Reviewerly/exHarmony huggingface dataset:
Bot2Vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks

Recently, due to the rapid growth of online social networks (OSNs) such as Facebook, Twitter, Weibo, etc. the number of machine accounts/social bots that mimic human users has increased. Along with the development of artificial intelligence (AI), social bots are designed to become smarter and more sophisticated in their efforts at replicating the normal behaviors of human accounts. Constructing reliable and effective bot detection mechanisms is this considered crucial to keep OSNs clean and safe for users. Despite the rapid development of social bot detection platforms, recent state-of-the-art systems still encounter challenges which are related to the model's generalization (and whether it can be adaptable for multiple types of OSNs) as well as the great efforts needed for feature engineering. In this paper, we propose a novel approach of applying network representation learning (NRL) to bot/spammer detection, called Bot2Vec. Our proposed Bot2Vec model is designed to automatically preserve both local neighborhood relations and the intra-community structure of user nodes while learning the representation of given OSNs, without using any extra features based on the user's profile. By applying the intra-community random walk strategy, Bot2Vec promises to achieve better user node embedding outputs than recent state-of-the-art network embedding baselines for bot detection tasks. Extensive experiments on two different types of real-word social networks (Twitter and Tagged) demonstrate the effectiveness of our proposed model. The source code for implementing the Bot2Vec model is available at: https://github.com/phamtheanhphu/bot2vec
Fast-convergent federated learning with class-weighted aggregation

Recently, federated learning has attracted great attention due to its advantage of enabling model training in a distributed manner. Instead of uploading data for centralized training, it allows devices to keep local data private and only send parameters to server. Then the server aggregates local models to derive a global model. In this paper, we study the aggregation problem in federated learning, especially with non-independently and identically distributed data. Since existing scheme may degrade the representative of local models after aggregation, we propose to reallocate weights of local models based on contributions to each class. Then two class-weighted aggregation strategies are developed to improve the communication efficiency in federated learning. Evaluation shows that the proposed schemes reduce 30.49% and 23.59% of communication costs compared with FedAvg.
Room-temperature plexcitonic strong coupling: Ultrafast dynamics for quantum applications

Strong light–matter interaction is at the heart of modern quantum technological applications and is the basis for a wide range of rich optical phenomena. Coupling a single quantum emitter strongly with electromagnetic fields provides an unprecedented control over its quantum states and enables high-fidelity quantum operations. However, single-emitter strong coupling is exceptionally fragile and has been realized mostly at cryogenic temperatures. Recent experiments have, however, demonstrated that single-emitter strong coupling can be realized at room temperature by using plasmonic nanocavities that confine optical fields via surface plasmons strongly on metal surfaces and facilitate sub-picosecond light–matter interaction. Here, we outline recent theoretical developments and experimental demonstrations of room-temperature strong coupling in the plasmonic platform, from emitter ensembles down to the single emitter limit, before placing a focus on selective studies that explore and provide insight into applications of plexcitonic strong coupling including sensing of single biological molecules, qubit entanglement generation, and reconfigurable single-photon sources and provide an outline of research directions in quantum sensing, quantum information processing, and ultrafast spectroscopy.
Multiview Pseudo-Labeling for Semi-supervised Learning from Video

We present a multiview pseudo-labeling approach to video learning, a novel framework that uses complementary views in the form of appearance and motion information for semi-supervised learning in video. The complementary views help obtain more reliable "pseudo-labels" on unlabeled video, to learn stronger video representations than from purely supervised data. Though our method capitalizes on multiple views, it nonetheless trains a model that is shared across appearance and motion input and thus, by design, incurs no additional computation overhead at inference time. On multiple video recognition datasets, our method substantially outperforms its supervised counterpart, and compares favorably to previous work on standard benchmarks in self-supervised video representation learning.
A Novel Parallelizable Chaotic Image Encryption Scheme Based on Elliptic Curves

Most of the computational time in many chaotic image encryption schemes is spent generating the required chaotic sequences.Since chaotic systems are defined by recurrence relations, they are often generated sequentially.In this paper, we propose a chaotic image encryption scheme which enables pixel-level parallelism to boost the computational speed of generating chaotic sequences.We use a group defined over elliptic curve (EC) points and the addition operator to generate a discrete chaotic sequence and use it to construct an image encryption scheme.The proposed scheme is designed such that encryption and decryption operations are highly parallelizable to take advantage of readily available parallel processing platforms such as GPU acceleration, DSPs and multi-core CPUs.Complexity analysis indicates that the proposed scheme is more efficient than existing EC-based image encryption schemes.Practical experiments on a quad-core CPU show that the proposed scheme can achieve a speedup of 3.93, confirming its superior parallelization efficiency in comparison with existing parallel image encryption schemes.We also provide detailed analysis of the immunity of the proposed scheme to all common cryptanalysis attacks.Results reveal that the proposed technique shows promising performance in terms of security and efficiency.
Driving assistance system based on data fusion of multisource sensors for autonomous unmanned ground vehicles

Autonomous unmanned ground vehicle is a product of technological development. At present, it has become a trend to provide multi-view real-time environmental state for autonomous unmanned ground vehicles. And more and more object detection algorithms will be applied in autonomous driving. In the recent years, multi-sensor-based driving assistance systems have become a way to solve these problems. In this paper, we introduce a driving assistance system based on data fusion of multiple sensors for autonomous driving. On the one hand, six fisheye cameras and twelve ultrasonic radars are used to collect surround-view environmental state. On the other hand, this system uses a low-light camera and a lidar to provide forward-view environmental state. In the meantime, we design panoramic mosaic algorithm, surround-view data fusion algorithm and forward-view data fusion algorithm. Besides, object detection algorithm is used to provide forward-view detection results. This driving assistance system will advance research into autonomous unmanned ground vehicles.
Streaming Transformer Asr With Blockwise Synchronous Beam Search

The Transformer self-attention network has shown promising performance as an alternative to recurrent neural networks in end-to-end (E2E) automatic speech recognition (ASR) systems. However, Transformer has a drawback in that the entire input sequence is required to compute both self-attention and source-target attention. In this paper, we propose a novel blockwise synchronous beam search algorithm based on blockwise processing of encoder to perform streaming E2E Transformer ASR. In the beam search, encoded feature blocks are synchronously aligned using a block boundary detection technique, where a reliability score of each predicted hypothesis is evaluated based on the end-of-sequence and repeated tokens in the hypothesis. Evaluations of the HKUST and AISHELL-1 Mandarin, LibriSpeech English, and CSJ Japanese tasks show that the proposed streaming Transformer algorithm outperforms conventional online approaches, including monotonic chunkwise attention (MoChA), especially when using the knowledge distillation technique. An ablation study indicates that our streaming approach contributes to reducing the response time, and the repetition criterion contributes significantly in certain tasks. Our streaming ASR models achieve comparable or superior performance to batch models and other streaming-based Transformer methods in all tasks considered.
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions

Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions enable controllable image generation and support a wide range of semantic editing operations, such as zoom or rotation. The discovery of such directions is often done in a supervised or semi-supervised manner and requires manual annotations which limits their use in practice. In comparison, unsupervised discovery allows finding subtle directions that are difficult to detect a priori. In this work, we propose a contrastive learning-based approach to discover semantic directions in the latent space of pre-trained GANs in a self-supervised manner. Our approach finds semantically meaningful dimensions compatible with state-of-the-art methods.
Bolt damage identification based on orientation-aware center point estimation network

With the development of deep learning, object detection algorithms based on horizontal box are widely used in the field of damage identification. However, damages can be in any direction and position, and they are not necessarily horizontal or vertical. This article proposes a bolt damage identification network, namely, orientation-aware center point estimation network, which models a damage as a center point of its rotated bounding box. The proposed orientation-aware center point estimation network uses deep layer aggregation network to search center points and regress to all other damage properties, such as size and angle. A loss function is designed to improve the optimization efficiency of network. Orientation-aware center point estimation network is applied to bolt damage detection, and comparison with the well-known Faster Region-Convolutional Neural Network (a benchmark using horizontal bounding box) demonstrates the accuracy of the proposed method. Finally, videos were utilized to verify the capability of the proposed orientation-aware center point estimation network in real-time detection of bolt damages.
Blockchain, Self-Sovereign Identity and Digital Credentials: Promise Versus Praxis in Education

Blockchain’s versatility is primarily due to its immutable and almost indestructible nature. These attributes have caught the attention of researchers and developers interested in applications and environments where the need for the integrity of identity and content are as paramount as the safe delivery and record of transactions. Self-sovereign digital identity in particular is often cited as a human right that nation states need to embrace with as much conviction as education and lifelong learning are considered to be a public good. Although the blockchain has long been identified as an opportunity for driving much-needed change in the core processes of the education sector, use cases to date have been limited in scope and execution, with blockchain advocates and education policy makers seemingly disconnected on fundamental issues such as governance, self-sovereignty, interoperability, choice of blockchain platforms and overall trust in standards and the integrity of the infrastructure. This article is primarily interested in the affordances of the technology as a public good for the education sector. It levers on the lead author’s perspective as a mediator between the blockchain and education sectors in Europe on high-profile blockchain in education projects to provide a snapshot of the challenges and workable solutions in the blockchain-enabled, European digital credentials sector.
Phase error compensation based on Tree-Net using deep learning

The nonlinear effect in phase shifting profilometry (PSP) is an essential source of phase error in 3D measurement. In this paper, we propose a universal phase error compensation method with a three-to-three deep learning framework (Tree-Net). Perfectly meeting the phase error compensation requirements, Tree-Net can construct six-step phase-shifting patterns from three-step. As a result, this compact network of fringe-to-fringe transformation has excellent performance when coping with different PSP systems after only one training. Experimental results demonstrate that the phase error can be reduced by about 90% in three-step PSP, which verified the effectiveness, universality, and robustness of the proposed method.
A robust and fast multispectral pedestrian detection deep network

Multispectral pedestrian detection is a difficult task, especially with pedestrian images of different sizes. In most convolutional neural network (CNN) models, the shared receptive fields of each layer are of the same size, which constrains detection results of multiple scales pedestrians. In this paper, we propose a dynamic selection scheme to adaptive adjust receptive field size in multispectral pedestrian detection. Specifically, a network in network (NIN) is employed to combine visible and thermal information. Selective kernel networks (SKNets) which uses selective kernel unit with different kernel size are employed. To effectively fuse the feature representation in each layer, a build block is designed, in which different features are fused. Feature pyramid is employed to integrate feature information in each layer. We empirically show that our method outperforms existing 8 state-of-the-art methods on one multispectral dataset and 4 state-of-the-art methods on another multispectral dataset. Detailed analyses show that our proposed method can capture multispectral pedestrian detection of different scales, which confirms the effective of SKNets for adaptively resizing the receptive field sizes. In addition, our method can operate at 14 frames per second (fps) on a GPU.
How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset

Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) topics by extensively annotating them with question intent categories, answer types, wikified entities, topic categories, and result type metadata from a commercial web search engine. Based on this data, we introduce a framework for identifying challenging queries. DL-HARD contains fifty topics from the official DL 2019/2020 evaluation benchmark, half of which are newly and independently assessed. We perform experiments using the official submitted runs to DL on DL-HARD and find substantial differences in metrics and the ranking of participating systems. Overall, DL-HARD is a new resource that promotes research on neural ranking methods by focusing on challenging and complex topics.
“That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems

Research in the social sciences has shown that expectations are an important factor in explanations as used between humans: rather than explaining the cause of an event per se, the explainer will often address another event that did not occur but that the explainee might have expected. For AI-powered systems, this finding suggests that explanation-generating systems may need to identify such end user expectations. In general, this is a challenging task, not the least because users often keep them implicit; there is thus a need to investigate the importance of such an ability. In this paper, we report an empirical study with 181 participants who were shown outputs from a text classifier system along with an explanation of why the system chose a particular class for each text. Explanations were both factual, explaining why the system produced a certain output or counterfactual, explaining why the system produced one output instead of another. Our main hypothesis was explanations should align with end user expectations; that is, a factual explanation should be given when the system's output is in line with end user expectations, and a counterfactual explanation when it is not. We find that factual explanations are indeed appropriate when expectations and output match. When they do not, neither factual nor counterfactual explanations appear appropriate, although we do find indications that our counterfactual explanations contained at least some necessary elements. Overall, this suggests that it is important for systems that create explanations of AI systems to infer what outputs the end user expected so that factual explanations can be generated at the appropriate moments. At the same time, this information is, by itself, not sufficient to also create appropriate explanations when the output and user expectations do not match. This is somewhat surprising given investigations of explanations in the social sciences, and will need more scrutiny in future studies.
A blind color digital image watermarking method based on image correction and eigenvalue decomposition

With the rapid development of Internet technology, the copyright protection of color images has become more and more important. In order to fulfill this purpose, this paper designs a blind color digital image watermarking method based on image correction and eigenvalue decomposition (EVD). Firstly, all the eigenvalues of the pixel block in the color host image are obtained by EVD. Then, the sum of the absolute value of the eigenvalues is quantified by the variable quantization steps to embed the color watermark image that encrypted by affine transform and encoded by the Hamming code. If the watermarked image is processed by geometric attack, then the attacked image can be corrected by using the geometric attributes. Finally, the inverse embedding process is performed to extract the color watermark. Moreover, the advantages of the proposed method are shown as follows: (1) all Peak Signal-to-noise Ratio (PSNR) values are greater than 42 dB; (2) the average Structural Similarity Index Metric (SSIM) values are greater than 0.97; (3) the maximum embedded capacity is 0.25bpp; (4) whole running-time is less than 20 s; (5) the key space is more than 2450; (6) most Normalized Cross-correlation (NC) values are more than 0.9. Compared with the related methods, the experimental results show that the proposed method not only has better watermark invisibility and larger watermark capacity, but also has higher security and stronger robustness against geometric attacks.
EdgeDuet: Tiling Small Object Detection for Edge Assisted Autonomous Mobile Vision

Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance, situational awareness, and safety inspection, where it is crucial to detect both small and large objects in crowded scenes. Prior studies either perform object detection locally on-board or offload the task to the edge/cloud. Local object detection yields low accuracy on small objects since it operates on low-resolution videos to fit in mobile memory. Offloaded object detection incurs high latency due to uploading high-resolution videos to the edge/cloud. Rather than either pure local processing or offloading, we propose to detect large objects locally while offloading small object detection to the edge. The key challenge is to reduce the latency of small object detection. Accordingly, we develop EdgeDuet, the first edge-device collaborative framework for enhancing small object detection with tile-level parallelism. It optimizes the offloaded detection pipeline in tiles rather than the entire frame for high accuracy and low latency. Evaluations on drone vision datasets under LTE, WiFi 2.4GHz, WiFi 5GHz show that EdgeDuet outperforms local object detection in small object detection accuracy by 233.0%. It also improves the detection accuracy by 44.7% and latency by 34.2% over the state-of-the-art offloading schemes.
Hierarchical delay-memory echo state network: A model designed for multi-step chaotic time series prediction

Predicting for long-term dynamics of complex systems from observations is a challenging topic in the field of time series modeling and analysis, and is continually under research. Noteworthily, multi-step prediction requires accurate learning of dynamics and correlations between historical data for predicting future behavior. In this paper, we proposed a modified recurrent neural network named hierarchical delay-memory echo state network (HDESN) for solving the task of multi-step chaotic time series prediction. The HDESN uses multiple reservoirs with delay-memory capabilities, which can simultaneously discover and explore the information of short-term and long-term memory hidden in the historical sequence, and extract the valuable evolution patterns through deep topology and hierarchical processing. Moreover, to ensure high-quality prediction results and reduce the computational burden as much as possible, we further design a phase-space representation strategy which can calculate a compact topology and delay-memory coefficient according to the chaotic characteristics of the data. Compared with other improved ESN-based models, the proposed HDESN does not have a larger memory capacity to capture potential evolution law hidden in the complex system layer by layer, but can also adaptively determine a suitable network architecture to reflect the mapping relations in chaotic phase space. The experimental results on two benchmark chaotic systems and a real-world meteorological dataset demonstrate that the proposed HDESN model obtains satisfactory performance in multi-step chaotic time series prediction.
A Hybrid Blockchain Model for Trusted Data of Supply Chain Finance

Supply chain finance is an effective way to solve the problem of capital turnover of construction enterprises and stabilize economic growth under epidemic situation. Blockchain technology can solve the problems in the current supply chain finance business, such as incorrect information between banks and enterprises, lack of visibility in transaction process, and possible joint fraud in the core enterprise model. However, it still has problems such as inconvenient information verification, data fraud, and difficulty to achieve a balance between efficiency, security and cost. This paper presents a hybrid chain model combining PANDA (a consensus algorithm based on public chain) and X-Alliance (a consensus algorithm based on alliance chain). Such proposed hybrid chain model can process the transaction of each account in parallel, asynchronize from other unrelated accounts in the network, provide more reliable data storage and authority management, and ensure the ownership of change tracking data, which has higher performance and lower protection cost while ensuring data security and privacy security. According to the data of network crawling, the experimental results show that the throughput can reach 1200tps when four nodes are deployed. The model serves for the supply chain financial data management of engineering projects, making trade authenticity review, risk assessment and credit transmission of core enterprises more efficient. On this basis, each participant can carry out analysis and give early warning of capital flow, timely analyze and calculate the authenticity of transaction background. The proposed method provides a reference for the normal operation of the project fund under the COVID-19 epidemic.