Reviewerly/exHarmony huggingface dataset:
Sociable and Ergonomic Human-Robot Collaboration through Action Recognition and Augmented Hierarchical Quadratic Programming

The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with multiple objectives and constraints, arising from the collaborative task or the human. In this regard, we propose vision techniques to perform human action recognition and image classification, which are integrated into an Augmented Hierarchical Quadratic Programming (AHQP) scheme to hierarchically optimize the robot's reactive behavior and human ergonomics. The proposed framework allows one to intuitively command the robot in space while a task is being executed. The experiments confirm increased human ergonomics and usability, which are fundamental parameters for reducing musculoskeletal diseases and increasing trust in automation.
Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning

The internet makes it easier for people to connect to each other and has become a platform to express ideas and share information with the world. The growth of the internet has indirectly led to the development of social networking sites. The reviews posted by people on these sites implies their opinion, and analysis over reviews is required to understand their intent. In this paper, natural language processing technique and machine learning algorithms are applied to classify the text data. The contributions of the proposed approach are three-fold: 1) chi square selector is applied to select the k-best features, 2) support vector machines is executed to classify the reviews (hyperparameters of the SVM classifier are tuned using GridSearch approach), and 3) bagging algorithm is applied with the base classifier over the newly built SVM classifier. The number of base classifiers of the bagging algorithm is varied accordingly. The results of the proposed approach are compared to the similar existing work, and hence, it is found to achieve better results as compared to the existing systems.
CLOSE: Curriculum Learning on the Sharing Extent Towards Better One-Shot NAS

One-shot Neural Architecture Search (NAS) has been widely used to discover architectures due to its efficiency. However, previous studies reveal that one-shot performance estimations of architectures might not be well correlated with their performances in stand-alone training because of the excessive sharing of operation parameters (i.e., large sharing extent) between architectures. Thus, recent methods construct even more over-parameterized supernets to reduce the sharing extent. But these improved methods introduce a large number of extra parameters and thus cause an undesirable trade-off between the training costs and the ranking quality. To alleviate the above issues, we propose to apply Curriculum Learning On Sharing Extent (CLOSE) to train the supernet both efficiently and effectively. Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum). To support this training strategy, we design a novel supernet (CLOSENet) that decouples the parameters from operations to realize a flexible sharing scheme and adjustable sharing extent. Extensive experiments demonstrate that CLOSE can obtain a better ranking quality across different computational budget constraints than other one-shot supernets, and is able to discover superior architectures when combined with various search strategies. Code is available at https://github.com/walkerning/aw_nas .
High Speed and Robust RGB-Thermal Tracking via Dual Attentive Stream Siamese Network

To meet the need of robust tracking in some cases (e.g., extremely illumination and thermal crossover), plenty of RGB-T tracking methods have been proposed in recent years. However, many of them can hardly meet the real-time standard since they are difficult to balance the robust-ness and the speed. To address such problems, we propose a new Dual Attentive Siamese Network (DASN) for RGB-T tracking. Specifically, we use a dual-stream siamese deep learning network to model tracking as a similarity measure task. In addition, to promote the information propagation procedure between two modalities and suppress the inter-ference of background, we design the channel attention module (CAFE Module) and the channel-spatial attention module (CSAFE Module). What's more, the dual modality region proposal sub-network and the strategy of selecting proposal are constructed to boost the performance. The proposed DASN is trained end-to-end offline. Extensive ex-periments on three real RGB-T tracking datasets show that our tracker achieves very competive results with a high tracking speed over 140 frames per second. Code is released at https://github.com/easycodesniper-atk/SiamCSR.git.
Doubly-Fused ViT: Fuse Information from Vision Transformer Doubly with Local Representation

Vision Transformer (ViT) has recently emerged as a new paradigm for computer vision tasks, but is not as efficient as convolutional neural networks (CNN). In this paper, we propose an efficient ViT architecture, named Doubly-Fused ViT (DFvT), where we feed low-resolution feature maps to self-attention (SA) to achieve larger context with efficiency (by moving downsampling prior to SA), and enhance it with fine-detailed spatial information. SA is a powerful mechanism that extracts rich context information, thus could and should operate at a low spatial resolution. To make up for the loss of details, convolutions are fused into the main ViT pipeline, without incurring high computational costs. In particular, a Context Module (CM), consisting of fused downsampling operator and subsequent SA, is introduced to effectively capture global features with high efficiency. A Spatial Module (SM) is proposed to preserve fine-grained spatial information. To fuse the heterogeneous features, we specially design a Dual AtteNtion Enhancement (DANE) module to selectively fuse low-level and high-level features. Experiments demonstrate that DFvT achieves state-of-the-art accuracy with much higher efficiency across a spectrum of different model sizes. Ablation study validates the effectiveness of our designed components.
Multi Scale Ship Detection Based on Attention and Weighted Fusion Model for High Resolution SAR Images

Ship detection in SAR images is a challenging problem. CNN-based ship detection method in SAR images has achieved remarkable results. Due to the multi scale of the ships and interference from complex sea conditions or nearshore background in SAR images, many false alarms and missed detections can occur in ship detection. To solve these problems, a multi-scale ship detection network in SAR images based on attention and weighted fusion is proposed in this paper. First, a higher-resolution detect head is added based on the YOLOv5 framework for detecting tiny-scale ships in SAR images. Then, the coordinate attention block is introduced to refine the location features of ship targets and suppress the interference of complex background. Finally, in the feature fusion stage, adaptive weighted feature fusion is used to reduce feature redundancy. Experiments on the SSDD dataset show the effectiveness of the proposed method.
Metaheuristic feature selection for software fault prediction

AbstractAbstractAchieving bug-free software is hard as most of the time there are hidden defects. Predicting the rate of faultiness in software modules before commercializing is still a challenge to software engineers. Early detection of fault prone modules not only helps the software project manager to get good quality software, but also to sustain their customer satisfaction. In this paper, two classifiers and a meta-heuristic feature selection based models are proposed for software fault prediction. We perform a comparison study of different classifiers such as Support Vector Machine, Random Forest and meta-heuristic feature selection technique such as Particle Swarm Optimization (PSO). We have conducted experiments on nine data sets collected from GitHub Repository. In this study F-measure, accuracy and AUC are considered as performance measures. We found that, the classification accuracy of proposed model is not degraded, after reducing 75.15%, of the total features on an average, by PSO. We also found that the prediction performance of Random Forest is better than other model such as Support Vector Machine.Subject Classification: 68M0768M1568P15Keywords: Software fault predictionMachine learningSupport vector machineRandom forestFeature selectionPSO
Speeding Up Recommender Systems Using Association Rules

Recommender systems are considered one of the most rapidly growing branches of Artificial Intelligence. The demand for finding more efficient techniques to generate recommendations becomes urgent. However, many recommendations become useless if there is a delay in generating and showing them to the user. Therefore, we focus on improving the speed of recommendation systems without impacting the accuracy. In this paper, we suggest a novel recommender system based on Factorization Machines and Association Rules (FMAR). We introduce an approach to generate association rules using two algorithms: (i) apriori and (ii) frequent pattern (FP) growth. These association rules will be utilized to reduce the number of items passed to the factorization machines recommendation model. We show that FMAR has significantly decreased the number of new items that the recommender system has to predict and hence, decreased the required time for generating the recommendations. On the other hand, while building the FMAR tool, we concentrate on making a balance between prediction time and accuracy of generated recommendations to ensure that the accuracy is not significantly impacted compared to the accuracy of using factorization machines without association rules.
Privacy-Preserving Viewport Prediction using Federated Learning for 360° Live Video Streaming

Predicting the user's viewport scanpath is an essential task for 360° viewport-based adaptive streaming. It informs the system which parts of content should be streamed with high quality for bandwidth saving over the best-effort Internet. However, in light of growing privacy concerns among consumers and increasingly strict data privacy legislation, user data collection and storage have been constrained. This paper proposes a novel privacy-preserving framework employing Federated Learning (FL) for online viewport prediction in a live 360° video streaming scenario. In this framework, the user data is only collected and processed on the client-side in the current viewing session and not shared with external parties, e.g., servers, and other clients. We evaluated the framework over a widely-used dataset and measure the computation and transmission time of the proposed streaming system. The experiments show that our framework provides high prediction accuracy and achieves real-time computation requirements of live video streaming. On privacy preservation, our results demonstrate that in a tile-based 360° video streaming system, the user identification rate can be decreased by 18.11 percentage points in 4 x 3 tiles per frame and 9.65 percentage points in 16x9 tiles per frame. The code will be publicly available to further contribute to the community.
Semantic consistency for graph representation learning

In graph learning, it is fundamental to integrate the features from graph structure and node attributes. Towards this end, graph convolution technique has been devised based on the premise that the similarity of node attributes between two nodes is semantically consistent with their topological proximity. However, many real-networks are found to exhibit the semantic inconsistency, i.e., the phenomenon that directly connected nodes are dissimilar in their attributes. This work is concerned with two related issues: how do we quantitatively measure the semantic consistency between node attributes and graph structure? can we leverage this information to facilitate graph representation? To answer those questions, we first introduce a novel metric to evaluate the semantic consistency in a graph, and then we identify a set of key designs to encode the local semantic consistency information into a type of ego's node feature. Then, we fuse this new node feature with the original node attributes by concatenating the two parts using the semantic consistency metric as weight factor. Experiments on real-world datasets show that linear classifier (e.g. multilayer perceptrons) based on our unsupervised feature learning scheme achieves strong performance across the datasets, especially on the datasets with low semantic consistency, compared to the popular supervised GCNs and other competitive unsupervised graph representation learning models.
Smart Homecare Research Translation Into Broader Practice: Enablers, Barriers and Directions

Smart homecare utilises advanced technologies to support, improve and promote remote healthcare in homes and communities through collecting and analysing health data and sharing this knowledge with carers and clinicians. With the continuous growth in the world’s older population, smart homecare becomes increasingly crucial in providing in-home care for older adults, allowing the vital healthcare dollars to go further into other critical care needs. In addition, with the rise in the development and utilisation of innovative technologies in healthcare settings, it is vital to ensure that these technologies are guided and approved by the corresponding regulatory bodies such as FDA (Foods and Drug Administration) in the USA and TGA (Therapeutic Good Administration) in Australia. With this premise, this paper identifies four dimensions for researchers to consider when developing smart homecare solutions for in-home remote care: Technology, Data, People, and Operational Environment. The essential interplays amongst these four dimensions are discussed to identify the various enablers and barriers in the successful delivery of smart homecare solutions. As the primary output of this paper, it proposes a conceptual framework to achieve practical in-home care for the older population living independently with the support of technology, while addressing the challenges such as security and privacy of patient data. Secondly, a comprehensive and practical guide featuring seven phases is presented to support and direct researchers in implementing smart homecare solutions for remote care. The proposed framework and the guide aim to make smart homecare research practical and truly translational into broader practice.
PreTraM: Self-supervised Pre-training via Connecting Trajectory and Map

Deep learning has recently achieved significant progress in trajectory forecasting. However, the scarcity of trajectory data inhibits the data-hungry deep-learning models from learning good representations. While pre-training methods for representation learning exist in computer vision and natural language processing, they still require large-scale data. It is hard to replicate their success in trajectory forecasting due to the inadequate trajectory data (e.g., 34K samples in the nuScenes dataset). To work around the scarcity of trajectory data, we resort to another data modality closely related to trajectories—HD-maps, which is abundantly provided in existing datasets. In this paper, we propose PreTraM, a self-supervised Pre-training scheme via connecting Trajectories and Maps for trajectory forecasting. PreTraM consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM reduces their errors by 5.5% and 6.9% relatively on the nuScenes dataset. We show that PreTraM improves data efficiency and scales well with model size. Our code and pre-trained models will be released at https://github.com/chenfengxu714/PreTraM .
Cluster Based Secure Multi-party Computation in Federated Learning for Histopathology Images

Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or gradients can expose private training data samples. To address this challenge, we adopt secure multiparty computation (SMC) to establish a privacy-preserving federated learning framework. In our proposed method, the hospitals are divided into clusters. After local training, each hospital splits its model weights among other hospitals in the same cluster such that no single hospital can retrieve other hospitals’ weights on its own. Then, all hospitals sum up the received weights, sending the results to the central server. Finally, the central server aggregates the results, retrieving the average of models’ weights and updating the model without having access to individual hospitals’ weights. We conduct experiments on a publicly available repository, The Cancer Genome Atlas (TCGA). We compare the performance of the proposed framework with differential privacy and federated averaging as the baseline. The results reveal that compared to differential privacy, our framework can achieve higher accuracy with no privacy leakage risk at a cost of higher communication overhead.
Achievable CCA2 Relaxation for Homomorphic Encryption

Homomorphic encryption (HE) protects data in-use, but can be computationally expensive. To avoid the costly bootstrapping procedure that refreshes ciphertexts, some works have explored client-aided outsourcing protocols, where the client intermittently refreshes ciphertexts for a server that is performing homomorphic computations. But is this approach secure against malicious servers? We present a CPA-secure encryption scheme that is completely insecure in this setting. We define a new notion of security, called funcCPA, that we prove is sufficient. Additionally, we show: Namely, funcCPA-security lies strictly between CPA-security and CCA2-security (under reasonable assumptions), and has an interesting relationship with circular security, though it is not known to be equivalent.
An evaluation of decision on paradigm shift in higher education by digital transformation

Digital transformation has become one of the main goals of educational institutions, particularly higher education institutions, in recent years. The research concludes finally solving the problem cited i.e., the world is on transformation mostly because of the digital development and diversification, where the higher education is also shifting as a compelled switch over in methodological approaches, and it needs an assessment for the decision criteria with a proper process and the justification. The research is quantitative in nature and is conducted with a sample of 462 teachers engaged in higher education in Poland. The statistical test Anova has been the main base for concluding the outcome. One of the research question and objective is about the real assessment of decision criteria in paradigm shift in higher education is answered with these variables showing a varied opinion that the paradigm shift has been there in the higher education, still it is not in the unfulfilled state. Poland's higher education sector is digitally transformed in their activities of teachers, where everyone has shifted their teaching pedagogy to online.
Intelligent Internet of Things in Mammography Screening Using Multicenter Transformation Between Unified Capsules

Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast cancer mortality rate by AI-driven healthcare. However, it is a huge challenge to extend the conventional single-center into the multicenter mammography screening, thus improving the effectiveness and robustness of intelligent IoT-based devices. To address this problem, we utilize multicenter mammograms by the modified capsule neural network and propose a novel framework called multicenter transformation between unified capsules (MLT-UniCaps) in this article. The proposed MLT-UniCaps is composed of Attentional Pose Embedding, Dynamic Source Capsule Traversal, and Adaptive Target Capsule Fusion to realize an intelligent remote assistant diagnosis. Attentional Pose Embedding extracts feature vectors via variations in position, orientation, scale, and lighting as the poses through an adversarial convolutional neural network with an attention-based layer. Based on the pose presentation, Dynamic Source Capsule Traversal deploys a dynamic routing mechanism between neurons to build a source cancer classifier for single-center mammography screening. Using the source cancer classifier, Adaptive Target Capsule Fusion integrates various centers of mammograms as the universal cancer detectors and optimizes heterogeneous distribution among them by the transformation-likelihood maximization. Owing to the three components, MLT-UniCaps effectively improves the results of single-center mammography screening and works in the multicenter breast cancer diagnosis. By comprehensive experiments on 58 965 samples, the proposed MLT-UniCaps obtains 90.1% of overall classification accuracy on single-center trials and 73.8% of overall F1 score on multicenter trials. All the experimental results illustrated that our MLT-UniCaps, an intelligent IoT-based clinical tool, inures the benefit of mammography screening.
K-Means

So far, we have covered supervised learning algorithms for which the classes are predefined and the class of each instance of the training dataset is known in advance. The problem was to find a model that correctly classifies instances into their appropriate classes with a minimal cost (i.e., minimal error rate).
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data

While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we propose a method to generate attribution maps for the detected objects in order to better understand the behavior of such models. These maps indicate the importance of each 3D point in predicting the specific objects. Our method works with black-box models: We do not require any prior knowledge of the architecture nor access to the model's internals, like parameters, activations or gradients. Our efficient perturbation-based approach empirically estimates the importance of each point by testing the model with randomly generated subsets of the input point cloud. Our sub-sampling strategy takes into account the special characteristics of LiDAR data, such as the depth-dependent point density. We show a detailed evaluation of the attribution maps and demonstrate that they are interpretable and highly informative. Furthermore, we compare the attribution maps of recent 3D object detection architectures to provide insights into their decision-making processes.