Reviewerly/exHarmony huggingface dataset:
When services computing meets blockchain: Challenges and opportunities

Services computing can offer a high-level abstraction to support diverse applications via encapsulating various computing infrastructures. Though services computing has greatly boosted the productivity of developers, it is faced with three main challenges: privacy and security risks, information silo, and pricing mechanisms and incentives. The recent advances of blockchain bring opportunities to address the challenges of services computing due to its build-in encryption as well as digital signature schemes, decentralization feature, and intrinsic incentive mechanisms. In this paper, we present a survey to investigate the integration of blockchain with services computing. The integration of blockchain with services computing mainly exhibits merits in two aspects: i) blockchain can potentially address key challenges of services computing and ii) services computing can also promote blockchain development. In particular, we categorize the current literature of services computing based on blockchain into five types: services creation, services discovery, services recommendation, services composition, and services arbitration. Moreover, we generalize Blockchain as a Service (BaaS) architecture and summarize the representative BaaS platforms. In addition, we also outline open issues of blockchain-based services computing and BaaS.
BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View

On-board 3D object detection in autonomous vehicles often relies on geometry information captured by LiDAR devices. Albeit image features are typically preferred for detection, numerous approaches take only spatial data as input. Exploiting this information in inference usually involves the use of compact representations such as the Bird's Eye View (BEV) projection, which entails a loss of information and thus hinders the joint inference of all the parameters of the objects' 3D boxes. In this paper, we present a fully end-toend 3D object detection framework that can infer oriented 3D boxes solely from BEV images by using a two-stage object detector and ad-hoc regression branches, eliminating the need for a post-processing stage. The method outperforms its predecessor (BirdNet) by a large margin and obtains state-of the-art results on the KITTI 3D Object Detection Benchmark for all the categories in evaluation. Source code is available at https://github.com/AlejandroBarrera/BirdNet2.
Real-time Face Mask and Social Distancing Violation Detection System using YOLO

With the recent outbreak and rapid transmission of the COVID-19 pandemic, the need for the public to follow social distancing norms and wear masks in public is only increasing. According to the World Health Organization, to follow proper social distancing, people in public places must maintain at least 3ft or 1m distance between each other. This paper focuses on a solution to help enforce proper social distancing and wearing masks in public using YOLO object detection on video footage and images in real time. The experimental results shown in this paper infer that the detection of masked faces and human subjects based on YOLO has stronger robustness and faster detection speed as compared to its competitors. Our proposed object detection model achieved a mean average precision score of 94.75% with an inference speed of 38 FPS on video. The network ensures inference speed capable of delivering real-time results without compromising on accuracy, even in complex setups. The social distancing method proposed also yields promising results in several variable scenarios.
From Amazon.com to Amazon.love: How Alexa is redefining companionship and interdependence for people with special needs

Abstract The trend of incorporating assistive conversational agents into people's lives has followed the unprecedented expansion in the usage of artificial intelligence (AI). Amazon, in particular, has been a key trendsetter in this area through its Alexa‐powered devices. Alexa is an intelligent personal assistant (IPA) that performs tasks, such as playing music, providing news and information, and controlling smart home appliances. While this IPA is widely utilized, it is especially gaining attention and growing usage by people with special needs. Even though the importance of the utilization of AI by people with special needs has been widely acknowledged in the extant literature, a sizeable gap exists in the marketing literature in relation to the assessment of the managerial and societal implications of IPAs when used by people with special needs. Accordingly, this study aims to examine (a) the stages of relationship development between Alexa and consumers with special needs, and (b) the potential opportunity of this relationship for Amazon in relation to their corporate image. The findings indicate that a relationship between Alexa and consumers with special needs is established as it helps them regain their independence and freedom. This relationship provides an opportunity for Amazon in enhancing its overall image for providing solutions to facilitate the lives of people with special needs.
DIGITNET: A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset

This paper introduces a novel deep learning architecture, named DIGITNET, and a large-scale digit dataset, named DIDA, to detect and recognize handwritten digits in historical document images written in the nineteen century. To generate the DIDA dataset, digit images are collected from 100,000 Swedish handwritten historical document images, which were written by different priests with different handwriting styles. This dataset contains three sub-datasets including single digit, large-scale bounding box annotated multi-digit, and digit string with 250,000, 25,000, and 200,000 samples in Red-Green-Blue (RGB) color spaces, respectively. Moreover, DIDA is used to train the DIGITNET network, which consists of two deep learning architectures, called DIGITNET-dect and DIGITNET-rec, respectively, to isolate digits and recognize digit strings in historical handwritten documents. In DIGITNET-dect architecture, to extract features from digits, three residual units where each residual unit has three convolution neural network structures are used and then a detection strategy based on You Look Only Once (YOLO) algorithm is employed to detect handwritten digits at two different scales. In DIGITNET-rec, the detected isolated digits are passed through 3 different designed Convolutional Neural Network (CNN) architectures and then the classification results of three different CNNs are combined using a voting scheme to recognize digit strings. The proposed model is also trained with various existing handwritten digit datasets and then validated over historical handwritten digit strings. The experimental results show that the proposed architecture trained with DIDA (publicly available from: https://didadataset.github.io/DIDA/) outperforms the state-of-the-art methods.
Enhancing the security of blockchain-based software defined networking through trust-based traffic fusion and filtration

With the rapid development of Internet-of-Things (IoT), more smart devices can be connected to the Internet, resulting in a dramatic increase of data transmission and communication. Software-Defined Networking (SDN), which separates the control planes and data planes, is considered as a promising solution to provide the scale and versatility necessary for IoT. However, SDN still suffers from several challenges, i.e., the centralized control plane would be a single point of failure. With the wide adoption of blockchain applications, such technologies can have a positive impact on SDN’s performance, i.e., blockchains allow non-confident individuals to interact with each other without the need for a central authority. However, attackers can still inject traffic to influence blockchain nodes from normal operations. Motivated by the recent development of blockchains and SDN, in this work, we focus on blockchain-based SDN and develop BSDNFilter, an IDS-based security mechanism that builds a trust-based filtration by using traffic fusion and aggregation to handle and reduce malicious traffic. Through collaborating with an IT organization, our evaluation in a real blockchain-based SDN environment demonstrates that our BSDNFilter is able to achieve better filtration performance against flooding attacks than similar approaches.
Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks

Deep learning (DL) algorithms have been proven to be very effective in a wide range of computer vision applications, such as segmentation, classification, and detection. DL models can automatically assess complex medical image scenes without human intervention and can be applied as a second reader to provide an additional opinion for the physician. To predict the axillary lymph node (ALN) metastatic status in patients with early-stage breast cancer, a deep learning-based computer-aided prediction system for ultrasound (US) images was proposed. A total of 153 women with breast tumor US images were involved in this study; there were 59 patients with metastasis and 94 patients without ALN metastasis. A deep learning-based computer-aided prediction (CAP) system using the tumor region and peritumoral tissue in ultrasound (US) images were employed to determine the ALN status in breast cancer. First, we adopted Mask R–CNN as our tumor detection and segmentation model to obtain the tumor localization and region. Second, the peritumoral tissue was extracted from the US image, which reflects metastatic progression. Third, we used the DL model to predict ALN metastasis. Finally, the simple linear iterative clustering (SLIC) superpixel segmentation method and the LIME explanation algorithm were employed to explain how the model makes decisions. The experimental results indicated that the DL model had the best prediction performance on tumor regions with 3 mm thick peritumoral tissue, and the accuracy, sensitivity, specificity, and AUC were 81.05% (124/153), 81.36% (48/59), 80.85% (76/94), and 0.8054, respectively. The results indicated that the proposed CAP system could help determine the ALN status in patients with early-stage breast cancer. The results reveal that the proposed CAP model, which combines primary tumor and peritumoral tissue, is an effective method to predict the ALN status in patients with early-stage breast cancer.
A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

The prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes. We used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCA In this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance. The results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.
A novel blockchain-based privacy-preserving framework for online social networks

Online social networks (OSNs) are nowadays an important field of applications thanks to the recent surge in online interaction. However, the illegal disclosure of user's private data can cause damaging consequences and even threaten the safety of users' life. The privacy issues of OSNs have become a matter of great concern for many people. In recent years, there are some research works to address this privacy issue, yet they do not always focus on providing the normal social network services for users, such as data sharing, data retrieval and data access services. Therefore, it is a challenge to ensure the security of sensitive data while providing efficient and privacy-preserving social network services for users. In this paper, we propose a novel blockchain-based privacy-preserving framework for online social networks, called BPP. Combined blockchain and public-key cryptography technique, the BPP framework can achieve secure data sharing, data retrieving, and data accessing with fairness and without worrying about potential damage to users' interest. Specifically, based on blockchain and public key encryption with keyword search technique, a secure, fair and efficient keyword search algorithm is proposed, with which the BBP framework realises privacy preservation of user's query and then obtain accurate query results with assurance and without needing for any further verification operation in online social network. Finally, we implement a prototype of our framework and deploy it to a locally simulated network. The extensive experiments and security analysis demonstrate the security, efficacy and efficiency of our proposed framework.
Unsupervised deep clustering via adaptive GMM modeling and optimization

Supervised deep learning techniques have achieved success in many computer vision tasks. However, most deep learning methods are data hungry and rely on a large number of labeled data in the training process. This work introduces an unsupervised deep clustering framework and studies the discovery of knowledge from a set of unlabeled data samples. Specifically, we propose a new network structure for both representation learning and GMM (Gaussian Mixture Model)-based representation modeling. In the training process of our proposed network, we not only adjust the Gaussian components to better model the distribution of representations, but also adjust the data representations towards their associating Gaussian centers to provide more adaptive support for the GMM. In this way, we take the data representations as the supervisory signal for the update of the GMM parameters and the GMM as the supervisory signal for the update of the representations, yet keeping the entire deep clustering as unsupervised. Consequently, we train the network based on an objective function with two learning targets. With the first target, we learn a GMM to model the representations properly and make each Gaussian component to be compact as much as possible. With the second target, we improve the inter-cluster distance by adapting the cluster centers to be further away from their neighbors. Thus, the training procedure simultaneously improves the intra-cluster compactness and inter-cluster separability for all the evolved clusters. Experimental results on eight datasets show that the proposed method can improve the clustering performance in comparison with the existing state of the art techniques.
SoFA: Source-data-free Feature Alignment for Unsupervised Domain Adaptation

Applying a trained model on a new scenario may suffer from domain shift. Unsupervised domain adaptation (UDA) has been proven to be an effective approach to solve the problem of domain shift by leveraging both data from the scenario that the model was trained on (source) and the new scenario (target). Although the source data are available for training the source model, there is no guarantee that the source data will still be available when applying UDA in the future due to emerging regulations on privacy of data. This results in the in-applicability of most existing UDA methods in the absence of source data. This paper proposes a source-data-free feature alignment (SoFA) method to address this problem by only using the trained source model and unlabeled target data. The source model is used to predict the labels for target data, and we model the generation process from predicted classes to input data to infer the latent features for alignment. Specifically, a mixture of Gaussian distributions is induced from the predicted classes as the reference distribution. The encoded target features are then aligned to the reference distribution via variational inference to extract class semantics without accessing source data. Relationship of the proposed method and the theory of domain adaptation is provided to verify the performance. Experimental results show the proposed method achieves higher or comparable accuracy compared to the existing methods in several cross-dataset classification tasks. Ablation studies are also conducted to confirm the importance of latent feature alignment to adaptation performance.
Coarse- and Fine-grained Attention Network with Background-aware Loss for Crowd Density Map Estimation

In this paper, we present a novel method Coarse- and Fine-grained Attention Network (CFANet) for generating high-quality crowd density maps and people count estimation by incorporating attention maps to better focus on the crowd area. We devise a from-coarse-to-fine progressive attention mechanism by integrating Crowd Region Recognizer (CRR) and Density Level Estimator (DLE) branch, which can suppress the influence of irrelevant background and assign attention weights according to the crowd density levels, because generating accurate fine-grained attention maps directly is normally difficult. We also employ a multi-level supervision mechanism to assist the backpropagation of gradient and reduce overfitting. Besides, we propose a Background-aware Structural Loss (BSL) to reduce the false recognition ratio while improving the structural similarity to groundtruth. Extensive experiments on commonly used datasets show that our method can not only outperform previous state-of-the-art methods in terms of count accuracy but also improve the image quality of density maps as well as reduce the false recognition ratio.
A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level between the extracted ROI and the ground truth (GT) was found by using the Jaccard and Dice similarity measuring method. According to the Jaccard similarity measuring method, WSCFMM showed an overall improvement of 4.72%, whereas the Dice similarity measuring method provided an overall improvement of 2.65% against the SCMM. Besides, WSCFMM significantly stabilized and reduced the execution time by showing an improvement of 83.71%. The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.
LWRpro: An Energy-Efficient Configurable Crypto-Processor for Module-LWR

Saber, the only module-learning with rounding-based algorithm in NIST's third round of post-quantum cryptography (PQC) standardization process, is characterized by simplicity and flexibility. However, energy-efficient implementation of Saber is still under investigation since the commonly used number theoretic transform can not be utilized directly. In this manuscript, an energy-efficient configurable crypto-processor supporting multi-security-level key encapsulation mechanism of Saber, is proposed. First, an 8-level hierarchical Karatsuba framework is utilized to reduce degree-256 polynomial multiplication to the coefficient-wise multiplication. Second, a hardware-efficient Karatsuba scheduling strategy and an optimized pre-/post-processing structure is designed to reduce the area overheads of scheduling strategy. Third, a task-rescheduling-based pipeline strategy and truncated multipliers are proposed to enable fine-grained processing. Moreover, multiple parameter sets are supported in LWRpro to enable configurability among various security scenarios. Enabled by these optimizations, LWRpro requires 1066, 1456 and 1701 clock cycles for key generation, encapsulation, and decapsulation of Saber768. The post-layout version of LWRpro is implemented with TSMC 40 nm CMOS process within 0.38 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The throughput for Saber768 is up to 275k encapsulation operations per second and the energy efficiency is 0.15 uJ/encapsulation while operating at 400 MHz, achieving nearly 50× improvement and 31× improvement, respectively compared with current PQC hardware solutions.
Bee-foraging learning particle swarm optimization

Numerous particle swarm optimization (PSO) algorithms have been developed for solving numerical optimization problems in recent years. However, most of existing PSO algorithms have only one search phase. There is no strengthened search phase for the well-performed particles, and also no re-initialization phase for the exhausted particles. These issues may still restrict the performance of PSO for complex optimization problems. In this paper, inspired by the bee-foraging search mechanism of artificial bee colony algorithm, a novel bee-foraging learning PSO (BFL-PSO) algorithm is proposed. Different from existing PSO algorithms, the proposed BFL-PSO has three different search phases, namely employed learning, onlooker learning and scout learning. The employed learning phase works like traditional one-phase-based PSO, while the onlooker learning phase performs strengthened search around those well-performed particles to exploit promising solutions, and the scout learning phase re-initializes those exhausted particles to introduce new diversity. The proposed BFL-PSO is comprehensively evaluated on CEC2014 benchmark functions, and compared with state-of-the-art PSO algorithms as well as artificial bee colony algorithms. The experimental results show that BFL-PSO achieves very competitive performance in terms of solution accuracy. In addition, the effectiveness of the newly introduced onlooker learning and scout learning phases in BFL-PSO is verified.