Reviewerly/exHarmony huggingface dataset:
Smart Surveillance System for Illegal Garbage Dumping

Effective garbage management is a key challenge faced by urban areas across the world Garbage dumping in unauthorized places is a common problem in most cities, and it poses a serious threat to public health and the environment. To address this issue, we propose a real-world surveillance platform that detects and reports instances of garbage dumping in unauthorized areas using vision-based action recognition techniques. Our approach involves training a deep neural network on a dataset of garbage dumping actions to recognize and classify instances of this behaviour in real-time video footage. The system operates in three stages detection, tracking, and classification. In the detection phase, we use object detection techniques to locate potential dumping events in video frames. In the tracking phase, we track the detected object across subsequent frames to obtain a trajectory that helps in understanding the context of the action. In the classification phase, we classify the action as garbage dumping or non-dumping using a convolutional neural network. Our system achieves an accuracy of 96.2% on a test dataset, demonstrating its effectiveness in detecting garbage dumping events in real-world surveillance scenarios. The proposed system can be integrated into existing surveillance infrastructure to improve the efficiency of garbage management systems and enhance public health and environmental protection.
Automated Hyperparameter Optimization of Convolutional Neural Network (CNN) for First-Break (FB) Arrival Picking

Abstract The Convolutional Neural Network (CNN) has been used successfully to enhance the First-break (FB) automated arrival picking of seismic data. Determining an optimized FB model is challenging as it needs to consider several hyperparameters (HPs) combinations. Tuning the most important HPs manually is infeasible because of a higher number of HP combinations to be tested. Three state-of-the-art automated hyperparameter optimization (HPO) techniques are applied to a CNN model for robust FB arrival picking classification. A CNN model with 4 convolutional (Conv) layers followed by one fully connected (FC) and one output layer is designed to classify the seismic event as FB or non-FB. To control overfitting, dropout (DO), batch normalization are used after every two Conv layers, in addition to only the DO layer after FC. The number and size of kernels, DO rate, Learning rate (Lr), and several neurons in the FC layer are fine-tuned using random search, Bayesian, and Hyper Band HPO techniques. The findings are experimentally evaluated and compared in terms of four performance metrics with respect to classification performance. The five hyperparameters mentioned above are fine-tuned in 13 search spaces for each of the three HPO techniques. From experimental results, applying random search HPO to CNN yields the best accuracy and F1-score of 96.26%, with the best HP combination of 16, 16, 32, and 64 for numbers of kernels in four Conv layers respectively; 2, 2, 2, 5 for the size of kernels in each Conv layer; 0, 0.45, 0.25 for DO rate in each of DO layers; 240 for numbers of neurons in FC layer; and 0.000675 for Lr. In terms of loss on test data, the above combination of HP gives the lowest test loss of 0.1191 among all techniques, making it a robust model. This model outperforms all the other models in terms of precision (96.27%) and recall. Moreover, all HPO models outperformed the baseline in terms of all metrics. The use of DO after Conv layers and FC layers is highly recommended. Moreover, the use of kernel size relatively smaller (i.e. 2) produces the best classification performance. According to the best HP combination results, there is also no harm to use a relatively higher number of neurons in the FC layer than the Conv layer in FB arrival picking classification. The optimal values of Lr range from 0.0001 to 0.000675 depending on the HPO techniques. The model developed in this study improves the accuracy of the auto-picking of FB seismic data and it is anticipated our model to be used more widely in future studies in the processing of seismic data.
Intelligent Course Recommender for Professional Development

Abstract One of the major challenges faced in oil and gas industry today is talent management of its workforce. Training the workforce and suggesting the right training courses to the individuals is important in talent management and career development. There are many training recommendation systems available using different machine learning approaches such as collaborative filtering, neural networks and hybrid models. In this paper, an intelligent recommendation system is proposed by blending machine learning algorithms, natural language processing (NLP) and text analytics combined with organizational preferences. This framework gives a recommender system considering the user profiles, training preferences for his or her organization where each set of organizational units will have unique training recommendation requirements considering organizational functional behavior. The proposed mechanism uses machine learning algorithms at different stages of its learning process and ensemble them in a unique fashion such that desirable results are achieved to the user satisfaction.
Organization of Content Policy on University Library Websites: Strategies for Promoting Publishing Projects

In the era of digital transformation, the forms of providing information and working with users are changing. As you know, libraries of educational institutions are not only a place for storing and distributing educational resources among readers, but also carry out work to popularize resources and promote them in the educational environment. The university libraries, performing informational, educational and cultural functions, use the tools of websites to solve the tasks set. The success of the entire educational organization depends on a competent and convenient content policy. The article is devoted to the content analysis of sections of the websites of libraries of pedagogical universities. The purpose of the study: to study the content of the websites of libraries of pedagogical universities. In the course of the work, statistical and comparative methods were used, a content analysis of 38 pages was carried out, the main tasks were identified and problems for further research were identified. Main conclusions: relevance of content, reflection of links with external partners and publishers, ease of navigation, modern design are important conditions for interaction between the library and the reader and, as a result, increased interest in publishing projects and library activities. Different types and variety of content increase audience engagement, reduce the time of interaction between participants in the educational process. The main tasks facing libraries are: building up external and internal relations to promote publishing projects, work to popularize the developments of the university's authors in the external information space, the use of modern research on the impact of web resource design on readers. The results can be used to develop courses and conduct training events for industry professionals.
PENGARUH PENGGUNAAN FERRITE BEAD DAN VARIASI JENIS BUSI TERHADAP TORSI DAN DAYA PADA SEPEDA MOTOR YAMAHA SOUL GT TAHUN 2012

&lt;p&gt;&lt;em&gt;The purpose of this research was to determine: (1) The effect of Ferrite Bead on Torque and Power on the Yamaha Soul GT 2012 motorcycle. (2) the effect of use variations of spark plug type on Torque and Power on the Yamaha Soul GT 2012 motorcycles. (3) The effect of Ferrite Bead and variations of spark plug on Torque and Power on the Yamaha Soul GT 2012 motorcycle. This research is a quantitative type using experimental methods. The data analysis technique used is descriptive analysis. The research was conducted using a DYNOJET tool. The population in this research was the Yamaha Soul GT 2012 motorcycle.&lt;/em&gt;&lt;em&gt;From this results of the research, it can be concluded: (1) The use of Ferrite Bead can increase a Torque and Power on Yamaha Soul GT 2012 motorcycle. The results without the use of Ferrite Bead produces a torque of 9,77 N.m at 4502 rpm and power of 7,23 HP at 6107 rpm, while testing using a Ferrite Bead in the middle of the spark plug wire produces a torque of 10,22 N.m at 4660 rpm and power of 7,33 HP at 5438 rpm. There was an increase in torque of 4,61% and an increase of power in 1,38 %. (2) The use of variations of the spark plugs type can increase a torque and power on Yamaha Soul GT 2012 motorcycle. The highest increase in torque and power occurs in the use of Platinum spark plugs. Test result using a Standar spark plug (C7HSA) produce a torque of 9,77 N.m at 4502 rpm and power of 7,23 HP at 6107 rpm, while testing using a Platinum spark plug (CR7HGP) obtained torque of 10,21 N.m at 4650 rpm and power of 7,33 HP at 6209 rpm, and testing using a Iridium spark plug (NGK CR6HIX) obtained torque of 10,04 N.m at 4632 rpm and power of 7,27 HP at 6466 rpm. An increase in torque on the use of platinum spark plugs by 4,5 % and power by 1,38 %. While the increase in torque on the use of iridium spark plugs is 2,76 % and the power is 0,55 %. (3) Interaction of the use of Ferrite Bead and variations of spark plugs type can increase a Torque and Power on Yamaha Soul GT 2012 motorcycle. The test results show the highest Torque and Power namely the use of Ferrite Bead in the middle of spark plugs wire and the use of Platinum spark plugs with a Torque value of 10,38 N.m at 4664 rpm and a power of 7,4 HP at 6294 rpm. There was an increase in torque by 6,24% and an increase in power by 2,4 % from standard conditions.&lt;/em&gt;&lt;/p&gt;
DiffCSP: Finding Browser Bugs in Content Security Policy Enforcement through Differential Testing

The Content Security Policy (CSP) is one of the de facto security mechanisms that mitigate web threats.Many websites have been deploying CSPs mainly to mitigate crosssite scripting (XSS) attacks by instructing client browsers to constrain JavaScript (JS) execution.However, a browser bug in CSP enforcement enables an adversary to bypass a deployed CSP, posing a security threat.As the CSP specification evolves, CSP becomes more complicated in supporting an increasing number of directives, which brings additional complexity to implementing correct enforcement behaviors.Unfortunately, the finding of CSP enforcement bugs in a systematic way has been largely understudied.In this paper, we propose DiffCSP, the first differential testing framework to find CSP enforcement bugs involving JS execution.DiffCSP generates CSPs and a comprehensive set of HTML instances that exhibit all known ways of executing JS snippets.DiffCSP then executes each HTML instance for each generated policy across different browsers, thereby collecting inconsistent execution results.To analyze a large volume of the execution results, we leverage a decision tree and identify common causes of the observed inconsistencies.We demonstrate the efficacy of DiffCSP by finding 29 security bugs and eight functional bugs.We also show that three bugs are due to unclear descriptions of the CSP specification.We further identify the common root causes of CSP enforcement bugs, such as incorrect CSP inheritance and hash handling.We confirm the risky trend of client browsers deriving completely different interpretations from the same CSPs, which raises security concerns.Our study demonstrates the effectiveness of DiffCSP for identifying CSP enforcement bugs, and our findings have contributed to patching 12 security bugs in major browsers, including Chrome and Safari. I. INTRODUCTIONContent Security Policy (CSP) [4,5,6] is a protection mechanism that has become a de facto measure of security mitigation.A web developer declares a CSP for their website in the web response header or the response body via a meta tag.This instructs the browser to honor the CSP and govern the execution and inclusion of various web resources, such as scripts or images.The CSP specification has evolved into the level 3 specification (CSP3) [6] since its adoption by major browsers, including Chrome, Firefox, Edge, and Safari. 1 XSS attack payload:2
Estimation of OCR and Compression Index by Different Methods

Preconsolidation stress ( $${\sigma }_{c}^{\mathrm{^{\prime}}}$$ ) is an important parameter to understand the stress history of the soil and in calculating settlements. Accurate determination of settlement depends largely on the accuracy of $${\sigma }_{c}^{\mathrm{^{\prime}}}$$ . Several researchers have proposed different methods to obtain $${\sigma }_{c}^{\mathrm{^{\prime}}}$$ from e-log $${\sigma }^{\mathrm{^{\prime}}}$$ plot. Determination of preconsolidation stress relies on the graphical approach of Casagrande method. This method depends on identification of the point of maximum curvature on the e-log $${\sigma }^{\mathrm{^{\prime}}}$$ curve which is highly subjective and leaves room for errors. Several methods have been proposed in the literature for interpretation of preconsolidation stress based on curve fitting rather than subjective judgment. These approaches are based on graphical interpretation of void ratio (e) versus effective stress ( $${\sigma }^{\mathrm{^{\prime}}}$$ ), log e versus log $${\sigma }^{\mathrm{^{\prime}}}$$ , log (1 + e) versus log $${\sigma }^{\mathrm{^{\prime}}}$$ plots. The methods used in this study are semi-logarithmic [1, 2, 3, 4], bi-logarithmic [5, 6, 7] and based on variation of constrained modulus with effective stress [8]. In this study, test data taken from the three (Egypt, California and India) different locations around the world is analysed. Compression index (Cc) and over consolidation ratio (OCR) are determined using the above methods and the variation is studied.
Characterizing the VPN Ecosystem in the Wild

With the increase of remote working during and after the COVID-19 pandemic, the use of Virtual Private Networks (VPNs) around the world has nearly doubled. Therefore, measuring the traffic and security aspects of the VPN ecosystem is more important now than ever. VPN users rely on the security of VPN solutions, to protect private and corporate communication. Thus a good understanding of the security state of VPN servers is crucial. Moreover, properly detecting and characterizing VPN traffic remains challenging, since some VPN protocols use the same port number as web traffic and port-based traffic classification will not help. In this paper, we aim at detecting and characterizing VPN servers in the wild, which facilitates detecting the VPN traffic. To this end, we perform Internet-wide active measurements to find VPN servers in the wild, and analyze their cryptographic certificates, vulnerabilities, locations, and fingerprints. We find 9.8M VPN servers distributed around the world using OpenVPN, SSTP, PPTP, and IPsec, and analyze their vulnerability. We find SSTP to be the most vulnerable protocol with more than 90% of detected servers being vulnerable to TLS downgrade attacks. Out of all the servers that respond to our VPN probes, 2% also respond to HTTP probes and therefore are classified as Web servers. Finally, we use our list of VPN servers to identify VPN traffic in a large European ISP and observe that 2.6% of all traffic is related to these VPN servers.
The Future is Democratized Graphs

Graphs (a.k.a networks) are ubiquitous nowadays in many application domains (e.g., retail and eCommerce, transportation and logistics, healthcare, pharmaceuticals, and life sciences) as they provide powerful abstractions to model complex structures and relationships. Consequently, graph data management tools are expected to play a pivotal role in diverse applications such as customer analytics, fraud detection and prevention, supply chain management, and scientific data analysis. Markets and Markets anticipates the global graph database market size is expected to grow from USD 1.9 billion in 2021 to USD 5.1 billion by 2026 (Markets and Markets 2022). Given such growth opportunities, it is paramount for graph data management tools to be user-friendly, efficient, and scalable to support their growing demands from diverse end users and applications.
Finding the Cyclic Covers of a String

We introduce the concept of cyclic covers, which generalizes the classical notion of covers in strings. Given any nonempty string X of length n, a factor W of X is called a cyclic cover if every position of X belongs to an occurrence of a cyclic shift of W. Two cyclic covers are distinct if one is not a cyclic shift of the other. The cyclic cover problem requires finding all distinct cyclic covers of X. We present an algorithm that solves the cyclic cover problem in $$\mathcal {O}(n \log n)$$ time. This is based on finding a well-structured set of standard occurrences of a constant number of factors of a cyclic cover candidate W, computing the regions of X covered by cyclic shifts of W, extending those factors, and taking the union of the results.
Pattern Selection for Large Networks

In the preceding chapter, we have discussed the selection of canned patterns (i.e., the CPS problem) from a large collection of small- or medium-sized data graphs (i.e., graph databases). In this chapter, we present a framework called TATTOO (daTa-driven cAnned paTtern selecTiOn from netwOrks) that takes a data-driven approach to the CPS problem for large networks. Given a network G, a user-specified plug b, TATTOO automatically selects canned patterns from G that satisfy b.
Internal Longest Palindrome Queries in Optimal Time

Palindromes are strings that read the same forward and backward. Problems of computing palindromic structures in strings have been studied for many years with a motivation of their application to biology. The longest palindrome problem is one of the most important and classical problems regarding palindromic structures, that is, to compute the longest palindrome appearing in a string T of length n. The problem can be solved in $$\mathcal {O}(n)$$ time by the famous algorithm of Manacher [Journal of the ACM, 1975]. In this paper, we consider the problem in the internal model. The internal longest palindrome query is, given a substring T[i..j] of T as a query, to compute the longest palindrome appearing in T[i..j]. The best known data structure for this problem is the one proposed by Amir et al. [Algorithmica, 2020], which can answer any query in $$\mathcal {O}(\log n)$$ time. In this paper, we propose a linear-size data structure that can answer any internal longest palindrome query in constant time. Also, given the input string T, our data structure can be constructed in $$\mathcal {O}(n)$$ time.
Maintenance of Patterns

In the preceding chapters, we have introduced the pivotal role patterns play in PnP interfaces and how they can be automatically selected from the underlying graph data sources. Observe that these patterns are selected at a particular time point. However, real-world graph repositories are dynamic in nature. A study Zou et al. (2008) reported that approximately 4,000 new structures were added daily to the SCI finder database. Similarly, new compounds are added to the PubChem and Drugbank daily. Consequently, these patterns may grow stale quickly over time and adversely impact efficient visual query formulation (Huang et al. 2021).
The World of Visual Graph Query Interfaces—An Overview

Several visual interfaces such as Refinery Kairam et al. (2015) and Apollo Chau et al. (2011) facilitate exploration of large networks using keyword-based search on node and edge attributes. These systems enable bottom-up search through query results where the output (results) serves in part as input (queries). They visualize the most relevant items returned by the system w.r.t. a query along with their connections using a graph view. An end user is expected to browse these items and associated attributes which may spur insights and further exploration. However, these systems are limited to keyword-based queries and do not support subgraph queries where a query topology needs to be formulated. This significantly limits the practical usage of these systems for graph databases. Consequently, bottom-up search is limited only to browsing intermediate result graphs as patterns are not relevant here. Recall from Sect. 1.6, such bottom-up search may have a considerable cognitive impact on end users in the context of subgraph querying.
Background

This chapter provides an overview of key concepts that serve as background for the rest of the book. First, we discuss relevant terminology related to graphs and subgraph query. This is followed by a brief discussion on cognitive load and usability that play pivotal roles in the design of visual subgraph query interfaces. While these two concepts are studied exhaustively in cognitive psychology, information visualization, and HCI communities, they have not received adequate attention from the graph data management community. Table 2.1 shows the key symbols related to graphs that we shall be using throughout this book.
Pattern Selection for Graph Databases

In the preceding chapters, we emphasize patterns (basic and canned) as the basic building blocks of PnP interfaces. In this chapter, we present a state-of-the-art technique for selecting them to aid the construction of PnP interfaces for a large collection of small- or medium-sized data graphs (i.e., graph database). Recall that such datasets are prevalent nowadays in a variety of domains such as cheminformatics, bioinformatics, drug discovery, and computer vision. To this end, we first present a framework called CATAPULT (Huang et al. 2019) (Canned pAttern selecTion for fAst graPh qUery formuLaTion) that is at the core to address this problem. Then, we convey how it is utilized as the building block for AURORA, a novel PnP interface for graph databases (Bhowmick et al. 2020). Briefly, CATAPULT comprises of three key components, namely small graph clustering, cluster summary graph (CSG) generator, and canned pattern selector (Fig. 6.1). Given a data graph collection, the small graph clustering module performs clustering of the underlying data graphs using features such as frequent subtrees and maximum (connected) common subgraphs. Then, each cluster is summarized as a cluster summary graph (CSG) by “integrating” all data graphs in that cluster.
THE DYNAMIC RELATIONSHIP BETWEEN STOCK MARKET DEVELOPMENT AND INCLUSIVE GROWTH IN A RETROSPECTIVELY-TENDED-ECONOMY

The purpose and main focus of this paper is to inquire into the dynamic nature of stock market and inclusive growth series in a retrospectively-tended-economy like Nigeria. Lags of the previous periods of the indicators were confirmed to be very fundamental building blocks to understanding the current behaviour of the phenomenal growth of the two indicators and their metrics. We reviewed the nature and types of lag models and innovatively interpreted them in a spectacular manner that unveiled the uniqueness of the impacts of such past periods on current behaviour of both stock market and inclusive growth. Applying the autoregressive distributed lag model for the period 1980 to 2020, our findings show that current performance of the market and inclusive growth are somewhat inter-period growth-persistence-averse, and stock market size is ephemeral and susceptible to shadow and cosmetic largeness. We recommend that Lags should be considered as important and indispensable inputs into the policy and stock market and inclusive growth modelling processes and procedures in Nigeria.&#x0D; Keywords: Stock Market Development; Inclusive Growth; Dynamic Effects; Retrospectively- Tended-Economy.