Reviewerly/exHarmony huggingface dataset:
Pedestrian Multi-object Tracking Based on ResNeXt and FairMOT

Multi-object tracking is an important branch in the field of computer vision. To address the shortcomings of the current paradigm of following detection-based multi-object tracking, this paper proposes an improved algorithm based on FairMOT. Firstly, ResNeXt50 is used as the backbone network, which makes the model more capable of feature extraction, secondly, a normalization-based attention module (NAM) is added to Resblock to suppress less significant weights and focus more on the desired target regions to extract more effective features. The MOTA metric and IDF1 metric achieve 68.8% and 68.1% respectively on the MOT17 dataset. The experimental results demonstrate the performance of the proposed algorithm with some advantages.
Application of Copula Function in Shanghai and Shenzhen Index

For the description of the correlation between the Shanghai index return rate and the Shenzhen's, the copula model is selected between the normal copula function and the t-copula function. The results show that the binary t-copula with linear correlation coefficient of 0.9289 and degree of freedom of 4 is closer to the sample correlation coefficient; Kendall rank correlation coefficient and Spearman rank correlation coefficient of normal copula are closer to the sample.
The Effect of Performance Appraisal System on Organizational Commitment: Case Study Gaza Electricity Distribution Corporation

This study aims to investigate the effect of performance appraisal system on organizational commitment in Gaza Electricity Distribution Corporation (GEDCO). In total, 269 employees from the Gaza Electricity Distribution Company's six branches took part in the study, with a response rate of 82.16%. The analysis was conducted using a quantitative research methodology. To analyse the structural equation model and look at the study hypotheses, the (AMOS) tool was utilized.. The study found that there is a direct, weak, positive significant effect for performance appraisal system on organizational commitment. The results also denote that the dimensions (appraisal method (AM) and (appraisal feedback (AF) have the most impact on organizational commitment. The dimension (appraisal process (AP) was the least influential on organizational commitment.
Blockchain of Cryptocurrency Using a Proof-of-Work-Based Consensus Algorithm with an SHA-256 Hash Algorithm to Make Secure Payments

A blockchain is a distributed database featuring a decentralized digital network. A blockchain contains a set of data that can store information in blocks on an electronic platform in an advanced setup that uses a systematic framework via the Internet. Blockchains are popular because of their roles in cryptocurrency networks—such as Bitcoin—which must have a secure payment system. This method of transaction is perceived as an imaginative technique for online payments because it enables productive, straightforward, and secure exchanges. The blockchain of Bitcoin turned innovation into success by building a peer-to-peer monetary transaction network for secure transactions—a network that connects global financial markets with a circulated framework without any weak links. The purpose of this research is to provide the best hybrid algorithm for cryptocurrency so that secure payments can be made. For example, because booking a hotel room reservation becomes complex when that hotel is located in other country. Currency exchanges and funds transfers have become expensive because of high exchange rates, so using cryptocurrency to make hotel room reservations is a cheaper option.
Blockchain Networks for Cybersecurity Using Machine-Learning Algorithms

The application of blockchains along with machine-learning (ML) algorithms will provide safe data transactions because it is an unchanging kind of disseminated record that is suitable for loading information without depending on outside data. In addition, a few machine-learning algorithms using blockchains can prevent ransomware attacks. Even though a lot of work has gone into using blockchains with algorithms for network safety, too few exhaustive overviews have been conducted on blockchain frameworks that use such algorithms for online protection. A new advancement in the Internet of Things (IoT), in cutting-edge intelligent systems (ISs), can hold sufficient information to conduct big data analyses with high accuracy and low inertness in security. Thus, for conducting efficient and effective analytics on blockchain data, a neural network of deep learning (DL) could be a better option because it can identify all the layers. The research in this chapter focuses on cybersecurity in blockchains by using neural networks. The proposed method, which combines a multilayer perceptron (MLP) with long short-term memory (LSTM) models for online security to establish an arrangement that is based on market patterns, provides security to data transactions such as online payments.
The Acceptance of Artificial Intelligence in the Commercial Use of Crypto-Currency and Blockchain Systems

According to the literature review of technology acceptance designs, artificial intelligence is presented as a tool for technical and commercial use of crypto-currency, and it is based on several dimensions of multidimensional attractiveness hence the notion of blockchain. We are focused on an acceptance test according to the axes and technical orientations in order to know the tricks of changing the thoughts and behaviors of the human being. At this level, several theories have been linked (the New Technology Acceptance Model, Standardized framework for integrating new technologies, Theory of Predicted Behavior, using concepts from both the theory of flow and behavioral reasoning (TOF)) in a single model. To concretize our approach, we opt for a quantitative approach based on the PCA method (principal components analysis) in order to reduce the items and to answer the hypotheses posed at the beginning. We note from the results of our study the existence of a strong significance between the dimension “The technology tools of commercial intervention” and the variable to be explained “Technological efficiency” which is slightly significant which allows us to determine a deep acceptability of the blockchain systems.
Revolutionizing Waste Management: The Garbage Bank Solution and Its IT-Driven Implementation for Eco-Friendly Hygiene

The high yield of domestic household waste is a significant problem for environmental hygiene, where waste management is still not a top priority to provide profit opportunities for the community and the government. Thus, to campaign for ecological hygiene solutions by applying information technology in the form of a Garbage Bank, which can help deal with domestic waste, especially plastic waste.&#x0D; The Waste bank application is built using the CodeIgniter framework, which has the concept of Model, View, Controller (MVC), which has functions such as a model for the database framework, views of the system view and controllers as actions for executing user commands. The system was also developed using the extreme programming method with the advantage of being fast in development, simple and object-oriented. The resulting system has conducted a feasibility test to conclude that it is following its function.&#x0D; This research resulted in a system of three parts: the admin section, which can manage data on partners, customers, pick-ups to withdraw funds and reports. The partner section manages customer data, waste, pick-up and reports, and the customer section registers, views the history of saving waste, withdraws funds, and views types of waste and partners entirely done online.
Educational Opportunities

Journal AWWAVolume 115, Issue 8 p. 74-74 Educational Opportunities Educational Opportunities First published: 03 October 2023 https://doi.org/10.1002/awwa.2168Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume115, Issue8October 2023Pages 74-74 RelatedInformation
SITE SELECTION FOR ENGINEERING STRUCTURES AND EARTHQUAKE

An earthquake is a natural event and it is quite easy and beneficial to convert it to wealth rather than disaster. A strong earthquake exerts its catastrophe on the buildings/structures found in the soil grounds. There is not even a single case study that creates catastrophe where structures/buildings are erected on/in the rocky ground whose extensive planar discontinuities are free of expansive clay minerals. Every strong earthquake has proven this reality. The last earthquake with a magnitude of 7.7 hit Pazarcik/Kahramanmaras/Turkey on the 6th of February. The epicenter is at the fertile soil plain of the township Çigdemtepe (Crocushill)/Pazarcik which has been erected on the weak rock of clayey limestone. Despite that, casualty (the number of dead and injured) is zero in the township of Crocushill) whereas a very severe disaster has occurred in (a) Battalgazi and (b) Harran soil plains which are orderly 160 and 200 km away from the epicenter. The reasons behind this fact are “the seismic energy attenuates in strong rock over a thousand times more than that in thick (t&gt;20 m) soil mantle” and “wave velocity in rock is practically 30 times higher than that in saturated soil”. It should also be noted that there is no liquefaction in the rock. If the locations of engineering structures are chosen other than irrigable agricultural areas, fertile soils and possible landslide areas, no problems will be encountered. It is not important where and when the earthquake will occur, but where it will collapse. These hazardous areas also overlap with agricultural areas.
PHNN: A Prompt and Hybrid Neural Network-Based Model for Aspect-Based Sentiment Classification

Aspect-based sentiment classification (ABSC) is an important task in natural language processing (NLP) that aims to predict the sentiment polarity of different aspects in a sentence. The attention mechanism and pre-trained models are commonly used in ABSC tasks. However, a single pre-trained model typically does not perceive downstream tasks very well, and the attention mechanism usually neglects the syntactic information of sentences. In this paper, we propose a prompt and hybrid neural network (PHNN) model, which utilizes the prompt and a hybrid neural network structure to solve the ABSC task. More precisely, it first uses the prompt to convert an input sentence into cloze-type text and utilizes RoBERTa to deal with the input. Then, it applies the graph convolutional neural network (GCN) combined with the convolutional neural network (CNN) to extract the syntactic features of the sentence while using bi-directional long short-term memory (BiLSTM) to obtain the semantic features of the sentence. Further, it utilizes the multi-head attention (MHA) mechanism to learn attention in the sentence and aspect words. Finally, the sentiment polarity of the aspect words is obtained by using the softmax function. Experiments on three benchmark datasets show that PHNN has the best performance compared with other baselines, validating the efficiency of our model.
EDUCATION MANAGEMENT SYSTEMS

The higher education system strives to make every possible aspect of study better by incorporating technology and digitalization into the process. As we all move towards personalization and user experience, the education industry can do the same by implementing learning management systems for various schools and programs in school. In this age where knowledge comes to the fore, the importance of knowledge is emphasized once again and the education system can use student knowledge to become more effective and enrich the body. However, this is only possible if the data is stored in a unified manner. The concept of e-learning is not new in education. However, the COVID-19 pandemic has increased the need for schools to support technology systems to support learning. The need for schools to use online platforms to provide solutions to their students is increasing as the pros outweigh the cons. Simply put, e-learning is learning or teaching through digital resources such as software programmers, mobile devices, and the Internet. Key Words: support technology, e-learning optics, digitalization, software programmers, mobile devices
Remote sensing solar simulation laboratory for polarimetric scene simulation

The Applied Sensing Laboratory at the University of Dayton has constructed a solar simulation laboratory to support polarimetric remote sensing research. The laboratory contains a number of highly accurate and repeatable motion stages that allow for automated positioning and control of imaging sensors, light sources, and object geometries. The laboratory contains both collimated (direct sun) and diffuse (downwelling) light sources that we have spectrally tuned to match expected solar irradiance under a range of outdoor conditions. In this work we describe the capabilities of the laboratory and the measures that have been taken to date for calibrating the laboratory environment to mimic outdoor solar conditions. The laboratory can support complex, automated experiments that can precisely control the dominant parameters of interest in polarimetric remote sensing. We demonstrate example data for laboratory use cases of interest to the polarimetric imaging community that include polarimetric bidirectional reflectance distribution function (pBRDF) measurements and generation of curated datasets to support polarimetric phenomenology studies and deep learning algorithm training for a host of polarimetric remote sensing applications.
Non-Fungible Tokens and the Threat of Wash Trading

Non-fungible tokens are a revolutionary concept that combines art, authenticity, proof of ownership and enables large-scale commerce. Their value does not come from their use in financial transactions, but from the fact that they are linked to specific assets, whether digital or real. Non-fungible tokens thus represent a new driving force in the areas of digital ownership. Owners of these tokens can earn huge sums at a time when the art market is on the verge of revolution. The non-fungible token market experienced enormous growth in 2021 with traders investing a huge amounts of billions of dollars worth of cryptocurrencies into digital collectibles. Since 2021 the transaction activity in this area cooled, although the number of active investors continued to grow in 2022. While the popularity of certain non-fungible token collectibles can fluctuate depending on market conditions, some traders may try to manipulate the prices of certain non-fungible tokens to make them appear more valuable. The token will be sold at a higher price to a new wallet, which is also controlled by the original owner. Transactions between wallet addresses are saved on a blockchain and can be accessed publicly, so that anyone can see when the token was traded and for how much it was sold. However, wallet addresses contain no identifying information making it very difficult to discern who is behind a transaction and whether two addresses are owned by the same individual. This process is called wash trading and its analysis is a goal of this contribution.
Radar signal deinterleaving method exploiting correlation of multi-parameter time series

Signal deinterleaving is a critical technique in the process of radar electronic reconnaissance. However, the performance of the traditional signal deinterleaving algorithm is degraded dramatically when faced with high pulse loss ratios and noise pulse interference. To deal with this deficiency, a radar signal deinterleaving method exploiting the correlation of multi-parameter time series is proposed. TOA, RF, and PW are used to construct a pulse feature representation. The bidirectional recurrent neural network is used to explore the long-term temporal patterns in the pulse stream and extract the characteristics of the pulse time series context via supervised learning. Simulated experimental results showed that the proposed method could obtain robust performance under non-ideal conditions, which can achieve an accuracy of over 90% even if the pulse loss ratio reached 70%.
Landslide disaster risk analysis in Pacet District, Mojokerto Regency, East Java

Pacet District, Mojokerto Regency, East Java is one of the areas prone to landslides. Landslides in the area that occur can threaten lives and cause environmental damage and financial losses. This study aims to analyze the risk of landslides in Pacet District, Mojokerto Regency, East Java, and apply Early Warning System (EWS) technology based on the Internet of Things (IoT). This type of research is both qualitative and quantitative, the results of the study are based on the level of threat of landslides. Designing a landslide early warning tool is not easy to do because it has to be adapted to local situations and conditions. This program will be directed at technology transfer by implementing an Internet of Things (IoT)-based Early Warning System (EWS).
The Ethics of Using “Pre-made Designs” in Commerce

The distinction between creative work that is entirely original and that which is only based on the work of others is becoming increasingly difficult to discern as graphic design software gets easier to use and more widely available. This study examines the ethical consequences of utilizing graphic design software, particularly in regard to copyright legislation and responsible design practices. This article analyses how pre-made design components and templates found in software programmes might infringe on copyright and how designers can traverse these legal concerns to preserve ethical design practices. In addition, the essay explores the role that graphic design software may play in either nurturing or impeding original creativity, as well as the ethical issues that come into play when choosing between developing original ideas and employing pre-made design pieces. In conclusion, this section of the essay provides guidelines for designers on how they should approach the usage of graphic design tools in an ethical and responsible manner. This article gives insight into the ethical issues designers must consider while producing new designs and navigating the difficulties of copyright law by examining the ethical consequences of utilizing graphic design tools.
Global Path Planning Method Based on Improved Beetle Antenna Search Algorithm for Unmanned Surface Vehicle

In this paper, the constraint function of the maximum turning angle is designed in the fitness function of the Beetle Antenna Search algorithm (BAS) according to the characteristic that unmanned surface vehicles (USV) are not easy to turn sharply. To solve the problem of strong randomness and poor stability of the global path planning of BAS, the dynamic search step size that changes with the distance from the obstacle is used in the Improved Beetle Antenna Search algorithm (IBAS), instead of the fixed step size used in the original algorithm. Simulation results show that IBAS improves the stability and success rate of global path planning for USV. The planned path is smooth, continuous, safe, and effective. IBAS is more suitable for the path planning of USV than the original algorithm.
Towards Secure Data Storage in Web 3.0: Ciphertext-Policy Attribute-Based Encryption

As the next-generation internet paradigm, Web 3.0 aims to build a free, equal and decentralized Internet. Different from the data monopoly of the Internet oligarchy in Web 2.0, users in Web 3.0 are fairly endowed with the right to store, share, access and manage data. However, as data leakage, tampering and loss may result in a forfeiture of users’ data control, the security of data storage has become a key and essential prerequisite to safeguard users’ data rights. Blockchain has gained widespread acknowledgment as a prospective technology for solving data security. Nonetheless, its primary suitability for large-scale datasets is limited, owing to the substantial communication and storage overheads. Fortunately, a distributed data storage network (DDSN) can compensate for accommodating large-scale datasets by providing sizeable and inexpensive storage space. In this article, we first summarize the data storage advantages of blockchain and DDSN, and analyze their security challenges in terms of access control, authenticity and confidentiality. To address these challenges, we propose a general and versatile data security storage framework, where we adopt ciphertext-policy attribute-based encryption (CP-ABE) to enable user-centric fine-grained access control and decentralized authorization. Furthermore, to reduce storage costs while protecting data authenticity, we propose a ciphertext split storage model for CP-ABE. In this model, the CP-ABE ciphertext is divided into the attribute component and the data component based on functional structure to realize on-chain/off-chain split storage. In addition, by embedding the ambiguity factor required for decryption and the attributes in the access policy into the index, we design an efficient attribute-based keyword search mechanism to resolve the conflict between confidentiality and availability caused by encryption. Finally, we demonstrate the effective performance of the proposed framework through numerical results.
POSTER: Stopping Run-Time Countermeasures in Cryptographic Primitives

White-box cryptographic implementations with masking and shuffling have been proposed to protect against key extraction attacks. However, higher-order Differential Computation Analysis (HO-DCA) and its variants have been developed to break these countermeasures without having to perform reverse engineering. These non-invasive attacks are, however, costly to perform and can also be prevented by adapting novel constructions of masking and shuffling methods. This poster presents a simple binary injection attack on state-of-the-art masking and shuffling countermeasures. By injecting just a few lines of assembly code, the attacker can nullify run-time random sources, rendering randomness ineffective in hiding key-sensitive intermediate values. Our proposed attack mainly consists of hijacking the GOT entries and function calls to overcome run-time countermeasures protecting white-box cryptographic implementations.