Reviewerly/exHarmony huggingface dataset:
Identification of Facial Emotions Using Reinforcement model under Deep Learning

This paper addresses the identification of facial emotions using a reinforcement model under deep learning. Close-to-perception ability presents a more exhaustive recommendation on human-machine interaction (HMI). Because of the Transfer Self-training (TST), and the Representation Reinforcement Network (RRN), this study offers an active FER arrangement. Two modules are considered for depiction support arranging such as Surface Representation Reinforcement (SurRR) and Semantic Representation Reinforcement (SemaRR). SurRR highlights are detracting component communication centers in feature maps and match face attributes in different facets. Worldwide face settings are semantically sent in channel and dimensional facets of a piece. RRN has a limit concerning involved origin when the edges and computational complication are considerably belittled. Our technique was tried on informational indexes from CK+, RaFD, FERPLUS, and RAFDB, and it was viewed as 100 percent, 98.62 percent, 89.64 percent, and 88.72 percent, individually. Also, the early application exploration shows the way that our strategy can be utilized in HMI.
Smaller Representation of Compiled Regular Expressions

We consider the problem of running the regex pattern matching in a space-efficient manner. Given a regex, we suggest a bit-packing scheme for representing a compiled regex in a compressed way, which is its position automaton. Our scheme reduces its representation size further by relying on the homogeneous property of position automata and practical features of regexes. We implement the proposed scheme and evaluate the memory consumption using a practical regex benchmark dataset. Our approach produces a much smaller representation compared to two common FA representations. In addition, experimental results show that our bit-packing regex engine is effective for matching regexes that have large compiled forms, by showing less memory consumption compared to the current state-of-the-art regex engine (RE2).
Deep Learning Model Optimization

This chapter reviews recent work on compressing and accelerating deep neural networks, which has attracted much attention from the deep learning community and has made great strides in the past few years. This chapter divides these methods into four categories: (1) parameter pruning and sharing; (2) low-rank factorization; (3) transfer/compact convolutional filters; and (4) knowledge distillation. For parameter pruning- and sharing-based solutions, the redundancy in model parameters is explored, and redundant and noncritical parameters are removed. For low-rank methods based on solutions, decomposition techniques and matrix/tensor decomposition can be used to estimate the information parameters of CNNs. For the transfer/compact convolution solutions, a special structured convolution filter is designed to reduce the parameter space and save storage/computation. Knowledge distillation methods learn distillation models and train more compact neural networks to reproduce the output of larger networks.
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching

Cross-device user matching is a critical problem in numerous domains, including advertising, recommender systems, and cybersecurity. It involves identifying and linking different devices belonging to the same person, utilizing sequence logs. Previous data mining techniques have struggled to address the long-range dependencies and higher-order connections between the logs. Recently, researchers have modeled this problem as a graph problem and proposed a two-tier graph contextual embedding (TGCE) neural network architecture, which outperforms previous methods. In this paper, we propose a novel hierarchical graph neural network architecture (HGNN), which has a more computationally efficient second level design than TGCE. Furthermore, we introduce a cross-attention (Cross-Att) mechanism in our model, which improves performance by 5% compared to the state-of-the-art TGCE method.
Categorical Embeddings for Tabular Data using PyTorch

Deep learning has received much attention for computer vision and natural language processing, but less for tabular data, which is the most prevalent type of data used in industry. Embeddings offer a solution by representing categorical variables as continuous vectors in lowdimensional space. PyTorch provides excellent support for GPU acceleration and pre-built functions and modules, making it easier to work with embeddings and categorical variables. In this research paper, we apply a feedforward neural network model in PyTorch to a multiclass classification problem using the Shelter Animal Outcome dataset. We calculate the probability of an animal's outcome belonging to each of the 5 categories. Additionally, we explore feature importance using two common techniques: MDI and permutation. Understanding feature importance is crucial for building better models, improving performance, and interpreting and communicating results. Our findings demonstrate the usefulness of embeddings and PyTorch for deep learning with tabular data and highlight the importance of feature selection for building effective machine learning models.
Performance Analysis of Human Activity

This project aims to develop an AI-powered gym assistant using Jupyter Notebook and MediaPipe, a popular computer vision library, to count the repetitions of three joint exercises: curls, squats, and sit-ups. The system will provide real-time feedback and monitoring, allowing users to track their progress and improve performance. The proposed method utilizes MediaPipe, which offers pre-trained machine-learning models for human pose estimation and hand tracking. These models will accurately detect and track critical body joints and hand movements during the exercises. The system will then analyze the detected poses to identify the repetitions of each activity based on predefined movement patterns and pose thresholds. Jupyter Notebook will be used as the development environment for coding and testing the system. Python programming language and MediaPipe’s Python API will be employed to implement the pose estimation and repetition counting algorithms. The system will also incorporate a user-friendly interface, allowing users to interact with the gym assistant and receive feedback on their exercise performance. The completed project will provide an AI-powered gym assistant that can accurately count the repetitions of curls, squats, and sit-ups in real-time. Additionally, this project will contribute to the advancement of the field of fitness technology by showcasing the potential of combining computer vision and artificial intelligence techniques for gym monitoring and performance tracking. The results of this project have the potential to benefit fitness enthusiasts, trainers, and researchers alike, providing contributions to the field of fitness technology.
FPGA Implementation of Enhanced Montgomery Modular for Fast Multiplication

This Paper proposed an enhanced Montgomery and efficient implementation of Modular Multiplication. Cryptographyoprocess is usedufor providingphigh informationmsecurity when a data is transferredmfrom transmitter to receiver. Various using methods like RSA, ECC, the Digital Signature Algorithm. The propose Montgomery algorithm usin RSA algorithim of cryptography is implemented in two different input both the inputs are 8 bit input. Coding have been done in Verilog language and the results are simulated on Vivado Software. For physical testing, we have used an FPGA NESYS 4 DDR hardware board that have Artix-7 FPGA chip on it produced by digilent company. The propose method shows good results in term of the number of slice flip flop, LUTs, and number of IOBs and power consumption. The proposed method shows better results as compare to other previous methods in term of different result parameters.
Web Application Vulnerabilities and Best Practices: A Comprehensive Analysis

Web applications are integral to our digital lives, but they are also prone to numerous security vulnerabilities that can lead to data breaches, unauthorized access, and other malicious activities. This research paper aims to provide a comprehensive analysis of common web application vulnerabilities and propose best practices for mitigating these risks. The study examines a wide range of vulnerabilities, including SQL injection, cross-site scripting (XSS), cross-site request forgery (CSRF), and insecure direct object references. By exploring real-world examples and conducting in-depth analysis, this research aims to raise awareness and provide practical recommendations for developers, security professionals, and organizations to enhance the security of their web applications.
Natural Language Processing (NLP)-Driven Subjective Answer Ranking

Exams for universities and boards are administered offline each year. Many students show up for subjective exams. It took a lot of work to manually evaluate such a big number of papers. The evaluation's quality can occasionally fluctuate depending on the evaluator's attitude. The evaluation process takes a lot of time and effort. Objective or multiple choice questions are commonly found in competitive and entrance tests. These tests are reviewed using a machine because that is how they were administered, making evaluation simple. The manual evaluation of subjective papers is a difficult and taxing undertaking. A major obstacle when utilizing artificial intelligence (AI) to analyze subjective articles is a lack of understanding and acceptance of the findings. There have been numerous attempts to use computer science to evaluate student responses. To accomplish this objective, the majority of the effort, however, needs standard counts or precise terms. There are also not enough carefully selected data sets. In order to evaluate descriptive responses automatically, this paper proposes a novel approach that makes use of various machine learning, natural language processing, and tools like WorldNet, Word2vec, word mover's distance (WMD), cosine similarity, Multinomial Naive Bayes (MNB), and term frequency-inverse document frequency (TF-IDF). Responses are assessed using solution statements and keywords, and a machine learning model is built to forecast the grades of responses. Overall, the results indicate that WMD outperforms cosine similarity. The machine learning model could also be employed independently with appropriate training. Without the MNB model, experimentation produces an accuracy of 88%. Using MNB, the error rate is further decreased by 1.3%. Keywords - Subjective answer evaluation, big data, machine
Synergic quantum generative machine learning

We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparameters and report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration between the generators and discriminator, thus, we call it quantum synergic generative learning. We present numerical evidence that the synergic approach, in some cases, compares favorably to recently proposed quantum generative adversarial learning. In addition to the results obtained with quantum simulators, we also present experimental results obtained with an actual programmable quantum computer. We investigate how a quantum computer implementing generative learning algorithm could learn the concept of a maximally-entangled state. After completing the learning process, the network is able both to recognize and to generate an entangled state. Our approach can be treated as one possible preliminary step to understanding how the concept of quantum entanglement can be learned and demonstrated by a quantum computer.
Agent-Based Music Live Coding: Sonic adventures in 2D

This article describes agent-based music live coding, an approach for music performance and composition based on programming a set of agents in a 2D plane. This style of programming draws from the tradition of agent-based models and facilitates interactive algorithmic control of data-driven sound synthesis methods such as wave terrain synthesis or corpus-based concatenative synthesis. The main elements are a ‘terrain’, which may be used to access different types of data, a set of agents and their trajectories, and a set of synthesis functions associated to agents. An implementation using the SuperCollider language is demonstrated.
D. I. Mendeleev’s Yu. periodic law, G. Shcherbakov’s cosmogeochemical system and prospects for the development of mineralogical and geochemical investigations

The law of cosmogeochemical differentiation discovered by the remarkable Russian geochemist Yu. G. Shcherbakov and the geochemical classification of elements formulated on its basis on the scale of the Periodic system of D. I. Mendeleev are analyzed. Based on new research results, it is shown that signs of the fundamental cosmic distribution code of chemical elements are quite clearly preserved in terrestrial geological and biological objects.
Gauging the Media Discourse and the Roots of Islamophobia Awareness in Spain

This article analyses the media discourse about Islamophobia in Spain. Specifically, an overview of all the appearances of the term in four Spanish newspapers (ABC, El Mundo, El País, and La Vanguardia) is provided with the aim of finding out when the term was first used and became standard language. The study also demonstrates the links with the public interest and identifies the ideological and terminological attitudes in the discourse of each newspaper. The corpus includes 1475 news articles since the first reference (in 1987) to the term Islamophobia and May 2022, which were quantitatively examined in two steps. While the first was manual and served to document the historical background, the second allowed us to monitor the media content by means of Sketch Engine. Furthermore, the searches for the term “islamofobia” in Google Trends from Spain were also reviewed. The main findings show that both terrorist attacks in Western countries and the controversies surrounding freedom of speech are key to the emergence and normalisation of the concept, particularly since 2015. However, the interest of each newspaper differs, with El País covering the topic most frequently. This left-wing newspaper offers some notable variations in terminology as well. While the three right-wing newspapers consistently relate Islamophobia to threat, the vocabulary used in El País underpins the victimisation of the Arab-Islamic population. According to the Sketch Engine analysis, the usual terms that occur in combination with Islamophobia are racism, terrorism, violence, hate, anti-semitism, and xenophobia. Finally, Google Trends data confirmed the peak in public interest in the Barcelona and Cambrils attacks (17A).
A Blockchain Approach for Migrating a Cyber-Physical Water Monitoring Solution to a Decentralized Architecture

Water is one of the most important resources in our lives, and because of this, the interest in water management systems is growing constantly. A primary concern regarding urban water distribution is how to build robust solutions to facilitate water monitoring flows with the support of consumer involvement. Crowdsensing solutions contribute to the involvement in social platforms for increased awareness about the importance of water resources based on incentives and rewards. Blockchain is one of the technologies that has become increasingly popular in the last few years. The possibility of using this architecture in such different sectors while integrating emerging concepts, such as crowdsensing, the Internet of Things, serious gaming, and decision support systems, offers a lot of alternatives and approaches for designing modern applications. This paper aims to present how these technologies can be combined in order to migrate the functionalities of a water distribution management system from a centralized architecture to a decentralized one by leveraging blockchain technologies. The proposed application was designed to facilitate incident reporting flows in public water distribution networks. The proposed solution was to migrate the rewarding mechanisms using the Ethereum infrastructure. The novelty of this solution is determined by the introduction of this decentralized approach into the architecture and also by increasing customer interest by offering tradeable rewards and dynamic subscription discounts. This results in a new decentralized architecture that allows for more transparent interactions between the water provider and clients and increases customer engagement to contribute to water reporting flows.
Multivariate Statistical Methods Used in Determining the Hydrogeochemical Surfaces of Groundwater in Yusufiya District

Abstract For the importance of groundwater and being the second natural source of water after surface water, it is necessary to know the quality, features and hydrological surfaces of this water. For this reason, 15 wells were chosen in the Al Yusufiyah sub-district to study the quality of groundwater based on multivariate statistical methods as well as the Piper chart. These methods proved successful in characterizing the study of groundwater chemistry and can be relied upon for ground and surface water in other regions. The quality of the groundwater in Al Yousifiyah, according to Piper’s scheme, the alkalinity increases with the presence of sulfate and chloride, depending on the type of soil. From the analysis of the correlation matrix, the totals were found to be Ca, Mg, and Cl., Ca, Mg, Cl, and SO4, as well as Ca, Na, Mg, Cl, SO4, and HCO3. From the mass analysis, the first K, second Na, third Ca, fourth Mg, SO4 and fifth Cl, HCO3 were obtained. Finally, from the main component analysis, hydrogeochemical facies were found: Cl, SO4, Ca, HCO3, Mg, and Na, Respectively.
Experiential Observations: An Ontology Pattern-Based Study on Capturing the Potential Content within Evidences of Experiences

Modelling the knowledge behind human experiences is a complex process: it should take into account, among others, the activities performed, human observations and the documentation of the evidence. To represent this knowledge in a declarative way means to support data interoperability in the context of cultural heritage artefacts, as linked datasets on experience documentation have started to appear. With this objective in mind, we describe a study based on an ontology design pattern for modelling experiences through observations, which are considered indirect evidence of a mental process (i.e., the experience). This pattern highlights the structural differences between types of experiential documentation, such as diaries and social media, providing a guideline for the comparability between different domains and for supporting the construction of heterogeneous datasets based on an epistemic compatibility. We have performed not only a formal evaluation over the pattern but also an assessment through a series of case studies. This approach includes (a) the analysis of interoperability among two case studies (reading through social media and historical sources); (b) the development of an ontology for collecting evidences of reading, which reuses the proposed pattern; and (c) the inspection of experience in humanities datasets.
Fault diagnosis of train control onboard equipment based on Attention-Capsule Network

Abstract The onboard equipment of the train is a key component to ensure the safe and efficient operation of the train. Therefore, improving the fault detection ability of onboard equipment is crucial for the safe operation of trains. The driving log record file (AE-LOG) was generated by the CTCS-300T series control system (AE-LOG) as the starting point, and the improved capsule network (CAPSNET) as the research method was studied. By using the capsule network, this model greatly improves the attributes of text characteristics in high-dimensional space and low-dimensional space. At the same time, the routing method of the fusion attention mechanism is used to improve the efficiency of feature selection. Through experimental verification of operating data provided by a certain road bureau, and comparing with traditional models, the accuracy of the model in the control vehicle device fault diagnosis of the control of the vehicle is significantly improved, which shows the effectiveness of the model.
Calculation of Evaporation Losses for Center Pivot Sprinkler Irrigation Systems Type (Lindsay) by Effect of Timer Percentage of Speed and the Increase of Sprayer Nozzles from Soil Surface

Abstract The experiment was conducted during summer season (July, August, September) 2019 in some field in Anbar governorate, Saqlawiya city, Gisaba area to calculate water losses by evaporation through water depths by calculating the discharges that come from nozzles and water depths to collect in side cans while calculating uniformity coefficient above soil surface. The results showed that the increase of spray nozzles in soil surface at 1.0m gave the lowest depths from evaporated water by changing the timer speed percent; where it was 0.18mm 100% speed, while increased to 3.39mm at 20% speed compared 0.66mm at 100% speed till 5.91mm at 20% speed when expanding the sprayer nozzles to 1.50m from soil surface. The 100% speed gave low percentage of losses (1.79%) and the high percentage was obtained at 20% speed (33.70%) at 1.0m height, which is better that height of 1.50m in rationalizing water in order to irrigate new areas that water was not enough for. this is due to variation in the height of spray nozzles above the ground, as well as variations in temperature and wind speed steadfastly speed up the system.
Simple Knowledge Graph Completion Model Based on Differential Negative Sampling and Prompt Learning

Knowledge graphs (KGs) serve as a crucial resource for numerous artificial intelligence tasks, significantly contributing to the advancement of the AI field. However, the incompleteness of existing KGs hinders their effectiveness in practical applications. Consequently, researchers have proposed the task of KG completion. Currently, embedding-based techniques dominate the field as they leverage the structural information within KGs to infer and complete missing parts. Nonetheless, these methods exhibit limitations. They are limited by the quality and quantity of structural information and are unable to handle the missing entities in the original KG. To overcome these challenges, researchers have attempted to integrate pretrained language models and textual data to perform KG completion. This approach utilizes the definition statements and description text of entities within KGs. The goal is to compensate for the latent connections that are difficult for traditional methods to obtain. However, text-based methods still lag behind embedding-based models in terms of performance. Our analysis reveals that the critical issue lies in the selection process of negative samples. In order to enhance the performance of the text-based methods, various types of negative sampling methods are employed in this study. We introduced prompt learning to fill the gap between the pre-training language model and the knowledge graph completion task, and to improve the model reasoning level. Simultaneously, a ranking strategy based on KG structural information is proposed to utilize KG structured data to assist reasoning. The experiment results demonstrate that our model exhibits strong competitiveness and outstanding inference speed. By fully exploiting the internal structural information of KGs and external relevant descriptive text resources, we successfully elevate the performance levels of KG completion tasks across various metrics.