Reviewerly/exHarmony huggingface dataset:
Livestock data – Is it there and is it FAIR? A systematic review of livestock farming datasets in Australia

The global adoption of the FAIR principles for scientific data: findable, accessible, interoperable and reusable, has been relatively slow in agriculture, compared to other disciplines. A recent review of the literature showed that the use of precision farming technologies and the development and adoption of open data standards was particularly low in extensive livestock farming. However, a plethora of public datasets exist that have the potential to be used to inform precision farming decision tools. Using extensive livestock farming in Australia as example, we investigate the quantity and quality of datasets available via a systematic dataset review. This systematic review of datasets begins with a search of open data catalogues and querying these to find datasets. Software scripts are developed and used to query the Application Programming Interfaces (APIs) of many of the large data catalogues in Australia, while catalogues without public APIs are queried manually via available web portals. Following the systematic search, a combined list of all datasets is collated and tested for FAIRness and other quality metrics. The contribution of this work is the resulting overview of the state of open datasets within the livestock farming domain on the one hand, but also the development of a systematic dataset search strategy, reusable methods and software scripts.
Network Alignment with Holistic Embeddings

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different types of network information or modalities. In this paper, we propose a novel end-to-end alignment framework that can leverage different modalities to compare and align network nodes in an efficient way. In order to exploit the richness of the network context, our model constructs multiple embeddings for each node, each of which captures one modality or type of network information. We then design a late-fusion mechanism to combine the learned embeddings based on the importance of the underlying information. Our fusion mechanism allows our model to be adapted to various types of structure of the input network. Experimental results show that our technique outperforms state-of-the-art approaches in terms of accuracy on real and synthetic datasets, while being robust against various noise factors.
η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities

Recently, the recurrent neural network, or its most popular type—the Long Short Term Memory (LSTM) network— has achieved great success in a broad spectrum of real-world application domains, such as autonomous driving, natural language processing, sentiment analysis, and epidemiology. Due to the complex features of the real-world tasks, current LSTM models become increasingly bigger and more complicated for enhancing the learning ability and prediction accuracy. However, through our in-depth characterization on the state-of-the-art general-purpose deep-learning accelerators, we observe that the LSTM training execution grows inefficient in terms of storage, performance, and energy consumption, under an increasing model size. With further algorithmic and architectural analysis, we identify the root cause for large LSTM training inefficiency: massive intermediate variables. To enable a highly-efficient LSTM training solution for the ever-growing model size, we exploit some unique memory-saving and performance improvement opportunities from the LSTM training procedure, and leverage them to propose the first cross-stack training solution, η-LSTM, for large LSTM models. η-LSTM comprises both software-level and hardware-level innovations that effectively lower the memory footprint upper-bound and excessive data movements during large LSTM training, while also drastically improving training performance and energy efficiency. Experimental results on six real-world large LSTM training benchmarks demonstrate that η-LSTM reduces the required memory footprint by an average of 57.5% (up to 75.8%) and brings down the data movements for weight matrices, activation data, and intermediate variables by 40.9%, 32.9%, and 80.0%, respectively. Furthermore, it outperforms the state-of-the-art GPU implementation for LSTM training by an average of 3.99× (up to 5.73×) on performance and 2.75× (up to 4.25) on energy. We hope this work can shed some light on how to design high logic utilization for future NPUs.
A deep learning approach for Indian sign language gestures classification with different backgrounds

Abstract The sign language recognition system recently has drawn the attention of various researchers as there is no universal sign language, moreover, it consists of many patterns and postures. Many methods for extracting features and classifying sign language have been proposed in the literature, most of them are based on machine learning techniques. In this article, a deep learning method has been adopted by designing a Convolution Neural Network (CNN) model to extract the sign language features where for classification softmax layer is used. All alphabets in simple as well as complex backgrounds have been considered, where data is collected from 100 subjects in different lighting conditions. The effect of various optimization techniques (Adam, Sgdm, RMSProp), activation functions (ReLU and Leaky ReLU) for generalization ability is also observed. The proposed approach has succeeded in attaining the testing accuracy of 99.10%, 92.69%, and 95.95% on the Indian sign language dataset with simple, complex backgrounds and on a mixed background scenarios, respectively. The model is also tested on NUS dataset-I, NUS dataset-II, their combination, and achieved the accuracy of 100%, 95.95%, and 97.22% respectively.
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction

Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recently, some deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. In these work, the attention mechanism is used to select the user interested items in historical behaviors, improving the performance of the CTR predictor. Normally, these attentive modules can be jointly trained with the base predictor by using gradient descents. In this paper, we regard user interest modeling as a feature selection problem, which we call user interest selection. For such a problem, we propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper. More specifically, we use a differentiable module as our wrapping operator and then recast its learning problem as a continuous bilevel optimization. Moreover, we use a meta-learning algorithm to solve the optimization and theoretically prove its convergence. Meanwhile, we also provide theoretical analysis to show that our proposed method 1) efficiencies the wrapper-based feature selection, and 2) achieves better resistance to overfitting. Finally, extensive experiments on three public datasets manifest the superiority of our method in boosting the performance of CTR prediction.
User Scheduling for Federated Learning Through Over-the-Air Computation

A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process. FL not only reduces the communication needs but also helps to protect the local privacy. Although FL has these advantages, it can still experience large communication latency when there are massive edge devices connected to the central parameter server (PS) and/or millions of model parameters involved in the learning process. Over-the-air computation (AirComp) is capable of computing while transmitting data by allowing multiple devices to send data simultaneously by using analog modulation. To achieve good performance in FL through AirComp, user scheduling plays a critical role. In this paper, we investigate and compare different user scheduling policies, which are based on various criteria such as wireless channel conditions and the significance of model updates. Receiver beamforming is applied to minimize the mean-square-error (MSE) of the distortion of function aggregation result via AirComp. Simulation results show that scheduling based on the significance of model updates has smaller fluctuations in the training process while scheduling based on channel condition has the advantage on energy efficiency.
From Explainable to Reliable Artificial Intelligence

Artificial Intelligence systems are characterized by always less interactions with humans today, leading to autonomous decision-making processes. In this context, erroneous predictions can have severe consequences. As a solution, we design and develop a set of methods derived from eXplainable AI models. The aim is to define “safety regions” in the feature space where false negatives (e.g., in a mobility scenario, prediction of no collision, but collision in reality) tend to zero. We test and compare the proposed algorithms on two different datasets (physical fatigue and vehicle platooning) and achieve quite different conclusions in terms of results that strongly depend on the level of noise in the dataset rather than on the algorithms at hand.
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization

Tuning complex machine learning systems is challenging. Machine learning typically requires to set hyperparameters, be it regularization, architecture, or optimization parameters, whose tuning is critical to achieve good predictive performance. To democratize access to machine learning systems, it is essential to automate the tuning. This paper presents Amazon SageMaker Automatic Model Tuning (AMT), a fully managed system for gradient-free optimization at scale. AMT finds the best version of a trained machine learning model by repeatedly evaluating it with different hyperparameter configurations. It leverages either random search or Bayesian optimization to choose the hyperparameter values resulting in the best model, as measured by the metric chosen by the user. AMT can be used with built-in algorithms, custom algorithms, and Amazon SageMaker pre-built containers for machine learning frameworks. We discuss the core functionality, system architecture, our design principles, and lessons learned. We also describe more advanced features of AMT, such as automated early stopping and warm-starting, showing in experiments their benefits to users.
Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models

Insurance claim severity data are characterized by complex distributional phenomenons, where flexible density estimation tools such as the finite mixture models (FMM) are necessary. However, maximum likelihood estimations (MLE) often produce unstable tail estimates for the FMM. Motivated by this challenge, this article presents a maximum weighted likelihood estimator (MWLE) for robust estimations of heavy-tailed FMM. Under some regularity conditions, the proposed MWLE is consistent and asymptotically normal. Since the MWLE has a probabilistic interpretation, we are able to develop two distinctive versions of the Generalized Expectation-Maximization (GEM) algorithm to estimate the MWLE parameters more efficiently and reliably than the standard gradient-based algorithms. We apply the proposed MWLE to two simulation studies and a real motor insurance dataset to demonstrate that it better extrapolates the extreme losses than the MLE, without sacrificing the flexibility of the FMM in capturing the small attritional claims.
A distinctive landmark-based face recognition system for identical twins by extracting novel weighted features

High similarity in facial appearances of twins has complicated the facial feature-based recognition task. This paper introduces a Distinctive Landmark-based Face Recognition (DLFR) system to mitigate the problem. Novel features are proposed based on the number of key points obtained from a modified scale-invariant feature transform algorithm and also the most distinctive landmark region of the face. A slightly altered genetic algorithm is used to optimize weights for the features. The weighted features are classified using a support vector machine classifier . Extensive experiments on 440 face images of identical twins and non-twin individuals show the higher performance of the DLFR system in comparison to a previous work in distinguishing between identical twins and individuals with similar facial images. Because of substantial differences of the DLFR system with most conventional face recognition systems, the work could motivate the research community to push forward the embodied novel idea further.
Using Web Standards to Design Accessible Data Visualizations in Professional Communication

Introduction: Data visualization is a reliable tool for professional communication practitioners to synthesize and present data for a variety of audiences. However, data visualizations have a range of accessibility concerns, including visual acuity, color/contrast difficulties, color blindness, and size/scale issues. Data visualizations should therefore be designed following web standards for complex images to ensure that they are accessible to audiences with diverse needs. Key concepts: Drawing from work in professional communication and disability studies, practitioners recognize that users have varied accessibility needs. “Universal design” as a guiding principle is less helpful than targeted approaches to design that reflect actual user needs. Such targeted approaches should follow web standards for accessible design because they enable interaction with newer accessibility technologies and put more control in the hands of users. Key lessons: Follow these best practices to create visually accessible data visualizations. 1. Design the visual for accessibility by using whitespace, creating contrast, maintaining size/scale, and labeling the visual clearly. 2. Implement the visual using web standards to create semantic connections between the visual and text for both users and accessibility technologies. This goal can be achieved with textual description, overview/data/presentation context, or ARIA semantic links. 3. Test the visual for accessibility through user tests and industry-standard tools. Implications for practice: Web standards provide a blueprint for designing accessible data visualizations for online spaces, but professional communicators should be aware of the coding expertise and necessary infrastructure needed to deploy these visuals. Nevertheless, with increasing use of public-facing data visualizations to convey information on global issues, such as COVID-19, the need for these visuals to be accessible to all audiences becomes paramount.
Machine Learning Algorithm for Classification

Abstract Recently, machine learning methods have a good performance in the field of classification tasks. Summarizing and comparing the performances of different classifiers in the application of their specific classification tasks has a reference significance. In this paper, five classical machine learning classifiers, including GMM, Random Forest, SVM, XGBoost, and Naive Bayes, are compared to show their computing characteristics. The advantages and disadvantages are analysed in this paper. Based on the different datasets, namely different specific classification tasks, the different classifiers perform similarly. However, the SVM-based classifier has the lowest accuracy while processing the text data to apply the text classification task. This result shows that if the classification task is difficult, the accuracy would not be high. This research summarizes the performances of different machine learning methods in the application of specific classification tasks. And this research has a reference significance for the machine learning-based classifiers.
Query-driven video event processing for the internet of multimedia things

Advances in Deep Neural Network (DNN) techniques have revolutionized video analytics and unlocked the potential for querying and mining video event patterns. This paper details GNOSIS, an event processing platform to perform near-real-time video event detection in a distributed setting. GNOSIS follows a serverless approach where its component acts as independent microservices and can be deployed at multiple nodes. GNOSIS uses a declarative query-driven approach where users can write customize queries for spatiotemporal video event reasoning. The system converts the incoming video streams into a continuous evolving graph stream using machine learning (ML) and DNN models pipeline and applies graph matching for video event pattern detection. GNOSIS can perform both stateful and stateless video event matching. To improve Quality of Service (QoS), recent work in GNOSIS incorporates optimization techniques like adaptive scheduling, energy efficiency, and content-driven windows. This paper demonstrates the Occupational Health and Safety query use cases to show the GNOSIS efficacy.
Early Warning Pedestrian Crossing Intention From Its Head Gesture using Head Pose Estimation

The development of the autonomous driving system is still a hot topic. Especially in the area of the interaction between the autonomous driving system and road crossing pedestrians. Recognizing the pedestrian crossing intention is one of the crucial topics for the smart autonomous driving system. The autonomous driving system must be safe enough for both its user and pedestrian. Many research, approach, and method has been developed to detect or predict pedestrian crossing intention. But unfortunately, many previous works predict the pedestrian crossing intention that already does cross the road. There is still few research about how to predict early pedestrian crossing intention. Usually when they want to cross the road or before they do cross the road, the pedestrian gives unique gestures like looking at the incoming vehicle. Thus, an early warning pedestrian crossing intention system has been developed using a different approach, by its head gesture. The pedestrian crossing intention could be predicted by classifying its head pose angle. Experiment show very well that the proposed method able to predict early pedestrian crossing intention by its head gesture and give an early warning sign to the autonomous driving system with the accuracy of the classification 97.2%. We believe our work could bring benefits to the autonomous driving system to increase the safeties of its system.
Benchmark Set Reduction for Cheap Empirical Algorithmic Studies

The present paper introduces a benchmark set reduction strategy that can degrade the experimental evaluation cost for the algorithmic studies. Algorithm design is an iterative development process within in a test-revise loop. Starting a devised algorithm's initial version, it needs to be tested for revealing its advantages and drawbacks. Its shortcomings lead the designers to modify the algorithm. Then, the modified algorithm is again tested. This two-step pipeline is repeated until the algorithm meets the expectations such as delivering the state-of-the-art results on a set of benchmarks for a specific problem. That recurring testing step can be a burden for whom with limited computational resources. The mentioned computational cost can mainly occur due either high per instance runtime cost or having a large benchmark set. This study focuses on the cases when a target algorithm needs to be assessed across large benchmark sets. The idea is to automatically extract problem instance representation through an instance-algorithm performance data. The derived representation in the form of latent features is utilized to determine a small yet a representative subset of a given large instance set. The proposed strategy is investigated on the Traveling Thief Problem of 9720 instances. The corresponding performance data is collected by the help of 21 TTP algorithms. The resulting computational analysis showed that the proposed method is capable of substantially minimizing the benchmark instance set size.
Chatbot : A Question Answering System for Student

In the last few years there has been a fast growing up of the use of Chatbots in various fields, such as Customer Service, Marketing, Educational, Health Care and many others. This paper develops a Question Answering System in educational domain. Using Word Embedding, Latent Dirichlet Allocation and Text summarization method build 3 knowledge bases. According question sentence analysis then LineBot retrieve data from these knowledge bases and provides proper answers to the student. Comparison experimental group-used LineBot hits and reading time in e-learning system, are more than control group.
Proximal Policy Based Deep Reinforcement Learning Approach for Swarm Robots

Artificial intelligence technology is becoming more active in all areas of our lives day by day. This technology affects our daily life by more developing in areas such as industry 4.0, security and education. Deep reinforcement learning is one of the most developed algorithms in the field of artificial intelligence. In this study, it is aimed that three different robots in a limited area learn to move without hitting each other, fixed obstacles and the boundaries of the field. These robots have been trained using the deep reinforcement learning approach and Proximal policy optimization (PPO) policy. Instead of uses value-based methods with the discrete action space, PPO that can easily manipulate the continuous action field and successfully determine the action of the robots has been proposed. PPO policy achieves successful results in multi-agent problems, especially with the use of the Actor-Critic network. In addition, information is given about environment control and learning approaches for swarm behavior. We propose parameter sharing and behavior-based method for this study. Finally, trained model is recorded and tested in 9 different environments where the obstacles are located differently. With our method, robots can perform their tasks in closed environments in the real world without damaging anyone or anything.
AVGuard: A Forensic Investigation Framework for Autonomous Vehicles

Autonomous vehicles (AVs) rely on on-board sensors and computation capabilities to drive on the road with limited or no human intervention. However, autonomous driving decisions can go wrong for numerous reasons, leading to accidents on the road. The AVs lack a proper forensics investigation framework, which is essential for various reasons such as resolving insurance disputes, investigating attacks, compliance with autonomous driving safety guidelines, etc. To design robust and safe AVs, identifying the actual reason behind any incident involving the AV is crucial. Hence, it is essential to collect meaningful logs from different autonomous driving modules and store them in a secure and tamper-proof way. In this paper, we propose AVGuard, a forensic investigation framework that collects and stores the autonomous driving logs. The framework can generate and verify proofs to ensure the integrity of collected logs while preventing collusion attacks among multiple dishonest parties. The stored logs can be used later by investigators to identify the exact incident. Our proof-of-concept implementation shows that the framework can be integrated with autonomous driving modules efficiently without any significant overheads.