Reviewerly/exHarmony huggingface dataset:
Flash Freezing Flash Boys: Countering Blockchain Front-Running

Front-running, the practice of benefiting from advanced knowledge of pending transactions, has proliferated in the cryptocurrency space with the emergence of decentralized finance. Front-running causes devastating losses to honest participants—estimated at $280M each month—and endangers the fairness of the ecosystem. We present Flash Freezing Flash Boys (F3B), an architecture to address front-running attacks by relying on a commit-and-reveal scheme where the contents of a transaction are encrypted and later revealed by a decentralized secret-management committee (SMC) when the transaction has been committed by the underlying consensus layer. To maintain legacy compatibility, we design F3B to be agnostic to the underlying consensus algorithm and compatible with existing smart contracts. A preliminary exploration of F3B shows that with a secret-management committee consisting of 8 and 128 members, F3B presents between 0.1 and 2.2 seconds of transaction-processing latency, respectively.
AutoML and Neural Architecture Search for Gaze Estimation

This paper focuses on employing the AutoKeras neural architecture search tool and Automatic Machine Learning (AutoML) methods to find an optimal gaze estimation system. The Network Architecture Search (NAS) means automatically tuning already existing deep neural network configurations using a dataset of interest. The algorithm will search in the architectural space for a better neural model along with its optimized parameters. In the paper context, an AutoML solution will perform similarly, but using just ‘pure’ ML models. Considering “Appearance-based Gaze Estimation in the Wild” (MPIIGaze) and “Columbia Gaze Data Set” (CAVE) datasets, the experiments showed results comparable to those of with manually designed models.
An IoT Digital Twin for Cyber-Security Defence Based on Runtime Verification

A security decoupling approach for IoT device communications is presented, based on a Digital Twin with runtime verification capabilities. The solution proposed assumes that a local agent (security module) can be deployed to the IoT device by the IoT server. The runtime verification approach implemented in the Digital Twin detects possible violations of protected communications from either a remote device or a local compromised process and provides timely and valuable information for countering a potential cyber-security attack. Moreover, only a subset of the observed traffic needs to be monitored, which induces negligible overhead and allows deploying the Digital Twin in IoT devices with limited computational resources. Runtime verification was implemented by adopting a rule-based approach for monitoring parametric events, i.e. the packets that carry data.
Biophilic Enriched Virtual Environments for Industrial Training: a User Study

Immersive Virtual Reality (IVR) training offers the capability to industrial workers to acquire skills and address complex tasks by immersing them in a safe and controlled virtual environment (VE). However, in the literature, IVR training is mainly based on principles of standardization and efficiency without considering the operators’ well-being. A novel design approach consists of the introduction in the VE of Positive Computing to improve workers’ well-being by applying the Biophilia hypothesis. In this work, we explored the possibility of introducing biophilic elements in a VE training scenario that would support psychological well-being and human potential. However, the introduction of virtual elements not related to the training task may distract operators, impairing their performance. We selected as a training scenario the assembly of a real truck engine. It is accomplished in a workstation, and operators do not interact with the surrounding VE. Therefore, we placed the training area into four different types of VEs: 3D Minimal (MIN), 3D Minimal Biophilic enriched (MIN+BIO), 3D Realistic (REAL), and 3D Realistic Biophilic enriched (REAL+BIO). We compared the MIN and REAL scenarios with the respective biophilic enriched scenarios. The performance of 40 participants was evaluated in terms of completion time, object fixation time, training task accuracy, knowledge accuracy, cognitive load, and user experience. The results revealed that introducing biophilic elements in a VR training environment attracts users’ attention in the idle phase of the training. In contrast, they keep concentrating on the task without worsening their performance during the task accomplishment.
Obstacle Collision Prediction Model for Path Planning Using Obstacle Trajectory Clustering

A probabilistic path planning is one of the major areas of interest in the field of navigation. Toward the goal of path planning, collision prediction is one of the key research challenges in order to achieve the high accuracy of a probabilistic path. In order to build a collision forecasting mode, we have proposed a multilayer collision prediction model for path planning in the dynamic environment. The binary classifier is used to classify the obstacles, that is, whether the obstacle is a dynamic or a static obstacle. The trajectory clustering is done with the help of predicting the obstacle trajectory and identifying the turning points of an obstacle over the same trajectory. The points that are generated by the obstacle trajectory clustering model are used by the time series location forecasting model after performing data de-noising and outlier detection, to generate the obstacle-free points over the working space in the dynamic environment. This time series location forecasting model provides the location of all the obstacles over the time series model that is used as an independent variable for the collision prediction model. Finally, the collision prediction model provides the most appropriate points that can be considered in the collision-free path and generate an obstacle-free probabilistic path over the dynamic environment.
PSS: Progressive Sample Selection for Open-World Visual Representation Learning

We propose a practical open-world representation learning setting where the objective is to learn the representations for unseen categories without prior knowledge or access to images associated with these novel categories during training. Existing open-world representation learning methods make assumptions, which are often violated in practice and thus fail to generalize to the proposed setting. We propose a novel progressive approach which does not depend on such assumptions. At each iteration our approach selects unlabeled samples that attain a high homogeneity while belonging to classes that are distant to the current set of known classes in the feature space. Then we use the high-quality pseudo-labels generated via clustering over these selected samples to improve the feature generalization iteratively. Experiments demonstrate that the proposed method consistently outperforms state-of-the-art open-world semi-supervised learning methods and novel class discovery methods over nature species image retrieval and face verification benchmarks. Our training and inference code are released. ( https://github.com/dmlc/dgl/tree/master/examples/pytorch/hilander/PSS ).
Mining Syntactic Relationships via Recursion and Wandering on A Dependency Tree for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis aims to determine the emotional orientation of a particular aspect of an online comment. Considering that syntax and even sentences themselves are a specific graph structure, most researchers' recent work mainly focuses on the dependency tree. They construct sentences into tree structures according to dependency parser. However, because a sentence can contain many aspects, it is difficult to correctly associate each aspect with the corresponding important part of the sentence by directly using the original dependency tree. To strengthen this association, a reconstructed dependency tree with aspect as the root is constructed by fine-tuning the original dependency tree for each aspect. We propose a neural network model named ARWAT, which performs on the reconstructed tree to learn informative contextual words and grammatical information effectively. Extensive experiment results demonstrate the superior performance of our proposed model against multiple baselines on five benchmark datasets.
Towards edible drones for rescue missions: design and flight of nutritional wings

Drones have shown to be useful aerial vehicles for unmanned transport missions such as food and medical supply delivery. This can be leveraged to deliver life-saving nutrition and medicine for people in emergency situations. However, commercial drones can generally only carry 10 %–30 % of their own mass as payload, which limits the amount of food delivery in a single flight. One novel solution to noticeably increase the food-carrying ratio of a drone, is recreating some structures of a drone, such as the wings, with edible materials. We thus propose a drone, which is no longer only a food-transporting aircraft, but itself is partially edible, increasing its food-carrying mass ratio to 50 %, owing to its edible wings. Furthermore, should the edible drone be left behind in the environment after performing its task in an emergency situation, it will be more biodegradable than its non-edible counterpart, leaving less waste in the environment. Here we describe the choice of materials and scalable design of edible wings, and validate the method in a flight-capable prototype that can provide 300 kcal and carry a payload of 80 g of water.
Learning from pseudo-labels: Self-training Electronic Components Detector for Waste Printed Circuit Boards

Electronic components (EC) detection is relevant for Printed Circuit Board (PCB) manufacturing, quality inspection, and assurance (cyber-security). Moreover, at end-of-life electronic devices, the PCBs become Waste PCBs (WPCBs), the primary source of high-value materials in electronic waste. However, the recycling process is challenging because of the WPCB’s high composition diversity. EC detection in WPCBs can reduce the uncertainty about WPCBs composition and help select a better recycling process. Nevertheless, large fully-annotated datasets for the PCB domain are available at a high cost. For WPCBs, there is only one publicly available dataset, the PCB-DSLR, and it is partially labeled. As for PCBs, the FICS-PCB dataset is available. However, it contains images of modern and clean PCBs, all in good condition, while the first has broken and dirty parts. Thus, we propose a self-training strategy for object detection algorithms without needing a fully labeled dataset. In the strategy, the YOLOv5 model is used to detect the eight types of electronic components present in FICS-PCB in PCB-DSLR, which initially has labels only for integrated circuits. The process occurs through the interaction between a teacher model, trained with the FICSPCB data, and a student model, which is trained by joining the FICS-PCB data and the teacher-generated labels for PCB-DSLR. The results show that the student model performs better in the electronic components detection task, obtaining greater precision and sensitivity when compared to the teacher model. Since we are dealing with partially labeled data, we provide a low-dimensional representation of the detections of both models using t-SNE.
Jointly Learning Span Extraction and Sequence Labeling for Information Extraction from Business Documents

This paper introduces a new information extraction model for business documents. Different from prior studies which only base on span extraction or sequence labeling, the model takes into account advantage of both span extraction and sequence labeling. The combination allows the model to deal with long documents with sparse information (the small amount of extracted information). The model is trained end-to-end to jointly optimize the two tasks in a unified manner. Experimental results on four business datasets in English and Japanese show that the model achieves promising results and is significantly faster than the normal span-based extraction method. The code is also available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://bit.ly/3iYztCL. It will be available on github.
Vision-based Distributed Multi-UAV Collision Avoidance via Deep Reinforcement Learning for Navigation

Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems is considered a challenging task. It needs to ensure collision-free path planning in real-time, especially when the multi-UAV systems can become very crowded on certain occasions. In this paper, we presented a vision-based decentralized collision-avoidance policy learning method for multi-UAV systems. The policy takes depth images and inertial measurements as sensory inputs and outputs UAV's steering commands, and it is trained together with the latent representation of depth images using a policy gradient-based reinforcement learning algorithm and autoencoder in the multi-UAV three-dimensional workspaces. Each UAV follows the same trained policy and acts independently to reach the goal without colliding or communicating with other UAVs. We validate our method in various simulated scenarios. The experimental results show that our learned policy can guarantee fully autonomous collision-free navigation for multi-UAV in three-dimensional workspaces, and its navigation performance will not be greatly affected by the increase in the number of UAVs.
Collaborative Multilingual Continuous Sign Language Recognition: A Unified Framework

Current continuous sign language recognition systems generally target on a single language. When it comes to the multilingual problem, existing solutions often build separate models based on the same network and then train them with their corresponding sign language corpora. Observing that different sign languages share some low-level visual patterns, we argue that it is beneficial to optimize the recognition model in a collaborative way. With this motivation, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> unified framework for multilingual continuous sign language recognition. Our framework consists of a shared visual encoder for visual information encoding, multiple language-dependent sequential modules for long-range temporal dependency learning aimed at different languages, and a universal sequential module to learn the commonality of all languages. An additional language embedding is introduced to distinguish different languages within the shared temporal encoders. Further, we present a max-probability decoding method to obtain the alignment between sign videos and sign words for visual encoder refinement. We evaluate our approach on three continuous sign language recognition benchmarks, i.e., RWTH-PHOENIX-Weather, CSL and GSL-SD. The experimental results reveal that our method outperforms the individually trained recognition models. Our method also demonstrates better performance compared with state-of-the-art algorithms.
View-Normalized and Subject-Independent Skeleton Generation for Action Recognition

Skeleton-based action recognition has attracted great interest in computer vision. For this task, a challenging problem concerns the large intraclass variances of skeleton data, which are mainly caused by diverse viewpoints and subjects, and greatly increase the difficulty of modeling actions through a network. To address the above problem, we propose a variance reduction (VaRe) framework for skeleton-based action recognition, which consists of a view-normalization generative adversarial network (VN-GAN), a subject-independent network (SINet) and a classification network. First, the VN-GAN is responsible for reducing view-induced intraclass variances. Specifically, this network, comprising a generator and a discriminator, is aimed at learning a mapping from a diverse-view skeleton distribution to a unified-view skeleton distribution in an unsupervised manner, thereby generating a view-normalized skeleton. Second, taking the view-normalized skeleton as input, the SINet focuses on reducing the influences of the personal habits of subjects on action recognition. To generate SI skeleton data, the SINet automatically adjusts the human pose according to the human kinematic structure under a classification loss constraint. Finally, without the interference of view- and subject-induced variances, the classification network can concentrate more on learning discriminative action features to predict classes. Furthermore, by combining the joint and bone modalities, the proposed framework achieves competitive performance on three benchmarks: NTU RGB+D, NTU-120 RGB+D and Northwestern-UCLA Multiview Action 3D.
Multi-party Updatable Delegated Private Set Intersection

With the growth of cloud computing, the need arises for Private Set Intersection protocols (PSI) that can let parties outsource the storage of their private sets and securely delegate PSI computation to a cloud server. The existing delegated PSIs have two major limitations; namely, they cannot support (1) efficient updates on outsourced sets and (2) efficient PSI among multiple clients. This paper presents "Feather", the first lightweight delegated PSI that addresses both limitations simultaneously. It lets clients independently prepare and upload their private sets to the cloud once, then delegate the computation an unlimited number of times. We implemented Feather and compared its costs with the state of the art delegated PSIs. The evaluation shows that Feather is more efficient computationally, in both update and PSI computation phases.
Mixed Reality Based Visual Analytics

Having a novel human-computer interaction mode, mixed reality expands the design space of visualization and interactive interface, confirming itself a broad application prospect. Recently, mixed reality technologies have undergone rapid developments, and many researchers have applied it into the field of visual analytics. In this situation, first the concept and current developments of mixed reality are introduced, the differences between the mixed reality environment and the traditional interactive environment from aspects of display and interaction are discussed, the advantages and limitations of mixed reality visual analytics are analyzed, and related research projects are presented. In addition, based on the three application scenarios of mixed reality visual analytics, the augmenting visualization, the situated analytics, and the collaborative analytics, current works of the mixed reality visual analysis application are also introduced. Finally, current progress are summarized and the opportunities and challenges of mixed reality visual analytics are discussed.
Maliciously Secure Massively Parallel Computation for All-but-One Corruptions

The Massive Parallel Computing (MPC) model gained wide adoption over the last decade. By now, it is widely accepted as the right model for capturing the commonly used programming paradigms (such as MapReduce, Hadoop, and Spark) that utilize parallel computation power to manipulate and analyze huge amounts of data. Motivated by the need to perform large-scale data analytics in a privacy-preserving manner, several recent works have presented generic compilers that transform algorithms in the MPC model into secure counterparts, while preserving various efficiency parameters of the original algorithms. The first paper, due to Chan et al. (ITCS ’20), focused on the honest majority setting. Later, Fernando et al. (TCC ’20) considered the dishonest majority setting. The latter work presented a compiler that transforms generic MPC algorithms into ones which are secure against semi-honest attackers that may control all but one of the parties involved. The security of their resulting algorithm relied on the existence of a PKI and also on rather strong cryptographic assumptions: indistinguishability obfuscation and the circular security of certain LWE-based encryption systems. In this work, we focus on the dishonest majority setting, following Fernando et al. In this setting, the known compilers do not achieve the standard security notion called malicious security, where attackers can arbitrarily deviate from the prescribed protocol. In fact, we show that unless very strong setup assumptions as made (such as a programmable random oracle), it is provably impossible to withstand malicious attackers due to the stringent requirements on space and round complexity. As our main contribution, we complement the above negative result by designing the first general compiler for malicious attackers in the dishonest majority setting. The resulting protocols withstand all-but-one corruptions. Our compiler relies on a simple PKI and a (programmable) random oracle, and is proven secure assuming LWE and SNARKs. Interestingly, even with such strong assumptions, it is rather non-trivial to obtain a secure protocol.
CycleResume: A Cycle Learning Framework with Hybrid Attention for Fine-Grained Talent-Job Fit

Online recruitment platforms are becoming powerful tools for job hunters to look for jobs and for companies to search for outstanding candidates. To liberate the workforce for resume assessment, there is an increasing attention on developing learning-based systems for automatic talent-job fit. Existing approaches suffer from several drawbacks, such as simply ignoring the interactions between talent’s skills and experiences and only utilizing the final output representations of resume and job-post for matching. To address these issues, we propose a new deep cycle learning-based framework for talent-job fit. In particular, we first propose a noise-incorporated co-attention mechanism to measure resume consistency in a fine-grained manner. Then a multi-scale CNN is utilized for job representation learning, and the matching score between resume and job is measured via multi-scale attention. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on a self-collected resume-job dataset. The results show that our method achieves state-of-the-art performance, and we demonstrate that the proposed modules can be utilized as off-the-shelf blocks for similar application tasks.
Quantum Computing Technology and Roadmap

The progress of Quantum Computing has significantly accelerated in recent years due to increased resources for research and development across the world. The focus has changed from just building quantum devices to establishing a quantum computing system including software and applications. An overview of the recent advancements of superconducting quantum computing systems is provided including the achievements made to scale processors to 127-qubit and scale up to 4000 qubits in 2025. Three key metrics are defined, scale, quality, and speed to holistically measure performance. The computational capabilities of today's quantum chips can be extended by adding classical resources using circuit knitting techniques to reach quantum advantage.