Reviewerly/exHarmony huggingface dataset:
Design of intelligent real-time safety supervision system for pension institutions based on LBS

Abstract With the gradual ageing of China's population and the single-child family system becoming the backbone of society, the traditional pension way of relying on children at home not only does not guarantee the quality of life of the elderly, but also brings great pressure to the work and life of young people. The way of relying on pension institutions to provide for the aged is becoming gradually accepted by society. In elderly care institutions, safety supervision systems provide an important link to protect the safety of the elderly, thereby ensuring accurate analysis and supervision of the elderly persons’ activity routes and activity rules, and thus allowing an early warning to be given in a dangerous situation. The system realises the real-time positioning and monitoring of the elderly activity track, and can give early warning and timely rescue to the elderly who enter into any dangerous area. The system includes a server monitoring program and mobile APP; ASP.NET Technology to develop the monitoring program; SQL Server 2012 for data storage; and model view controller (MVC) architecture for the APP to improve the efficiency of program development. The supervision program comprises the functions of personnel information management, fence management, positioning service, activity track record, danger warning and rapid rescue. The APP deploys the location-based service (LBS) technology of Android, uses Baidu map API to obtain location information and draw activity track, and stores the information in the mobile phone SQLite database. The APP is installed on mobile phones, bracelets and other devices carried by the elderly and staff, which can record the current position of the person in real-time and simultaneously transmit it to the computer. When the elderly enter into a dangerous area, the system can quickly select the nearest staff and inform them for rescue, so as to ensure the safety of the elderly. After simulation test, the system has now reached the preset function.
Topic Analysis of Indonesian Comment Text Using the Latent Dirichlet Allocation

People's ideas expressed in the social media comments section are so diverse and numerous that it is difficult to identify the topic of discussion. The purpose of this work is to apply the Latent Dirichlet Allocation method to analyze conversational topics in Indonesian texts via comments on social media YouTube. The stages of the research procedure began with data collecting, preprocessing, matrix term documents, topic modeling, and categorization. The dataset utilized is Indonesian commentary text on YouTube about COVID-19, which totaled 142,790 comments from 371 videos between March and September 2020. This research yielded six issue categories: “Hope - Prayer,” “Policy - Regulation,” “Pandemic Impact,” “News - Information,” “Health Protocol,” and “Development of Cases.” The modeling findings have an Adjusted Rand Index (ARI) score of 0.84 and a coherence value of 0.572. As a result, the findings indicate that the use of LDA is appropriate for Indonesian comments with short texts and extensive data coverage. The main contribution of this study is to investigate the main themes that are frequently discussed by users in the commentary text in order to discover the opinions that emerge in the community. The findings of this study can provide insight into the key thought themes that emerge in society.
Oracle Recognition of Oracle Network Based on Ant Colony Algorithm

Recent studies have shown that compared with traditional social networks, networks in which users socialize through interest recommendation have obvious homogeneity characteristics. Recommending topics of interest to users has become one of the main objectives of recommendation systems in such social networks, and the widespread data sparsity in such social networks has become the main problem faced by such recommendation systems. Particularly, in the oracle interest network, this problem is more difficult to solve because there are very few people who read and understand the Oracle. To address this problem, we propose an ant colony algorithm based recognition algorithm that can greatly expand the data in the oracle interest network and thus improve the efficiency of oracle interest network recommendation in this paper. Using the one-to-one correspondence between characters and translation in Oracle rubbings, the Oracle recognition problem is transformed into character matching problem, which can skip manual feature engineering experts, so as to realize efficient Oracle recognition. First, the coordinates of each character in the oracle bones are extracted. Then, the matching degree value of each oracle character corresponding to the translation of the oracle rubbings is assigned according to the coordinates. Finally, the maximum matching degree value of each character is searched using the improved ant colony algorithm, and the search result is the Chinese character corresponding to the oracle rubbings. In this paper, through experimental simulation, it is proved that this method is very effective when applied to the field of oracle recognition, and the recognition rate can approach 100% in some special oracle rubbings.
Isodual and self-dual codes from graphs

Binary linear codes are constructed from graphs, in particular, by the generator matrix [In|A] where A is the adjacency matrix of a graph on n vertices. A combinatorial interpretation of the minimum distance of such codes is given. We also present graph theoretic conditions for such linear codes to be Type I and Type II self-dual. Several examples of binary linear codes produced by well-known graph classes are given.
Machine Unlearning: Its Need and Implementation Strategies

Generally when users share information about themselves on some online platforms, they knowingly or unknowingly allow this data to be used by the companies behind these companies for various purposes including selling this information to advertisers as well as using it to better enrich their predictive models. In the event of the user changing their minds on allowing such data about them to be able to be used by the companies, it becomes a strenuous task for the companies to get rid of the influence of this collected data, especially when it has been used to train their machine learning models. Recent legislations by governing bodies, like the European Union, grant people the right to choose where data about them may be used, including a right to have their data and its resulting influence be completely removed from a company's databases and machine learning models. To be able to do this at scale new machine unlearning solutions need to be invented. In this paper, we look at some of these early models of machine unlearning strategies that have been proposed.
Inter observer errors of cast-off stains using FARO zone 3D

The estimated volume of the swing path is referred to as the Path Volume Envelope (PVE) which defines a volume in space from which the path of a blood-releasing object is contained within. FARO Zone 3D is a software program that allows for the calculation of the PVE; however, there are no current studies which look at how well this method performs across various analysts for a simple, human created cast-off pattern. A total of seventeen participants were provided data for a single cast-off pattern that was produced from human motion using a wooden dowel rod as the "weapon". The known three-dimensional motion of the object which created the cast-off pattern was documented using a motion capture system but remained unknown to the participants. The results show that an approximate maximum width of the PVE of±15 cm is enough to encompass the results of all participants in this study. In addition, the X, Y and Z position of the center of the PVE was found to have a standard deviation of 0.1 cm, 5.1 cm, and 9.9 cm, respectively. The standard deviations of the rotations for the PVE around the horizontal and vertical axes (i.e., yaw and pitch) were found to be 2.6° and 1° respectively. Overall, the combined results of all participants were able to fit within a rectangular prism with an overall width of± 15 cm which appears to be a reasonable value for simple scenarios, since it can define an approximate location from where an object was swung relative to a wall or another object at a crime scene. A PVE of greater or lesser maximum width may be required depending on the complexity of the cast-off pattern, the weapon used, and other factors.
Gollector: Measuring Domain Name Dark Matter from Different Vantage Points

This paper proposes Gollector, a novel tool for measuring the domain name space from different vantage points. Whereas such measurements have typically been conducted from a single (or few) vantage point, our proposed solution combines multiple measurements in a single system. Gollector allows us to express the relative difference in the covered domain name space, and the temporal characteristics, as domain name dark matter. We leverage a three-week trace from four vantage points, by applying the tool to three security-related use cases: early domain registration detection, data leakage in a split-horizon situation, and a proposed method for subdomain enumeration. We release the Gollector source code to the research community to support future research in this field.
Fuzzy Financial Fraud Risk Governance System in an Information Technology Environment

The financial fraud risk assessment requires expertise concerning the audit methodology and the risk assessment of business processes. The assessment of financial fraud risks is a significant challenge for independent, external auditors chiefly when dealing with the audit in an information technology (IT) environment. A meta-analysis financial fraud risk governance model taking into account an IT environment and based on fuzzy inference system is proposed in this paper. The fuzzy set theory and fuzzy logic are employed to deal with actual business audits, which do not always concern dichotomic fraud risk conditions. The inputs of the proposed fuzzy financial fraud risk governance system concern meta-analysis input factors related to the fundamental domains of risk in IT audit comprising the (i) effective identity and access protocol, (ii) system development, (iii) control of business operations, and (iv) change in systems or applications. Results demonstrate that the proposed approach enables supporting the operational risk management to promote the operational efficiency by identifying, measuring, and disclosing events (risk conditions) both in terms of qualitative (stratification) and quantitative (score) analysis. The fuzzy IT financial fraud risk system is able to work as a first barrier to reflect the adequacy of the information technologies and systems used to avoid risk in the financial fraud governance.
An Empirical Study of Different Techniques for the Improvement of Quality of Service in Cloud Computing

In a large, heterogeneous, and distributed environment, the computing infrastructure expands, and resource management becomes a challenging task. In a cloud world, one experiences problems of resource distribution, triggered by items like heterogeneity, dynamism, and errors, with uncertainty and distribution of resource. Unfortunately, to manage these environments, applications, and resource behaviors, current resource management techniques, frameworks, and mechanisms are insufficient. In recent years, the computer system is mostly based on cloud computing. Service level agreement (SLA) and quality of service (QoS) decrease by the minimum utilization of resources. Proper utilization of resources reduces the SLA violation and maximize QoS. Proper management of resources managed by service algorithms. This research paper analyzes the different types of resource management strategies which play the vital role mange the resources computing resources.
Research on cross‐hierarchical graph network recommendation algorithm based on random walk and convolutional neural network

Abstract Convolutional network (CNN) has been widely used in processing various graphics and network data analysis tasks. Therefore, facing the recommendation problem of large‐scale heterogeneous interaction, some studies have raised the problem of whether graph convolution neural network (GCN) can optimize the integration of node features and topology in complex graphs with rich information, but GCN may not be able to adaptively learn some deep related information between topology and node features, Therefore, the ability of GCN in some classification tasks may be seriously hindered. Therefore, this article proposes a heterogeneous network recommendation algorithm based on random walk and convolutional neural network (RW‐CNN), which can not only obtain the deep information of network structure but also aggregate the node representation. The core idea is to use the rule items based on meta‐path similarity to constrain the implicit representation of users and goods and make full use of the rich structural and semantic information of heterogeneous information networks. Then, combined with convolution neural network, the cross‐correlation information between project and user nodes is processed, so as to fully mine the semantic features of nodes. Finally, through the attention mechanism and fully connected network, the global user feature vector and project feature vector of path fusion at different levels are obtained. Finally, the two characteristic matrices are multiplied to obtain the prediction score.
Demonstration + Natural Language: Multimodal Interfaces for GUI-Based Interactive Task Learning Agents

We summarize our past five years of work on designing, building, and studying Sugilite, an interactive task learning agent that can learn new tasks and relevant associated concepts interactively from the user’s natural language instructions and demonstrations leveraging the graphical user interfaces (GUIs) of third-party mobile apps. Through its multi-modal and mixed-initiative approaches for Human-AI interaction, Sugilite made important contributions in improving the usability, applicability, generalizability, flexibility, robustness, and shareability of interactive task learning agents. Sugilite also represents a new human-AI interaction paradigm for interactive task learning, where it uses existing app GUIs as a medium for users to communicate their intents with an AI agent instead of the interfaces for users to interact with the underlying computing services. In this chapter, we describe the Sugilite system, explain the design and implementation of its key features, and show a prototype in the form of a conversational assistant on Android.
A CNN Based Q-learning Algorithm for Path Planning of Automated Guided Vehicle

For path planning of Automated Guided Vehicle (AGV), an improved neural network based on Q-learning algorithm is proposed to overcome the problems of slow convergence speed, too many iterations and unstable convergence performance existing in the traditional Q-learning algorithm. The idea is to introduce a convolutional neural network (CNN) into Q-learning algorithm to achieve the value function approximation of Q-learning algorithm and solve the dimension disaster problem in practical application. The improved CNN based Q-learning algorithm is more convenient for the selection of action and the update of Q. The simulation results show that the improved CNN based Q-learning algorithm is stable and has faster convergence speed and less iteration times.
Study of recognition of electronic invoice image

Electronic invoice has been gradually popularized in financial reimbursement. The automatic recognition of electronic invoice image can help financial personnel save processing time and reduce entry errors, so it is great significance. This paper provides a method for recognition of electronic invoice image, which mainly includes invoice preprocessing, invoice positioning, invoice cutting, sample training and building of convolution neural network model. Experimental results show that this method has high recognition accuracy and can effectively help financial staff reduce their workload.
Framelet Sets and Associated Scaling Sets

In time–frequency analysis, an increasing interest is to develop various tools to split a signal into a set of non-overlapping frequency regions without the influence of their adjacent regions. Although the framelet is an ideal tool for time–frequency analysis, most of the framelets only give an overlapping partition of the frequency domain. In order to obtain a non-overlapping partition of the frequency domain, framelet sets and associated scaling sets are introduced. In this study, we will investigate the relation between framelet (or scaling) sets and the frequency domain of framelets (or frame scaling functions). We find that the frequency domain of any frame scaling function always contains a scaling set and the frequency domain of any FMRA framelet always contains a framelet set. Moreover, we give a simple approach to construct various framelet/scaling sets from band-limited framelets and frame scaling functions.
Camelot: AI-Powered Campaign Management System

AI-Powered tools and technologies have proven to be successful in solving a variety of business problems including sales and marketing optimization. The social media marketing campaign is something widely used today but requires deep domain expertise and human efforts and hence an expensive approach. This problem is most prevalent in Small and Medium Enterprises (SMEs) where the lack of a Cost-effective solution hinders their ability to leverage the power of social media outlets for revenue and mind share growth. In this paper, we propose a system and methods for Automatic marketing Campaign generation using AI models with a Data-driven approach. Kaggle dataset of supermarket analysis was used for experimentation and Natural Language Generation (NLG) technology was used with canned queries for Text Generation, lastly, images were retrieved from the database using Deep Learning-Based Object detection to make the campaign more visually appealing to the clients. This methodology reduces the human efforts required earlier, is more efficient, and helps improve the marketing campaign’s reach by further publishing it on social media. This approach achieved satisfactory good results and responses after validation by the industry experts.
Application of Geostatistical Tools to the Geochemical Characterization of the Peloritani Mts (Sicily, Italy) Aquifers

Sources of groundwater contaminants in inhabited areas, located in complex geo-tectonic contexts, are often deeply interlocked, thus, making the discrimination between anthropic and natural origins difficult. In this study, we investigate the Peloritani Mountain aquifers (Sicily, Italy), using the combination of probability plots with concentration contour maps to retrieve an overall view of the groundwater geo-chemistry with a special focus on the flux of heavy metals. In particular, we present a methodology for integrating spatial data with very different levels of precision, acquired before and during the “geomatic era”. Our results depict a complex geochemical layout driven by a geo-puzzle of rocks with very different lithological natures, hydraulically connected by a dense tectonic network that is also responsible for the mixing of deep hydrothermal fluids with the meteoric recharge. Moreover, a double source, geogenic or anthropogenic, was individuated for many chemicals delivered to groundwater bodies. The concentration contour maps, based on the different data groups identified by the probability plots, fit the coherency and congruency criteria with the distribution of both rock matrices and anthropogenic sources for chemicals, indicating the success of our geostatistical approach.
Robustness-Aware Filter Pruning for Robust Neural Networks Against Adversarial Attacks

Today, neural networks show remarkable performance in various computer vision tasks, but they are vulnerable to adversarial attacks. By adversarial training, neural networks may improve robustness against adversarial attacks. However, it is a time-consuming and resource-intensive task. An earlier study analyzed adversarial attacks on the image features and proposed a robust dataset that would contain only features robust to the adversarial attack. By training with the robust dataset, neural networks can achieve a decent accuracy under adversarial attacks without carrying out time-consuming adversarial perturbation tasks. However, even if a network is trained with the robust dataset, it may still be vulnerable to adversarial attacks. In this paper, to overcome this limitation, we propose a new method called Robustness-aware Filter Pruning (RFP). To the best of our knowledge, it is the first attempt to utilize a filter pruning method to enhance the robustness against the adversarial attack. In the proposed method, the filters that are involved with non-robust features are pruned. With the proposed method, 52.1 &#x0025; accuracy against one of the most powerful adversarial attacks is achieved, which is 3.8&#x0025; better than the previous robust dataset training while maintaining clean image test accuracy. Also, our method achieves the best performance when compared with the other filter pruning methods on robust dataset.
A Generic Method to Rapidly Release Internet Services on Commercial Platforms

The prosperous development of Internet services such as O2O, IoT, and Web API has brought new vitality to service commercial platforms. However, these services involve online and offline business, which are widely diverse without a unified design and development standard. In addition, Internet services update frequently, which leads to repeat releases on commercial platforms. Therefore, in this paper, we present a generic method to rapidly release Internet services on commercial platforms. The method uses a highly abstract metamodel to express service business extensively and realizes service functions by executing metamodel objects. This method has wide versatility. Meanwhile, it extends the DevOps theory to solve the frequent changes of service functions during use after the release. Finally, we verified the usability of this method in the elderly healthcare domain.
Few-Shot Learning Via Dependency Maximization and Instance Discriminant Analysis

We study the few-shot learning (FSL) problem, where a model learns to recognize new objects with extremely few labeled training data per category. Most of previous FSL approaches resort to the meta-learning paradigm, where the model accumulates inductive bias through learning many training tasks so as to solve a new unseen few-shot task. In contrast, we propose a simple approach to exploit unlabeled data accompanying the few-shot task for improving few-shot performance. Firstly, we propose a Dependency Maximization method based on the Hilbert-Schmidt norm of the cross-covariance operator, which maximizes the statistical dependency between the embedded feature of those unlabeled data and their label predictions, together with the supervised loss over the support set. We then use the obtained model to infer the pseudo-labels for those unlabeled data. Furthermore, we propose an Instance Discriminant Analysis to evaluate the credibility of each pseudo-labeled example and select the most faithful ones into an augmented support set to retrain the model as in the first step. We iterate the above process until the pseudo-labels for the unlabeled data become stable. Following the standard transductive and semi-supervised FSL setting, our experiments show that the proposed method outperforms previous state-of-the-art methods on four widely used benchmarks, including mini-ImageNet, tiered-ImageNet, CUB, and CIFARFS.