Reviewerly/exHarmony huggingface dataset:
A Language-Agnostic Framework with Bidirectional Syntactic Graph Convolutional Networks for Cross-Lingual Aspect Term Extraction

Aspect term extraction is a vital sub-task of sentiment analysis, which aims to extract explicit product attributes in customer reviews. Unfortunately, many languages lack sufficient labeled data, so researchers focus on Cross-lingual Aspect Term Extraction (XATE) to fully use sufficient data in other languages. Most recent cross-lingual methods focus on semantic alignment and data augmentation, but lack research on language structure, including syntax and lexicality. To this end, we propose a Language-Agnostic framework with Bidirectional Syntactic Graph Convolutional Networks (LA-BSGCN) for XATE. It is based on the idea that the topological structures of syntactic dependencies and the lexical tags across different languages are similar. We design a multi-layer bidirectional GCN, which can encode the syntactic tree more accurately. Furthermore, to reduce the lexicality semantic gap between different languages, we encode named entity recognition (NER) and part of speech (POS) information into our model. We conduct six pairs of cross-lingual experiments on SemEval2016 Task5 datasets. The results show that our LA-BSGCN significantly reduces the semantic gap and outperforms the state-of-the-art methods. For reproducibility, our code for this paper is available at github.
Redesign Visual Transformer For Small Datasets

Nowadays, the self-attention mechanism has become a resound of visual feature extraction along with convolution. The transformer network composed of self-attention has developed rapidly and made remarkable achievements in visual tasks. The self-attention shows the potential to replace convolution as the primary method of visual feature extraction in ubiquitous intelligence. Nevertheless, the development of the Visual Transformer still suffer from the following problems: a) The self-attention mechanism has a low inductive bias, which leads to large data demand and a high training cost. b) The Transformer backbone network cannot adapt well to the low visual information density and performs unsatisfactorily under low resolution and small-scale datasets. To tackle the abovementioned two problems, this paper proposes a novel algorithm based on the mature Visual Transformer architecture, which is dedicated to exploring the performance potential of the Transformer network and its kernel self-attention mechanism on small-scale datasets. Specifically, we first propose a network architecture equipped with multi-coordination strategy to solve the self-attention degradation problem inherent in the existing Transformer architecture. Secondly, we introduce consistent regularization into the Transformer to make the self-attention mechanism acquire more reliable feature representation ability in the case of insufficient visual features. In the experiments, CSwin Transformer, the mainstream visual model, is selected to verify the effectiveness of the proposed method on the prevalent small datasets, and superior results are achieved. In particular, without pre-training, our accuracy on the CIFAR-100 dataset is improved by 1.24% compared to CSwin.
Edge Resource Autoscaling for Hierarchical Federated Learning Over Public Edge Platforms

Federated learning promises to empower ubiquitous end devices to collaboratively learn a shared model in a privacy-preserving manner. To reduce the enormous and expensive wide-area-network (WAN) traffic incurred by the traditional two-tiered cloud-device federated learning, hierarchical federated learning over cloud-edge-device has been proposed recently. With hierarchical federated learning, edge servers are leveraged as intermediaries to perform local model aggregations to reduce the model updates aggregated by the centralized cloud. Considering the emerging public edge platforms such as Aliyun Edge Node Service that rent edge servers to users in an on-demand manner, we present AutoEdge, an edge server autoscaling framework for hierarchical federated learning. The goal of AutoEdge is to autoscale edge servers against dynamical device participants in a cost-efficient manner. Achieving this goal is challenging since the underlying long-term optimization problem is NP-hard involves the future system information. To attack these challenges, AutoEdge first applies regularization technique to decompose the long-term problem into a set of solvable fractional subproblems. Then, adopting a randomized dependent rounding scheme, AutoEdge further rounds the fractional solutions to a near-optimal and feasible integral solution. AutoEdge achieves outstanding performance guarantee, as verified by both rigorous theoretical analysis and extensive trace-driven simulations.
Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.
RSG-Search: Semantic Traffic Scene Retrieval Using Graph-Based Scene Representation

Browsing specific traffic scene in large-scale dataset is an increasing demand from researchers, self-driving community and insurance companies. It is easy to search scenes with specific tags such as "rain", "snow", or "on highway". However, searching specific scene configurations, like "two vehicles waiting for a person crossing the road", is still an open problem. In this paper, we provide RSG-search, a scene-graph based traffic scene retrieval method, based on our previous research on traffic scene-graph generation. By previously translating open datasets to scene graphs, we can ignore irrelevant details, and efficiently search specific scene configuration among thousands of traffic scenes. Experiment results shows that our graph searching method is able to retrieve results for a given query with high accuracy. Our method simplifies the task of scene retrieval, opening opportunities for new applications.
Blockchain infrastructure and services for biomedical applications

Characteristics of blockchain technologies, such as decentralization, anonymity, auditability, transparency, reliability and immutability, have inspired numerous potential applications in the field of biomedicine. The goal of this research is to point out the possibilities of using blockchain technologies in biomedical applications, with a focus on extended medical supply chain management and transparent end-to-end data provisioning. As a possible approach, we propose a blockchain-based platform for storing data from the medical supply chain, data about and measured using various medical devices, patient medical records, personal health records, sensor data, and others, in order to provide all stakeholders with access to relevant and accurate data and raise the level of trust. We present the components needed for the implementation of the proposed system. Finally, we discuss the benefits and challenges related to the implementation of the proposed system.)
SecuCar: Data Loss Prevention for Cloud assisted VSS based on Public Auditing Technique

Intelligent and networked vehicle is a popular developing trend in future. However, Data Loss Prevention (DLP) is an import problem need to be solved. Especially, in the Vehicle Sharing System (VSS), the data authority is hard to be guaranteed based on the current technology. VSS provides the users with a convenient way to access vehicles nearby. However, the driving data security which can be used to record the driving conditions cannot be guaranteed by either cloud server or the vehicle. The camera in the cab is also not accepted because it will leak the user's privacy. This paper solves this problem based on the cryptography with the public auditing technology. We design a multi-signature scheme so that the OBU and the PDA equipment of the vehicle renter can jointly sign a signature as a meta data for the driving information and upload it to the cloud server together with the raw data. In the event of a dispute, such as a vehicle accident, anyone can conduct an integrity checking with batch jobs on the stored data in the cloud server, so that the cloud and the vehicle renter cannot deny these information. We think that our scheme can be used to prevent some obvious data modification in cloud or as a part of the reference for accident identification. We also use the provable security technology to prove that our protocol is secure, and we realize its core algorithms. The experimental result shows that our scheme is efficient.
Accelerate Supercomputing through Cross-Region Interconnection

Supercomputing has become an important tool for science and technology innovation. But existing supercomputers are difficult to meet the stringent requirements of supercomputing tasks on computing scale, deadlines, high flexibility and low prices. Cross-region interconnection supercomputing (CIS) and computing power scheduling have become a feasible solution, which can significantly shorten the total completion time of supercomputing tasks, average the load between supercomputers, and improve the benefits of all supercomputers. In this paper, we designed the CIS framework, modeled the supercomputing task and compute node mapping problem with two constraints of deadline and storage resources, designed the solving algorithm based on Ant Colony Optimization (ACO), analyzed the algorithm and evaluated its performance using simulation experiments. The experiment results demonstrate that, in comparison to FCFS and Min-Min algorithms, the ACO algorithm can schedule supercomputing tasks quickly and efficiently, shortening 12.9% the overall supercomputing task completion time.
An improved butterfly optimization algorithm for UAV path planning in complex environment

The three-dimensional (3-D) path planning of unmanned aerial vehicles (UAV) is a multi-objective optimization problem. In this work, an improved butterfly optimization algorithm (IBOA) based on the virtual center butterfly (VCB) and Neighborhood dimension perturbation learning (NDPL) is proposed to solve the problem. Since BOA executes a global search using pairwise interactions between two random butterflies (random operators). It makes the algorithm susceptible to missing the optimal solution, resulting in a lack of exploration capability. Therefore, we designed a novel VCB strategy to improve the exploration capability of the algorithm by creating attraction or repulsion effects on butterflies during the global phase. In the exploitation phase, the other individuals gravitate toward the best individual. If the best individual falls into the local extremes, the algorithm converges prematurely and with low precision. A new NDPL method is proposed that enhances the local mining capability of the algorithm by constructing a neighborhood matrix and learning from individuals in the neighborhood. The results of the simulation experiment in four scenarios demonstrate that the IBOA can effectively acquire a practical and effective path, and it performs better than the other six algorithms.
Capturing High-order Interactions for Interest-aware News Recommendation

Personalized news recommendation aims to help users find the news of their interests and alleviate information overload. Accurately modeling user interactions is the key to mining user interests. Existing methods usually learn user interactions via sequential or attention models, which ignores the complex transformational relations in user behavior sequences. Although graph neural network can solve the above problem to a certain extent by aggregating neighbor information, it only can propagate information from adjacent item, while in reality user interactions are often high-order. In this paper, we propose a novel method to extract user interests and interactions, which constructs each user behavior sequence as a hypergraph to capture high-order interactions. Specifically, we use knowledge graph as auxiliary information to enrich news semantics, thus enhancing the representation of nodes in the hypergraph. Then, we stack multiple convolutional layers to model the multi-hop high-order information propagation in the hypergraph. Furthermore, we explore time series representations in user behavior sequences to enhance user interest. Extensive experiments on two real-world datasets show the effectiveness of our method in improving news recommendation performance.
SET: a squeeze-and-excitation transformer for offline signature verification

Offline handwritten signature verification, which is widely used in finance, commerce, and criminal forensic identification, plays an essential role in the fields of biometrics and document forensics. The development in deep learning has led to significant advances in signature verification over the past decade. However, it is still challenging to distinguish between skilled forgeries and genuine signatures because both are close similarities with only subtle differences in strokes. With this paper, we develop a novel squeeze-and-excitation transformer structure (named SET) for feature extraction and signature verification. SET comprises four stages and receives a two-channel signature pair consisting of reference and query signatures as input. The SET block is the core of each stage, which is utilized to enhance the feature learning ability and strengthen the association between feature channels. We evaluate the proposed SET on several public datasets (CEDAR, BHSig-B, and BHSig-H). Experimental results demonstrate that our approach outperforms existing methods.
Research on Real-Time Motion Classification and Counting Algorithm Based on Video

High quality exercise data can often provide a basis for people to formulate scientific fitness plans. At present, the methods of motion recognition and counting through manual quantization or wearable devices with sensors are poor in convenience, visualization, efficiency and accuracy. This paper presents a real-time motion classification and counting method using computer vision technology, which makes up for the shortcomings of the previous methods. Firstly, a lightweight human posture estimation network MPE is designed to extract human bone point data, and then the data of human bone points are processed for motion classification. Finally, based on the classification results, the counting algorithm proposed in this paper is used for real-time motion counting. The counting algorithm can customize the counting standard to standardize the motion action. This method can meet the counting scene of a single person doing a variety of movements alternately. This paper mainly carries out experiments on five kinds of sports including squats, sit-ups, push-ups, pull-ups and jumping jacks. The experimental results show that the PCKh index of MPE network reaches 85.9 on MPII dataset and the AP index reaches 65.0 on COCO dataset. It also performs well in lightweight, with only 5.8G FLOPs and 5.6M model parameters. In addition, the classification accuracy of KNN classification network based on MPE is 95% on self-made dataset. The accuracy of counting is 93%, and the accuracy of counting ± 1 is 97%.
User Information Perception in Virtual Reality Environment

With the development of virtual reality (VR) technology, panoramic video, a new method that is the fusion of VR technology and panoramic video technology, have gradually emerged and developed rapidly. Nowadays, VR panoramic video is becoming the most important application of virtual reality technology. Although the amount of video information is large, the user acceptance rate is relatively low and the perception is weak, which has a certain negative impact on the popularity of VR. Currently, there are few types of research on the VR information perception field, and the existing researches lack a great division of user characteristics and panoramic video types. Therefore, this paper mainly discusses the following two questions: Is the user’s information perception level in the VR environment significantly better than that in the traditional media environment? Do event types and user characteristics in VR videos affect users’ perception of information? In response to the questions, a total of 20 participants were recruited for the research. Through the analysis of the statistical calculation, this paper draws the conclusion: Media type has a significant impact on user information perception and the perception in the VR environment is significantly better than that of traditional media. Besides, this paper also finds a phenomenon: the user characteristics and the proportion of event types in the video have an impact on the user information perception effect. It shows that the higher the level of education, the better the information perception effect (ages between 20-30); the higher the proportion of mobile events and emergencies in the video, the better the user information perception effect.
Investigating a Pressure Sensitive Surface Layer for Vehicle Localization

Roads are one of the most important transportation routes in the world, yet the way we build roads has remained the same for decades. The road system’s structure is virtually unchanged, and there is little use beyond the primary function of load transfer. However, the road could be an essential data source for different applications. This paper presents an algorithm for detecting and tracking vehicles passing over the road surface using real-time load data. We detect individual tires based on local pressure maxima on the surface and track them using a multiple-target tracker. Our algorithm subsequently identifies individual vehicles performing pattern matching with the tracked wheels. We tested the algorithm in the Cyber-Physical Mobility Lab because there is yet to be a system for real-world testing, and cyber-physical labs are more flexible and less expensive than real-world testing. In our test run, we achieved a vehicle detection accuracy and recall of 100% and a localization accuracy of a few centimeters.
Jamming-Resilient Over-the-Air Computation for Federated Learning in Edge Intelligence

By making full use of the accumulative feature of wireless signals, the over-the-air computation makes it possible for the federated learning to aggregate all the parameters simultaneously from the distributed learning agents, which lessens the delay of updating the parameters caused by the collisions of wireless signals. However, the accumulative feature in the over-the-air computation also opens the door to the jamming attackers. Specifically, an adversary can easily tamper the aggregated result in the over-the-air computation by launching a simple jamming signal. Thus, it is significant to improve the jamming resilience of the over-the-air computation so that it can be adopted in a wider range of wireless scenarios. In this paper, we propose the first jamming-resilient over-the-air computation algorithm for the federated learning via the multiple channel technique. Specifically, in our jamming-resilient algorithm, the learning agents randomly and uniformly choose one of the multi-channels to update their parameters, and the server drop a fraction of results according to a greedy screening rule. A theoretical analysis with some rigorous proofs is presented to show that the expected aggregated result by our algorithm is asymptotically close to the authentic value when the data on the learning agents has a uniform distribution. An extensive simulation is also conducted to show the correctness of our algorithm.
Heuristic Once Learning for Image &amp; Text Duality Information Processing

Few-shot learning is an important mechanism to minimize the need for the labeling of large amounts of data and taking advantage of transfer learning. To identify image/text input with duality property, this research proposes a “Heuristic once learning (HOL)” mechanism to investigate multi-modal input processing similar to human-like behavior. First, we create an image/text data set of big Latin letters composed of small letters and another data set composed of Arabic, Chinese and Roman numerals. Secondly, we use Convolutional Neural Networks (CNN) for pre-training the dataset of letters to get structural features. Thirdly, using the acquired knowledge, a Self-organizing Map (SOM) and Contrastive Language-Image Pretraining (CLIP) are tested separately using zero-shot learning. Siamese Networks and Vision Transformer (ViT) are also tested using one-shot learning by knowledge transfer to identify the features of unknown characters. The research results show the potential and challenges to realize HOL and make a useful attempt for the development of general agents.
MixOT: Graph Representation Learning based on Mix-order Sampling and Transport Aggregator for Social Networks

Graph Neural Networks (GNNs), one of the highly regarded graph representation learning methods, have achieved excellent performance in social networks. Graph convolution performs neighborhood aggregation, which is one of the most important graph operations. However, one layer in these methods considers only the nearest neighbors, and most existing GNNs are inherently limited by over-smoothing when going deeper into larger receptive domains. In this paper, we propose MixOT address this problem, which follows the idea of decoupling representation transformation and propagation in graph convolution operations. In MixOT, we first design an unbiased node sampling probability to precompute the mixed-order matrix. Then, we use top-k truncation to sample the neighborhood information of each order, thus implementing a sampler that avoids ‘‘neighbor explosion Based on this, we develop a transport aggregation method that efficiently measures the importance of neighbors of each order using optimal transport theory. Extensive experiments on graph benchmark datasets show that MixOT outperforms state-of-the-art GNN baselines in node classification and effectively alleviates the over-smoothing problem.