Reviewerly/exHarmony huggingface dataset:
Generalized Ridge Regression-Based Channelwise Feature Map Weighted Reconstruction Network for Fine-Grained Few-Shot Ship Classification

Fine-grained ship classification (FGSCR) has many applications in military and civilian fields. In recent years, deep learning has been widely used for classification tasks, and its success is inseparable from that of big data. However, ship images are valuable, with only a few images of a specific category being obtained, leading to the fine-grained few-shot ship classification problem. In addition, feature map channels contain distinct characteristics and discriminative details, which significantly influence FGSCR. Intuitively, channels with distinct characteristics should be assigned larger weights for classification, but most few-shot learning methods treat the channels equally. Therefore, we propose a generalized ridge-regression-based channelwise feature map weighted reconstruction network to address these issues. First, we reconstruct the query feature map by assigning different weights to the support feature map channels using the generalized ridge regression method. The channels with large discriminative details contribute more toward reconstruction. Second, we propose a support channel weight module to calculate the channel weight matrix used in the generalized ridge regression method. Finally, based on the reconstructed query feature map, we can calculate the reconstruction error. The reconstruction error is adopted as the distance metric. Our proposed method achieves excellent performance on the fine-grained ship, bird, aircraft, and WHU-RS19 datasets compared with other representative few-shot learning methods. Considering the limited studies on the fine-grained few-shot ship classification problem, we believe that our work is of great significance.
Fault detective: Automatic fault-detection for solar thermal systems based on artificial intelligence

Fault-Detection (FD) is essential to ensure the performance of solar thermal systems. However, manually analyzing the system can be time-consuming, error-prone, and requires extensive domain knowledge. On the other hand, existing FD algorithms are often too complicated to set up, limited to specific system layouts, or have only limited fault coverage. Hence, a new FD algorithm called Fault-Detective is presented in this paper, which is purely data-driven and can be applied to a wide range of system layouts with minimal configuration effort. It automatically identifies correlated sensors and models their behavior using Random-Forest-Regression. Faults are then detected by comparing predicted and measured values. The algorithm is tested using data from three large-scale solar thermal systems to evaluate its applicability and performance. The results are compared to manual fault detection performed by a domain expert. The evaluation shows that Fault-Detective can successfully identify correlated sensors and model their behavior well, resulting in coefficient-of-determination scores between R²=0.91 and R²=1.00. In addition, all faults detected by the domain experts were correctly spotted by Fault-Detective. The algorithm even identified some faults that the experts missed. However, the use of Fault-Detective is limited by the low precision score of 30% when monitoring temperature sensors. The reason for this is a high number of false alarms raised due to anomalies (e.g., consecutive days with bad weather) instead of faults. Nevertheless, the algorithm shows promising results for monitoring the thermal power of the systems, with an average precision score of 91%.
Fraud analytics: a research

Fraud is as old as humankind and appears in many types and forms. Popular examples are credit card fraud, tax evasion, identity theft, insurance fraud, counterfeit, click fraud, anti-money laundering, and payment transaction fraud. In earlier research we defined fraud as an uncommon, well-considered, imperceptibly concealed, time-evolving, and carefully organized crime. Nowadays, fraud is typically tackled using state-of-the-art analytical techniques with many accompanying challenges. It is the purpose of this article to highlight twelve research topics (RTs) that we believe prioritize high on the agenda of contemporary fraud analytics models. We do this by reviewing fraud analytics from a data, model, performance, and deployment perspective.
Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm

For training the present Neural Network (NN) models, the standard technique is to utilize decaying Learning Rates (LR). While the majority of these techniques commence with a large LR, they will decay multiple times over time. Decaying has been proved to enhance generalization as well as optimization. Other parameters, such as the network’s size, the number of hidden layers, dropouts to avoid overfitting, batch size, and so on, are solely based on heuristics. This work has proposed Adaptive Teaching Learning Based (ATLB) Heuristic to identify the optimal hyperparameters for diverse networks. Here we consider three architectures Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (BiLSTM) of Deep Neural Networks for classification. The evaluation of the proposed ATLB is done through the various learning rate schedulers Cyclical Learning Rate (CLR), Hyperbolic Tangent Decay (HTD), and Toggle between Hyperbolic Tangent Decay and Triangular mode with Restarts (T-HTR) techniques. Experimental results have shown the performance improvement on the 20Newsgroup, Reuters Newswire and IMDB dataset.
Dependable modulation classifier explainer with measurable explainability

The Internet of Things (IoT) plays a significant role in building smart cities worldwide. Smart cities use IoT devices to collect and analyze data to provide better services and solutions. These IoT devices are heavily dependent on the network for communication. These new-age networks use artificial intelligence (AI) that plays a crucial role in reducing network roll-out and operation costs, improving entire system performance, enhancing customer services, and generating possibilities to embed a wide range of telecom services and applications. For IoT devices, it is essential to have a robust and trustable network for reliable communication among devices and service points. The signals sent between the devices or service points use modulation to send a password over a bandpass frequency range. Our study focuses on modulation classification performed using deep learning method(s), adaptive modulation classification (AMC), which has now become an integral part of a communication system. We propose a dependable modulation classifier explainer (DMCE) that focuses on the explainability of modulation classification. Our study demonstrates how we can visualize and understand a particular prediction made by seeing highlighted data points crucial for modulation class prediction. We also demonstrate a numeric explainability measurable metric (EMM) to interpret the prediction. In the end, we present a comparative analysis with existing state-of-the-art methods.
A novel conservative chaos driven dynamic DNA coding for image encryption

Recently, many image encryption algorithms based on hybrid DNA and chaos have been developed. Most of these algorithms utilize chaotic systems exhibiting dissipative dynamics and periodic windows/patterns in the bifurcation diagrams along with co-existing attractors in the neighborhoods of parameter space. Therefore, such algorithms generate several weak keys, thereby making them prone to various chaos- specific attacks. In this paper, we propose a novel conservative chaotic standard map-driven dynamic DNA coding (encoding, addition, subtraction and decoding) for image encryption. It is the first hybrid DNA and conservative chaos-based image encryption algorithm having effectively infinite key space. The proposed image encryption algorithm is a dynamic DNA coding algorithm i.e., for the encryption of each pixel different rules for encoding, addition/subtraction, decoding etc. are randomly selected based on the pseudorandom sequences generated with the help of the conservative chaotic standard map. We propose a novel way to generate pseudo-random sequences through the conservative chaotic standard map and also test them rigorously through the most stringent test suite of pseudo-randomness, the NIST test suite, before using them in the proposed image encryption algorithm. Our image encryption algorithm incorporates unique feed-forward and feedback mechanisms to generate and modify the dynamic one-time pixels that are further used for the encryption of each pixel of the plain image, therefore, bringing in the desired sensitivity on plaintext as well as ciphertext. All the controlling pseudorandom sequences used in the algorithm are generated for a different value of the parameter (part of the secret key) with inter-dependency through the iterates of the chaotic map (in the generation process) and therefore possess extreme key sensitivity too. The performance and security analysis has been executed extensively through histogram analysis, correlation analysis, information entropy analysis, DNA sequence-based analysis, perceptual quality analysis, key sensitivity analysis, plaintext sensitivity analysis, classical attack analysis, etc. The results are promising and prove the robustness of the algorithm against various common cryptanalytic attacks.
Arabic Span Extraction-based Reading Comprehension Benchmark (ASER) and Neural Baseline Models

Machine reading comprehension (MRC) requires machines to read and answer questions about a given text. This can be achieved through either predicting answers or extracting them. Extracting answers from text involves predicting the first and last index of the answer span within the paragraph. Training machines to answer questions requires datasets that are created for such a purpose. The lack of availability of benchmarking datasets for the Arabic language has hindered research into machine reading comprehension from Arabic text. The aim of this article is to propose an Arabic Span-Extraction-based Reading Comprehension Benchmark (ASER) and complement it with neural baseline models for performance evaluations. Detailed steps are depicted for building and evaluating ASER, which is an Arabic dataset created manually for the task of machine reading comprehension. It contains 10,000 records from different domains and is divided into training and testing sets. The results of ASER evaluation led to the conclusion that it is a challenging benchmark since the answers have varying lengths and human performance resulted in an exact match of 42%. On the other hand, two main baseline models were the focus of ASER experimentation: the sequence-to-sequence (Seq2Seq) model with different neural networks and the bidirectional attention flow (BIDAF) model. These experiments were implemented using different embeddings, and the results showed an exact match with lower values than human performance.
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls

Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of bag-of-words retrievers. At the same time, deep language models have been shown to outperform traditional bag-of-words rerankers. However, it is unclear how to integrate PRF directly with emergent deep language models. This article addresses this gap by investigating methods for integrating PRF signals with rerankers and dense retrievers based on deep language models. We consider text-based, vector-based and hybrid PRF approaches and investigate different ways of combining and scoring relevance signals. An extensive empirical evaluation was conducted across four different datasets and two task settings (retrieval and ranking). Text-based PRF results show that the use of PRF had a mixed effect on deep rerankers across different datasets. We found that the best effectiveness was achieved when (i) directly concatenating each PRF passage with the query, searching with the new set of queries, and then aggregating the scores; (ii) using Borda to aggregate scores from PRF runs. Vector-based PRF results show that the use of PRF enhanced the effectiveness of deep rerankers and dense retrievers over several evaluation metrics. We found that higher effectiveness was achieved when (i) the query retains either the majority or the same weight within the PRF mechanism, and (ii) a shallower PRF signal (i.e., a smaller number of top-ranked passages) was employed, rather than a deeper signal. Our vector-based PRF method is computationally efficient; thus, this represents a general PRF method others can use with deep rerankers and dense retrievers.
Multi-initial-Center Federated Learning with Data Distribution Similarity-Aware Constraint

Federated Learning (FL) has recently attracted high attention since it allows clients to collaboratively train a model while the training data remains local. However, due to the inherent heterogeneity of local data distributions, the trained model usually fails to perform well on each client. Clustered FL has emerged to tackle this issue by clustering clients with similar data distributions. However, these model-dependent clustering methods tend to perform poorly and be costly. In this work, we propose a distribution similarity-based clustered federated learning framework FedDSMIC, which clusters clients by detecting the client-level underlying data distribution based on the model’s memory of training data. Furthermore, we extend the assumption about data distribution to a more realistic cluster structure. The center models are learned as good initial points to obtain common data properties in the cluster. Each client in a cluster gets a more personalized model by performing one step of gradient descent from the initial point. The empirical evaluation on real-world datasets shows that FedDSMIC outperforms popular state-of-the-art federated learning algorithms while keeping the lowest communication overhead.
The impact of the digital divide on synchronous online teaching in Kazakhstan during COVID-19 school closures

This paper examines how factors associated with the digital divide such as ICT access, digital skills, and outcomes influenced synchronous online teaching in urban and rural schools in Kazakhstan during COVID-19 school closures. In addition to school location, this paper addresses how the speed and steadiness of the internet connection, and teacher characteristics such as age, qualification, and experience influenced teacher usage of synchronous teaching and learning mediums. Data in this paper consists of a nationally representative sample of nearly 4,000 teachers. This study found that the digital divide narrows when schools provide teachers with ICT access. While both, the speed of the internet and rural–urban residency have statistically significant effects on the use of ICT tools by teachers when considered separately, the interaction term between these two covariates was not statistically significant. Results indicated that age, experience, teacher workload and professional qualification were important determinants in teachers’ ability to engage in synchronous teaching.
FedADMM: A federated primal-dual algorithm allowing partial participation

Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is appealing because of its ability to accommodate heterogeneity in client compute and storage resources, non-i.i.d. data assumptions, and data privacy. Our contribution is to offer a new federated learning algorithm, FedADMM, for solving non-convex composite optimization problems with non-smooth regularizers. We prove the convergence of FedADMM for the case when not all clients are able to participate in a given communication round under a very general sampling model.
Optimal Linear Multiple Estimation for Landmark-Based Planning via Control Synthesis

A common way to implement navigation in mobile robots is through the use of landmarks. In this case, the main goal of the controller is to make progress toward a goal location (stability), while avoiding the boundary of the environment (safety). In our previous work, we proposed a method to synthesize global controllers for environments with a polyhedral decomposition; our solution uses a Quadratically Constrained Quadratic Program with Chance Constraints to take into account the uncertainty in landmark measurements. Building upon this work, we introduce the concept of virtual landmarks, which are defined as linear combinations of actual landmark measurements that minimize the uncertainty in the resulting control actions. Interestingly, our results show that the first minimum-variance landmark is independent of the feedback control matrix, thus decoupling the design of the landmark from the one of the controller; attempting to derive additional, statistically independent landmarks, however, requires solving non-convex problems that involve also the controller. Numerical experiments demonstrate that, by minimizing the variance of the inputs, the resulting controller will be less conservative, and the robot is able to complete navigation tasks more effectively (faster and with less jitter in the trajectory).
Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds

A recent emphasis of distributed learning research has been on federated learning (FL), in which model training is conducted by the data-collecting devices. Existing research on FL has mostly focused on a star topology learning architecture with synchronized (time-triggered) model training rounds, where the local models of the devices are periodically aggregated by a centralized coordinating node. However, in many settings, such a coordinating node may not exist, motivating efforts to fully decentralize FL. In this work, we propose a novel methodology for distributed model aggregations via asynchronous, event-triggered consensus iterations over the network graph topology. We consider heterogeneous communication event thresholds at each device that weigh the change in local model parameters against the available local resources in deciding the benefit of aggregations at each iteration. Through theoretical analysis, we demonstrate that our methodology achieves asymptotic convergence to the globally optimal learning model under standard assumptions in distributed learning and graph consensus literature, and without restrictive connectivity requirements on the underlying topology. Subsequent numerical results demonstrate that our methodology obtains substantial improvements in communication requirements compared with FL baselines.
Decentralized Federated Learning for Over-Parameterized Models

Modern machine learning, especially deep learning, features models that are often highly expressive and over-parameterized. They can interpolate the data by driving the empirical loss close to zero. We analyze the convergence rate of decentralized stochastic gradient descent (SGD), which is at the core of decentralized federated learning (DFL), for these over-parameterized models. Our analysis covers the setting of decentralized SGD with time-varying networks, local updates and heterogeneous data. We establish strong convergence guarantees with or without the assumption of convex objectives that either improves upon the existing literature or is the first for the regime.
Multi-Agent Pathfinding for Deadlock Avoidance on Rotational Movements

Deadlock is always a challenging problem for multi-agent pathfinding, especially when the system is in high scales in terms of number of agents and map size. Some recent studies showed that the agents can learn to resolve the deadlock problem through reinforcement learning. However, most of them are not designed for non-holonomic robots, which are commonly applied in warehouses. In particular, the rotation movement may cause the agents staying at the same locations for a long time, and the deadlock happens more frequently especially in dense environment. In this paper, an algorithm called MAPF-rot with a deadlock breaking scheme is proposed to tackle the deadlock problem arising from the rotation movement in the multi-agent pathfinding problem. Experiments are performed to demonstrate the efficiency of the proposed algorithm.
Lightweight Monocular Depth Estimation with an Edge Guided Network

Monocular depth estimation is an important task that can be applied to many robotic applications. Existing methods focus on improving depth estimation accuracy via training increasingly deeper and wider networks, however these suffer from large computational complexity. Recent studies found that edge information are important cues for convolutional neural networks (CNNs) to estimate depth. Inspired by the above observations, we present a novel lightweight Edge Guided Depth Estimation Network (EGD-Net) in this study. In particular, we start out with a lightweight encoder-decoder architecture and embed an edge guidance branch which takes as input image gradients and multi-scale feature maps from the backbone to learn the edge attention features. In order to aggregate the context information and edge attention features, we design a transformer-based feature aggregation module (TRFA). TRFA captures the long-range dependencies between the context information and edge attention features through cross-attention mechanism. We perform extensive experiments on the NYU depth v2 dataset. Experimental results show that the proposed method runs about 96 fps on a Nvidia GTX 1080 GPU whilst achieving the state-of-the-art performance in terms of accuracy.
Differential Privacy Made Easy

Data privacy has been a significant issue for many decades. Several techniques have been developed to make sure individuals' privacy but still, the world has seen privacy failures. In 2006, Cynthia Dwork gave the idea of Differential Privacy which gave strong theoretical guarantees for data privacy. Many companies and research institutes developed differential privacy libraries, but in order to get differentially private results, users have to tune the privacy parameters. In this paper, we minimized these tunable parameters. The DP-framework is developed which compares the differentially private results of three Python based differential privacy libraries. We also introduced a new very simple DP library (GRAM - DP), so that people with no background in differential privacy can still secure the privacy of the individuals in the dataset while releasing statistical results in public.