Reviewerly/exHarmony huggingface dataset:
Hardware Accelerator for Transformer based End-to-End Automatic Speech Recognition System

Hardware accelerators are being designed to offload compute-intensive tasks such as deep neural networks from the CPU to improve the overall performance of an application, specifically on the performance-per-watt metric. Encoder-decoder-based sequence-to-sequence models such as the Transformer model have demonstrated state-of-the-art results in end-to-end automatic speech recognition systems (ASRs). The Transformer model being intensive on memory and computation poses a challenge for an FPGA implementation. This paper proposes an end-to-end architecture to accelerate a Transformer for an ASR system. The host CPU orchestrates the computations from different encoder and decoder stages of the Transformer architecture on the designed hardware accelerator with no necessity for intervening FPGA reconfiguration. The communication latency is hidden by prefetching the weights of the next encoder/decoder block while the current block is being processed. The computation is split across both the Super Logic Regions (SLRs) of the FPGA, mitigating the inter-SLR communication. The proposed design presents an optimal latency, exploiting the available resources. The accelerator design is realized using high-level synthesis tools and evaluated on an Alveo U-50 FPGA card. The design demonstrates an average speed-up of $32 \times$ compared to an Intel Xeon E5-2640 CPU and $8.8 \times$ compared to NVIDIA GeForce RTX 3080 Ti Graphics card for a 32-bit floating point single precision model.
Inferring stances of silent-participants in Twitter chatter using Label Propagation

Measuring public opinion has many interesting applications. It is a known fact that in social media networks, the majority of the users consume information without taking part in the discourse. Quantifying the opinions and stances on day to day happenings with a silent majority may not be possible with just Natural Language Processing tools. In this work we explore the potential possibility of using graph methods to propagate the found opinions of users to silent participants. For this work we use the Twitter chatter during the early pandemic times since there was a lot of polarizing ideas that was being discussed in Twitter. We also know there was some misinformation relating to important medical facts and it was important to convey proper political decisions to the user base. It also follows that understanding where users stand on certain aspects such as masks and vaccinations can be used in better informing the public about matters such as health and safety. Therefore it is important to capture a comprehensive opinion about these important matters. We report the findings of our experimental study into using Label Propagation to find user stances of silent participants. We use several implementations of Label Propagation Algorithm including some which are derivatives of the traditional method to test our hypothesis. We also present our results which perform with 0.94 with F-1 (macro) against our baselines which are at 0.49 F-1(macro).
Information: From Cognitive-Sentient Exploration of Reality to Predictive Big Data Assisted Informational Era

In this article it is shown that information is a concept not only useful and used for the communication systems and data operation, but also for the understanding of the matter non-living and living structuration. On this basis it is described the Informational Model of Consciousness and the functions and operability of the Informational System of the Human Body, allowing to define and understand the Cognitive-Sentient Exploration of Reality used as a tool by our predecessors to create some archaic models of the universe and human, remained up to date as references from philosophic point of view, and still operating nowadays in the creative sciences and arts fields in our informational era. Introducing the concept of information-related matter, it was possible to explain the informational processes in human body and eukaryotic/prokaryotic cells, the basic units of life, and to show that in the living entities an informational system operating similarly with that of the human can be identified and defined as Informational System of the Living Structures. The predictive properties of the Cognitive-Sentient Exploration of Reality is nowadays assisted by the predictive big data analysis, helping in various scientific domains where a high quantity of data was accumulated, including in the bioengineering field and human health, particularly for the prediction of the evolution of COVID-19 cases in the world, with examples for Romania, Spain and USA. Multiple applications involving artificial intelligence and the informational models of human and living structures for a healthy society are mentioned, in particular in neurosciences, biosciences and bioengineering. The priority of the Romanian concepts on information and the important contribution of the Romanian informational science oriented to understand the brain functions/operability and applications in biomedical field is highlighted.
A Frequent Construction Mining Scheme Based on Syntax Tree

"Natural language processing (NLP) is one of the main research directions in artificial intelligence. One of the goals of NLP is to identify various semantic information in the text. Currently, the mainstream semantic recognition tasks focus more on using the semantic information of each word in the text to perform semantic analysis of the entire sentence. The research on semantics in cognitive linguistics indicates that semantics is determined by both the words contained in the sentence and the arrangement of the words. Linguists refer to permutations and combinations containing certain semantic information as constructions. Since the construction plays an essential role in semantic information, identifying various constructions in text is a crucial work of semantic recognition tasks. Based on this background, the main works performed in this paper are as follows: 1) The definition and program representation of constructions and the corresponding constraints in NLP tasks are proposed. 2) A frequent construction mining algorithm is proposed to extract frequent structures that meet the construction requirements in the grammar structure tree. Based on the above works, the corresponding construction database can be extracted for the specified natural language corpus, which is helpful for more effective text semantic analysis."
The Teaching Media for Mangrove Photosynthesis E-Booklets With a Socioscientific Issue Approach Through PBL Improve Students' Critical Thinking

The low ability of students' critical thinking is due to the need for more use of media to support the development of students' thinking skills. This study aims to produce SSI e-booklet teaching media and test the effectiveness of PBL in increasing students' critical thinking. This research belongs to the development research type developed using the ADDIE model, with analysis, design, development, implementation, and evaluation stages. The subjects involved in this study were 29 junior high school students. Data collection in the study was carried out using observation, interview, and test methods. The instrument used is in the form of a test in the form of free essay questions. Data analysis used the free sample t-test and the gain score. The research analysis results showed that the validity level of the SSI e-booklet on mangrove photosynthesis was in a very good category. The effectiveness of PBL learning using the SSI e-booklet is very effective. There are differences in students' critical thinking between the control and experimental classes. Based on these results, applying the SSI e-booklet media through PBL has proven effective in improving students' critical thinking skills.
Worksheets and Joyful Teaching Strategies in Teaching English to Students of English Village of Parit Baru

English lessons are not compulsory in public elementary schools. It makes English a challenge for students in Indonesia's foreign language communication from an early age. This study aimed to find student worksheets and fun learning strategies, including singing and mentoring activities, to be implemented in teaching English to students. This research includes case study research. This study used a sampling technique to select five fifth-grade students who participated in this program. Data collection was carried out using participant observation techniques, and the documents included were student worksheets, photos, and activity videos. Data analysis techniques using thematic analysis techniques. The study results show that student worksheets and fun learning strategies can help students in English Village learn English through all teaching stages. It was concluded that alternative learning in the form of English Village using student worksheets and fun learning strategies had been proven to help students learn English by providing brief material that is clear and easy to understand.
Controlling Project Execution in the Era of Soft Computing and Machine Learning

During the execution control of their initiatives, organisations use a variety of devices to aid independent direction. Regardless, they are still insufficient in the face of skewed statistics and shifting management approaches. The absence of frameworks for controlling the execution of enterprises has an impact on the nature of their categorization in terms of supporting independent direction. The presentation of soft registering methods, which provide heartiness, effectiveness and flexibility at apparatuses, is an optional arrangement. This research provides a technique for project execution control based on Soft Computing and ML, which contributes to the executives’ ability to further improve the project. The proposed method allows for AI and the replacement of fuzzy inference frameworks in project evaluation. The results are derived from seven calculations involving space apportioning, neural architecture, gradient descent and genetic algorithms. Adoption of the proposed framework, which has been included in this paper for project personnel, signifies a change in the nature of venture evaluation. The obtained result ensures that the apparatuses are in perfect working order to assist the independent direction in projecting the executive associations.
A Comprehensive Review on Explainable AI Techniques, Challenges, and Future Scope

Artificial Intelligence (AI) has been making remarkable advancements in recent years and has the potential to revolutionize many aspects of our lives. From self-driving cars to healthcare systems, AI can make tasks easier, faster, and more accurate. However, the increasing reliance on AI has raised concerns about its transparency, accountability, and interpretability. eXplainable AI (XAI) is a field that focuses on explaining the predictions made by AI systems. This has become increasingly important as AI is being used in sensitive and critical applications such as medical diagnoses, financial risk assessments, and criminal justice decisions. It is essential to ensure that the decisions made by AI systems are transparent, trustworthy, and can be justified to stakeholders. The paper explores the challenges associated with creating explainable AI systems and the different techniques that are being developed to overcome these challenges. Further, it presents a summary of the strengths and weaknesses of various XAI techniques. The paper will provide an overview of the state-of-the-art in XAI and highlight the need for further research in this field.
Generative Artificial Intelligence: Opportunities and Challenges of Large Language Models

Artificial Intelligence (AI) research in the past decade has led to the development of Generative AI, where AI systems create new information from almost nothing after learning from trained models. Generative AI can create original work, like an article, a code, a painting, a poem, or a song. Google Brain initially used Large Language Models (LLM) for context-aware text translation, and Google went on to develop Bidirectional Encoder Representations from Transformers (BERT) and Language Model for Dialogue Applications (LaMDA). Facebook created OPT-175B and BlenderBot, while OpenAI innovated GPT-3 for text, DALL-E2 for images, and Whisper for speech. GPT-3 was trained on around 45 terabytes of text data at an estimated cost of several million dollars. Generative models have also been developed from online communities like Midjourney and open-source ones like HuggingFace. On November 30, 2022, OpenAI launched ChatGPT, which used natural language processing (NLP) techniques and was trained on LLM. There was excitement and caution as OpenAI’s ChatGPT reached one million users in just five days, and in January 2023 reached 100 million users. Many marveled at its eloquence and the limited supervision with which it generated code and answered questions. More deployments followed; Microsoft’s OpenAI-powered Bing on February 7, 2023, followed by Google’s Bard on February 8, 2023. We describe the working of LLM and their opportunities and challenges for our modern world.
Aspect-Based Sentiment Classification: Survey

Through a variety of Internet applications that act as a rich source of information, Web 2.0 makes it easier for people to voice their opinions. The latent information in textual expressions can be processed and analyzed to disclose the user's or people's sentiments. Sentiment analysis, also identified as opinion mining, review mining, attitude mining, and other similar terms, entails the algorithmic extraction of opinions and perspectives from textual material. One of the three primary forms of sentiment analysis is aspect level sentiment analysis, which uses granule-level processing to determine sentiment orientation by utilizing the various characteristics of entities. Aspect-oriented sentiment analysis has been significantly impacted by the advancement of deep learning and machine learning techniques. This study provides a comprehensive overview of the present state of machine learning-based aspect-based sentiment analysis.
Scheduling the Tasks and Balancing the Loads in Cloud Computing Using African Vultures-Aquila Optimization Model

Load balancing among VMs is crucial for optimizing the delivery of cloud services, both in terms of the money spent and the time spent. Transferring a running virtual machine from one physical host to another is known as “live VM migration,” and it is used in the cloud to balance the system load. While this method has been offered to lessen downtime while moving overburdened VMs, it is still expensive and requires a sizable amount of memory to implement. To address these limitations, we provide a Task-based-System Load Balancing approach based on the African Vultures-Aquila Optimization Model (AVAO), which attains system load balancing by moving only the surplus of tasks from an overcrowded VM. In addition, we develop an optimization model for applying AVAO to the transfer of these supplementary activities to the new host VMs. We employ an augmented version of the cloud simulator (Cloudsim) package with AVAO as its task scheduling typically to test the suggested strategy. Based on the simulation findings, it is clear that the proposed method considerably shortens the time required for the load balancing practice when compared to the conventional methods. In terms of both makespan time and throughput, the system's performance is significantly enhanced if the proposed method is proven to be valid.
A Comparative Analysis of Recent Face Detection Methods Implemented for Age and Gender Detection

Age and gender detection is an upcoming technology, which is gaining importance. Age and gender detection is a subjective task. Even humans have trouble recognizing it sometimes. Our system helps them recognize what age range and gender the person is. To select the absolute best method, we have done a comparative analysis of different face detection algorithms, these are Dlib, OpenCV, and MediaPipe, and implemented them on pretrained neural network model of age and gender detection. Then, this system was implemented in hardware using Jetson Nano.
AI-Powered Blockchain in Politics

The interaction between blockchain technology, artificial intelligence (AI), and international politics is studied in this chapter. The intersection of Blockchain and AI can potentially disrupt old political paradigms and restructure power structures. Furthermore, the decentralized and transparent nature of blockchain, combined with AI’s analytical power, has the potential to usher in new governance, diplomacy, and policymaking systems. The chapter analyzes how these technologies may influence global political processes, state interactions, and power dynamics. It also examines issues such as data privacy problems, security dangers, and the necessity for robust legal frameworks. Finally, the study emphasizes the importance of political leaders understanding and engaging with these transformational technologies to ensure their responsible and equitable usage in the changing environment of international politics.
Exploration of Chinese Teaching Strategies in Primary Schools under the Background of New Curriculum Reform

With the continuous deepening of the new curriculum reform, the most important thing in current Chinese teaching is to improve teaching efficiency.To improve the quality and efficiency of Chinese teaching, it is necessary to improve the efficiency of Chinese classroom teaching.Chinese teaching in primary schools is a fertile ground to enhance students' critical thinking ability, improve their ideological quality, and cultivate their innovative consciousness, spirit and ability.Only by skillfully integrating the activation of educational methods and innovative teaching forms into Chinese teaching can we continuously promote the development of teaching reform and further improve the level of quality education.This paper discusses some problems existing in the process of effective teaching of Chinese in primary schools under the background of new curriculum reform, and puts forward some effective suggestions, such as cultivating students' problem thinking, guiding students to actively explore, and attaching importance to moral education infiltration, in order to inspire primary school Chinese teachers to be student-oriented, realize effective teaching and truly realize students' all-round development.
Security threats and measures to overcome in superior cloud

Cloud computing is among the most widely used technologies due to its ability to reduce costs, reliability and fast access with increasing flexibility and scalability for computer processes. In recent years, cloud computing has grown rapidly in the IT industry, and IT organizations have expressed concerns regarding security issues with cloud computing. Cloud Computing has some level of risks because essential services are frequently outsourced to a third party, and as third party handles the data stored in the cloud it is harder to maintain data security and confidentiality. This paper provides an overview of security issues or menaces in the cloud and proposes some measures to address them.
Securing cloud environments: A comparative analysis of RSA and MRGA for enhanced data protection

Cloud computing revolutionizes the way businesses are stored and managed, and ensures robust security measures. This paper explores the use of Rivest-Shamir-Adleman (RSA) and the Magic Rectangle Generating Algorithm (MRGA) to enhance security in cloud environments. This highlights the growing significance of cloud computing and the need for effective security measures. It then introduces RSA and MRGA as cryptographic algorithms with their unique strengths and applications. It further delves into the benefits of using RSA and MRGA for data protection, key management, authentication, and identity management in cloud environments. The potential of these algorithms to address security challenges in the cloud and improve the overall data security is discussed. Additionally, the abstract acknowledges the limitations and considerations associated with the implementation of the RSA and MRGA. This emphasizes the importance of this research in enhancing cloud security and provides insights into the potential of RSA and MRGA as security measures in the cloud.
Evolutionary Programming in Politics

This chapter addresses the convergence of evolutionary programming (EP) and international relations, a relatively unknown field in the digital age. EP, a subfield of artificial intelligence (AI), uses methods inspired by biological evolution, including reproduction, mutation, and selection, to maximize solutions to problems. While its influence is recognized in various fields, its implications on politics remain explored to a limited extent. According to this study, EP can offer novel insights into understanding and managing complex international relations dynamics. Specifically, it can aid in modelling and simulating international interactions, forecasting global political trends, and informing diplomacy and conflict resolution policymaking. However, the use of EP in this context is fraught with difficulties. Data privacy concerns, ethical considerations, and the risk of misinterpretation can all arise. Therefore, a responsible and informed approach is crucial when employing EP in international relations. This chapter aims to stimulate additional research and discussion on this interdisciplinary application of EP.
A comparative study of a novel approach with baseline attributes leading to sentiment analysis of Covid-19 tweets

The Natural Language Processing research domain experienced an enormous surge in large-scale event analysis due to the rise of Covid-19, or the Coronavirus situation, in the last few years around the world. The WHO declared it as a pandemic on 11th March 2020. Quite naturally, an event of such a high impact attracted a majority of the researchers to cultivate experimentations, and report any breakthrough on the same, as we continue to explore the mass psychological states and emotional viewpoints about it from the social networks and micro-blogs. In this chapter, we take an approach to descriptively study and summarize several recent and high-quality research works on Covid-19 sentiment analysis, knowledge discovery, predictive analysis, topic classification, learning models, etc. We compare and contrast the methodology of these works, the data and models that were worked with, and the respective contributions. Simultaneously, we also show a novel direction for developing a corpus from Covid-19 tweets and introducing two distinct attributes, namely word-trend detection, and relevant-word identification. Consequently, we relate such words in a word-relevance and occurrence rating scale. We then convert such attributes into corresponding word vectors and train a simple bidirectional long short-term network. We obtain the highest validation accuracy of 87.08%. Finally, we compare our results with previous benchmark experiments based on their prediction accuracy. With this work, we aim to bridge our contribution with that of some related works to gain a better sentiment scenario from the web.