Reviewerly/exHarmony huggingface dataset:
Margin Maximization of Text Classification based on Support Vector Machine

Abstract: This project focuses on developing a margin maximization model for topic categorization using Support Vector Machines (SVM). Topic categorization aims to classify text documents into predefined topic categories. The proposed model leverages SVM's ability to create a hyperplane with maximum margin between different topics, enhancing the classification performance. The project begins with dataset preparation, where a labeled dataset covering a wide range of topics is gathered. The text data undergoes preprocessing, including cleaning, normalization, and conversion into numerical representations. Feature extraction techniques such as TF-IDF or word embeddings are employed to capture important features related to topics. The dataset is split into training and testing sets, with the training set used to train the SVM model. The SVM model is trained with a focus on maximizing the margin, utilizing techniques like soft-margin SVM or the kernel trick to handle non-linear separable data. Hyperparameters of the SVM model are tuned to optimize its performance. The trained SVM model is then used to predict the topics of the testing data, and its performance is evaluated using standard metrics such as accuracy, precision, recall, and F1 score. The findings of this project demonstrate the effectiveness of the margin maximization model for topic categorization using SVM
Fall Detection for Elderly People Using Machine Learning

Abstract: Falling down is among the most common causes of medical attention required by the elderly people . Elderly people often injure themselves from falling down more especially when they are living alone. After a fall occurs, medical attention needs to be provided promptly in order to reduce the risk of victim . Several technologies have been developed which utilize webcams to monitor the activities of elderly people. However, the cost of operation and installation is expensive and only applicable for. Fall is one the major cause of death for older people. Detecting the fall plays a major role in saving lives. There are three different types of fall detection commonly used , such as wearable, ambient sensor and vision-based methods.If elderly people falls then it will put severe effect on their health and technology is helping humans in every aspect of their life and in this paper author is using machine learning algorithm to predict FALL scenarios by analysing their movements. In propose paper author has used SVM and Decision Tree algorithms to train SISFALL dataset and this trained model can be used to predict fall scenarios from new test data. Sensors will be embedded with elderly people’s body and this sensor will record their movement such as Heart Rate, EEG and circulation and then give this input to ML model and ML model will predict current scenario or posture and alert to elderly people
Face Recognition using SVM with the Help of Kernel Characteristics Function

Abstract: This project focuses on building a face recognition system using the Labeled Faces in the Wild (LFW) dataset.The dataset includes diverse facial images of individuals across various age groups, such as kids, women, men, and the elderly. The model employs a Support Vector Machine (SVM) with a radial basis function (RBF) kernel for effective classification. Preprocessing techniques like normalization, grayscale conversion, and face alignment enhance the dataset's quality. The system's performance is assessed using accuracy, precision, recall metrics, and a Receiver Operating Characteristic (ROC) curve. The project aims to create a reliable and versatile face recognition solution applicable in security, authentication, and surveillance scenarios
Impact of Forensic Accounting on Fraud Detection in Zimbabwes Public Sector Expenditure Cycle

Abstract: The objective of this study was to evaluate the influence of forensic accounting on the discovery of fraudulent activities within the public sector of Zimbabwe, specifically examining the expenditure cycles of government departments. The investigation was sparked by the Auditor General's disclosure of governance problems and the rise in qualified audit opinions observed from 2018 to 2022. During that time period, the government imposed additional restrictions, such as a directive to freeze payments for all contracts that had been submitted for payment, a requirement to conduct due diligence checks on all contracts with the assistance of internal audit staff, and the blacklisting of 19 companies in an effort to get value for money, as well as a reduction in the huge appetite for business travel by government officials (Treasury Circular number 5 of 2023) served as another impetus for the research.
Introduction to blockchain and smart contracts development

In this chapter, with a focus on the Ethereum blockchain, different development tools and environments are presented. The component functions of a sample Ethereum token standard are also discussed. This chapter closes with a discussion of different decentralized application architectures.
FAIR Data Digest #23

Open data of 16.2 million Dutch vehicles that can be used to enrich Wikidata
CYBERSECURITY AND PRIVACY: GUARDIANS OF DIGITAL INTEGRITY

Mohd Nafees; Prof. (Dr.) Pramod Kumar
Pengaruh Pemberian Obat Anti Tuberkulosisi (OAT) Terhadap Jumlah Trombosit pada Pasien TB Paru di BKPM Purwokerto

Pulmonary tuberculosis (TB) is an infection of the lungs characterized by pulmonary infiltrates and the formation of caseous granulomas, fibrosis, and cavities. It can be caused by the risk of transmission of the acid-fast bacillus Mycobacterium tuberculosis. One supportive treatment for pulmonary TB is the administration of anti-tuberculosis drugs (OAT). The content of rifampicin in the anti-tuberculosis drug can cause side effects of hemorrhagic disorders, namely thrombocytopenia. This study aims to determine the effect of giving OAT on platelet counts in TB patients in treatment phases of 0, 2, and 6 months at BKPM Purwokerto (Balai Kesehatan Paru Masyarakat). This was a type of analytic observational study with a cross-sectional design. Patients with pulmonary TB in treatment phase 0 were 15 at two months and 15 at six months in this study. Sampling was carried out by purposive sampling with inclusion and exclusion criteria. From this analysis, it is known that the data used the one-way ANOVA test. The results obtained have a p-value of 0.101 (p 0.05), so it can be concluded that there is no effect of the administration of OAT on platelet counts in TB patients in the treatment phases of 0, 2, and 6 months.
Can Machine Translate Dialogue Acts: Evidence from Translating Dialogues from English to Arabic

Machine translation advances translation quality at morphological, syntactic, and semantic levels. The pragmatic level of machine translation is also evolving, but challenges remain due to cultural and contextual issues on the one hand and machine translation deficiencies on the other. While computational studies have made strides in automating translation tasks, linguistic-oriented research in this area remains sparse. In response to this gap, this study seeks to assess the effectiveness of Neural Machine Translation, as exemplified by Google Translate, in translating dialogue acts inherent in natural English conversations into Arabic, drawing upon Austin's theory of speech acts and leveraging a corpus of authentic sources [1] . Our findings highlight certain challenges in the machine’s identification of the performative functions of the utterances in conversations, viz. directives, expressive and representatives. Such challenges emanate from specific linguistic features of English conversations (e.g., idiomatic expressions, polysemous words, and deixis) and the lack of contextual information in everyday discourse. These challenges ultimately impede the faithful representation of speakers' intentions in the translated output. Keywords: Neural Machine Translation; dialogue acts; English; Arabic translation quality [1] https://www.eslfast.com/robot/ and https://helenadailyenglish.com/english-conversations-in-real-life-with-common-phrases-meaning-example
利用多尺度感興趣區域之細微關係提供圖片字幕

&lt;p&gt;隨著機器學習的蓬勃發展，圖片字幕生成(Image Captioning)的技術愈來愈進步。近期的Image Captioning引入區域提取網路(Region proposal Networks，RPN)與注意力機制(Attention Mechanism)。Image Captioning 透過 RPN 提取圖片中特定的物件區域，可以降低雜訊被當作視覺特徵的機率；注意力機制讓模型更專注在物件到文字的轉換。但是目前研究成果還存在著缺陷，RPN 與注意力機制皆專注於單一物件區域。它們缺少物件與物件之間更細膩的視覺特徵。上述的缺陷導致字幕生成器生成不明確的關係描述。為了提高Image Captioning 生成關係描述的細膩度，本研究提出透過不同物件之間多尺度感興趣區域之關係特徵的Image Captioning模型。本研究架構有 RPN、全卷積神經網路(Fully Convolutional Neural Networks，FCNN)以及長短期記憶(Long Short-term Memory，LSTM)單元。相較於現有的研究成果，在視覺特徵上，除了物件區域外，我們將進一步提取不同物件之間的多尺度 ROIs。由於某些多尺度 ROIs 是屬於雜訊，因此利用並交比(Intersection-over-Union)進行篩選。每一個ROI都先經由FCNN萃取出視覺特徵，再通過融合機制與排序網路獲得已排序的融合特徵，最後利用 LSTM 學習此特徵到完整句子的轉換。在訓練過程中額外透過階層式屬性的輔助監督，使字幕生成器能夠針對如何生成細膩的屬性進行學習。本研究提出的架構能夠在動態的圖片上，使用更精確的動詞描述物件動作。並且在基於 n-gram 的方法上，獲得更高的分數。&lt;/p&gt; &lt;p&gt; &lt;/p&gt;&lt;p&gt;With the rapid development of machine learning, the technique of Image Captioning is be coming more and more advanced. Recent researches of Image Captioning introduce Region Proposal Networks (RPN) and Attention Mechanism. Through RPN, we can extract features of specific object region in the image and reduce the probability of noises being treated as visual features. Attention mechanism makes the models to focus more on the mapping of object and caption. However, the current research results have deficiencies. Both RPN and Attention Mechanism only focus on the single object region instead of fine-grained visual features. Aforementioned deficiencies cause mistakes that caption generator generates uncertain rela tionships. In this paper, to improve exquisiteness of relationship descriptions for Image Cap tioning, we propose the Image Captioning model which generates sentence with multi-scale regions of interest (ROIs) between two different objects. Our proposed architecture includes Region Proposal Networks, Fully Convolutional Neural Networks and Long Short-term Memory cells. Compared to the existing research results, we extract not only object regions but multi-scale ROIs between two different objects on visual features. Some of Multi-scale ROIs are noises that can be screened by utilizing Intersection-over-Union (IoU). Each ROI utilizes FCNN to extract the visual features, followed by obtaining sorted fusion features with fusion mechanism and sorting network, and lastly learning transformation between this features to a whole sentence by LSTM. Caption generator can focus on learning how to generate fine grained attributes with hierarchical attribute supervisions on the training stage. The architecture proposed in this study can use more precise verbs to describe object actions on dynamic pic tures. Furthermore, our architecture outperforms on metrics based n-gram.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Neural Syntonicity: A Constructionist Approach to the Development of Image Recognition Tools Used to Teach Students about Powerful Ideas in Artificial Intelligence

Although Artificial Intelligence (AI) is already being used in a variety of ways to support creativity and education, there are still limitations when it comes to understanding how AI becomes intelligent, its impacts and how to manipulate, tinker with and explore future uses. This work builds on the idea of “syntonicity” as a cognitive tool where learners benefit from their existing understanding of intelligence while learning about AI. This work presents a learning framework called “Neural Syntonicity” which describes the syntonic relationship between the student’s thoughts and reflections while learning how to use and train AI Image Recognition tools. In this project we: 1) developed a series of Machine Learning Image Recognition software tools that students can manipulate and tinker with, 2) developed a “microworld” of activities and learning materials that supports a conducive learning environment for students to learn about Image Recognition, and 3) developed scenarios that allow students to explore their own cognitive labels of visual Image Recognition while using these tools. The research also aims to help students uncover “Powerful Ideas” and learn technical knowledge in Artificial Intelligence like: prediction, data clustering, accuracy, data bias, training and societal impacts. Using a mixed methods approach of Design Based Research, we conducted studies with three different groups of students. Through the analysis, we found that all groups of students gained confidence with using AI, and learned new technical skills in AI. Students were also able to demonstrate through a variety of examples that bias is a factor that can be controlled in AI systems as well as in the human mind.
Online Small Groups Talk in English Collaborative Prewriting Phase Viewed from Social Presence Frame

During the COVID-19, the students of Indonesian tertiary conducted teaching and learning activities with distance learning or distance learning in the English academic writing course. The novelty of the current study is that small groups talk in the collaborative prewriting phase via WhatsApp using social presence framework. This study investigated what students talked about while doing academic writing tasks at a private university in Indonesia. The methodology applied a descriptive quantitative. The instruments used were observation and questionnaires. We conducted observation in chatrooms. The questionnaire was distributed to find out the students’ responses to the prewriting process from the fifteen participants. For the observation data, we used the content analysis technique, textual data were coded into social presence indicators using Nvivo12 software. The questionnaire was validated by employing content and construct validity with two expert judgments. The findings showed that four groups constructed collaborative floors, positive responses, organization, language, content, task negotiation, and shared resources with different contributions. In performing identity online, eight dominant students in each group took more roles than others. The result of students’ responses was that they perceived discussion online in the collaborative prewriting phase. The study recommends that small online groups talk in the synchronous joint prewriting phase promotes social presence and assist the students in enhancing critical thinking skill and academic writing.
Development of an Automatic Chronological Record Creation System Using Voice AI to Facilitate Information Aggregation and Sharing in the Event of a Disaster

Abstract Objective: In the event of a disaster, the chain of command and communication of each relevant agency is important. In this study, a chronological record creation system using voice AI (V-CRS) was developed, and an experiment was conducted to determine whether the obtained information could be quickly and easily summarized in chronological order. Methods: After a lecture by Japanese Disaster Medical Assistant (DMAT) Team members and 8 medical clerks on how to use the developed tool, a comparison experiment was conducted between manual input and V-CRS utilization of the time to compile disaster information. Results: Results proved that V-CRS can collect information gathered at headquarters more quickly than handwriting. It was also suggested that even medical clerks who have never been trained to record information during disasters could record information at the same speed as trained DMAT personnel. Conclusion: V-CRS can transcribe audio information even in situations where technical terms and physical units must be recorded, such as radiation disasters. It has been proven that anyone can quickly organize information using this method, to some extent.
Key Committing Security of AEZ and More

For an Authenticated Encryption with Associated Data (AEAD) scheme, the key committing security refers to the security notion of whether the adversary can produce a pair of distinct input tuples, including the key, that result in the same output. While the key committing security of various nonce-based AEAD schemes is known, the security analysis of Robust AE (RAE) is largely unexplored. In particular, we are interested in the key committing security of AEAD schemes built on the Encode-then-Encipher (EtE) approach from a wide block cipher. We first consider AEZ v5, the classical and the first dedicated RAE that employs the EtE approach. We focus our analysis on the core part of AEZ to show our best attacks depending on the length of the ciphertext expansion. In the general case where the Tweakable Block Cipher (TBC) is assumed to be ideal, we show a birthday attack and a matching provable security result. AEZ adopts a simpler key schedule and the prove-then-prune approach in the full specification, and we show a practical attack against it by exploiting the simplicity of the key schedule. The complexity is 227, and we experimentally verify the correctness with a concrete example. We also cover two AEAD schemes based on EtE. One is built on Adiantum, and the other one is built on HCTR2, which are two wide block ciphers that are used in real applications. We present key committing attacks against these schemes when used in EtE and matching proofs for particular cases.
Sistem Pendukung Keputusan Pemilihan Pegawai Honorer Kelurahan Medan Sinembah Menerapkan Metode ROC dan MOORA

An honorary employee is someone who works for a company that is eligible to be employed as a daily employee for a predetermined period of time and also with a definite salary from the company he works for. The problem experienced in this study is about the process of selecting honorary employees. The selection of honorary employees so far has only looked at education, even though in the process of selecting honorary employees several criteria must be used. The criteria in this study are Education, Age, Height, Distance from Home, Work Experience, Character and Test Values. From the problems encountered, it is very appropriate if the Medan Sinembah Village party implements a Decision Support System. The method used in this study is ROC and MOORA, this method was chosen because it is able to provide the best decision based on predetermined criteria where the best alternative is alternative A18 on behalf of "Andry" with a value of Yi = 0.328
Pendampingan Pembuatan Media Pembelajaran Berbasis Aplikasi Canva Bagi Guru SMP Srijaya Negara

Community service activities carried out on this occasion are related to improving teacher competence based on Law No. 14 of 2005 concerning Teachers and Lecturers. The method of community service is mentoring the teachers of Srijaya Negara Junior High School. The service results showed increased understanding and ability of the Teachers of Srijaya Negara Junior High School to make Canva-based learning media. Mentoring activities for making Canva-based learning media went well and efficiently. The teachers showed enthusiasm for creating learning media using Canva. The enthusiastic attitude of the teachers can be seen from the enthusiasm and desire to complete Canva-based learning media. In addition, the understanding and knowledge of teachers in utilizing Canva to create learning media can be seen from the initial test (pre-test) and the final test (post-test).
Exploring the Impact of Chat GPT and Critical Thinking on Consumer Engagement in Cognitive Marketing: An Empirical Study with Early Adopters

This study aims to examine the role of critical thinking in revolutionizing cognitive marketing by exploring the impact of Chat GPT on enhancing consumer engagement. The study employs a quantitative survey approach to collect data from early adopters, using constructs such as critical thinking, perceived ease of use, perceived usefulness, task technology fit (TTF), consumer engagement, and intention to use Chat GPT. The study is novel in that it examines the role of critical thinking in optimizing the use of Chat GPT for consumer engagement, which has not been explored extensively in previous research. A novel construct, namely Propensity to Diffusion of Innovation, is identified that adds to the diffusion of innovation theory. The findings of this study reveal propensity to the diffusion of Chat GPT is positively related to customer engagement, while critical thinking partially moderates the relationship between them. The results are expected to provide new insights into how critical thinking can be leveraged to improve the effectiveness of cognitive marketing strategies using Chat GPT, ultimately benefiting businesses and consumers alike.
COMPRESSION STUDY FOR IMAGE OPTIMIZATION TECHNIQUES AVAILABLE FOR JOOMLA-BASED WEBSITES

Nowadays Images represent approximately about half website size, Which effect the performance of website therefore optimization become necessary to avoid increasing image transfer size as well as page load time. Many studies showed that image optimization is lacking on most websites. This study introduced image optimization approaches used with joomla websites. The paper ends up with all optimization approaches reduce total page size and approve overall performance with different level of optimization.
FACTORS AFFECTING A STUDENT'S PREFERENCE FOR ONLINE PROFESSIONAL COURSES: A CONJOINT APPROACH

Online professional courses have changed the learning methods,especially after covid 19.These professional courses proved to be very useful and broke the geographical boundaries for learning.This research paper aimed to study the preferred attributes of the online professional courses offered by the different institutions and their relative importance from a student's perspective. This paper applied the conjoint method to identify the preferred attributes of online courses. The paper found the highest utility associated with the renowned resource person, with a good research background,etc,and contemporary content.The main aim of joining the courses is to upgrade with new knowledge and skills which makes them employable and improve their earning potential. The utilities for a year duration with reasonable course fee is found positive.The positive reviews from previous students stimulate the chance of opting for the courses and most of the students prefer evening timings and live sessions for these courses.
The effect of articulate storyline-based interactive learning media to facilitate student learning independence

The problem in this study is whether there is an influence of interactive learning media-based articulate storyline to facilitate student learning self-reliance at SMKN 3 Serang City. This study aims to determine the effect of interactive learning media based on an articulate storyline to facilitate student learning self-reliance at SMKN 3 Serang City. The study was conducted in SMKN 3 Serang City, with samples of 35 students in Multimedia class XI. In this study, writers have used correlational quantitative methods, with data collection techniques of observation, questionnaires and documentation and based on the results of correlation analysis, using the T-test. Where the tcount is 40,282, and the table value is 2,035. Then tcount &gt; ttable means that interactive learning media is influenced based on articulate storylines to facilitate student learning independence. The contribution of variable x to variable y is 98.01%, whereas 1.99% is affected by other factors, such as the internal being reviewed from physiological and psychological factors.