature of IoT security. Thus, a huge divide in the total adoption and actualization of IoT in diverse areas of human endeavour. This review formalizes the IoT concept, illuminating deep insights into possible solutions to the heterogeneous nature of IoT's security challenges, emerging issues, gaps, opportunities, foresight, and recommendations.
Trustworthy AI

The pursuit of responsible AI raises the ante on both the trustworthy computing and formal methods communities.
Optimizing Federated Learning in Distributed Industrial IoT: A Multi-Agent Approach

In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reinforcement on Federated</i> ” (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.
Spatial and temporal distribution characteristics of haze and pollution particles in China based on spatial statistics

In recent years, the occurrence and frequency of haze have constantly been increasing, bringing severe threats to people's daily lives. To this end, this paper discusses the spatial and temporal distribution characteristics of severe haze in China, analyzes the interaction between haze pollution and the influence of economy and energy structure on haze in 31 provinces of China. It provides references for the treatment of haze weather and the prevention and control of air pollution in China. This paper mainly adopts the spatial autocorrelation method. The data processed mainly includes API (Air Pollution Index) and meteorological station data. Combined with the statistical yearbook data, this paper conducts multi-aspect research and exploration. By using statistical methods to study the haze distribution in China, we found that the haze and PM2.5 concentrations were mainly distributed in Beijing-Tianjin-Hebei, Shandong Province, the northern northwest, southeastern Sichuan, and Chongqing. Haze distribution has obvious seasonality, more in winter and less in summer. There are also regional differences in the concentration distribution of urban pollutants. The concentration of SO2 and absorbable particles are relatively high in northern cities. In contrast, that of southern cities is relatively low and changes with seasonal changes.
Chaotic color image encryption based on 4D chaotic maps and DNA sequence

This paper combines a four-dimensional chaotic system with DNA technology to design a color image encryption algorithm. The algorithm converts each pixel value into two 4-bit binary for WSSM scrambling. The proposed 4D chaotic system is used to generate four chaotic sequences to determine the rules of DNA encoding, decoding and calculation. Finally, the three matrices of R, G, and B are divided into blocks and scrambled. Security analysis is carried out, including histogram, key sensitivity, entropy, correlation, anti-differential attack, anti-noise attack, occlusion attack and other aspects. All experimental results show that the proposed image encryption algorithm can effectively resist various attacks.
Blockchain-based Supply Chain Traceability for COVID-19 personal protective equipment

The COVID-19 pandemic has severely impacted many industries, in particular the healthcare sector exposing systemic vulnerabilities in emergency preparedness, risk mitigation, and supply chain management. A major challenge during the pandemic was related to the increased demand for Personal Protective Equipment (PPE), resulting in critical shortages for healthcare and frontline workers. This is due to the lack of information visibility combined with the inability to precisely track product movement within the supply chain, requiring a robust traceability solution. Blockchain technology is a distributed ledger that ensures a transparent, safe, and secure exchange of data among supply chain stakeholders. The advantages of adopting blockchain technology to manage and track PPE products in the supply chain include decentralized control, security, traceability, and auditable time-stamped transactions. In this paper, we present a blockchain-based approach using smart contracts to transform PPE supply chain operations. We propose a generic framework using Ethereum smart contracts and decentralized storage systems to automate the processes and information exchange and present detailed algorithms that capture the interactions among supply chain stakeholders. The smart contract code was developed and tested in Remix environment, and the code is made publicly available on Github. We present detailed cost and security analysis incurred by the stakeholders in the supply chain. Adopting a blockchain-based solution for PPE supply chains is economically viable and provides a streamlined, secure, trusted, and transparent mode of communication among various stakeholders.
Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain

The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML. Most of the current Transfer DML (TDML) methods are not applicable to the scenario where data are drawn from heterogeneous domains. Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace. However, these approaches lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. This motivates us to develop a general flexible heterogeneous TDML (HTDML) framework. In particular, any (linear/nonlinear) DML algorithms can be employed to learn the source metric beforehand. Then the pre-learned source metric is represented as a set of knowledge fragments to help target metric learning. We show how generalization error in the target domain could be reduced using the proposed transfer strategy, and develop novel algorithm to learn either linear or nonlinear target metric. Extensive experiments on various applications demonstrate the effectiveness of the proposed method.
Agile, Stage-Gate and their combination: Exploring how they relate to performance in software development

This exploratory study investigates the relationship of plan-driven Stage-Gate and flexible Agile models with new product development performance through an original conceptualization that focuses on their underlying principles for managing uncertainty and the resulting changes. While Stage-Gate attempts to control uncertainty up-front to avoid later changes, Agile seeks to adapt to uncertainty and accommodate changes for a longer proportion of the development process. In addition, we examine the interaction effects of combining the two models. The analysis of survey data on 181 software developers shows that the adoption of Stage-Gate principles is negatively associated with speed and cost performance. For Agile, the use of sprints is positively related to new product quality, on-time and on-budget completion, while early and frequent user feedback would seem to prolong time-to-market. Finally, the results highlight a nuanced interaction between Stage-Gate and Agile, both positive and negative depending on the principles considered.