Reviewerly/exHarmony huggingface dataset:
RESUME SUMMARIZER AND JOB DESCRIPTION MATCHER USING NATURAL LANGUAGE PROCESSING AND SPACY

In the HR recruitment process, one pivotal stage is "Summarizing &amp; Screening." At this juncture, recruiters grapple with the laborious task of manually sifting through a myriad of resumes to shortlist suitable candidates. This project aims to streamline and automate this process using two innovative tools. Firstly, the "Resume Summarizer" leverages advanced natural language processing techniques to swiftly extract pertinent details from candidates' resumes, offering a concise summary that underscores their skills, experience, and qualifications. This summary can be tailored to match specific job requirements, facilitating recruiters in the comparative evaluation of multiple candidates. Secondly, the "Job Description Matcher" assists recruiters in expeditiously and accurately aligning job descriptions with candidate resumes. By employing natural language processing, this tool identifies keywords and phrases that align with the job opening's prerequisites, generating a compatibility score to rank resumes according to their alignment with the job description. This innovation greatly expedites candidate selection and enhances recruiters' efficiency. Importantly, this project employs a unique approach, incorporating regular expressions to extract essential information from resumes, setting it apart from existing NLP-based models and systems. Keywords: Natural Language Processing, Text Analysis, Data Extraction, Keyword matching, Regular Expressions, Optical Character Recognition (OCR), N-Gram Model, Name Entity Recognition (NER), Unsupervised NER.
Robotic Choreography Creation Through Symbolic AI Techniques

Symbolic Artificial Intelligence (AI) techniques in robotic choreography contribute to the broader field of human-robot interaction and address the main limitations of learning approaches in terms of computational requirements. A general symbolic approach leveraging dance sequence rules and constraints can (1) reduce computational effort and (2) allow the possibility to easily insert aesthetic and musical constraints. In this direction, this paper presents a preliminary approach based on symbolic AI techniques that is able to plan a robotic choreography based on a general problem description and music input. The algorithm is adaptable using different symbolic AI techniques, and by considering different user requirements. For the demonstration phase, a challenge in a Master course on AI was organized, with resulting choreographies available in a public repository. The possibility to easily insert aesthetic constraints also allows to a more intuitive and expressive communication between humans and robots. This has implications not only in the field of entertainment but also in different areas like healthcare and education.
Performance Evaluation of Container Identification Detection Algorithm

The identification of container surfaces carries a large amount of crucial information regarding production and logistics. Research on the detection and identification of containers is lacking in both academia and industry, and the efficiency is low due to the need for manual completion of related tasks. In order to tackle this problem, we have created a large-scale text detection dataset for container surface identification called IdentificationText. This dataset consists of 12,000 high-resolution images, providing bounding boxes annotations for text detection tasks. We have discussed downstream applications of the IdentificationText dataset as well as our annotation techniques used in the dataset’s creation. The text in this dataset exhibits challenges such as deformations, multi-direction, and multi-scale. We conducted extensive experiments to evaluate the effectiveness and difficulty of this dataset using advanced text detection methods. In our experiments, we found that repeated textures and vertical text at multiple scales would cause missed detections, which was an extremely serious problem. The experimental results indicate that it is challenging for current text detection methods to locating text on container surfaces. Achieving higher accuracy in detecting text on containers requires more in-depth research. The experimental results serve as the benchmark performance for the IdentificationText dataset, providing reference for future researchers.
OFFENSES IN DECENTRALIZED SYSTEMS

Introduction. The article is devoted to the study of the problem of offenses in decentralized systems, in particular in blockchain networks. The author analyzes the main types of offenses that can occur in these systems, such as fraud, various attacks, and others. Since decentralized networks have no centralized control, they become more vulnerable to various types of attacks and abuses. Understanding the nature of decentralized networks can help to solve the problem of crimes in these systems more effectively, and understanding the principles of these networks can help to develop effective and transparent methods of solving such crimes. Summary of the main results of the study. Taking into account the results of the research, an explanation is provided as to how decentralized networks such as blockchain are organized, what are the offenses in such networks, what are attacks in decentralized systems. A detailed list of the main types of attacks, other types of offenses and abuses in decentralized systems is also provided, a description and explanation is provided for each type, and specific examples are also provided for some of them. Conclusions. For the first time, a list of the main offenses in decentralized networks such as blockchain has been unified and provided in the context of criminal law.
Time alignment quantum illumination based on single real-time coincidence counting

We propose and demonstrate an improved quantum illumination protocol based on the time correlation of twin photons, for the high signal-to-noise ratio (SNR) of target detection and signal reconstruction in the strong noise environment. The Hong-Ou-Mandel (HOM) interferometer is applied after the spontaneous parametric down-conversion (SPDC) process to construct a probing twin-beam in which the photon times are precisely aligned between the beams. At the radar receiver, we put forward a single real-time coincidence counting (SRCC) method on a series of time slices to reconstruct the probe signals of pulse radar and calculate the SNR advantages against the conventional pulse radar, as well as the quantum illumination (QI) protocol. Our main achievements in this research are the realization of real-time detection of quantum information while acquiring a higher SNR than QI and classical illumination (CI) protocols, as well as its demonstration of strong robustness to noise and losses, which also proposes what we believe to be a novel way for quantum target detection.
ChatGPT as a Commenter to the News: Can LLMs Generate Human-Like Opinions?

ChatGPT, GPT-3.5, and other large language models (LLMs) have drawn significant attention since their release, and the abilities of these models have been investigated for a wide variety of tasks. In this research we investigate to what extent GPT-3.5 can generate human-like comments on Dutch news articles. We define human likeness as `not distinguishable from human comments', approximated by the difficulty of automatic classification between human and GPT comments. We analyze human likeness across multiple prompting techniques. In particular, we utilize zero-shot, few-shot and context prompts, for two generated personas. We found that our fine-tuned BERT models can easily distinguish human-written comments from GPT-3.5 generated comments, with none of the used prompting methods performing noticeably better. We further analyzed that human comments consistently showed higher lexical diversity than GPT-generated comments. This indicates that although generative LLMs can generate fluent text, their capability to create human-like opinionated comments is still limited.
Simplifying Cloud Management with Cloudless Computing

Cloud computing has transformed the IT industry, but managing cloud infrastructures remains a difficult task. We make a case for putting today's management practices, known as "Infrastructure-as-Code," on a firmer ground via a principled design. We call this end goal Cloudless Computing: it aims to simplify cloud infrastructure management tasks by supporting them "as-a-service," analogous to serverless computing that relieves users of the burden of managing server instances. By assisting tenants with these tasks, cloud resources will be presented to their users more readily without the undue burden of complex control. We describe the research problems by examining the typical lifecycle of today's cloud infrastructure management, and identify places where a cloudless approach will advance the state of the art.
Detecting Fraudulent Wallets in Ethereum Blockchain Combining Supervised and Unsupervised Techniques - Using Autoencoders and XGboost

The illicit activity in Blockchain reached an all-time high in 2021. In this work, we combined two machine learning techniques, Autoencoder (AE) and Extreme Gradient Boosting (XGBoost), to improve the performance of predicting illicit activity at the account level. The choice of autoencoding technique allows us to be able to detect new MOs (modus operandi) from fraudsters. With an Autoencoder trained only with healthy accounts, we are not misleading the model to focus on specific MOs as it happens with tree-based models. This allows us to introduce a dimension that could capture future fraudulent behaviours. Furthermore, the dataset was generated considering the real applicability of the model, i.e. it mimics what can realistically be obtained in a practical situation. With this approach, we were able to improve the state-of-the-art performance. In our test set the precision-recall AUC (area under the curve) of our final model increased by 12,07% when compared with our baseline.
Double embedding reversible information hiding based on chunking

A reversible data hiding method based on double embedding in chunking is proposed for existing data hiding algorithms to improve the embedding capacity and generate a secret-laden image with a high visual effect. Firstly, the original image is chunked and different reversible embedding methods are applied to different parts of the sub-blocks for the secret information embedding operation. To improve the embedding capacity, the secret information continues the pre-processing operation and adopts a hierarchical embedding method to improve the utilization of each embeddable point. The original prediction method is also improved to generate a steeper error histogram with more embeddable pixel points. In addition, the use of a suitable prediction and embedding method produces a compensating reduction effect when performing double embedding, reducing the number of invalid displacements and improving the quality of the carrier image. It is experimentally verified that the proposed method outperforms some state-of-the-art RDH work with higher embedding capacity as well as lower distortion. For a standard Lena image, a peak signal-to-noise ratio of up to 66.21 dB can be obtained when the embedding capacity is 10000 bits.
A reversible data hiding algorithm based on image region division

Many reversible data hiding algorithms only scan images in a fixed sequence, leading to low embedding capacity and poor stego-image quality. This paper proposes an adaptive algorithm that divides images into smooth and texture regions based on complexity, and uses different embedding algorithms for each region. Three predictors are used to generate asymmetric histograms and increase embedding capacity through two rounds of embedding in smooth regions. Pixel value ordering (PVO) is used for embedding fewer data in texture regions to maintain image quality. Experimental results show that the algorithm has high embedding capacity and low visual distortion.
LANGUAGE-SPECIFIC LINKS BETWEEN BILINGUAL PARENT-SHARED BOOK READING AND CHILDREN’S EMERGENT LITERACY

Parent-child book reading presents benefits to the development of monolingual children’s language ability, but the effect on bi/multilingual children is still undetermined. The positive connection between the frequency of parent-shared book reading and expressive vocabulary that children could express or use for Spanish-English language using children (age from 4-5) has been shown, while other research failed to get the same result in other bilingual children. Ye &amp; Stephanie (2022) investigated the relationship between parent-child reading and children’s literacy achievement with a huge number of samples of bilingual families, participants of children aged from 2-6 years old. They determined that parent-child reading could mediate the relationship between children’s literacy learning and socioeconomic status. However, they didn’t know the languages used by parents during shared book reading practices, although the information about languages spoken by parents at home was available in the table showed in this study. So, in this study, I will consider collecting this information to gain a better understanding of language-specific links between bilingual parent shared book reading and children’s emergent literacy.
Automated Machine Learning: A Comprehensive Survey and Framework for Advancements

Abstract: Machine learning has become an indispensable tool in numerous domains, and the selection of an appropriate machine learning algorithm can significantly impact the success of a project. To address the challenge of algorithm selection, we present an Automated Machine Learning (AutoML) platform designed to empower users with the ability to effortlessly compare the performance of two selected machine learning algorithms on their datasets. This platform offers a user-friendly interface, guiding users through the entire process, from uploading their datasets to selecting algorithms and making informed decisions. The platform provides a streamlined workflow that allows users to easily upload datasets of their choice, select two machine learning algorithms from a pre-defined set, and compare their performance based on a variety of evaluation metrics. Users can visualize and analyze the results to gain insights into how different algorithms impact their data. Additionally, the platform offers assistance in identifying the most suitable algorithm for achieving optimal results. With the rapid evolution of machine learning techniques, this AutoML platform addresses the need for a user-friendly, efficient, and accessible tool to assist practitioners and researchers in algorithm selection. By simplifying the comparative analysis of machine learning models, this platform empowers users to make informed decisions that enhance the effectiveness of their data-driven projects.
An Android Application for Attendance Using Geofencing

Abstract: Students' participation should be recorded when school activities are taught in the classroom. The traditional method is to record statistics, which takes a lot of time and interferes with teaching activities in the classroom. It is clear that the engagement process for teaching activities such as social studies, online courses, and large lectures is not possible as an academic. This article introduces mobile participation as a classroom-based learning environment. Students submit their attendance and address information through the Student Registration APP. Teachers can set attendance locations to control whether students need to go to a designated location for attendance registration or remote registration registration. Experimental results show that the system is flexible and intuitive, can save participation and analysis time, improve teaching performance, and meet information needs for participation in teaching activities.
Effectiveness of information communication, and Technology (ICT) tools in teaching mixture in grade seven science in one of the schools in Bhutan

The emergence of Information and Communication Technologies (ICTs) has brought about a significant shift in the way lessons are delivered and learners are engaged in their learning process. The integration of ICT tools into science education has been shown to improve the overall quality and efficacy of instructional practices. This quantitative study investigated the effectiveness of integrating ICT tools such as an overhead projector, internet, and smartphones into the teaching of grade seven science in one of the schools in Bhutan. The results of the descriptive analysis revealed a mean difference between the two tests was 9.82. The inferential analysis indicated a significant mean difference between the two tests with (p &lt; 0.05, d = 0.813), suggesting that the incorporation of ICT tools was effective in teaching grade seven science. Additionally, a strong positive correlation (r = 0.787) was observed between students' attitudes towards ICT tools and their test scores. Based on these findings, this study recommends that teachers integrate ICT tools into their science instructions. The implementation of various ICT tools in the classroom can provide students with a comprehensive and engaging learning experience, enabling them to better comprehend and apply key scientific concepts.
Generating Adversarial Examples Using LAD

Nowadays, Machine learning models are widely used in many fields and employed to solve problems from different sectors. However, we often face issues when running these models in case the training data is insufficient. These issues happen when the dataset available is small or only part of it is available because of its sensitive content or even because the dataset is imbalanced. Thus, synthetic data generation is needed to provide data similar to actual data. We have proposed the use of the Logical Analysis of Data methodology to generate adversarial data from any given dataset. For our study, we have used an intrusion detection dataset, and the results demonstrate the potential of Logical Analysis of Data by evaluating adversarial datasets using various machine learning classifiers.
CenAD: Collaborative Embedding Network for Anomaly Detection with Leveraging Partially Observed Anomalies

Leveraging observed anomalies in anomaly detection can significantly improve detection accuracy. Assuming that observed anomalies cover all anomaly distributions, existing methods commonly learn the anomaly distributions from these observed anomalies and assign each object an anomaly score according to the similarities between it and observed anomalies. However, these observed anomalies may partially cover anomaly distributions, which severely restrains the performance in detecting uncovered anomalies. To address this issue, we propose a novel collaborative embedding network for this task, named CenAD. By leveraging partially observed anomalies, the collaborative learning derives a loss with maximum neighbor dispersion and minimum volume estimation as guidance to make anomalies more dispersed. Each object is assigned to an anomaly score by its contributions to data dispersion, which distinguishes these anomalies from the entire data effectively. To investigate the effectiveness of CenAD with partially observed anomalies, we conduct extensive results on several datasets to validate the superiority of our method, in which we obtain average improvement up to 13.92% in AUC-ROC and 29.44% in AUC-PR compared with previous methods.
THE ROLE OF ARTIFICIAL INTELLIGENCE IN VIDEO GAME DEVELOPMENT

In this paper, we study the vital role of artificial intelligence (AI) in the development of video games, with a focus on various aspects of AI application in this industry. In the introduction, we discuss both the development of video games and the role of AI systems in the user experience, defining the progression of AI’s role in video games. In the following section, we investigate how in-game entities and AI collaborate. Here, we analyze basic concepts such as Non-Playable Characters (NPCs) and how AI enhances their intelligence and reactivity in the game. The mechanisms of AI in video games are a crucial point of consideration in the next part of the paper. We explain how various AI techniques are used for decision-making, player tracking, and adapting the game to their actions. Furthermore, we explore the use of AI in video games beyond NPC control, examining examples such as procedurally generated worlds and player experience modeling. This application of AI contributes to a deeper and more dynamic player experience. In the modern application of AI in video games, we delve into advanced uses of machine learning and deep neural networks in game development. Here, we consider how AI is used for game personalization, user data analysis, and enhancing graphics and sound. Finally, we discuss the future of video games and the role of neural networks in their development. We predict the growth of AI in various aspects of games and how it will shape the future player experience. In conclusion, we assert that artificial intelligence has become an indispensable part of video game development, and its impact will inevitably expand in the future, enabling increasingly rich, dynamic, and personalized games for players worldwide.
Gray Learning From Non-IID Data With Out-of-Distribution Samples

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-independent and identically distributed (non-IID) datasets comprising both in-and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in-and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed gray learning (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics.