Reviewerly/exHarmony huggingface dataset:
Face Recognition Based on Lightweight Convolutional Neural Networks

Face recognition algorithms based on deep learning methods have become increasingly popular. Most of these are based on highly precise but complex convolutional neural networks (CNNs), which require significant computing resources and storage, and are difficult to deploy on mobile devices or embedded terminals. In this paper, we propose several methods to improve the algorithms for face recognition based on a lightweight CNN, which is further optimized in terms of the network architecture and training pattern on the basis of MobileFaceNet. Regarding the network architecture, we introduce the Squeeze-and-Excitation (SE) block and propose three improved structures via a channel attention mechanism—the depthwise SE module, the depthwise separable SE module, and the linear SE module—which are able to learn the correlation of information between channels and assign them different weights. In addition, a novel training method for the face recognition task combined with an additive angular margin loss function is proposed that performs the compression and knowledge transfer of the deep network for face recognition. Finally, we obtained high-precision and lightweight face recognition models with fewer parameters and calculations that are more suitable for applications. Through extensive experiments and analysis, we demonstrate the effectiveness of the proposed methods.
Cluster Tendency Methods for Visualizing the Data Partitions

Clustering is widely used technique for grouping of data objects based on similarity features. The similarity features are derived from the similarity or dissimilarity metrics like Euclidean, cosine etc. Traditional clustering methods such as k-means, and other graph-based techniques are major techniques for discovery of clusters. However, these methods require user interference for determining the number of clusters initially. Determining the number of clusters for given data is known as cluster tendency. There is chance for getting poor clustering results when using either k-means or graph-based clustering methods with intractable value of ‘k’ by user. Thus, it is required to focus on cluster tendency methods for knowing prior knowledge about number of clusters in clustering. This paper presents the various visual access tendency (VAT) methods for good assessment of number of clusters.
Application of Artificial Intelligence in Computer Network Technology

Artificial intelligence technology is a very popular advanced technology in recent years. Its existence is not only in many media publicity, but also in all aspects of society and work can be very good development and application. Among them, the most extensive application of artificial intelligence technology belongs to the research application of artificial intelligence technology in computer network technology, because the research and application in this respect, its impact, scope and breadth are not comparable to other.
Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification

One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated data include: using transfer learning, data augmentation and training the models with less expensive image-level annotations (weakly-supervised learning). However, it is not clear how to combine the use of transfer learning in a CNN model when different data sources are available for training or how to leverage from the combination of large amounts of weakly annotated images with a set of local region annotations. This paper aims to evaluate CNN training strategies based on transfer learning to leverage the combination of weak and strong annotations in heterogeneous data sources. The trade-off between classification performance and annotation effort is explored by evaluating a CNN that learns from strong labels (region annotations) and is later fine-tuned on a dataset with less expensive weak (image-level) labels.As expected, the model performance on strongly annotated data steadily increases as the percentage of strong annotations that are used increases, reaching a performance comparable to pathologists ([Formula: see text]). Nevertheless, the performance sharply decreases when applied for the WSI classification scenario with [Formula: see text]. Moreover, it only provides a lower performance regardless of the number of annotations used. The model performance increases when fine-tuning the model for the task of Gleason scoring with the weak WSI labels [Formula: see text].Combining weak and strong supervision improves strong supervision in classification of Gleason patterns using tissue microarrays (TMA) and WSI regions. Our results contribute very good strategies for training CNN models combining few annotated data and heterogeneous data sources. The performance increases in the controlled TMA scenario with the number of annotations used to train the model. Nevertheless, the performance is hindered when the trained TMA model is applied directly to the more challenging WSI classification problem. This demonstrates that a good pre-trained model for prostate cancer TMA image classification may lead to the best downstream model if fine-tuned on the WSI target dataset. We have made available the source code repository for reproducing the experiments in the paper: https://github.com/ilmaro8/Digital_Pathology_Transfer_Learning.
A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario

We intend to create a new risk assessment methodology that combines the best characteristics of both risk score and machine learning models. More specifically, we aim to develop a method that, besides having a good performance, offers a personalized model and outcome for each patient, presents high interpretability, and incorporates an estimation of the prediction reliability which is not usually available. By combining these features in the same approach we expect that it can boost the confidence of physicians to use such a tool in their daily activity. In order to achieve the mentioned goals, a three-step methodology was developed: several rules were created by dichotomizing risk factors; such rules were trained with a machine learning classifier to predict the acceptance degree of each rule (the probability that the rule is correct) for each patient; that information was combined and used to compute the risk of mortality and the reliability of such prediction. The methodology was applied to a dataset of patients admitted with any type of acute coronary syndromes (ACS), to assess the 30-days all-cause mortality risk. The performance was compared with state-of-the-art approaches: logistic regression (LR), artificial neural network (ANN), and clinical risk score model (Global Registry of Acute Coronary Events - GRACE). The proposed approach achieved testing results identical to the standard LR, but offers superior interpretability and personalization; it also significantly outperforms the GRACE risk model and the standard ANN model. The calibration curve also suggests a very good generalization ability of the obtained model as it approaches the ideal curve. Finally, the reliability estimation of individual predictions presented a great correlation with the misclassifications rate. Those properties may have a beneficial application in other clinical scenarios as well. [abridged]
Efficient Range Proofs with Transparent Setup from Bounded Integer Commitments

We introduce a new approach for constructing range proofs. Our approach is modular, and leads to highly competitive range proofs under standard assumption, using less communication and (much) less computation than the state of the art methods, and without relying on a trusted setup. Our range proofs can be used as a drop-in replacement in a variety of protocols such as distributed ledgers, anonymous transaction systems, and many more, leading to significant reductions in communication and computation for these applications. At the heart of our result is a new method to transform any commitment over a finite field into a commitment scheme which allows to commit to and efficiently prove relations about bounded integers. Combining these new commitments with a classical approach for range proofs based on square decomposition, we obtain several new instantiations of a paradigm which was previously limited to RSA-based range proofs (with high communication and computation, and trusted setup). More specifically, we get:
SynSig2Vec: Forgery-Free Learning of Dynamic Signature Representations by Sigma Lognormal-Based Synthesis and 1D CNN

Handwritten signature verification is a challenging task because signatures of a writer may be skillfully imitated by a forger. As skilled forgeries are generally difficult to acquire for training, in this paper, we propose a deep learning-based dynamic signature verification framework, SynSig2Vec, to address the skilled forgery attack without training with any skilled forgeries. Specifically, SynSig2Vec consists of a novel learning-by-synthesis method for training and a 1D convolutional neural network model, called Sig2Vec, for signature representation extraction. The learning-by-synthesis method first applies the Sigma Lognormal model to synthesize signatures with different distortion levels for genuine template signatures, and then learns to rank these synthesized samples in a learnable representation space based on average precision optimization. The representation space is achieved by the proposed Sig2Vec model, which is designed to extract fixed-length representations from dynamic signatures of arbitrary lengths. Through this training method, the Sig2Vec model can extract extremely effective signature representations for verification. Our SynSig2Vec framework requires only genuine signatures for training, yet achieves state-of-the-art performance on the largest dynamic signature database to date, DeepSignDB, in both skilled forgery and random forgery scenarios. Source codes of SynSig2Vec will be available at https://github.com/LaiSongxuan/SynSig2Vec.
Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.
A Survey of Aggregation-Disaggregation in Large Markov Chains

This paper surveys results since 1984 on aggregation-disaggregation for large Markov chains. The topics covered include alternative forms of the aggregate equations; alternative forms ofthe disaggregation equations; alternative schemes for iterative aggregation-disaggregation; aggregation for generalized birth-and-death (row-continuous) processes; and aggregation for nearly completely decomposable systems. Implementation issues are described, including overrelaxation, Gauss-Seidel variants, and block scaling. Many topics for further research are identified, including adaptive aggregation, multilevel aggregation, parallel computation, bounds, and applications.
CioSy: A Collaborative Blockchain-Based Insurance System

The insurance industry is heavily dependent on several processes executed among multiple entities, such as insurer, insured, and third-party services. The increasingly competitive environment is pushing insurance companies to use advanced technologies to address multiple challenges, namely lack of trust, lack of transparency, and economic instability. To this end, blockchain is used as an emerging technology that enables transparent and secure data storage and transmission. In this paper, we propose CioSy, a collaborative blockchain-based insurance system for monitoring and processing the insurance transactions. To the best of our knowledge, the existing approaches do not consider collaborative insurance to achieve an automated, transparent, and tamper-proof solution. CioSy aims at automating the insurance policy processing, claim handling, and payment using smart contracts. For validation purposes, an experimental prototype is developed on Ethereum blockchain. Our experimental results show that the proposed approach is both feasible and economical in terms of time and cost.
Boundary Detection with BERT for Span-level Emotion Cause Analysis

Emotion cause analysis (ECA) has been an emerging topic in natural language processing, which aims to identify the reasons behind a certain emotion expressed in the text.Most ECA methods intend to identify the clause which contains the cause of a given emotion, but such clause-level ECA (CECA) can be ambiguous and imprecise.In this paper, we aim at span-level ECA (SECA) by detecting the precise boundaries of text spans conveying accurate emotion causes from the given context.We formulate this task as sequence labeling and position identification problems and design two neural methods to solve them.Experiments on two benchmark ECA datasets show that the proposed methods substantially outperform the existing ECA models 1 .
Peripersonal and reaching space differ: Evidence from their spatial extent and multisensory facilitation pattern

Peripersonal space (PPS) is a multisensory representation of the space near body parts facilitating interactions with the close environment. Studies on non-human and human primates agree in showing that PPS is a body part-centered representation that guides actions. Because of these characteristics, growing confusion surrounds peripersonal and arm-reaching space (ARS), that is the space one's arm can reach. Despite neuroanatomical evidence favoring their distinction, no study has contrasted directly their respective extent and behavioral features. Here, in five experiments (N = 140) we found that PPS differs from ARS, as evidenced both by participants' spatial and temporal performance and by its modeling. We mapped PPS and ARS using both their respective gold standard tasks and a novel multisensory facilitation paradigm. Results show that: (1) PPS is smaller than ARS; (2) multivariate analyses of spatial patterns of multisensory facilitation predict participants' hand locations within ARS; and (3) the multisensory facilitation map shifts isomorphically following hand positions, revealing hand-centered coding of PPS, therefore pointing to a functional similarity to the receptive fields of monkeys' multisensory neurons. A control experiment further corroborated these results and additionally ruled out the orienting of attention as the driving mechanism for the increased multisensory facilitation near the hand. In sharp contrast, ARS mapping results in a larger spatial extent, with undistinguishable patterns across hand positions, cross-validating the conclusion that PPS and ARS are distinct spatial representations. These findings show a need for refinement of theoretical models of PPS, which is relevant to constructs as diverse as self-representation, social interpersonal distance, and motor control.
Automated Comment Update: How Far are We?

Code comments are key to program comprehension. When they are not consistent with the code, maintenance is hindered. Yet developers often forget to update comments along with their code evolution. With recent advances in neural machine translation, the research community is contemplating novel approaches for automatically generating up-to-date comments following code changes. CUP is such an example state-of-the-art approach whose promising performance remains however to be comprehensively assessed. Our study contributes to the literature by performing an in-depth analysis on the effectiveness of CUP. Our analysis revealed that the overall effectiveness of CUP is largely contributed by its success on updating comments via a single token change (96.6%). Several update failures occur when CUP ignores some code change information (10.4%) or when it is otherwise misled by additional information (12.8%). To put in perspective the achievements of CUP, we implement HEBCUP, a straightforward heuristic-based approach for code comment update. Building on our observations on CUP successful and failure cases, we design heuristics for focusing the update on the changed code and for performing token-level comment update. HebCup is shown to outperform CUP in terms of Accuracy by more than 60% while being over three orders of magnitude (i.e., 1700 times) faster. Further empirical analysis confirms that the HebCup does not even overfit to the empirical analysis set. Overall, with this study, we call for more research in deep learning based comment update towards achieving state-of-the-art performance that would be unreachable by other less sophisticated techniques.
3-D Measurement Method for Multireflectivity Scenes Based on Nonlinear Fringe Projection Intensity Adjustment

The 3-D measurement methods based on fringe projection intensity adjustment (FPIA) have been widely adopted to measure the multireflectivity scenes. However, the signal-to-noise ratios (SNRs) of the captured fringe patterns in the FPIA-based methods are reduced because of a large range of reflectivity variations (LRRVs). In our research, a reflection model and a crosstalk model are built to analyze the disturbances induced by LRRVs on the SNRs in the captured fringe patterns, and then, a 3-D measurement method based on nonlinear FPIA (NLFPIA) is proposed to alleviate the LRRV-induced disturbances and improve the measurement accuracy. Moreover, to improve the efficiency of the proposed 3-D measurement method, we present a cyclic utilization of fringe patterns (CUFPs) technique to decrease the number of projection patterns. Experimental results show that the proposed NLFPIA-based 3-D measurement method reduces the root-mean-square error (RMSE) in the measurements of the two standard ceramic geometries by 52.9845% and 36.6393% compared with the FPIA-based method.
A survey on deep learning-based precise boundary recovery of semantic segmentation for images and point clouds

Precise localization of semantic segmentation is attracting increasing attention, and salient performances are dominated by deep learning-based methods, especially deep convolutional neural networks (DCNNs). However, the outputs from the final layer of DCNNs are not sufficiently localized for accurate object boundaries due to their invariance properties, which makes precise boundary recovery of semantic segmentation an academically challenging question. Both 2D and 3D objects suffer from the same problem. Considering this, this paper conducts a comprehensive survey of precise boundary recovery for semantic segmentation, focusing mainly on 2D images and 3D point clouds. Firstly, we formulate the problem of potential boundary recovery for semantic segmentation based on DCNNs, elaborate on the terminology as well as background concepts in this field. Then, we categorize boundary recovery methods into four strategies according to their techniques and network architectures to discuss how they obtain accurate boundaries of semantic segmentation. Next, publicly available datasets on which they have been assessed are argued. To compare these datasets, we design diagrams based on five indicators to help researchers judge which are the ones that best suit their tasks. Moreover, we further compare and analyze the performance of all the reviewed methods through experimental results. Finally, current challenges and prospective research issues are discussed extensively.
Transaction Throughput Optimization for Integrated Blockchain and MEC System in IoT

The integration of blockchain and mobile edge computing (MEC), as a secure, efficient, and reliable edge computing paradigm, has been widely applied in many applications, such as large-scale Internet of Things (IoT), Internet of Vehicles (IoV), and smart grid. However, due to the restricted transaction throughput of blockchain, the combination of blockchain and MEC in most existing works cannot support applications with frequent transaction requirements. In this paper, we propose an integrated blockchain and MEC (IBM) framework based on a space-structured ledger to meet the transaction demands for IoT applications. In the framework, a collaborative mining process is designed, where we consider the cooperation between mobile devices (MDs) and MEC servers. To promote mining efficiency, we further develop a high-performance consensus mechanism called reputation-based proof of work (Re-PoW), in which differentiated mining targets are assigned according to the reputation of MDs. In the Re-PoW consensus mechanism, heterogeneous capabilities and historical behaviors of MDs are all considered for accurately evaluating their reputation. In addition, we present an alternating optimization algorithm by jointly optimizing bandwidth allocation and computation resource allocation to further enhance the performance of the proposed scheme. Simulation results show that the proposed approach can achieve significant throughput improvement.
MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction

Visual Information Extraction (VIE) task aims to extract key information from multifarious document images (e.g., invoices and purchase receipts). Most previous methods treat the VIE task simply as a sequence labeling problem or classification problem, which requires models to carefully identify each kind of semantics by introducing multimodal features, such as font, color, layout. But simply introducing multimodal features can't work well when faced with numeric semantic categories or some ambiguous texts. To address this issue, in this paper we propose a novel key-value matching model based on a graph neural network for VIE (MatchVIE). Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics, and simply focuses on the strong relevancy between entities. Besides, we introduce a simple but effective operation, Num2Vec, to tackle the instability of encoded values, which helps model converge more smoothly. Comprehensive experiments demonstrate that the proposed MatchVIE can significantly outperform previous methods. Notably, to the best of our knowledge, MatchVIE may be the first attempt to tackle the VIE task by modeling the relevancy between keys and values and it is a good complement to the existing methods.