Reviewerly/exHarmony huggingface dataset:
Hercules: Boosting the Performance of Privacy-Preserving Federated Learning

In this paper, we address the problem of privacy-preserving federated neural network training with <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> users. We present <b>Hercules</b> , an efficient and high-precision training framework that can tolerate collusion of up to <inline-formula><tex-math notation="LaTeX">$N-1$</tex-math></inline-formula> users. <b>Hercules</b> follows the POSEIDON framework proposed by Sav et al. (NDSS’21), but makes a qualitative leap in performance with the following contributions: (i) we design a novel parallel homomorphic computation method for matrix operations, which enables fast Single Instruction and Multiple Data (SIMD) operations over ciphertexts. For the multiplication of two <inline-formula><tex-math notation="LaTeX">$h\times h$</tex-math></inline-formula> dimensional matrices, our method reduces the computation complexity from <inline-formula><tex-math notation="LaTeX">$O(h^{3})$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(h)$</tex-math></inline-formula> . This greatly improves the training efficiency of the neural network since the ciphertext computation is dominated by the convolution operations; (ii) we present an efficient approximation on the sign function based on the composite polynomial approximation. It is used to approximate non-polynomial functions (i.e., <monospace>ReLU</monospace> and <monospace>max</monospace> ), with the optimal asymptotic complexity. Extensive experiments on various benchmark datasets (BCW, ESR, CREDIT, MNIST, SVHN, CIFAR-10 and CIFAR-100) show that compared with POSEIDON, <b>Hercules</b> obtains up to 4% increase in model accuracy, and up to <inline-formula><tex-math notation="LaTeX">$60\times$</tex-math></inline-formula> reduction in the computation and communication cost.
The Security Investigation of Ban Score and Misbehavior Tracking in Bitcoin Network

Bitcoin P2P networking is especially vulnerable to networking threats because it is permissionless and does not have the security protections based on the trust in identities, which enables the attackers to manipulate the identities for Sybil and spoofing attacks. The Bitcoin node keeps track of its peer’s networking misbehaviors through ban scores. In this paper, we investigate the security problems of the ban-score mechanism and discover that the ban score is not only ineffective against the Bitcoin Message-based DoS (BM-DoS) attacks but also vulnerable to the Defamation attack as the network adversary can exploit the ban score to defame innocent peers. To defend against these threats, we design an anomaly detection approach that is effective, lightweight, and tailored to the networking threats exploiting Bitcoin’s ban-score mechanism. We prototype our threat discoveries against a real-world Bitcoin node connected to the Bitcoin Mainnet and conduct experiments based on the prototype implementation. The experimental results show that the attacks have devastating impacts on the targeted victim while being cost-effective on the attacker side. For example, an attacker can ban a peer in two milliseconds and reduce the victim’s mining rate by hundreds of thousands of hash computations per second. Furthermore, to counter the threats, we empirically validate our detection countermeasure’s effectiveness and performances against the BM-DoS and Defamation attacks.
Multi-Feature Fusion Transformer for Chinese Named Entity Recognition

Recently, an approach to enhancing semantic and entity boundaries through lexicons has been used in Chinese named entity recognition tasks to improve recognition rate. However, this approach ignores the radicals of Chinese characters. As a kind of characters that evolve from pictographs, a lot of information is embedded in the radicals of Chinese characters. This paper proposes a novel Chinese named entity recognition method, named Multi-feature Fusion Transformer (MFT), which exploits the information of the radicals of Chinese characters. Our model is able to further enhance the semantic information by adding the radical information of Chinese characters while using word features. We have also improved the basic structure of the Transformer model to make the model perform better in entity recognition. Our model is compared with other mainstream models on Resume dataset and Weibo dataset, the experimental results show that our model is significantly better than other models, with F1 score of 95.77% and 64.38% for the respective dataset.
Design and Development of Smart WheelChair

This paper sets out an extraordinary way of how the specially abled people can commute on a daily basis without any struggle, as mobility of physically challenged people is quite difficult without any external help. The paper proposes the design of an embedded system based smart wheelchair which enables users to operate it with minimal effort. It has a joystick module, bluetooth module, voice control and it can also be controlled using an android app. This helps the handicapped people in performing day to day chores unaccompanied by anyone.
Development of a desktop application for a complex heterogeneous evaluation system

Like many others, our institution has had to adapt our traditional testing and assessment of students due to the COVID-19 pandemic. This article describes the process applied to the development of a desktop application for a complex heterogeneous evaluation system for students of applied informatics. Qualitative research, case study together with experiments, and action research made it possible to examine the monitored phenomena more deeply in interconnected contexts. Prospective case studies with multiple cases were used to investigate the issue. According to the function, they are pedagogical evaluation case studies. In addition to verifying the effectiveness of the introduced materials, forms of education, and the flipped classroom approach, the study also focused on examining the relationship between their work on assigned tasks and the given issue, while heterogeneity in the evaluation was a key element. The main goal was to verify the effectiveness of the created materials in the implementation of various forms of education using the flipped classroom approach. Also, the possibilities of achieving the set educational goals during online teaching using instructional videos or scripts using a flipped classroom approach compared to a hybrid form of education and assessment were investigated. The contribution of the work and recommendations for pedagogical theory and practice are published in the paper.
Automatic Robotic Arm Based on Bluetooth Regulated for Progressed Surgical Task

The aim and objective of its purpose is to design a prototype that allows an android device to control a robotic arm through Bluetooth for surgical operations more accurately. The developed prototypes include two servo motor driven by the motor driver to place the object after picking it up certain distance, also for rotating robot arm in the desired direction. The movement of the robotic arm was controlled by programming the ATMEL microcontroller. The robotic arm has a grip that extends to 6cm and shuts to 1.5cm, can pick up and drop the object up to 350gm. Bluetooth module is used to wireless communication between the mobile application and the robot. The Stabilized output is achieved using IC L293D. Polymer material was used to build the bot arm structure, the “total weight” is reduced without affecting any parameter. To bring down the cost of whole prototype without comprising on efficiency the number of DC motors are reduced to two instead of traditional 4 motors setup. Hence the developed system is more flexible and the prototype can assist physicians during surgical procedures by picking and placing the surgical equipment, lowering the risk of contamination and human touch.
The Major Questions Doctrine and the Threat to Regulating Emerging Technologies

Emerging technologies offer the potential to improve health and quality of life but also pose notable risks to safety, wellbeing, and equity. Law and technology scholarship posits that robust policy and regulatory strategies are required to manage these complex benefits, risks, and uncertainties in the public interest. At the same time, the Supreme Court in its recent jurisprudence appears eager to revitalize nondelegation legal norms, especially through the major questions doctrine. This article argues the major questions doctrine and other aggressive implementations of nondelegation principles pose both direct and indirect challenges to the regulation of emerging technologies—which often involves not only novel rulemaking but also “inherited regulation,” or the process of extending existing rules to capture emerging technologies. Especially with a politically incapacitated Congress, the major questions doctrine could pose significant legal challenges (or uncertainty) and create new policy and political issues in administrative agency efforts to manage emerging technologies. Time and empirical study will be needed to determine exactly how and where aggressive nondelegation canons, such as the major questions doctrine, impact the regulation of emerging technologies. Accordingly, the article concludes by reflecting on coming challenges and opportunities for which regulators and stakeholders should be prepared.
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space

General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments. To address this issue, goal-conditioned reinforcement learning aims to acquire policies that can reach configurable goals for a wide range of tasks on command. However, such goal-conditioned policies are notoriously difficult and time-consuming to train from scratch. In this paper, we propose Planning to Practice (PTP), a method that makes it practical to train goal-conditioned policies for long-horizon tasks that require multiple distinct types of interactions to solve. Our approach is based on two key ideas. First, we decompose the goal-reaching problem hierarchically, with a high-level planner that sets intermediate subgoals using conditional subgoal generators in the latent space for a low-level model-free policy. Second, we propose a hybrid approach which first pre-trains both the conditional subgoal generator and the policy on previously collected data through offline reinforcement learning, and then fine-tunes the policy via online exploration. This fine-tuning process is itself facilitated by the planned subgoals, which breaks down the original target task into short-horizon goal-reaching tasks that are significantly easier to learn. We conduct experiments in both the simulation and real world, in which the policy is pre-trained on demonstrations of short primitive behaviors and fine-tuned for temporally extended tasks that are unseen in the offline data. Our experimental results show that PTP can generate feasible sequences of subgoals that enable the policy to efficiently solve the target tasks. <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> Supplementary video: sites.google.com/view/planning-to-practice
Fusing CNNs and Transformers for Deformable Medical Image Registration

Deformable medical image registration is fundamental for many medical image studies. In the field of deformable image registration, convolutional neural network (CNN) based methods cannot effectively deal with large deformation settings due to the local receptive fields. Transformers are good at modeling long-range dependency, but the lack of convolutional induction bias can lead to limited ability of grasping local details. In this paper, we propose a novel parallel architecture combining CNNs and Transformers, FTNet, to tackle this issue. FTNet contains convolutional branch and Transformer branch that extract features independently, with the proposed fusion strategy, the encoded features from both branches are efficiently fused. Extensive experiments on two brain image datasets demonstrate that our approach achieves superior registration accuracy against other state-of-the-art methods while maintaining desired diffeomorphic properties of deformation fields.
Blockchain-Based WDP Solution for Real-Time Heterogeneous Computing Resource Allocation

The utilization of cloud and edge computing has become one of the most prevailing resource supply mechanisms. Thousands of enterprise users and Internet of Things (IoT) devices have performed operations through services sold by computing resource providers. So far, the auction match is the main strategy for allocating resources, in which there exists a trusted third party playing a role as a broker to deal with resource allocation requests for both providers and consumers. The main concerns in resource allocation architecture are how and how long to solve the Winner Determination Problem (WDP), which is used to lay out the match outcome. It is not easy for a single broker with limited computing power to generate an optimal solution in a short period since its corresponding time complexity is regarded as the NP-hard problem. Meanwhile, it is hard for people to trust the third party thoroughly. That is, there are three potential issues, including centralization, data security, and untrustworthiness in traditional matching architecture. In particular, the real-time matching cannot be achieved to fulfill users who have urgent needs of computing resources. To solve above issues, we have designed a trustworthy and real-time decentralized computing resource allocation platform based on blockchain and smart contract. In order to optimize the allocation results, we improve the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for miners to reach the consensus mechanism. Experimental results and comparison analysis have demonstrated that potential defects could be addressed in the new method, and the real-time resource allocation can be preserved firmly despite the balance vibration of market supply and demand.
Towards Continuous Cyber Testing with Reinforcement Learning for Whole Campaign Emulation

Modern automated penetration testing uses rule-based procedures and model-checking concepts to search through all possible attacks on network models and identify those that violate some correctness or security property by generating an attack graph. By generating all possible attacks, modern, top-down approaches inherently do not isolate the few attacks that matter the most. This weakness is exacerbated in future network settings like 5G and Internet of Things (IoT) settings where networks are expected to have thousands of hosts (or more) and evolve over time. This has created a perception that the attack graph concept itself is inadequate, in turn hindering the automation of cyber testing. Recent research re-positions automated attack graph generation as a best practice in cyber defense by applying deep reinforcement learning (RL). While recent research into penetration testing with RL has seen a rapid growth in interest, a clear concept of operational use has not been defined. We define and provide formalism for the concept of whole campaign emulation (WCE). We present WCE as both a challenge problem and a framework for automating cyber T&E with RL. This manuscript captures an RL-oriented perspective on the past, present, and future of attack graph generation, and serves as a primer from researchers and practitioners alike. With WCE, organizations from small businesses to nation-states can feasibly institute continuous cyber T&E with low test costs and low disruption to operations.
CNN-Based Salient Object Detection on Hyperspectral Images Using Extended Morphology

Salient object detection in hyperspectral images is of interest in various image processing and computer vision applications. Many studies considering spectral information have been reported, extracting only low-level features from a hyperspectral image. This paper proposes a Convolutional Neural Network (CNN) based salient object detection method using hyperspectral imagery to utilise spatial and spectral information simultaneously. The proposed methodology incorporates Extended Morphological Profile (EMP) followed by a CNN to utilise the information from nearby pixels and high-level features simultaneously. We have evaluated the performance of the proposed approach on two independent datasets to verify the generalisation ability, viz. 1) Hyperspectral Salient Object Detection Dataset (HS-SOD) and 2) Pavia University dataset. An extensive quantitative analysis of the results revealed that the proposed method significantly outperforms other state-of-the-art methods by approximately ≥ 2% of AUC (Area Under receiver operating characteristic Curve) and F-measure and lower mean absolute error for both datasets.
Vehicle Target Detection from the Perspective of UAV Aerial Photography Based on Rotating Box

Nowadays, UAV aerial photography has become one of the important means for people to obtain information. The research on aerial vehicle target detection technology is of great significance to the fields of military investigation, intelligent transportation, intelligent security and so on. However, different from the ground view, the scene from the aerial view of UAV usually has the characteristics of complex background, more small targets and different target scales, which brings great challenges to the visual detection algorithm. In order to improve such problems, this paper proposes an RST-YOLOv5s algorithm. The algorithm takes YOLOv5s algorithm as the main body, designs a vehicle target detection method from the perspective of UAV aerial photography based on rotating frame regression, and improves its network structure by adding Swin Transformer. The aerial photos taken by UAV at different altitudes are made into data sets for training to realize the accurate detection of ground vehicle targets. The experimental results show that this method achieves better detection effect in the detection of ground vehicle targets by UAV.
A Computer Vision Model for Detecting Suspicious Behaviour from Multiple Cameras in Crime Hotspots Using Convolutional Neural Networks

Conventional security surveillance systems detect suspicious behaviours by the active participation of human operators constantly watching monitors showing video streams of activities captured from different cameras. The datasets are manually retrieved and analyzed after the occurrence of the incidents. This often leads to misinterpretation and late detection in a real-life environment. Recent studies have investigated the current surveillance systems for the challenges of the detection of suspicious incidents. The research question here is: How can suspicious breaking into run be detected being a common red flag leading to most crimes? This research develops a computer vision framework based on feature engineering, convolutional neural network (CNN), and median filtering to address the lacuna faced by the surveillance systems. Experiments were conducted on real-life image frames captured from multiple camera datasets. The proposed model outperforms the conventional approaches in terms of the detection of suspicious behavioural patterns with an average F1-score of 0.9661, a false positive rate of 0.0734, and an accuracy of 94.81%. The deployment of this proposed model in a crowded environment can help to augment the work of security personnel in raising awareness regarding possible crime at hot spots.
Relative Pose from SIFT Features

This paper derives the geometric relationship of epipolar geometry and orientation- and scale-covariant, e.g., SIFT, features. We derive a new linear constraint relating the unknown elements of the fundamental matrix and the orientation and scale. This equation can be used together with the well-known epipolar constraint to, e.g., estimate the fundamental matrix from four SIFT correspondences, essential matrix from three, and to solve the semi-calibrated case from three correspondences. Requiring fewer correspondences than the well-known point-based approaches (e.g., 5PT, 6PT and 7PT solvers) for epipolar geometry estimation makes RANSAC-like randomized robust estimation significantly faster. The proposed constraint is tested on a number of problems in a synthetic environment and on publicly available real-world datasets on more than 800 00 image pairs. It is superior to the state-of-the-art in terms of processing time while often leading more accurate results. The solvers are included in GC-RANSAC at https://github.com/danini/graph-cut-ransac .
Multimodal Emotion Classification With Multi-Level Semantic Reasoning Network

Nowadays, people are accustomed to posting images and associated text for expressing their emotions on social networks. Accordingly, multimodal sentiment analysis has drawn increasingly more attention. Most of the existing image-text multimodal sentiment analysis methods simply predict the sentiment polarity. However, the same sentiment polarity may correspond to quite different emotions, such as happiness vs. excitement and disgust vs. sadness. Therefore, sentiment polarity is ambiguous and may not convey the accurate emotions that people want to express. Psychological research has shown that objects and words are emotional stimuli and that semantic concepts can affect the role of stimuli. Inspired by this observation, this paper presents a new MUlti-Level SEmantic Reasoning network (MULSER) for fine-grained image-text multimodal emotion classification, which not only investigates the semantic relationship among objects and words respectively, but also explores the semantic relationship between regional objects and global concepts. For image modality, we first build graphs to extract objects and global representation, and employ a graph attention module to perform bilevel semantic reasoning. Then, a joint visual graph is built to learn the regional-global semantic relations. For text modality, we build a word graph and further apply graph attention to reinforce the interdependencies among words in a sentence. Finally, a cross-modal attention fusion module is proposed to fuse semantic-enhanced visual and textual features, based on which informative multimodal representations are obtained for fine-grained emotion classification. The experimental results on public datasets demonstrate the superiority of the proposed model over the state-of-the-art methods.
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation

Recent advances in unsupervised representation learning significantly improved the sample efficiency of training Reinforcement Learning policies in simulated environments. However, similar gains have not yet been seen for real-robot reinforcement learning. In this work, we focus on enabling data-efficient real-robot learning from pixels. We present Contrastive Pre-training and Data Augmentation for Efficient Robotic Learning (CoDER), a method that utilizes data augmentation and unsupervised learning to achieve sample-efficient training of real-robot arm policies from sparse rewards. While contrastive pre-training, data augmentation, demonstrations, and reinforcement learning are alone insufficient for efficient learning, our main contribution is showing that the combination of these disparate techniques results in a simple yet data-efficient method. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels, such as reaching, picking, moving, pulling a large object, flipping a switch, and opening a drawer in just 30 minutes of mean real-world training time. We include videos and code on the project website: https://sites.google.com/view/efficient-robotic-manipulation/home.