Reviewerly/exHarmony huggingface dataset:
A Gamified Research Tool for Incentive Mechanism Design in Federated Learning

Federated Learning (FL) enables multiple participants to collaboratively train AI models in a privacy-preserving manner, which incurs cost during the training processing. This can be a significant issue especially for business participants [8]. These costs include communication, technical, compliance, risk of market share erosion and free-riding problems (i.e., participants may only join FL training with low-quality data) [6]. Motivating participants to contribute high-quality data continuously and maintain a healthy FL ecosystem is a challenging problem. The key to achieving this goal is through effective and fair incentive schemes. When designing such schemes, it is important for researchers to understand how FL participants react under different schemes and situations. In this chapter, we present a multi-player game to facilitate researchers to study federated learning incentive schemes – FedGame (A demonstration video of the platform can be found at: https://youtu.be/UhAMVx8SOE8 . Additional resources about the platform will continuously be made available over time at: http://www.federated-learning.org/ .), by extending our previous work in [5]. FedGame allows human players to role-play as FL participants under various conditions. It serves as a tool for researchers or incentive mechanism designers to study the impact of emergent behaviors by FL participants under different incentive schemes. It can be useful for eliciting human behaviour patterns in FL and identifying potential loopholes in the proposed incentive scheme. After learning the behaviour pattern, FedGame can, in turn, further test any given incentive scheme’s competitiveness again schemes based on real decision patterns.
A Structural Approach to Dealing with High Dimensionality Parameter Search Spaces

In the field of robotics, searching for effective control parameters is a challenge as controllers become more complex. As the number of parameters increases, the dimensionality of the search problem causes results to become varied because the search cannot effectively traverse the whole search space. In applications such as autonomous robotics, quick training that provides consistent and robust results is key. Hierarchical controllers are often employed to solve multi-input control problems, but multiple controllers increases the number of parameters and thus the dimensionality of the search problem. It is unknown whether hierarchies in controllers allows for effective staged parameter optimisation. Furthermore, it is unknown if a staged optimisation approach would avoid the issues high dimensional spaces cause to searches. Here we compare two hierarchical controllers, where one was trained in a staged manner based on the hierarchy and the other was trained with all parameters being optimised at once. This paper shows that the staged approach is strained less by the dimensionality of the problem. The solutions scoring in the bottom 25% of both approaches were compared, with the staged approach having significantly lower error. This demonstrates that the staged approach is capable of avoiding highly varied results by reducing the computational complexity of the search space. Computational complexity across AI has troubled engineers, resulting in increasingly intense algorithms to handle the high dimensionality. These results will hopefully prompt approaches that use of developmental or staged strategies to tackle high dimensionality spaces.
PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection

Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure. Semantics come from the appearance and context of objects to the sensor, while geometric structure is the actual 3D shape of point clouds. Most detectors on LiDAR point clouds focus only on analyzing the geometric structure of objects in real 3D space. Unlike previous works, we propose to learn both semantic feature and geometric structure via a unified multi-view framework. Our method exploits the nature of LiDAR scans -- 2D range images, and applies well-studied 2D convolutions to extract semantic features. By fusing semantic and geometric features, our method outperforms state-of-the-art approaches in all categories by a large margin. The methodology of combining semantic and geometric features provides a unique perspective of looking at the problems in real-world 3D point cloud detection.
In-Situ Merge Sort Using Hand-Shaking Algorithm

In the present computer system, the data processing aspect, occupies the enormous processing frequency, approximately has the nearly 50% above CPU processing time is USES in the sort data. It can be seen that the data sorting algorithm has a high requirement on its own execution speed, so it is particularly important to implement a fast and good sorting algorithm. In this paper. The traditional merge sort algorithm uses two-way merge sort, which needs the same size of auxiliary space and data to be sorted, so it is necessary to improve it. The traditional merge sort method and an improved in-situ merge algorithm based on hand method are introduced, which is devoted to providing theoretical basis for improving the traditional data sort method.
Interpolation between Residual and Non-Residual Networks

Although ordinary differential equations (ODEs) provide insights for designing network architectures, its relationship with the non-residual convolutional neural networks (CNNs) is still unclear. In this paper, we present a novel ODE model by adding a damping term. It can be shown that the proposed model can recover both a ResNet and a CNN by adjusting an interpolation coefficient. Therefore, the damped ODE model provides a unified framework for the interpretation of residual and non-residual networks. The Lyapunov analysis reveals better stability of the proposed model, and thus yields robustness improvement of the learned networks. Experiments on a number of image classification benchmarks show that the proposed model substantially improves the accuracy of ResNet and ResNeXt over the perturbed inputs from both stochastic noise and adversarial attack methods. Moreover, the loss landscape analysis demonstrates the improved robustness of our method along the attack direction.
Quantum Noise Diffusion Mapping Based on Chaotic Recurrent Neural Network in Quantum Noise Cipher

We introduce a quantum noise diffusion mapping method based on chaotic recurrent neural network in quantum noise cipher system. Simulation results demonstrate the improved security performance in terms of increasing quantum noise masking number.
Policy Return: A New Method for Reducing the Number of Experimental Trials in Deep Reinforcement Learning

Using the same algorithm and hyperparameter configurations, deep reinforcement learning (DRL) will derive drastically different results from multiple experimental trials, and most of these results are unsatisfactory. Because of the instability of the results, researchers have to perform many trials to confirm an algorithm or a set of hyperparameters in DRL. In this article, we present the policy return method, which is a new design for reducing the number of trials when training a DRL model. This method allows the learned policy to return to a previous state when it becomes divergent or stagnant at any stage of training. When returning, a certain percentage of stochastic data is added to the weights of the neural networks to prevent a repeated decline. Extensive experiments on challenging tasks and various target scores demonstrate that the policy return method can bring about a 10% to 40% reduction in the required number of trials compared with that of the corresponding original algorithm, and a 10% to 30% reduction compared with the state-of-the-art algorithms.
An Improved Local Binary Descriptor Based Polar Gridding for Rotation Change

Rotation change is widespread in many computer vision applications. Robustness in rotation is an important evaluation for local descriptor. This paper proposes a fast and rotation-robust local binary descriptor using polar location, called rotation-invariant local difference binary descriptor (RLDB). RLDB computes binary test of average intensity and gradient in radial and tangent directions of grid cells partitioned by multiple log-polar grids. The proposed local descriptors need no extra work to deal with rotation specially. Integral image in polar coordinate and lookup table mapping between discretized polar coordinates and image pixel locations are proposed and applied to speed up the computation in the presented descriptors’ construction. The proposed descriptor is compared with some of state-of-the-art local descriptors. The experimental results indicate that the proposed descriptor has a better performance in large rotation change and viewpoint change. The proposed descriptor has comparable distinctive performance and construction speed owing to fast computing in polar coordinate.
BemTV: Hybrid CDN/Peer-to-Peer Architecture for Live Video Distribution over the Internet

Assuming that video streaming is now responsible for the absolute majority of the Internet traffic and considering that the audience uses WebRTC-enabled web browsers and mobile devices to access and retrieve content, this work proposes the development of a peer-to-peer overlay network to assist the delivery of video streaming events that use HTTP-based protocols without the need to install additional software. Using the peer-to-peer network, the client/server model becomes hybrid, where network nodes that are watching the same event can retrieve portions of the video content directly from the server or neighboring nodes. This approach has two main objectives; decrease the client/server traffic and consequently the economic cost of delivery while improving the quality of the users' experience, given that communication between neighboring nodes can support the flow of better quality videos between the points
Innovativeness, Firm Performance and Managers’ Educational Level: A Study of Selected SMEs in Yobe State, Nigeria

The objectives of the study are two folds: to investigates the relationship between innovativeness and firm performance, and to moderate such relationship with educational level of managers.In attempt to justify the effect of innovativeness on SMEs performance, structured questionnaire in a 5-point Likert Scale was applied in collecting data.Total of 259 respondents were drawn as sample.The data were analyzed using SPSS 25 version for descriptive statistics, normality and common method variance.Analyses of measurement model for internal consistency, discriminant validity, and composite reliability were conducted in PLS 3. Hypotheses of the study were confirmed by applying (SEM) technique in PLS-SEM.The current study moreover, indicates that managers' educational level moderates' relationship between innovativeness and firm performance.The study declares that educational level of managers affect relationship between innovativeness and performance of SMEs in Nigeria.Thus, the findings could be used by government to develop policy decision on loan advances to be based on one's level of education.Prior to this study, there has been no study that tries to blend managers' own level of education with innovativeness and firm performance with a view to assessing its moderating effect, this study fills this gap.
New Modes of Inference for Probabilistic SHM

In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labelling to describe what each the measured signals represent is often unavailable. In consequence, the algorithms used to implement SHM should be robust and adaptive, while accommodating for missing information in the training-data – such that new information can be included if it becomes available. By collecting three novel techniques for statistical learning (originally proposed in previous work) – including semi-supervised, active, and transfer learning – it is argued that probabilistic algorithms offer a natural solution to model the signals recorded from systems in practice.
Corrigendum to “Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics”

A Machine Learning Approach to Big Data Analytics" [1]
SOCMTD: Selecting Optimal Countermeasure for Moving Target Defense Using Dynamic Game

Moving target defense, as a 'game-changing' security technique for network warfare, realizes proactive defense by increasing network dynamics, uncertainty and redundancy.How to select the best countermeasure from the candidate countermeasures to maximize defense payoff becomes one of the core issues.In order to improve the dynamic analysis for existing decision-making, a novel approach of selecting the optimal countermeasure using game theory is proposed.Based on the signal game theory, a multi-stage adversary model for dynamic defense is established.Afterwards, the payoffs of candidate attack-defense strategies are quantified from the viewpoint of attack surface transfer.Then the perfect Bayesian equilibrium is calculated.The inference of attacker type is presented through signal reception and recognition.Finally the countermeasure for selecting optimal defense strategy is designed on the tradeoff between defense cost and benefit for dynamic network.A case study of attack-defense confrontation in small-scale LAN shows that the proposed approach is correct and efficient.
Adversarial Perturbation Attacks on ML-based CAD: A Case Study on CNN-based Lithographic Hotspot Detection

There is substantial interest in the use of machine learning (ML)-based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning. However, while deep learning methods have surpassed state-of-the-art performance in several applications, they have exhibited intrinsic susceptibility to adversarial perturbations—small but deliberate alterations to the input of a neural network, precipitating incorrect predictions. In this article, we seek to investigate whether adversarial perturbations pose risks to ML-based CAD tools, and if so, how these risks can be mitigated. To this end, we use a motivating case study of lithographic hotspot detection, for which convolutional neural networks (CNN) have shown great promise. In this context, we show the first adversarial perturbation attacks on state-of-the-art CNN-based hotspot detectors; specifically, we show that small (on average 0.5% modified area), functionality preserving, and design-constraint-satisfying changes to a layout can nonetheless trick a CNN-based hotspot detector into predicting the modified layout as hotspot free (with up to 99.7% success in finding perturbations that flip a detector’s output prediction, based on a given set of attack constraints). We propose an adversarial retraining strategy to improve the robustness of CNN-based hotspot detection and show that this strategy significantly improves robustness (by a factor of ~3) against adversarial attacks without compromising classification accuracy.
The EpiBench Platform to Propel AI/ML-Based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-Level Performance

During the COVID-19 pandemic, a significant effort has gone into developing ML-driven epidemic forecasting techniques. However, benchmarks do not exist to claim if a new AI/ML technique is better than the existing ones. The “covid-forecast-hub” is a collection of more than 30 teams, including us, that submit their forecasts weekly to the CDC. It is not possible to declare whether one method is better than the other using those forecasts because each team’s submission may correspond to different techniques over the period and involve human interventions as the teams are continuously changing/tuning their approach. Such forecasts may be considered “human-expert” forecasts and do not qualify as AI/ML approaches, although they can be used as an indicator of human expert performance. We are interested in supporting AI/ML research in epidemic forecasting which can lead to scalable forecasting without human intervention. Which modeling technique, learning strategy, and data pre-processing technique work well for epidemic forecasting is still an open problem. To help advance the state-of-the-art in AI/ML applied to epidemiology, a benchmark with a collection of performance points is needed and the current “state-of-the-art” techniques need to be identified. We propose EpiBench a platform consisting of community-driven benchmarks for AI/ML applied to epidemic forecasting to standardize the challenge with uniform evaluation protocol. In this paper, we introduce a prototype of EpiBench which is currently running and accepting submissions for the task of forecasting COVID-19 cases and deaths in the US states and We demonstrate that we can utilize the prototype to develop an ensemble relying on fully automated epidemic forecasts (no human intervention) that reaches human-expert level ensemble currently being used by the CDC.
A computational prediction method based on modified U-Net for cell distribution in tumor microenvironment

The tumor microenvironment (TME) is the internal environment in which tumors develop and consist of tumor cells, various immune cells, and interstitial cells. Understanding different cell distribution in TME can help predict clinical response of immunotherapy and offer guidance for therapeutic optimization. Current pathological practice utilizes multiplexed immunohistochemistry (mIHC) to make assessment of different types of cell distribution in TME. However, these staining methods are time- and cost consuming and staining results often require professional pathologists to interpret, which can be possibly influenced by subjectivity. In this work, we propose a computational prediction method for cell distribution in TME using a modified U-Net structure, which can learn useful features from the hematoxylin and eosin (H&amp;E) images and predict PanCK positive colon cancer cells and tumor infiltrating lymphocytes (TILs) at cellular-level. Our created datasets contain H&amp;E images and cellular-level segmentation labels annotated by board-certified pathologists according to the corresponding registered mIHC images. We optimize the structure of the traditional U-Net network, and extract multi-scale features by integrating the Inception block, so as to create a modified U-Net structure which can predict PanCK positive colon cancer cells and TILs distribution in TME with the accuracy of 84.6% on test set. Hence, this method shows the potential to predict different types of cell distribution in TME more objectively and efficiently.
Design of a New Distributed NoSQL Database with Distributed Hash Tables

Abstract Databases play a fundamental role in today’s world, being used by most companies, especially those that offer services through the Internet. Today there is a wide variety of database models, each adapted for use according to the specific requirements of each application. Traditionally, the relational models with centralized architectures have been used mostly due to their simplicity and general-purpose query language, which made relational systems suitable for almost any application. However, with the growth of the Internet in recent decades, both in the number of users and in the amount of information, those centralized models began to suffer availability and scalability issues. To address those issues, the use of decentralized architectures and alternative database models began to arise, eventually replacing relational databases and centralized architectures when the requirements on availability and scalability are high. Those database models alternative to the traditional relational model are grouped under the name of NoSQL (Not only Structured Query Language). In this article, we present a NoSQL database developed as an end of degree work, with a flexible data model based on documents and a fully decentralized architecture based on the Gossip protocol for node discovery and a distributed hash table, in particular the rendezvous hashing algorithm, used to distribute and replicate the data across all the nodes. The main goals of the system are to achieve high availability (the data should be almost always accessible) and high scalability (the system should be able to scale by increasing the number of nodes to increase its capacity both on data and number of users). High availability is achieved thanks to the replication of the data, while high scalability is achieved by its decentralized architecture, which allows multiple entry points from the requests, and the data distribution, effectively increasing the database capacity by increasing the number of nodes.
Attention-based two-layer long short-term memory model for automatic essay scoring

We propose a neural network architecture-based automatic essay scoring model which contains a two-layer long short-term memory (LSTM) and an attention mechanism layer. The Google word vector dataset, which includes the richer word information and contextual information than the local-trained word vector dataset, is used to generate the embedding word vector of the input layer of the model by pre-training. The lower layer of the two-layer LSTM network captures the context semantic information and hidden context dependency, and the upper layer extracts the deeper context dependency. The attention mechanism layer focuses on the information extracted from the upper hidden layer of two-layer LSTM and calculates the attention probability to highlight the importance of key information in the text. The dataset used for automatic essay scoring task is provided by the Hewlett Foundation, and the quadratic weighted kappa coefficient is used as the evaluation index of the model. The experimental results show that the proposed method outperforms other automatic essay scoring baseline models such as bidirectional LSTM, SKIPFLOW-LSTM, and so on, in terms of the value of quadratic weighted kappa coefficient.
Evaluation of Multimodal Semantic Segmentation using RGB-D Data

Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environments. To achieve this, we are exploring and evaluating a range of related technology and solutions, including AI-driven multimodal scene perception, fusion, processing, and understanding. This work reports our efforts on the evaluation of a state-of-the-art approach for semantic segmentation with multiple RGB and depth sensing data. We employ four large datasets composed of diverse urban and terrain scenes and design various experimental methods and metrics. In addition, we also develop new strategies of multi-datasets learning to improve the detection and recognition of unseen objects. Extensive experiments, implementations, and results are reported in the paper.
Hyperspherical Weight Uncertainty in Neural Networks

Bayesian neural networks learn a posterior probability distribution over the weights of the network to estimate the uncertainty in predictions. Parameterization of prior and posterior distribution as Gaussian in Monte Carlo Dropout, Bayes-by-Backprop (BBB) often fails in latent hyperspherical structure [, ]. In this paper, we address an enhanced approach for selecting weights of a neural network [] corresponding to each layer with a uniform distribution on the Hypersphere to efficiently approximate the posterior distribution, called Hypersphere Bayes by Backprop. We show that this Hyperspherical Weight Uncertainty in Neural Networks is able to model a richer variational distribution than previous methods and obtain well-calibrated predictive uncertainty in deep learning in non-linear regression, image classification and high dimensional active learning. We then demonstrate how this uncertainty in the weights can be used to improve generalisation in Variational Auto-Encoder (VAE) problem.