Reviewerly/exHarmony huggingface dataset:
Makeup Removal for Face Verification Based Upon Deep Learning

Makeup, derived from human`s pursuit of beauty, is widely accepted by the public. It changes the image of human`s appearance, brings more beautiful enjoyment and spiritual pleasure. Despite its popularization, it poses a huge challenge for face recognition, as altering the appearance reduces the accuracy of face recognition. In this paper, the purpose of the experiment is not only to generate non-makeup face images from makeup face images, but more importantly to retain identity information for face verification. To begin with, the original convolution layer is replaced by Resnet blocks. Furthermore, the idea and calculation method of feature matching are quoted. Experimental results demonstrate that this proposal generates non-makeup faces with few artifacts, that achieve 97.1% accuracy on Dataset1 and 94.3% accuracy on Dataset2 in face verification, which are better than discussed models.
Blockchain Technology Applications in Retail and Insurance Sectors: Cases from Suning and PingAn

Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network. As a novel technology, blockchain has a broad impact on various business practices. China is leading blockchain technology development all over the world. This paper studies the blockchain technology applications in the retail and insurance sectors in China. Specifically, we compared the similarities and differences of blockchain implementations between two typical cases-Suning and Ping An. Our research results provide meaningful implications and insights for blockchain practitioners who are keen in leveraging blockchain technologies in their businesses.
Classification of Food Reviews from Bengali Text using LSTM

People of this modern era are very much dependable on online reviews when it is the matter of purchasing any product. It is vital to bring out information from the huge amount of accessible text reviews. People of almost every age often visit restaurants. In today’s world food review is the fundamental requirement for visiting restaurants. But selecting a restaurant based on reviews is not quite an easy task. Deciding whether a food is worth having or not can be difficult. Several factors including the price, quality, taste, quantity can influence the actual worth of a food. From the perspective of a consumer, it is a dilemma to select a food appropriately. Food quality prediction can be a challenging task due to the high number of reviews that should be considered for the accurate prediction. Most people nowadays select restaurants based on their preferred food’s review. But the reviews present on the social platforms are mostly broad. People don’t find it useful to read the whole review. Therefore, a model which is capable of accepting reviews as input and is able to predict the food quality as output can become a great solution to this problem. So in this study, we have introduced a method which will be able to classify long Bengali food reviews into precise classes such as Good, Bad and Best using LSTM. The whole dataset which was used in our experiment has been collected from Facebook food review groups. Among them 80% was used for model training and 20% data used for the validation. Our model was able to classify 1000 Bengali review with 98% training and 80% validation accuracy.
Assessment of parathyroid gland cellularity by digital slide analysis

Computer-aided examination of digital tissue images has attracted attention in recent years. Application in the field of parathyroid pathology has not been studied previously. It holds a potential to assist in the examination of parathyroid gland adenoma or hyperplasia.To explore parathyroid cell detection of slide images by digital tissue analysis and compare the results to standard human processing.47 incisional biopsies of healthy appearing parathyroid glands were evaluated for their cellularity level. First, by the standard examination using microscopy by three independent pathologists. We compared the mean cellularity grading of the pathologists to the output of a computerized cell detection software.A disagreement was found between the standard human cellularity grading and the digital analysis output. However, the digital analysis reaches a 94% specificity and 48% sensitivity to predict high cellularity (>60% parenchymal cells).Digital analysis of parathyroid tissue can be used as a tool for hypercellularity elimination, therefore assisting in the diagnosis of parathyroid cell hyperplasia. Additional studies using more advanced algorithms are necessary for further precision enhancement.
OMNet: Object- Perception Multi-Branch Network for Pedestrian Re-Identification

In this paper, we propose a multi-branch model of spatial global attention mechanism to perform the task of pedestrian recognition. The main research direction of the previous pedestrian re-recognition is detection-recognition, which only correlates the appearance information between objects to ascertain the object's target. This paper proposes a multi-branch model result, using the spatial information of the recognition object, proposes a multi-branch network based on the global spatial attention mechanism, and establishes the object connection for pedestrian recognition through the spatial relationship between the main branch and the auxiliary branch. Another branch focuses on the longitudinal association of pedestrians through the attention mechanism, establishes the connection between key points of pedestrians, and optimizes the Mahalanobis distance between key points. The method proposed under this paper has improved accuracy and speed compared with preceding network performance in several pedestrian re-identification databases.
VarDenGrid: A New Variable Density Clustering Algorithm with Grid-Based Initialization

Among all clustering methods, the density-based clustering algorithm is one of the most powerful methods for discovering arbitrary-shaped clusters. However, almost all the renowned density-based algorithm requires user input(s), which are sometimes difficult to determine. Additionally, some of them like DBSCAN are unable to get variable density clusters. We introduce a new algorithm VarDenGrid in the paradigm of density-based clustering that will not require any user input(s) and also will be able to obtain variable density clusters. VarDenGrid starts with grid-based initialization of prospective clusters. Then the initial clusters are modified based on a novel density-based clustering algorithm proposed here. Each prospective cluster determines its density (Epsilon) by the maximum of the nearest neighbor distance of the objects in the cluster. We compared the results obtained using the new algorithm to that of well-known state-of-the-art density-based clustering algorithms DBSCAN and OPTICS on 15 real-world data sets from the UCI data repository and noted that performances of VarDenGrid favorably compare with these well-known state-of-the-art algorithms.
Application of Smart Service Robot Path Planning Based on Improved A* Algorithm

Aiming at the problems of long time-consuming path planning and low node search efficiency when the exhibition hall service robots autonomously pick up and deliver items, a global path planning improved A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm is proposed, which adds location information of the service robot on the basis of the traditional evaluation function. Contains the weight function of the cost function and heuristic function; in view of the low efficiency of A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm node search, it is proposed to determine the quadrant where the target is located, and at the same time only perform the depth-first expansion search to the quadrant where the target is located, thereby ignoring other unnecessary quadrants; This paper uses Bezier curve to smooth the planned path, so that the smoothed path is closer to the actual motion path. The experimental results show that the average path length obtained by the improved A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm path planning scheme is 5.34% less than the traditional A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm, and the average time required is reduced by 22.56%. While ensuring the optimal path, it can improve the efficiency of service robot path planning and make the driving trajectory Smoother.
Multimodal Deep Learning Model for Specific Emitter Identification

Specific Emitter Identification (SEI) is an approach to distinguish similar emitters, especially those of the same type and transmitting parameters, based on revealing the underlying physical association between the emitter hardware and the received signals of it. The process of conducting SEI consists of feature extraction and classifier designing, which directly determines the performance of SEI. Traditional SEI is realized by expert knowledge and machine learning techniques. Recently, general and self-adapted feature extraction methods and deep learning techniques have been widely studied to improve the SEI performance under certain scenarios. To provide a universal SEI method, the authors provide a novel multimodal deep learning model based on various signal features and deep network branches. Received signal features are extracted with multiple techniques and then are treated as independent inputs to corresponded deep model branches. The outputs of each model branch are further merged in fully-connected layer in the multimodal network. The proposed the final method is tested on a signal source dataset; the experimental results indicated that this novel scheme outperforms the typical single modal deep learning-based SEI methods.
Deep Metric Learning for Code Authorship Attribution and Verification

Code authorship identification can assist in identifying creators of malware, identifying plagiarism, and giving insights in copyright infringement cases. Taking inspiration from facial recognition work, we apply recent advances in metric learning to the problem of authorship identification and verification. The metric learning approach makes it possible to measure similarity in the learned embedding space. Access to a discriminative similarity measure allows for the estimation of probability distributions that facilitate open-set classification and verification. We extend our analysis to verification based on sets of files, a previously unexplored problem domain in large-scale author identification. On closed-set tasks we achieve competitive accuracies, but do not improve on the state of the art.
Online Auction Based Resource Allocation for Soft-Deadline Tasks in Edge Computing

With the development of edge computing (EC), more and more tasks are offloaded to edge servers (ESs). However, faced with a huge number of users offloading tasks to ESs, how to allocate resources reasonably and reduce the response time of the system are problems worth studying. In this paper, we design an online auction algorithm to deal with those two issues at the same time. We first introduce four task value functions to model the sensitivity to the delay of different tasks. Then, we construct a three-layer EC model. Based on it, we define the resources allocation problem as a social welfare (SW) maximization problem, which is NP-hard. To solve this problem, we utilize the master-dual technique to transform it into an online auction problem. Finally, an algorithm considering task classification is proposed, which realizes both resource allocation and latency reduction in a polynomial time. Experiment results show that our approach reduces the scheduling latency by an average of 38% while maintains SW at the same time.
Research on the Construction of Data Middle Platform for Smart Hydropower Station

In order to improve the data service level of hydropower station, solve the problems of data island and data heterogeneity, and explore the value of massive data, the data middle platform is needed to construct which can improve the refined operation and management level of smart hydropower station effectively and improve the utilization rate of data. Firstly, this paper introduces the origin, definition, and development of the data middle platform, then introduces the four-layers architecture and key technologies of data middle platform for smart hydropower station in detail. Finally, some practical application scenarios are introduced and analyzed which has a certain reference value for the construction and application of data middle platform in smart hydropower station.
Multi Feature Fusion Attention Learning for Clothing-Changing Person Re-Identification

Person re-identification (Re-ID) aims to match the same pedestrain identity images across different camera views. Because pedestrians will change clothes frequently for a relatively long time, while many current methods rely heavily on color appearance information or only focus on the person biometric features, these methods make the performance dropped apparently when it is applied to Clohting-Changing. To relieve this dilemma, we proposed a novel Multi Feature Fusion Attention Network (MFFAN), which learns the fine-grained local features. Then we introduced a Clothing Adaptive Attention (CAA) module, which can integrate multiple granularity features to guide model to learn pedestrain's biometric feature. Meanwhile, in order to fully verify the performance of our method on clothing-changing Re-ID problem, we designed a Clothing Generation Network (CGN), which can generate multiple pictures of the same identity wearing different clothes. Finally, experimental results show that our method exceeds the current best method by over 5% and 6% on the VCcloth and PRCC datasets respectively.
Storified narrative: Awake photo archives in digital humanities

Abstract The rise and development of digital humanities have brought new challenges and possibilities to the archival narrative. This article aims to explore the storified narrative path to awake photo archives in digital humanities. The authors clarify plot and plot unit definitions first, then focus on photo archives to analyze the path realization of the storified narrative. The authors also take the photo archives of Chinese Archivist Mr Wu Baokang, an example for a more specific explanation, which Mrs Wu Jiaqing authorizes. The plot-driven storified narrative path of photo archives involves two parts: story elements deconstruction and plot units representation, narrative clues construction, and plot generation. The plot-driven, storified narrative path can fully play the advantages of storifying and promoting the critical development of archival narrative under the research paradigm of digital humanities. By exploring the storified narrative path of archives, it is helpful to promote the activation, in-depth utilization, and widespread dissemination of archives, promote the integration of archival narrative with digital humanities, and provide support for digital humanities research.
A Low-Cost IMU-Based Wearable System for Precise Identification of Walk Activity Using Deep Convolutional Neural Network

The increasing popularity of smart wearable devices can be attributed to progress in research towards human activity recognition. With the use of tiny sensing units, the different human activities of day-to-day life can be identified with a high accuracy. However, as a fitness tracking product, smart wearables are not always accurate in determining actual physical motion details. For example, recognizing walk activity may not always be possible when the monitoring device is held in hand, or kept in pockets. For precise recognition of walk activity, movement of legs needs to be monitored. However, the movement of legs while walking must be distinguished from simple leg swing activities. The present work designs an IMU based sensing system that can prevent false identification of a mimicked walk or leg swing in sitting posture as a real walk activity, using conventional and convolutional deep learning algorithms. The system shows remarkable capability of identifying an actual walk from a mimicked walk activity using CNN 95% of the time.
Dynamic Heterogeneous Graph Representation

Graphs are gradually generated with multiple temporal heterogeneous interactions in real-world scenarios, containing both abundant structures and complex dynamics. Compared to static heterogeneous graphs, the dynamics express not only the changing graph topology but also the sequential evolution as well as multiple temporal preferences, indicating the necessity of dynamic heterogeneous graph modeling. This chapter focuses on simultaneously modeling both the evolving dynamics and heterogeneous semantics, and introduces three representative approaches, including DyHNE to handle structure changes via matrix perturbation theory based incremental learning, SHCF to tackle evolving sequences via heterogeneous sequential neural collaborative filtering, and THIGE to model multiple long- and short-term preferences via temporal heterogeneous GNNs.
Using Digital Technologies to Improve the Efficiency of Discussion Environments for Economics Students During COVID-19

The article is devoted to studying the impact of digital technologies on providing an efficient educational discussion environment against the background of isolation and social distancing caused by COVID-19 preventive measures. The course structuring allowed fostering a better understanding of educational activities. The use of popular online communication tools positively influenced educational communication effectiveness. The conscious attitude to training facilitated the formation of a corporate culture of engagement in the learning environment. Thematic and consulting support provided by academic assistants allowed forming a sense of confidence. The implementation of the concept of business gamification provoked an increase in students’ interest in practical training and entailed greater participation in learning. The relevance of the creative work topic enabled students to express their own view on professional activity and reinforced the desire for using the gained knowledge and experience in practice.
TopKConv: Increased Adversarial Robustness Through Deeper Interpretability

Vulnerability to adversarial inputs remains an issue for deep neural networks. Attackers can slightly modify inputs in order to cause adverse behavior in otherwise highly accurate networks. In addition to making these networks less secure for real world applications, this also emphasizes a misalignment between the features the network uses to make decisions and the ones humans use. In this work we propose that more interpretable networks should yield more robust ones since they are able to rely on features that are more understandable to humans. More specifically, we take inspiration from interpretability based approaches to adversarial robustness, and propose a sparsity based defense to counter the impact of overparameterization on adversarial vulnerability. Building off of the work of the Dynamic-K algorithm, which introduces dynamic routing to fully connected layers in order to encourage sparse, interpretable predictions, we propose TopKConv, a novel method of reducing the number of activation channels used to construct each convolutional feature map. The incorporation of TopKConv alongside Dynamic-k results in a significant increase in adversarial accuracy at no cost to benign accuracy. Further, this is achieved with no fine tuning of or adversarial training.
Method for Generating Explainable Deep Learning Models in the Context of Air Traffic Management

Model explainability, interpretability, and explainable AI have become major research topics, particularly for deep neural networks where it is unclear what features the network may have used to come to a particular output. This paper presents a method for understanding these features. The method processes auto-encoder outputs to generate feature vectors and allows a user to explore these features and gain insight into the network’s behavior. The method is applied to the U.S. air traffic management domain, an area rich in data and complexity, where use of deep learning is growing rapidly. In particular, the method is used to create models to predict throughput at major airports based on current and predicted weather and air traffic conditions. Models are created for two major U.S. airports of interest to NASA. For this application, the method is found to produce acceptable levels of prediction accuracy. It also produces explanations that relate input conditions to each other and to the predictions in ways that align with those of subject matter experts. Acceptance by subject matter experts is necessary if deep learning models are to be adopted in the air traffic management domain.
Grounding Fault Detection Method Of Intelligent Distribution Network Under Power Marketing Informatization

Distribution network lines generally have long distances, many branches, complex equipment and diverse geographical conditions. Once a grounding fault occurs, the fault point search time is long, and there is a certain false alarm. In view of this problem, an intelligent distribution network grounding fault detection method under power marketing informatization is proposed. The steady-state and transient fault characteristics and different detection methods after single-phase grounding are analyzed. Recent emerging detection methods, namely, the comparative zero sequence current waveform method, zero sequence voltage conduction time domain method and wide area synchronous transient recording method, are compared. The results show that the transient characteristic method can detect various grounding faults with relatively high accuracy and convenient implementation. The application effect of the transient characteristic method in the Daqing Oilfield is ideal. The accuracy of short-circuit and overcurrent fault location is more than 95%, and the accuracy of permanent ground fault detection is more than 75%, which greatly improves the accuracy of ground fault detection and enhances the reliability of the power grid.
Digital Signatures with Quantum Candies

Quantum candies (qandies) represent a type of pedagogical simple model that describes many concepts from quantum information processing (QIP) intuitively without the need to understand or make use of superpositions and without the need of using complex algebra. One of the topics in quantum cryptography that has gained research attention in recent years is quantum digital signatures (QDS), which involve protocols to securely sign classical bits using quantum methods. In this paper, we show how the "qandy model" can be used to describe three QDS protocols in order to provide an important and potentially practical example of the power of "superpositionless" quantum information processing for individuals without background knowledge in the field.
The State of Artificial Intelligence in Marketing With Directions for Future Research

Today, artificial intelligence (AI) is becoming increasingly important in both industry and academics. To investigate AI in marketing, we have used bibliometric study, social network analysis (SNA), main path analysis, and content analysis to examine the top 10 authors, top 20 most cited articles, and top 11 milestone papers from our 628 articles sample. Bibliometric study identified leading authors, documents, universities, countries, and sources of these articles. By using SNA, we spotted an academic social network of crucial publications. Moreover, we recognized eleven milestone articles that constitute the main knowledge flow in AI marketing through main path analysis. Finally, we discussed future directions based on our findings. Our study is one among a few studies that have used bibliometric analysis methods to analyze and visualize the citation network of the AI-marketing interface.