Reviewerly/exHarmony huggingface dataset:
A real-time and accurate detection approach for bucket teeth falling off based on improved YOLOX

Abstract. An electric shovel is a bucket-equipped mining excavator widely used in open-pit mining today. The prolonged direct impact between the bucket teeth and the ore during the mining process will cause the teeth to loosen prematurely or even break, resulting in unplanned downtime and productivity losses. To solve this problem, we propose a real-time and accurate detection algorithm of bucket teeth falling off based on improved YOLOX. Firstly, to solve the problem of poor detection effect caused by uneven illumination, the dilated convolution attention mechanism is added to enhance the feature expression ability of the target in complex backgrounds so as to improve the detection accuracy of the target. Secondly, considering the high computing cost and large delay of the embedded device, the deep separable convolution is used to replace the traditional convolution in the feature pyramid network, and the model compression strategy is used to prune the redundant channels in the network, reduce the model volume, and improve the detection speed. The performance test is carried out on the self-constructed dataset of WK-10 electric shovel. The experimental results show that, compared with the YOLOX model, the mean average precision of the algorithm in this paper reaches 95.26 %, only 0.33 % lower, while the detection speed is 50.8 fps, 11.9 fps higher, and the model volume is 28.42 MB, which is reduced to 29.46 % of the original. Compared with many other existing methods, the target detection algorithm proposed in this paper has the advantages of higher precision, smaller model volume, and faster speed. It can meet the requirements of real-time and accurate detection of the bucket teeth falling off.
App Development Part 2 Overview

If you have reached here, congratulations. You have acquired the required basic knowledge to create your own iPhone App and now are some more advanced tasks. That is quite a feat, and you should be proud to make it thus far. What you have learned until now are the core skills, but not enough for creating a professional-looking app. Besides, there are many features that Xcode offers to make your app well integrated into the Apple ecosystem. In this short chapter, we’ll review what lies ahead in the remainder of the book and then start our iCloud set-up.
CloudKit Overview

Ever since Apple announced CloudKit, a new avenue for cloud-based app development has emerged for developers. CloudKit offers a great avenue for apps that need interactions with other App users. Prior to CloudKit, developers used to create homegrown solutions from authentication to data sync, which is not an easy task to do besides being vulnerable to security breaches. CloudKit wraps all such complexities and provides a scalable architecture to develop data-persistent, cloud-based applications.
An Attack on a Proposed Construction of Small-State Stream Ciphers and Proposals for New Constructions

Abstract Small-state stream ciphers (SSCs) idea is based on using key bits not only in the initialization but also continuously in the keystream generation phase. A time-memory-data tradeoff (TMDTO) distinguishing attack was successfully applied against all SSCs in 2017 by Hamann et al. They suggested using not only key bits but also initial value (IV) bits continuously in the keystream generation phase to strengthen SSCs against TMDTO attacks. Then, Hamann and Krause proposed a construction based on using only IV bits continuously in the packet mode. They suggested an instantiation of an SSC and claimed that it is resistant to TMDTO attacks. We point out that accessing IV bits imposes an overhead on cryptosystems that might be unacceptable in some applications. More importantly, we show that the proposed SSC remains vulnerable to TMDTO attacks 1. To resolve this security threat, the current paper proposes constructions based on storing key or IV bits that are the first to provide full security against TMDTO attacks. Five constructions are proposed for different applications by considering efficiency. Designers can obtain each construction’s minimum volatile state length according to the desirable keystream, key and IV lengths.
WITHDRAWN: A Feature Fusion Method Based on Multi-Classification Losses for Fine-Grained Visual Categorization

This article has been withdrawn at the request of the authors. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
PRE-DAC: proxy re-encryption based dynamic access control for secure cloud data

With the rapid development of technologies such as cloud computing and Internet of Things, the amount of data outsourced to all kinds of remote cloud storage providers is growing. Out of security concerns, it is necessary for data owners to encrypt sensitive data before uploading it to the cloud, which makes it difficult to enforce access control of the outsourced data. Specifically, the time cost for user revocation is considerably high because of the large number of encryptions required for updating ciphertext in the cloud. In this paper, we utilize Proxy Re-Encryption (PRE) to propose PRE-DAC (PRE based Dynamic Access Control), an access control scheme for encrypted cloud data, where the efficiency of user revocation is significantly improved. Simulation results using real-world datasets show that the number of asymmetric encryptions for each revocation is prominently less than that of the existing schemes.
Heap memory vulnerability utilization method in Zig language

In software security, heap memory vulnerability is a common type of vulnerability. However, heap memory vulnerabilities can only lead to the collapse of the program in most cases. In order to achieve the purpose of getshell, it is still necessary to construct and use it. Zig language is a development-oriented programming language and will have more applications in the future. Based on the mechanism and principle of heap memory implementation in zig language, this paper finds several methods to exploit heap memory vulnerabilities, which can achieve the attack effect of writing eight-byte data at any writable address and applying for any address, respectively. On this basis, some corresponding defense methods are proposed, which provides ideas for the utilization and defense of heap memory vulnerabilities in binary security.
Fraud attack behavior analysis based on collaborative filtering algorithm of personalized recommender system

Personalized recommendation system is divided into offline system and online system, the two are interconnected and cooperate with each other, and there are some problems in the development process. In response to the above problems, user-led recommendation and project-guided recommendation can be improved respectively. In addition, a good, personalized recommendation system is inseparable from excellent design. It is mainly divided into four management systems: source data management, ETL management, data policy management and cluster management, with the emphasis on data policy management. The research shows that the attack model and attack cost of fraud attack have different effects on the attack performance. In response to this problem, the performance of the collaborative filtering algorithm based on singular value segmentation SVD under different attack models is analyzed experimentally, and the impact of different filling scales and attack scales on attack efficiency is analyzed with three evaluation parameters.
PC-TDA: a policy-customized trusted Docker architecture based on attribute-based signature

Docker has become the main service deployment mode of cloud platform because of its lightweight and portable characteristics. With the popularity of cloud services, the security of Docker becomes an increasingly concerned issue. On one hand, the security capability of Docker is insufficient currently. Docker is designed to reduce the attack surface mainly by simplifying the system functions. Meanwhile, there are only limited built-in security mechanisms for Docker which are incapable of controlling the runtime security for Docker. On the other hand, there lacks personalized security policy deployment and enforcement mechanism in Docker which cannot meet the special security needs of users for cloud services. Focusing on the above problems, this paper proposes a new security-on-demand framework for Docker called a “Policy-Customized Trusted Docker Architecture” (PC-TDA), which forces cloud service providers to provide secure Docker services satisfying the user customized security policies by introducing the attribute-based encryption mechanism. At the same time, PC-TDA uses the attribute-based signature mechanism to support the verification of the security status and security policies of cloud services. Based on the Kylin cloud, the PC-TDA is preliminarily implemented, and the verification results show the availability and rationality of our proposed architecture which can provide greater flexibility for cloud users to control Dockers.
A response generator with response-aware encoder for generating specific and relevant responses

The dialogue data usually consist of the pairs of a query and its response, but no previous response generators have exploited the responses explicitly in their training while a response provides significant information about the meaning of a query. Therefore, this paper proposes a sequence-to-sequence response generator with a response-aware encoder. The proposed generator exploits golden responses by reflecting them into query representation. For this purpose, the response-aware encoder adds a relevancy scorer layer to the transformer encoder that calculates the relevancy of query tokens to a response. However, golden responses are available only during training of the response generator and unavailable at inference time. As a solution to this problem, the joint learning of a teacher and a student relevancy scorer is adopted. That is, at the training time, both the teacher and the student relevancy scorers are optimized but the decoder generates a response using only the relevancy of the teacher scorer. However, at the inference time, the decoder uses that of the student scorer. Since the student scorer is trained to minimize the difference from the teacher scorer, it can be used to compute the relevancy of a prospective response. The proposed model is the first attempt to use a golden response directly for generating a query representation, whereas previous studies used the responses for its implicit and indirect reflection. As a result, it achieved higher dialogue evaluation score than the current state-of-the-art model for Reddit, Persona-Chat, and DailyDialog data sets.
Visual Narratives of the Covid-19 pandemic

&#x0D; &#x0D; &#x0D; &#x0D; Covid-19 has sparked a worldwide interest in understanding the dynamic evo- lution of a pandemic and tracking the effectiveness of preventive measures and rules. For this reason, numerous media and research groups have produced com- prehensive data visualisations to illustrate the relevant trends and figures. In this paper, we will look at a selection of Covid 19 data visualisations to evaluate and discuss the currently established visualisation tools in terms of their ability to provide a communication channel both within the data science team and between data analysts, domain experts and a general interested audience. Although there is no set catalogue of evaluation criteria for data visualisations, we will try to give an overview of the different core aspects of visualisation evaluation and their competing principles.&#x0D; &#x0D; &#x0D; &#x0D;
Effects of Corporate Governance Disclosure on Profitability of Public Listed Firms in Tanzania

This study investigated the effect of corporate governance information (CGI) disclosure on profitability of firms listed in the Dar es Salaam Stock Exchange (DSE). 21 listed companies were studied for a period ranging from 2006 to 2021 using a mixed research method with an explanatory sequential design. Return on equity (ROE) and Return on Assets (ROA) were employed as dependent variables in the panel data analysis. CGI disclosure was an independent variable. Geographical diversification, firm age, firm size, and sales growth were the control variables. Secondary data was obtained from DSE. Qualitative data was gathered via semi-structured interviews. Thematic analysis and a random effect model with two estimates (1 and 2) were utilized to analyze qualitative and quantitative data respectively. The findings suggested that there was a positive and significant effect of CGI disclosure on firm profitability. The findings adds to the body of knowledge by signifying stakeholder’s theory. The study concluded that Disclosure of CGI may undeniably result in increased profitability. We recommended that firm managers should pay a closer look at CGI disclosure, enhance their disclosure practices, and invest in disclosure strategies that will benefit stakeholders.
ComVis‐Sail: Comparative Sailing Performance Visualization for Coaching

Abstract During training sessions, sailors rely on feedback provided by the coaches to reinforce their skills and improve their performance. Nowadays, the incorporation of sensors on the boats enables coaches to potentially provide more informed feedback to the sailors. A common exercise during practice sessions, consists of two boats of the same class, sailing side by side in a straight line with different boat handling techniques. Coaches try to understand which techniques are that make one boat go faster than the other. The analysis of the obtained data from the boats is challenging given its multi‐dimensional, time‐varying and spatial nature. At present, coaches only rely on aggregated statistics reducing the complexity of the data, hereby losing local and temporal information. We describe a new domain characterization and present a visualization design that allows coaches to analyse the data, structuring their analysis and explore the data from different perspectives. A central element of the tool is the glyph design to intuitively represent and aggregate multiple aspects of the sensor data. We have conducted multiple user studies with naive users, sailors and coaches to evaluate the design and potential of the overall tool. image
Designing and Implementation of Novel Ensemble model based on ANFIS and Gradient Boosting methods for Hand Gestures Classification

Communication through hand gestures has always been the primary method worldwide. There are numerous methods implemented for the classification of hand gestures. However, most successful attempts use Convectional Neural Network (CNN) based methods, which are high in resource consumption and computational complexity, leading to problematic implementation in low-resource platforms. This study proposes a lightweight, efficient, and effective hand gesture recognition method using a novel ensemble model. The proposed ensemble method used XGBoost, CatBoost, and LightGBM gradient boosting algorithms as the initial classification. Then the Adaptive Network-based Fuzzy Inference System (ANFIS) algorithm was used to boost the accuracy. The dataset used in this study contains seven hand gesture classes and 350 samples, while each sample was generated using 30 frames summation of a sequence. The results were evaluated using two methods such as 10-fold cross-validation and confusion matrix-based parameters. The proposed ensemble algorithm outperformed eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosted Machine (LightGBM) algorithms in average accuracy, precision, recall, and f1-score, having 99%, 0.99, 0.97, and 0.98.
Assessing web security risks using dynamic Bayesian network

This paper presents an approach to assess security risks of web systems based on its deployment scenario. First, we propose a conceptual model specifying the characteristics of the cause-effect connections in the formation of security risks. From this conceptual model, we propose a multi-layered dynamic Bayesian network (MLDBN) for integrating temporal factors into the assessment of security risks. This MLDBN allows considering the variation of risk levels and risk likelihood while threat events and vulnerabilities emerging during the deployment process of a web system. An associated mechanism for generating the structure of MLDBN is also proposed. In each deployment scenario, security risks of each asset and security risks of the whole web system are assessed according to three tenets: loss of confidentiality, loss of integrity and loss of availability. The proposed solution is implemented in the form of a risk assessment tool, providing convenient risk assessment and risk monitoring functions for the managers and the experts.
Retrieving Keywords in Historical Vietnamese Stele Images Without Human Annotations

Stone engravings on Vietnamese steles are an invaluable resource for historians to study the life of the villagers in the past. Thanks to pictures taken of stampings of the steles, they can be investigated today in the form of digital images. Automatic keyword spotting is a promising means to access the textual content of the images, allowing to retrieve steles that contain a certain query term. In this paper, we present a complete pipeline for retrieving Chu Nom characters in Vietnamese steles that operates fully automatically on the original images, without the need for preprocessing, segmentation, or human annotation. It combines a self-calibration approach to character detection using deep convolutional neural networks with a graph-based approach to keyword spotting that compares templates of the search term with detected characters based on structural properties.
Multi-task Learning-based Text Classification with Subword-Phrase Extraction

Text classification using deep learning, which is trained with a tremendous amount of text, has achieved superior performance than traditional methods. In addition to its success, multi-task learning has become a promising approach for text classification; for instance, a multi-task learning approach employs named entity recognition as an auxiliary task for text classification. The existing MTL-based text classification methods depend on auxiliary tasks using supervised labels, which require large human and/or financial efforts to create. To reduce these efforts, this paper proposes a multi-task learning-based text classification framework which reduces the additional efforts on supervised label creation. A basic idea to realize this is that to utilize phrasal expressions consisting of subwords (called subword-phrase). To the best of our knowledge, there has been no text classification approach on top of subword-phrases, because subwords do not always express a coherent set of meanings. The proposed framework is new to add subword-phrase recognition as an auxiliary task, and to utilize subword-phrases for text classification. To realize the low-cost auxiliary recognition task, the framework extracts subword-phrases in an unsupervised manner. The experimental evaluation of the five popular datasets for text classification showcases the effectiveness of the involvement of the subword-phrase recognition as an auxiliary task. It also shows comparative results with the state-of-the-art method.
Empirical Analysis of Filter Feature Selection Criteria on Financial Datasets

High dimensionality is one of the data quality problems that affects the performance of machine learning models. Feature selection which aims to identify and remove as many redundant and irrelevant features as possible allows to boot the overall performance of the models while reducing the computational cost. However, the choice of an appropriate feature selection method is still a big challenge as there is no the best selection criterion that fits to all datasets. It is then essential to comparatively analyze the performance of feature selection criteria according to different characteristics of high-dimensional datasets, particularly large financial datasets where features are highly-correlated and redundant. In this paper, we explore nine different feature selection criteria which are typically categorized into two classes: (i) information theoretical based criteria and (ii) similarity based criteria over seven public financial datasets. To the best of our knowledge, no previous comprehensive empirical investigation has been carried out to demonstrate the positive effects of feature selection criteria on financial data. Experimental results indicate that the information theoretical-based methods suffer from a high computation time in case of high dimensional data (i.e., high number of features) while the similarity-based methods require significant computations to deal with high volume dataset (i.e., high number of samples).
An incorporation of deep temporal convolutional networks with hidden markov models post-processing for sensor-based human activity recognition

The use of sensors for human activity recognition (HAR) is one of the most active research fields. Several machine learning techniques for classifying human actions have been proposed in HAR. However, because they rely so heavily on the quality of handcrafted features, these techniques demand extensive feature engineering. Recent approaches to deep learning have attempted to provide comprehensive training. In this paper, we present a deep temporal convolutional neural network with Hidden Markov Chain for HAR post-processing. Our proposed method comprises an enhancement to ED-TCN [13], an efficient algorithm to generate very large, noisy data for training model with weight initialization, and a post-processing technique for smoothing the prediction of model. Experiments indicate that our proposed model outperforms other state-of-the-art models on the PAMAP2 and WISDM datasets, suggesting that our model is effective at recognizing human activity using sensors. Our method’s implementation is available on github: https://github.com/khaclinh/EFTCN-HMM.
Query-based Performance Comparison of Graph Database and Relational Database

A graph database is a type of NoSQL database that uses graph structure for semantic queries with nodes, edges, and properties to represent and store data. It has been applied in many fields, such as education, health, business, and social network, with many famous applications such as Google, Facebook, and eBay. One of the main advantages of the graph database is its effective performance in data queries. This paper presents a comprehensive comparison of the performance based on the execution time of a NoSQL graph database named Neo4J with a popular relational database system, MySQL, which is used as the underlying technology in developing a software system. Query types are categorized into four groups: selection/ search, recursion, aggregation, and pattern matching. We examined representative questions for each group and executed them on Neo4j and MySQL using a real-life dataset named Career Village. The results show that Neo4j’s data query performance is better than MySQL in most results.