Reviewerly/exHarmony huggingface dataset:
Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction

Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and likely destination areas. In this context, multi-modality is a fundamental aspect and its effective modeling can be beneficial to any architecture. Inferring accurate trajectories is nevertheless challenging, due to the inherently uncertain nature of the future. To overcome these difficulties, recent models use different inputs and propose to model human intentions using complex fusion mechanisms. In this respect, we propose a lightweight attention-based recurrent backbone that acts solely on past observed positions. Although this backbone already provides promising results, we demonstrate that its prediction accuracy can be improved considerably when combined with a scene-aware goal-estimation module. To this end, we employ a common goal module, based on a U-Net architecture, which additionally extracts semantic information to predict scene-compliant destinations. We conduct extensive experiments on publicly-available datasets (i.e. SDD, inD, ETH/UCY) and show that our approach performs on par with state-of-the-art techniques while reducing model complexity.
Federated Learning-based Driver Activity Recognition for Edge Devices

Video action recognition has been an active area of research for the past several years. However, the majority of research is concentrated on recognizing a diverse range of activities in distinct environments. On the other hand, Driver Activity Recognition (DAR) is significantly more difficult since there is a much finer distinction between various actions. Moreover, training robust DAR models requires diverse training data from multiple sources, which might not be feasible for a centralized setup due to privacy and security concerns. Furthermore, it is critical to develop efficient models due to limited computational resources available on vehicular edge devices. Federated Learning (FL), which allows data parties to collaborate on machine learning models while preserving data privacy and reducing communication requirements, can be used to overcome these challenges. Despite significant progress on various computer vision tasks, FL for DAR has been largely unexplored. In this work, we propose an FL-based DAR model and extensively benchmark the model performance on two datasets under various practical setups. Our results indicate that the proposed approach performs competitively under the centralized (non-FL) and decentralized (FL) settings.
CIOA: Circle-Inspired Optimization Algorithm, an algorithm for engineering optimization

This paper presents a new, robust and very efficient metaheuristic optimization algorithm, called Circle Inspired Optimization Algorithm (CIOA), for solving constrained and unconstrained engineering optimization problems. The inspiration for the proposed algorithm consists of well-known formulations of the trigonometric circle. CIOA is compared with five other very famous algorithms in ten benchmark function optimization problems, five real-world engineering constrained optimization problems, and also four structural optimization problems for plane and spatial trusses subjected to multiple and different types of constraints. The results obtained demonstrate that the proposed algorithm is more efficient than other famous algorithms, contributing to the accurate and fast solution of complex optimization problems.
Compression and regularized optimization of modules stacked residual deep fuzzy system with application to time series prediction

The double-input-rule-modules stacked deep fuzzy method (DIRM-DFM) has attracted much attention because of its interpretability and prediction accuracy. However, when confronted with high-dimensional data and large numbers of fuzzy rules, the original DIRM-DFM has three limitations. First, the identity mapping that appears in the depth layer causes degradation of the prediction performance. Second, redundant fuzzy rules increase the complexity of the structure. Third, the overfitting phenomenon in the parameter-learning process imposes restrictions on the generalization ability of the system. Therefore, achieving structural simplification and high-accuracy prediction remains challenging. In this paper, a compression and regularized optimization scheme for the modules stacked residual deep fuzzy system (CDIRM-RDFS) is presented to address the above-mentioned limitations. To overcome the first limitation, a residual approximation mechanism was developed to approximate the actual output layer by layer. To obtain more compact fuzzy modules, singular value decomposition was conducted to eliminate the unimportant fuzzy rules, thus surmounting the second limitation. Finally, to prevent overfitting, regularization terms combining the l1 norm with the l2 norm were added to the loss function to penalize the parameters in the learning process and thereby solve the third problem. The performance of the proposed CDIRM-RDFS was validated using various types of datasets, including seasonal and non-seasonal as well as cyclical and non-cyclical time series. Compared with some existing popular shallow and deep models, the experimental results indicate that the proposed methodology can simplify the system architecture while improving the accuracy, effectiveness, and robustness of the prediction model.
A semi-supervised label-driven auto-weighted strategy for multi-view data classification

Distinguishing the importance of views plays a key role in multi-view learning as each view often contributes differently to a specific task. However, existing strategies generally attach importance to each view blindly from a data representation perspective, which often fails to identify low-quality views then leading to poor performance. In this paper, by establishing a link between labeled data and the importance of different views, we propose a semi-supervised label-driven auto-weighted strategy that evaluates the importance of views from a labeling perspective to avoid the negative impact of unimportant or low-quality views. Based on this strategy, we propose a transductive semi-supervised auto-weighted multi-view classification model that learns the labels of multi-view data under the more reasonable weights of the views. The model is decoupled into three small-scale sub-problems that can efficiently be optimized. The experimental results on classification tasks show that our proposed method achieves very promising classification accuracy at a lower computational cost compared to other related methods, and the weight change experiments show that our proposed strategy can distinguish view importance more accurately than other related strategies especially when low-quality views are involved in multi-view learning.
HDPG

Traditional robot control or more general continuous control tasks often rely on carefully hand-crafted classic control methods. These models often lack the self-learning adaptability and intelligence to achieve human-level control. On the other hand, recent advancements in Reinforcement Learning (RL) present algorithms that have the capability of human-like learning. The integration of Deep Neural Networks (DNN) and RL thereby enables autonomous learning in robot control tasks. However, DNN-based RL brings both high-quality learning and high computation cost, which is no longer ideal for currently fast-growing edge computing scenarios.
Enabling efficient deep convolutional neural network-based sensor fusion for autonomous driving

Autonomous driving demands accurate perception and safe decision-making. To achieve this, automated vehicles are typically equipped with multiple sensors (e.g., cameras, Lidar, etc.), enabling them to exploit complementary environmental contexts by fusing data from different sensing modalities. With the success of Deep Convolutional Neural Network (DCNN), the fusion between multiple DCNNs has been proved to be a promising strategy to achieve satisfactory perception accuracy. However, existing mainstream DCNN fusion strategies conduct fusion by simply element-wisely adding feature maps extracted from different modalities together at various stages, failing to consider whether the features being fused are matched or not. Therefore, we first propose a feature disparity metric to quantitatively measure the degree of feature disparity between the fusing feature maps. Then, we propose a Fusion-filter as the Feature-matching techniques to tackle the feature-mismatching issue. We also propose a Layer-sharing technique in the deep layer of the DCNN to achieve high accuracy. With the assistance of feature disparity working as an additional loss, our proposed technologies enable DCNN to learn corresponding feature maps with similar characteristics and complementary visual context from different modalities. Evaluations demonstrate that our proposed fusion techniques can achieve higher accuracy on KITTI dataset with less computation resources consumption.
MATCHA

Fully Homomorphic Encryption over the Torus (TFHE) allows arbitrary computations to happen directly on ciphertexts using homomorphic logic gates. However, each TFHE gate on state-of-the-art hardware platforms such as GPUs and FPGAs is extremely slow (> 0.2ms). Moreover, even the latest FPGA-based TFHE accelerator cannot achieve high energy efficiency, since it frequently invokes expensive double-precision floating point FFT and IFFT kernels. In this paper, we propose a fast and energy-efficient accelerator, MATCHA, to process TFHE gates. MATCHA supports aggressive bootstrapping key unrolling to accelerate TFHE gates without decryption errors by approximate multiplication-less integer FFTs and IFFTs, and a pipelined datapath. Compared to prior accelerators, MATCHA improves the TFHE gate processing throughput by 2.3x, and the throughput per Watt by 6.3x.
A Nuanced perspective on blockchain technology and healthcare

The advantages of blockchain technology are numerous and its applications in healthcare industry are still under-estimated. In a context of increasing number of applications in healthcare sector it is important to adopt this technology in a way that is not at the origin of health issues for human beings. This short article discusses the potential side effects of a blockchain based healthcare industry that could mainly be due to the increasing needs in electricity for validation of all blockchain-based transactions\records. This note explains how and why this aspect could eventually generate some crypto-damages and ultimately might generate health issues. The aim of this essay is to provide some thoughts on the way blockchain technology could (or could not) be implemented to ensure a real sustainable, ethical and consistent healthcare system.
K-LM: Knowledge Augmenting in Language Models Within the Scholarly Domain

The use of superior algorithms and complex architectures in language models have successfully imparted human-like abilities to machines for specific tasks.But two significant constraints, the available training data size and the understanding of domain-specific context, hamper the pre-trained language models from optimal and reliable performance.A potential solution to tackle these limitations is to equip the language models with domain knowledge.While the commonly adopted techniques use Knowledge Graphs Embeddings (KGEs) to inject domain knowledge, we provide a Knowledge Language Model (K-LM) to use the Resource Description Framework (RDF) triples directly, extracted from world knowledge bases.The proposed model works in conjunction with Generative Pretrained Transformer (GPT-2) and Bidirectional Encoder Representations from Transformers (BERT) and uses a well-defined pipeline to select, categorize, and filter the RDF triples.In addition, we introduce heuristic methods to inject domain-specific knowledge in K-LM, leveraging knowledge graphs (KGs).We tested our approaches on the classification task within the scholarly domain using two KGs, and our results show that our proposed language model has significantly outperformed the baselines and BERT for each KG.Our experimental findings also help us conclude the importance of relevance of KG used over the quantity of injected RDF triples.Also, each of our proposed methods for injecting the RDF triples has increased the overall model's accuracy, demonstrating that K-LM is a potential choice for domain adaptation to solve knowledge-driven problems.
AraXLNet: pre-trained language model for sentiment analysis of Arabic

Abstract The Arabic language is a complex language with little resources; therefore, its limitations create a challenge to produce accurate text classification tasks such as sentiment analysis. The main goal of sentiment analysis is to determine the overall orientation of a given text in terms of whether it is positive, negative, or neutral. Recently, language models have shown great results in promoting the accuracy of text classification in English. The models are pre-trained on a large dataset and then fine-tuned on the downstream tasks. Particularly, XLNet has achieved state-of-the-art results for diverse natural language processing (NLP) tasks in English. In this paper, we hypothesize that such parallel success can be achieved in Arabic. The paper aims to support this hypothesis by producing the first XLNet-based language model in Arabic called AraXLNet, demonstrating its use in Arabic sentiment analysis in order to improve the prediction accuracy of such tasks. The results showed that the proposed model, AraXLNet, with Farasa segmenter achieved an accuracy results of 94.78%, 93.01%, and 85.77% in sentiment analysis task for Arabic using multiple benchmark datasets. This result outperformed AraBERT that obtained 84.65%, 92.13%, and 85.05% on the same datasets, respectively. The improved accuracy of the proposed model was evident using multiple benchmark datasets, thus offering promising advancement in the Arabic text classification tasks.
Major Challenges and Future Approaches in the Employment of Blockchain and Machine Learning Techniques in the Health and Medicine

According to the benefits in safeguarding and transferring medical information, illness assessment, evaluation of “Magnetic Resonance Mapping” images, and certain other disciplines, blockchain and machine learning (ML) technology has significantly piqued attention in the healthcare domains. Formerly, those chores have been performed out along with individuals; eventually, individuals acquired attraction because to its precision and efficiency. The proposed study will examine the activities and possible capabilities of learning algorithms and blockchain in the healthcare professions focusing on these fascinating facts. Primary and secondary data analysis has been executed, with primary analysis method consisting of a survey of 150 randomly picked medicine professionals with expertise in machine learning and blockchain. They gave their answers that were being subsequently transferred to figures and employed as response variable in SPSS examining. The length of time where learning and blockchain have been used in medicine is really the independent factor. To better understand the primary and small hurdles of integrating machine learning and blockchain, a correlation investigation was done. Thereafter, secondary methodology is employed to validate the primary study results.
Patch-DFD: Patch-based end-to-end DeepFake discriminator

Facial forgery by DeepFake has recently attracted more public attention. Face image contains sensitive personal information, abuse of such technology will grow into a menace. Since the difference between real and fake faces is usually subtle and local, the general detection framework of applying the backbone network to capture the global features of the entire face and then feeding it into the binary classifier is not optimal. In addition, patch-based schemes are widely used in various computer vision tasks, including image classification. However, how to extract features for location-specific and arbitrary-shaped patches while preserving their original information and spoof patterns as much as possible requires further exploration. In this paper, a novel deep forgery detector called Patch-DFD is proposed, which applies a patch-based solution of Facial Patch Mapping (FPM) to obtain several part-based feature maps, preserving original details of each facial patch to the greatest extent. Besides, the BM-pooling module aims to fix the size of the feature maps while reducing quantization errors. The local voting strategy is finally used to fuse the results of parts detectors, so as to more accurately identify the fake faces generated by deep generative models. Compared to typical patch-wise framework that takes patch inputs, our scheme is more efficient due to the absence of repeated convolution operations. Moreover, extensive experiments conducted on publicly available face forensics datasets have proved that the effectiveness of our framework.
Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches

The study’s goal is to analyse and predict customer reviews of insurance products using various machine learning techniques. We gathered consumer rating data from the Yelp website and filtered the initial data set to only include insurance reviews. Following cleaning, the filtered summary texts were graded as positive, neutral or negative sentiments, and the AFINN and Valence Aware Dictionary for Sentiment Reasoning (VADER) sentiment algorithms were used to rate those sentiments. Furthermore, the current investigation employs five supervised machine learning approaches to divide customer ratings of insurance companies into three sentiment groups. The results of the current study revealed that the majority of customer reviews for the insurance products were negative, with the average number of words with negative sentiment being higher. In addition, current research discovered that while all of the approaches (decision tree, K Neighbours classifier, support vector machine (SVM), logistic regression and random forest classifier) can correctly classify review text into sentiment class, logistic regression outperforms in high accuracy. We analysed and predicted customer review messages using a variety of machine learning methods, which could help companies better understand how customers respond to their products and services. As a result, companies can learn how to use machine learning methods to better understand the behaviour of their customers.
Bidirectional Matching Prototypical Network for Few-Shot Image Classification

Few-shot image classification (FSIC) is the task of generalizing a model to unknown categories by learning from a small number of labeled samples of some given categories. Recently, metric-based approaches have received lots of attention for their simplicity and effectiveness, but they often only use support set to generate inaccurate prototypes matching query set, ignoring the rich information contained in queries and the reversibility of the matching relationship between the two. In this letter, we propose a new simple and effective metric-based method called Bidirectional Matching Prototypical Network (BMPN), which has three innovations:1)It has an additional reverse matching process. This process uses queries to generate more accurate prototypes to improve the model&#x2019;s performance while also forcing the model to learn features far from the decision boundary to enhance generalization capabilities; 2)It has a lightweight coordinate attention feature extractor (CAFE). This module not only captures long-term dependence along one spatial direction but also saving the accurate position information of the other spatial direction, helping the model to locate the region of interest more accurately; 3)It has a joint loss function, including forward matching loss and reverse matching loss, and a progressive weighting strategy is used in the training process to balance the importance of the two. Our model is trained end-to-end, and the experimental results show that we have reached the most advanced performance on the two benchmark datasets.
RELU-Function and Derived Function Review

The activation function plays an important role in training and improving performance in deep neural networks (dnn). The rectified linear unit (relu) function provides the necessary non-linear properties in the deep neural network (dnn). However, few papers sort out and compare various relu activation functions. Most of the paper focuses on the efficiency and accuracy of certain activation functions used by the model, but does not pay attention to the nature and differences of these activation functions. Therefore, this paper attempts to organize the RELU-function and derived function in this paper. And compared the accuracy of different relu functions (and its derivative functions) under the Mnist data set. From the experimental point of view, the relu function performs the best, and the selu and elu functions perform poorly.
Collective Deep Reinforcement Learning for Intelligence Sharing in the Internet of Intelligence-Empowered Edge Computing

Edge intelligence is emerging as a new interdiscipline to push learning intelligence from remote centers to the edge of the network. However, with its widespread deployment, new challenges arise in terms of training efficiency and service of quality (QoS). Massive repetitive model training is ubiquitous due to the inevitable needs of users for the same types of data and training results. Additionally, a smaller volume of data samples will cause the over-fitting of models. To address these issues, driven by the Internet of intelligence, this paper proposes a distributed edge intelligence sharing scheme, which allows distributed edge nodes to quickly and economically improve learning performance by sharing their learned intelligence. Considering the time-varying edge network states including data collection states, computing and communication states, and node reputation states, the distributed intelligence sharing is formulated as a multi-agent Markov decision process (MDP). Then, a novel collective deep reinforcement learning (CDRL) algorithm is designed to obtain the optimal intelligence sharing policy, which consists of local soft actor-critic (SAC) learning at each edge node and collective learning between different edge nodes. Simulation results indicate our proposal outperforms the benchmark schemes in terms of learning efficiency and intelligence sharing efficiency.