Reviewerly/exHarmony huggingface dataset:
Analysis of Hate Tweets Using CBOW-based Optimization Word Embedding Methods Using Deep Neural Networks

In the recent years, governments, corporations, and academics have poured money into addressing the rising frequency of hate speech and expression, as well as the pressing need for effective solutions. Many methods for identifying hate speech have been developed and published on the Internet. This seeks to categorise textual information as non-hate or hate speech, with the algorithm being able to detect targeted features in the latter instance. which attempts to detect if textual content is non-hate or hate speech, with the algorithm recognising the desired qualities in the latter case Our proposed study uses Continuous Bag Of Word (CBOW)-based word embedding to try to predict the target term by analysing the context of the surrounding words, and feature extractors, deep learning-based structures explicitly capture the meanings of offensive speech. On the largest collection of hate speech data sets based on Twitter, our approaches are sorely tested. For hate speech identification, we investigate the influence of several extra-linguistic factors in combination with character n-grams. In addition, we provide a lexicon based on the most important words in our data. The proposed approach predicts 95% word embedding accuracy in real-time Twitter hate speech test data.
Tyro: A Mobile Inventory Pod for e-Commerce Services

Due to the increasing use of smartphones and Internet access, e-commerce start-ups are rapidly spreading throughout the world. Robotics and automation, together with cutting-edge technology like artificial intelligence (AI), machine learning, and deep learning concepts, are some of the factors contributing to the e-commerce sector's explosive growth. While start-up e-commerce services are falling behind in terms of automation and mechanical integrity, the e-commerce industry as a whole is performing well in contrast to other sectors. In the light of this, this study suggests a reasonably priced mobile inventory pod for e-commerce services that incorporates artificial intelligence and stores the requested items in the designated region of the warehouse. Picking goods from the rack is done by a robotic arm with four degrees of freedom, and the pod's eye is a camera module. AI helps to train the bot to take the right path, choose the right object, and identify obstacles in its path. Mecanum wheels allow us to manoeuvre the robot in any direction, even in confined spaces, and an Arduino microprocessor ensures that the entire system runs smoothly.
Utilizing Geochemical Analysis for Provenance Determination in Sedimentary Systems

Geochemical and isotopic analysis represents a potent approach for investigating sediment provenance, a crucial aspect for paleogeographic reconstructions, understanding the evolution of the Earth's crust and characterizing geology in areas that are no longer exposed at the surface. The sediment composition can be influenced by multiple processes including the weathering and erosion of the source rock, mixing with other sediment sources, grain size sorting and hydrodynamic processes such as water or wind transport. Additionally, climate and topography can impact the sediment composition by affecting the rate and nature of weathering and erosion as well as the transport and deposition of sediments. Moreover, post-depositional processes such as diagenesis and metamorphism have the potential to modify the original isotopic and geochemical composition of sediments. The objective of the present investigation is to conduct an overview of diverse geochemical proxies frequently employed in the evaluation of provenance as well as to assess their capabilities and limitations in estimating the origin of sediment. Additionally, statistical methodologies might be advantageous for sediment source determination, although it is crucial to acknowledge their restrictions and to merge them with traditional geochemical and isotopic techniques. A multidisciplinary strategy that encompasses several techniques and methodologies is probable to yield the most sturdy and dependable outcomes for identifying sediment provenance.
Creating of Applied Computer Developments in the Educational Process in the Training of Chemists-Technologists

Independent works (in the form of applied developments) have an influence on the formation of students of different areas of learning skills of professional orientation and communication in cooperation and skills of customer-performer relationships in active cognitive creative activity of students. The object of research is the process of creating computer developments related to chemical technology. The subject of research is the interaction of students of different specialties. This approach reveals the use of all functions of independent work, i.e.: cognitive, prognostic, educational, corrective and independent ones. It should be noted that this approach significantly expands the worldview of students-performers. In addition, there is an increase in students’ self-esteem and self-confidence. It is established that the most expedient for students majoring in 123 - Computer Engineering is the development of computer simulators of laboratory installations or production lines, specialized computer systems for experiments or laboratories, development of computer systems for process monitoring.
Accelerating Operations on Permutations Using Graphics Processing Units

GPUs (graphics processing units) are used in many modern computer systems. They consist of a large number of low-performance computing units that can be used for operations in parallel. In this case, the overall algorithm performance significantly increases compared to the CPU (central processing unit) use. Tests against a brute-force attack of cryptoprotocols that use operations on permutations, such as a three-pass cryptographic protocol, can be performed in parallel for different permutations. Therefore, it is important to determine the ability to use graphics processing units to accelerate operations on permutations. For this, the following tasks have been solved in this study: the most used operations on permutations have been defined; the program code that implements these operations and can use graphics processing units’ hardware has been developed; the performance of different approaches, including computing on CPU and various modes of computing on GPU, has been assessed. The results of the study indicate the effectiveness of using GPU hardware units to accelerate the multiplication of permutations, but if at least 5 such operations are performed simultaneously.
CachacaNER: a dataset for named entity recognition in texts about the cachaça beverage

Named Entity Recognition (NER) is the task of identifying and classifying tokens in texts corresponding to a set of pre-defined categories, such as names of people, organizations and locations. Datasets labeled for this task are essential for training supervised machine learning models. Although there are many datasets labeled with texts for English, in the Portuguese language they are scarcer. This work contributes to the creation and evaluation of a manually labeled dataset for the NER task, with texts in Brazilian Portuguese, in the specific domain of the beverage called Cachaça. This is a popular drink in Brazil, and of great economic importance. This is the first NER dataset in the beverage domain, and can be useful for other types of beverages with similar entity categories, such as wine and beer. We describe the process of data collection, creation of the dataset and its experimental evaluation. As a result, we created a dataset containing over 180,000 tokens labeled in 17 entity categories. The labeling obtained an agreement coefficient of 0.857 among the labelers, according to the Fleiss’ Kappa metric, which is considered almost perfect. In our experimental evaluation, we obtained a micro-F1 value equal to 0.933 in the test set. The size of the dataset, as well as the result of its experimental evaluation, are comparable to other datasets in the Portuguese language, even though ours has a greater number of entity categories.
UNIVERSITY STUDENTS' OPINIONS ON THE USE OF 3D HOLOGRAMS IN LEARNING ORGANIC CHEMISTRY

3D holograms are an effective tool for visualization, and their utilization in chemistry teaching can be beneficial in improving learning outcomes. However, studies on students’ opinions about holograms in chemistry teaching and learning are scarce. The research aimed to examine the views of chemistry students on the application of 3D holograms in organic chemistry learning at the university level. In this cross-sectional study, 55 first-year chemistry students at the University of Novi Sad (Serbia) participated. The sample consisted of students aged 18-20, of which 85.5% were female and 14.5% were male. An online questionnaire designed for this research was used to collect quantitative data. Data obtained after an eight-week application of 3D holograms in organic chemistry classes revealed that students have a positive opinion about the application of 3D holograms in organic chemistry classes. Therefore, the research results imply that teachers should apply 3D holograms in chemistry classes. Keywords: augmented reality, 3D holograms, chemistry education, students’ opinion
Supporting dynamic update scheme for assured deletion based-multi-copy association tree

Cloud storage services provide storage resources for resource-constrained users, which reduces their local overhead and computing cost. As an extension of cloud computing, fog computing introduces a fog layer between the cloud and users to deploy computing, storage, and other types of equipment, allowing users to operate outsourced data conveniently. Although cloud storage brings many conveniences to users, assured data deletion is still one of the crucial security challenges. This paper proposes an efficient and secure cloud data deletion scheme (SDUS-AD) that supports dynamic data updates in multi-copy scenarios. In this scheme, a new dynamic structure called Multi-Copy Association Tree is designed to realize the dynamic update of outsourced data efficiently and safely. A cloud-fog-user layer structure is used to meet the needs of resource-constrained users to update data and ensure the confidentiality of data privacy. Detailed performance analysis and simulation experiments show that SDUS-AD is efficient, safe, and feasible.
INTELLIGENT LEARNING IN STUDYING AND PLANNING COURSES – NEW OPPORTUNITIES AND CHALLENGES FOR OFFICERS

There were two projects at the National Defence University of Finland (NDU), which both ended by the end of 2022. One of them tried to find the answers to the main question: How artificial intelligence (AI) could be used to improve learning, teaching, and planning? The other tried to find the answer to the main question: What new skills do officers need when artificial intelligence is coming? We did literature reviews and found out that intelligent technology combined with data analytics can offer several improvements to traditional classroom teaching. From literature reviews, we also found some new skills that officers might need to be able to handle AI- based technologies. This is a position paper presenting the arguable opinions of the writers. We have found lots of benefits that the use of intelligent learning technology can bring, mainly by supporting individual learning paths. There is also an obvious need for AI officers who should have a deeper understanding of the AI-supported technology than normal officers. This project and some other similar projects have raised a lot of discussions, one seminar series about artificial intelligence and we do have some trained AI officers as well. Keywords: artificial intelligence, intelligent learning, supported studying, intelligent planning, characteristics of war
Visualization system of martial arts training action based on artificial intelligence algorithm

At present, there are many educational methods in the field of Chinese martial arts education, and various types of teaching institutions such as private martial arts schools, national sports institutes and general institutions of higher learning have been formed. However, the proportion of such groups who choose to receive professional martial arts education is very small compared to the population development base of China's entire higher martial arts education. The main reason is that martial arts training activities must be standardized and professional, and it is difficult to practice effectively in a home environment. Therefore, this paper develops a visualization system of martial arts training movements based on artificial intelligence system. Firstly, the theoretical basis of skeletal point extraction feature and posture quaternion in martial arts, the visualization of martial arts movements based on expression quaternions and the classification model of martial arts movements based on LSTM are introduced. For the human action visualization part, we use the Ogre rendering engine to display human actions and provide a data interface to control the movement of the virtual 3D human model. Then, the paper introduced the visualization system design of martial arts training movements, including system architecture design, system function analysis, and visualization module design. Finally, the system is disassembled through the test environment and evaluation indicators, action recognition effect analysis, and system visualization impact analysis, and the application effect of the system is introduced in detail. I hope it can help more people inherit and develop traditional sports and carry forward the spirit of martial arts, so that Wushu, a national traditional activity, can be better spread and popularized. This paper studies the artificial intelligence algorithm and applies it to the design process of martial arts training action visualization system, thus promoting the development of martial arts teaching.
THE APPLICATION OF INTERACTIVE LEARNING TASKS MADE BY USING DIGITAL HYBRID ILLUSTRATIONS IN THE TOPIC "HYDROCARBONS" IN EIGHTH-GRADE ORGANIC CHEMISTRY CLASSES

The content of organic chemistry is closely related to our everyday life, to nature, and to the human body. Illustrations play a big role in the acquisition of the course material, especially if those help to make the interpretation of the textual content easier. Hybrid illustrations are made up of combinations of realistic images (photographs, drawings) with abstract conventional elements (symbols, models, chemical equations). This type of illustration fuses difficult-to-interpret symbols often found in chemistry with everyday images that bring students closer to the content. The following study examines the use of digitally edited hybrid illustrations in interactive learning tasks that were used in the review and practice lessons on the Hydrocarbons topic in eighth-grade organic chemistry classes. The research took place in an experimental group of students from primary school in Novi Sad (Republic of Serbia), during which the students solved the given tasks on their cell phones via the Moodle platform. In the control group, teaching and learning took place in the traditional, or conventional way applying a lecture and a discussion method. After processing the Hydrocarbons topic, the experimental and control groups underwent the same testing process, the results of which prove the advantages of using the tasks created with the help of digital hybrid illustrations in the abstract parts of the curriculum. Keywords: digital learning, organic chemistry, hydrocarbons, hybrid illustrations
Outdoor Concert Hotel Acoustics

VAI Resort, situated across the street from the site of Super Bowl LVII, presents a number of acoustical challenges, not the least of which is a world-class, 360-degree outdoor performance venue. Performers will blast an 8-storey hotel located 250 ft away with music of all types, which audiences will enjoy from the balconies of their hotel rooms. The challenge of modeling such a venue comes in simplifying it down to manageable pieces. Does a balcony with a hotel room behind it behave like a Helmholtz resonator? How do the acoustics change if 90% of the balconies are open to the hotel rooms beyond? What if 50% are open? 25% are open? Such a variable acoustic space presents a significant modeling challenge. A methodology for tackling such a large-scale project is presented, with modeling results, auralizations, and comparisons to real-world measurements. Findings include a swing in RT60 of more than 25% when all balcony doors are opened versus closed.
ANALYSING THE MAJOR DETERMINANTS OF IMPLEMENTING ARTIFICIAL INTELLIGENCE IN ENHANCING CUSTOMER RELATIONSHIP MANAGEMENT IN PRIVATE BANKING COMPANIES

Technology advancements and the broad incorporation of digital practises into corporate operations have contributed significantly to the rapid development of customer relationship management during the last ten years. Because the firm's marketing activities have been using artificial intelligence (AI) to better understand its customers' shifting desires and requirements react more effectively to consumer enquiries, and deepen its connections with its clients, the company has been successful in reaching its goals. Automated responses to customer inquiries used to be monotonous and non-interactive; but, now that AI and other technologies are available, businesses are able to develop customised automation processes to enhance customer interactions and experiences. The abbreviation "Customer Relationship Management" (CRM) refers to a collection of computer software that is used to improve efficiency in dealing with customers and to boost sales.&#x0D; Artificial Intelligence (AI) is fast revolutionising how consumer enquiries are answered to within customer relationship management by examining wants and requirements, concentrating on developing unique customer interaction, and enhancing customer loyalty. This is being done within the context of Customer Relationship Management (CRM). The use of Artificial Intelligence (AI) in the workplace is fast becoming ubiquitous in the 21st century as an increasing number of businesses see its usefulness. The purpose of this empirical study is to investigate the key facets of AI that have an influence on Customer Relationship Management (CRM). The researchers intend to conduct their quantitative analysis using IBM SPSS, and a comprehensive report of their findings will be produced.
Engagement of Non-Governmental Organizations (NGOs) with Corporate Sector in Implementing CSR Projects: Experiences from Karnataka, India

The introduction of mandatory CSR with the “Chatterjee Model” has provided the necessary fillip and visibility to the partnerships between the corporate sector and NGOs. The development sector in India witnessed a significant change with the introduction of mandatory CSR. CSR funds became a major source of funding for the NGOs. In this context, the present study tries to understand the nature of emerging partnerships between NGOs and the corporate sector, the motivational factors and different dimensions from the NGO perspective. It is a descriptive exploratory study based on the purposively selected NGOs in Mysore and Bangalore districts of Karnataka. The study reveals that NGOs are engaging with companies mainly for accessing financial resources. Since the companies are having control over finances, companies lead the partnerships. There is good communication between NGOs and their business partners. Transformative changes are observed in some NGOs by improving their capabilities. Suggestions are made based on the study to realize maximum collaborative social value from the engagements.
Integrating Computer Vision and Crowd Sourcing to Infer Drug Use on Streets: A Case Study with 311 Data in San Francisco

Use of illegal drugs has been a bane of society for decades, and the problem is only increasing globally. Billions of dollars, time, effort and expertise among various stakeholders are being invested to combat this problem, but novel approaches are still needed. In this paper, we investigate the exciting possibility of leveraging big-data provided by the crowd to detect illegal drug use on streets. Our discussions with stakeholders revealed that drug use can be inferred via detection of any of the following components - syringes (including barrels and plungers), needles, needle caps, vials and tourniquets. We also learnt that in localities, where drug is common, these items are casually discarded on streets. Motivated by these, we conducted an investigation centered around San Francisco. We collected images from their SF311 platform where the public can actually upload their observations in the city for service. From more than 5 million images uploaded, we extracted 1, 759 images that contained one or more of drug use components mentioned above. We designed and validated state of the art Computer Vision techniques to both detect and localize components of interest with an uploaded image. Results demonstrate high utility, as we report later in the paper. To the best of our knowledge, our study is amongst the first to tackle the problem of detecting drug use automatically via AI techniques from crowd uploaded images. We believe that our study has practical impact.
Adversarial Training for Better Robustness

As the vulnerabilities of neural networks are gradually exposed, the security of deep learning attracts the thoughtful attention of researchers. Adversarial training is a promising way to enhance the robustness of deep learning models, which can defend against white-box targeted attacks by learning from the dedicated designed adversarial samples. In recent years, researchers have proposed many algorithms to promote the security of learning models, such as training effectiveness and decreasing limitations. In this survey, we propose a novel taxonomy to categorize the progress of adversarial training and analyze current constraints. Apart from introducing an overall picture of adversarial training in terms of adversarial attack/robustness, we make a conclusion and prospect of this area.