Reviewerly/exHarmony huggingface dataset:
Robust early-learning: Hindering the memorization of noisy labels

The \textit{memorization effects} of deep networks show that they will first memorize training data with clean labels and then those with noisy labels. The \textit{early stopping} method therefore can be exploited for learning with noisy labels. However, the side effect brought by noisy labels will influence the memorization of clean labels before early stopping. In this paper, motivated by the \textit{lottery ticket hypothesis} which shows that only partial parameters are important for generalization, we find that only partial parameters are important for fitting clean labels and generalize well, which we term as \textit{critical parameters}; while the other parameters tend to fit noisy labels and cannot generalize well, which we term as \textit{non-critical parameters}. Based on this, we propose \textit{robust early-learning} to reduce the side effect of noisy labels before early stopping and thus enhance the memorization of clean labels. Specifically, in each iteration, we divide all parameters into the critical and non-critical ones, and then perform different update rules for different types of parameters. Extensive experiments on benchmark-simulated and real-world label-noise datasets demonstrate the superiority of the proposed method over the state-of-the-art label-noise learning methods.
Public Sentiment Analysis of COVID-19 Vaccination Drive in India

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Contrastive learning, multi-view redundancy, and linear models

Self-supervised learning is an empirically successful approach to unsupervised learning based on creating artificial supervised learning problems. A popular self-supervised approach to representation learning is contrastive learning, which leverages naturally occurring pairs of similar and dissimilar data points, or multiple views of the same data. This work provides a theoretical analysis of contrastive learning in the multi-view setting, where two views of each datum are available. The main result is that linear functions of the learned representations are nearly optimal on downstream prediction tasks whenever the two views provide redundant information about the label.
Educational Information System Optimization for Artificial Intelligence Teaching Strategies

Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a detailed demand analysis was carried out, and the overall structure optimization of the network was given; the business process and data flow of the main modules of the website (resource center module and collaborative learning module) were optimized. In order to further enhance the local search ability of the algorithm, a multiclass interactive optimization algorithm is proposed in combination with the Euclidean distance-based clustering method, which changes the teaching mode from “one-person teaching” to “multiperson teaching.” This clustering method has lower complexity and is beneficial to enhance the utilization of neighborhood information. At the same time, in order to enhance the diversity of the population and strengthen the connection between the subgroups, after the teaching phase, the worst students in each subgroup are allowed to learn from the best teachers of the population, and after the learning phase, individuals in a random subgroup are allowed to learn from other subgroups. The algorithm was tested in the experimental environment of unconstrained, constrained, and an engineering problem. From the test results, it can be seen that the algorithm is not easy to fall into the local optimum. Compared with other algorithms, the solution accuracy is higher and the stability is better. And it performed well in engineering optimization problems, thus verifying the effectiveness of the strategy.
Quantum Collision Attacks on Reduced SHA-256 and SHA-512

In this paper, we study dedicated quantum collision attacks on SHA-256 and SHA-512 for the first time. The attacks reach 38 and 39 steps, respectively, which significantly improve the classical attacks for 31 and 27 steps. Both attacks adopt the framework of the previous work that converts many semi-free-start collisions into a 2-block collision, and are faster than the generic attack in the cost metric of time-space tradeoff. We observe that the number of required semi-free-start collisions can be reduced in the quantum setting, which allows us to convert the previous classical 38 and 39 step semi-free-start collisions into a collision. The idea behind our attacks is simple and will also be applicable to other cryptographic hash functions.
Transfer Learning Based Semantic Segmentation for 3D Object Detection from Point Cloud

Three-dimensional object detection utilizing LiDAR point cloud data is an indispensable part of autonomous driving perception systems. Point cloud-based 3D object detection has been a better replacement for higher accuracy than cameras during nighttime. However, most LiDAR-based 3D object methods work in a supervised manner, which means their state-of-the-art performance relies heavily on a large-scale and well-labeled dataset, while these annotated datasets could be expensive to obtain and only accessible in the limited scenario. Transfer learning is a promising approach to reduce the large-scale training datasets requirement, but existing transfer learning object detectors are primarily for 2D object detection rather than 3D. In this work, we utilize the 3D point cloud data more effectively by representing the birds-eye-view (BEV) scene and propose a transfer learning based point cloud semantic segmentation for 3D object detection. The proposed model minimizes the need for large-scale training datasets and consequently reduces the training time. First, a preprocessing stage filters the raw point cloud data to a BEV map within a specific field of view. Second, the transfer learning stage uses knowledge from the previously learned classification task (with more data for training) and generalizes the semantic segmentation-based 2D object detection task. Finally, 2D detection results from the BEV image have been back-projected into 3D in the postprocessing stage. We verify results on two datasets: the KITTI 3D object detection dataset and the Ouster LiDAR-64 dataset, thus demonstrating that the proposed method is highly competitive in terms of mean average precision (mAP up to 70%) while still running at more than 30 frames per second (FPS).
Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.
Government and economics in the digital economy

The digital economy brings both opportunities and challenges. After introducing the benefits and potential problems brought about by the digital economy, I then discuss the important role of government in ensuring that the digital economy functions properly. Finally, I propose several directions in which governments should concentrate their efforts to facilitate the sound development of the digital economy.
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution

Gradient Boost Decision Trees (GBDT) is a powerful additive model based on tree ensembles. Its nature makes GBDT a black-box model even though there are multiple explainable artificial intelligence (XAI) models obtaining information by reinterpreting the model globally and locally. Each tree of the ensemble is a transparent model itself but the final outcome is the result of a sum of these trees and it is not easy to clarify. In this paper, a feature contribution method for GBDT is developed. The proposed method takes advantage of the GBDT architecture to calculate the contribution of each feature using the residue of each node. This algorithm allows to calculate the sequence of node decisions given a prediction. Theoretical proofs and multiple experiments have been carried out to demonstrate the performance of our method which is not only a local explicability model for the GBDT algorithm but also a unique option that reflects GBDTs internal behavior. The proposal is aligned to the contribution of characteristics having impact in some artificial intelligence problems such as ethical analysis of Artificial Intelligence (AI) and comply with the new European laws such as the General Data Protection Regulation (GDPR) about the right to explain and nondiscrimination.
Autonomous Traffic System for Emergency Vehicles

An emergency can occur at any time. To overcome that emergency efficiently, we require seamless movement on the road to approach the destination within a limited time by using an Emergency Vehicle (EV). This paper proposes an emergency vehicle management solution (EVMS) to determine an efficient vehicle-passing sequence that allows the EV to cross a junction without any delay. The proposed system passes the EV and minimally affects the travel times of other vehicles on the junction. In the presence of an EV in the communication range, the proposed system prioritizes the EV by creating space for it in the lane adjacent to the shoulder lane. The shoulder lane is a lane that cyclists and motorcyclists will use in normal situations. However, when an EV enters the communication range, traffic from the adjacent lane will move to the shoulder lane. As the number of vehicles on the road increases rapidly, crossing the EV in the shortest possible time is crucial. The EVMS and algorithms are presented in this study to find the optimal vehicle sequence that gives EVs the highest priority. The proposed solution uses cutting-edge technologies (IoT Sensors, GPS, 5G, and Cloud computing) to collect and pass EVs’ information to the Roadside Units (RSU). The proposed solution was evaluated through mathematical modeling. The results show that the EVMS can reduce the travel times of EVs significantly without causing any performance degradation of normal vehicles.
DCMS: Cyber Security Breaches Survey 2021

This is the sixth in the annual series of reports from the UK Government's Department for Digital, Culture, Media &amp; Sport (DCMS). And the picture it paints isn't any prettier than the previous five. Quite the reverse, in fact.
No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence

Pre-trained models have been shown effective in many code intelligence tasks. These models are pre-trained on large-scale unlabeled corpus and then fine-tuned in downstream tasks. However, as the inputs to pre-training and downstream tasks are in different forms, it is hard to fully explore the knowledge of pre-trained models. Besides, the performance of fine-tuning strongly relies on the amount of downstream data, while in practice, the scenarios with scarce data are common. Recent studies in the natural language processing (NLP) field show that prompt tuning, a new paradigm for tuning, alleviates the above issues and achieves promising results in various NLP tasks. In prompt tuning, the prompts inserted during tuning provide task-specific knowledge, which is especially beneficial for tasks with relatively scarce data. In this paper, we empirically evaluate the usage and effect of prompt tuning in code intelligence tasks. We conduct prompt tuning on popular pre-trained models CodeBERT and CodeT5 and experiment with three code intelligence tasks including defect prediction, code summarization, and code translation. Our experimental results show that prompt tuning consistently outperforms fine-tuning in all three tasks. In addition, prompt tuning shows great potential in low-resource scenarios, e.g., improving the BLEU scores of fine-tuning by more than 26\% on average for code summarization. Our results suggest that instead of fine-tuning, we could adapt prompt tuning for code intelligence tasks to achieve better performance, especially when lacking task-specific data.
Using the Web to Interactively Learn to Find Objects

In order for robots to intelligently perform tasks with humans, they must be able to access a broad set of background knowledge about the environments in which they operate. Unlike other approaches, which tend to manually define the knowledge of the robot, our approach enables robots to actively query the World Wide Web (WWW) to learn background knowledge about the physical environment. We show that our approach is able to search the Web to infer the probability that an object, such as a "coffee,'' can be found in a location, such as a "kitchen.'' Our approach, called ObjectEval, is able to dynamically instantiate a utility function using this probability, enabling robots to find arbitrary objects in indoor environments. Our experimental results show that the interactive version of ObjectEval visits 28% fewer locations than the version trained offline and 71% fewer locations than a baseline approach which uses no background knowledge.
Vibration measurement using a pseudo-stereo system, target tracking and vision methods

A single high-speed camera pseudo-stereo system, with a four-mirror adapter, is optimised thanks to simulation, in order to measure vibrations. The measurement errors due to several sources of noise are quantified and the impact of key parameters on the measurement accuracy is analysed. A target tracking method, with removable stickers, is assessed in this context and used in combination with vision methods. The protocol is applied to measure the sub-millimeter displacement of a plate, and allows retrieving the full Frequency Response Function and associated Operational Modal Shapes. The results are compared with those obtained with reference methods using an accelerometer and a laser vibrometer. Finally, this optimised setup is used to study the relevance and potential limitations of the approach applied to an electro-acoustic source: a low-frequency loudspeaker.
Relationships Between Project Size, Agile Practices, and Successful Software Development: Results and Analysis

Large-scale software development succeeds more often when using agile methods. Flexible scope, frequent deliveries to production, a high degree of requirement changes and more competent providers are possible reasons.
Quantum link bootstrapping using a RuleSet-based communication protocol

Establishing end-to-end quantum connections requires quantified link characteristics, and operations need to coordinate decision making between nodes across a network. We introduce the RuleSet-based communication protocol for supporting quantum operations over distant nodes to minimize classical packet transmissions for guaranteeing synchronicity. RuleSets are distributed to nodes along a path at connection setup time, and hold lists of operations that need to be performed in real time. We simulate the RuleSet-based quantum link bootstrapping protocol, which consists of recurrent purifications and link-level tomography, to quantify the quantum link fidelity and its throughput. Our Markov-chain Monte Carlo simulation includes various error sources, such as the memory error, gate error, and channel error, modeled on currently available hardware. We found that when two quantum nodes, each with 100 memory qubits capable of emitting photons ideally to the optical fiber, are physically connected with a 10-km MeetInTheMiddle link, the Recurrent Single selection--Single error purification (RSs-Sp) protocol is capable of bringing up the fidelity from an average input ${F}_{r}=0.675$ to around ${F}_{r}=0.865$ with a generation rate of 1106 Bell pairs per second, as determined by simulated tomography. The system gets noisier with longer channels, in which case errors may develop faster than the purification gain. In such a situation, a stronger purification method, such as the double selection-based purification, shows an advantage for improving the fidelity. The knowledge acquired from bootstrapping can later be distributed to nodes within the same network, and used for other purposes such as route selection.
Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.
How to Securely Outsource Finding the Min-Cut of Undirected Edge-Weighted Graphs

Finding min-cut is a fundamental operation in graph theory. It has been widely used in many applications such as image segmentation and network partition. However, solving the min-cut problem is time-consuming for resource-constrained devices, especially when the graph is large and dense. In this paper, we explore how to securely solve the min-cut problem in the cloud environment, and propose two secure outsourcing schemes for the min-cut of undirected edge-weighted graphs. The first scheme is based on two non-colluded untrusted cloud servers, and the second one is under the single untrusted cloud server model. In the designed schemes, we develop a new technique that contains merging vertices and edges, inserting fake vertices and edges, shuffling vertices and randomizing weights of edges to protect the privacy of graphs. In order to realize the checkability of result from a single untrusted cloud server, we design a novel verification mechanism by outsourcing the min-cut of two related graphs. Besides, we also provide security analysis and the experimental evaluation. To the best of our knowledge, it is the first research on secure outsourcing of graph algorithm.
Robust Embedding with Multi-Level Structures for Link Prediction

Knowledge Graph (KG) embedding has become crucial for the task of link prediction. Recent work applies encoder-decoder models to tackle this problem, where an encoder is formulated as a graph neural network (GNN) and a decoder is represented by an embedding method. These approaches enforce embedding techniques with structure information. Unfortunately, existing GNN-based frameworks still confront 3 severe problems: low representational power, stacking in a flat way, and poor robustness to noise. In this work, we propose a novel multi-level graph neural network (M-GNN) to address the above challenges. We first identify an injective aggregate scheme and design a powerful GNN layer using multi-layer perceptrons (MLPs). Then, we define graph coarsening schemes for various kinds of relations, and stack GNN layers on a series of coarsened graphs, so as to model hierarchical structures. Furthermore, attention mechanisms are adopted so that our approach can make predictions accurately even on the noisy knowledge graph. Results on WN18 and FB15k datasets show that our approach is effective in the standard link prediction task, significantly and consistently outperforming competitive baselines. Furthermore, robustness analysis on FB15k-237 dataset demonstrates that our proposed M-GNN is highly robust to sparsity and noise.
Maximum Mean Discrepancy Gradient Flow

We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties. The MMD is an integral probability metric defined for a reproducing kernel Hilbert space (RKHS), and serves as a metric on probability measures for a sufficiently rich RKHS. We obtain conditions for convergence of the gradient flow towards a global optimum, that can be related to particle transport when optimizing neural networks. We also propose a way to regularize this MMD flow, based on an injection of noise in the gradient. This algorithmic fix comes with theoretical and empirical evidence. The practical implementation of the flow is straightforward, since both the MMD and its gradient have simple closed-form expressions, which can be easily estimated with samples.