Reviewerly/exHarmony huggingface dataset:
Analysis of Windows 11 Link File Artifact for Evidence Gathering

The objects created by the operating system are known as artifacts, and they contain crucial data about the actions taken by computer users. Thus, these artifacts are of great relevance to the forensic analyst. These artifacts can act as a evidence in a court of law to prove the digital crime. Link File or Shortcut file is one such object, presence of which confirms the usage of file in recent time. Link File are links between the executable and the applications. In this work, Link File is forensically analyzed and bring out its forensic value, knowledge it provides and perform few in-depth forensics examinations on Link File useful for analyst using open source FTK Imager tool. Lastly, we compared the Link File artifacts in various versions of Windows Operating System
Neighboring Envelope Embedded Stacked Autoencoder for Deep Learning on Hierarchically Structured Samples

A stacked autoencoder (SAE) is a widely used deep network. However, existing deep SAEs focus on original samples without considering the hierarchical structural information between samples. This limits the accuracy of the SAE. In recent years, state-of-the-art SAEs have suggested improvements in network structure, cost function, parameter optimization, and thereby the accuracy has been enhanced. However, the problem mentioned above is still not solved. Therefore, this paper is concerned with how to design a SAE that can conduct deep learning on hierarchically structured samples. This proposed SAE - neighboring envelope embedded stacked autoencoder (NE_ESAE) mainly consists of two parts. The first is the neighboring sample envelope learning mechanism (NSELM) that constructs sample-pairs by combining neighboring samples. In addition, the NSELM constructs multilayer sample spaces by multilayer iterative mean clustering, which considers similar samples and generates layers of envelope samples with hierarchical structural information. The second is an embedded stacked autoencoder (ESAE) to consider the original samples during training and in network structure, thereby finding the relationship of the samples with original features and deep features in a better manner. The experimental results show that our method has significantly better performance than some representative methods. Different from existing SAEs, the proposed NE_ESAE realizes deep learning on hierarchical structured samples, and makes SAE able to conduct cooperative deep sample and feature learning. The advantage that has been gained can be applied to other deep neural networks.
Teaching Evaluation Research of Professional Courses Based on Multinomial Random Forest with Improved Grassberger Entropy

In this paper, a multinomial random forest algorithm based on improved Grassberger entropy is proposed to evaluate the teaching effect of professional courses. In order to reduce the calculation deviation of the information gain, the improved Grassberger entropy is used to calculate the information gain, and the optimal split attribute of the split node is selected to train the multinomial random forest classifier. Multinomial random forest uses two multinomial distributions based on information gain to realize random selection of a split feature and split value respectively. This algorithm is used to construct the course teaching effect prediction system for analyzing the students' course learning effect. In addition, this paper also studies and determines the evaluation indicators that should be included in the curriculum teaching effect evaluation system, and constructs a student-oriented evaluation indicator system. The results of the experiment in this paper indicated that the model is able to exactly identify the student grades with different evaluation results from the evaluation index data containing noise, and the analysis outcomes of the importance of each evaluating indicator characteristic play a significant part in effectively improving teaching.
External Knowledge and Data Augmentation Enhanced Model for Chinese Short Text Matching

With the rapid development of the network, a large amount of short text data has been generated in life. Chinese short text matching is an important task of natural language processing, but it still faces challenges such as ambiguity in Chinese words and imbalanced ratio of samples in the training set. To address these problems, we propose an external knowledge and data augmentation enhanced model (EDM) for Chinese short text matching. EDM uses jieba, thulac and ltp to generate word lattices and employs HowNet as an external knowledge source for disambiguation. Additionally, dropout and EDA hybrid model is adopted for data augmentation to balance the proportion of samples in the training set. The experimental results on two Chinese short text datasets show that EDM outperforms most of the existing models. Ablation experiments also demonstrate that external knowledge and data augmentation can significantly improve the model.
Improving Cross-Domain Named Entity Recognition from the Perspective of Representation

Recently, cross-domain named entity recognition (cross-domain NER), which can reduce the high data annotation costs faced by fully-supervised methods, has drawn attention. Most competitive approaches mainly rely on pre-trained language models like BERT to represent words. As such, the original chaotic representations may bring challenges (e.g., entity span detection errors and entity type misclassification) for them. Motivated by this, this proposal proposes to improve cross-domain NER by refining the original representations.
USING MAGIC BOTTLE TO IMPROVE STUDENTS’ SPEAKING SKILLS

Speaking has been the main problem for majority of Malaysian students as they are afraid to make mistakes. In order for students to be understood while speaking in English, they need to be able to pronounce English words clearly. To solve this problem, a game ‘Magic Bottle’ was developed in this study. This research was conducted on a group of 27 students from Sekolah Menengah Kebangsaan Taman Universiti, Skudai, Johor Bahru. It was triangulated through analysis of PBD Assessment Reports, a set of pre-test and post-test, participant observation and interview. Early observation shows that none of the students passed their PBD Speaking Skills Assessment. Only 2 out of 27 students (7%) scored Band 2 in their PBD Assessment (Pelaporan Pentaksiran Bilik Darjah) for Speaking Skills. The rest, 25 out of 27 students (93%) only scored Band 1 in their Speaking Assessment. All the students failed in the first PBD Speaking Skills Assessment. In this study, students were taught about pronunciation using 'Magic Bottle'. In the game, students were exposed to the correct pronunciation of the difficult words. They were asked to read and role play the sentences in the game. The research showed that students had improved their pronunciation through ‘Magic Bottle’. The T-Test conducted at the end of the test shows that there was a significant improvement between the students’ post-test and pre-test results. Students had also shown improvement in their PBD Speaking Skills.
Extractions of Forensics Data with Wireshark

In this chapter, we will look at the challenges of obtaining forensics evidence from network capture files. First, we need to explain some basic concepts of forensics, so what exactly is it?
Network Traffic Forensics

In this chapter, we will review different characteristics of network connections and the traffic that is generated. It is an expansion on earlier topics as we need to extract the information from the communication traffic and identify what needs to be extracted from the data to be collected in a forensically sound manner.
Optimizations of Multi Optima Problems Using the Genetic Algorithm, Punch-Out Method, and Least Exponent Method

The Genetic Algorithm (GA) is a very effective global search algorithm. When the dimension of the optimization problem becomes great, it is not a simple task for methods, such as gradient methods. For the accuracy of a given solution, the search frequency of the variables necessary for a variable axis is far greater with the dimension increase of the solution vector. When the smoothness of the problem is good, the small frequency of the variable will do, with the progress of the generations. However, when the problem has many sharp peaks, such as in data regression, several frequencies of the variable in each variable axis are needed to result in a tremendous total number of individuals. In order to resolve this kind of ineffectiveness for many variables with a moderate number of individuals, a punch-out method is proposed for effectively finding the optimum. The least square method is revisited here to suggest least exponent method to take care of the outlier problem in data regression.
Performance and Resource Optimization

In this chapter, we will discuss a few performance and resource optimization techniques provided by Snowflake. This includes the Snowflake Search Optimization service, which improves the performance of certain types of lookup and analytical queries. We’ll also dive deeper into Snowflake caching.
Deep Clustering with Robust Autoencoder (DCRA)

Accordingly to Science Daily, 90 percent of all the data in the world has been generated in the last two years. However, the world is analyzing less than 1 percent of its data so far. With the advancement of high-performance computing, deep learning methods are readily applied to analyze large-scale high dimensional datasets. These machine learning methods have achieved significantly efficient training and inferencing as well as producing much more accurate predicted results. Clustering is an unsupervised machine learning method of identifying and grouping similar data points into the same cluster. Clustering plays a fundamental role in the data mining and machine learning community for grouping data into structures so that similar data points are assigned to similar groups. Furthermore, to process these huge amounts of high-dimensional data, deep learning becomes a key technique to learn and perform feature representation of data in latent space for many real world applications. In this paper, we propose deep clustering with robust autoencoder (DCRA), which jointly utilizes robust auto-encoder and deep clustering to perform feature representation and cluster assignments simultaneously. Multiple experiments using open public datasets have been conducted to evaluate our model’s performance. Our results show DCRA is capable of generating high quality clusters with high clustering accuracy of 90% above in high dimensional datasets. The decreasing training and test loss with increasing number of epochs also validates our results.
Secure Communication through Data Hiding based on Video Steganography

In the rapid sharing system there are many kind of inputs like text, image, audio or video, but the user’s needs are mainly focused on secure communication over the internet and intranet. We well thought-out for this kind of problem and this research are conceded for improving the security sharing. I proposed the system called Unified Mesh Steganography (UMS) technique. In this techniques some modern fashion used for implementation and the experimental evaluations are made using MATLAB. The technique attains some parameters like Embedding Capacity Level (ECL) and Transmission Ratio Level (TRL).
On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi supervised setting, in which the factors of variation are labeled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.
Education for an Emerging World: A Futuristic Perspective

Etymologically, the word Education has its origin from Latin words such as ‘educare’ which is to bring out or to nourish; ‘educere’ refers to lead out or to draw out; ‘educatum’ means the act of teaching or training. Education implies ‘a breeding, to bring up, and rearing.’ The Sanskrit word ‘shiksha’ is derived from the word ‘shas’ which means the root. Shiksha implies “to discipline, to control, to instruct and to teach.” There are a number of definitions that could be quoted and presented. The task at hand however is grounding education toward strategies, content and goals that direct students to face the challenges and problems of the future by creating an environment to nourish and worship life. The word life also includes all animate, inanimate, living and nonliving phenomenon for example, crystals, infectious proteins called prions and so on. It is now discovered that computer programmes are also considered "living" by scientists. Many viruses have DNA or RNA which has led to an opinion that they may have a commonality with the human species. The word ‘LIFE’ is used here in its broadest sense with the awareness that life is a pulsating, dynamic energy constantly emerging and changing, defying definition. This paper discusses the challenges of the future, the present technological developments, the adequacy of the present educational system in that context, and the enormous scope of the future of Education for the challenges that lie ahead. It attempts to visualize a possible alternate paradigm as an Indian model of educational system with enduring values for a changing world.
CORRUPTION DURING THE COVID-19 CRISIS: CHALLENGES AND POTENTIAL LESSONS

This conference opens up discussions on recent trends of corruption during the pandemic and how various jurisdictions have dealt with the vice. In light of the impact of economic crimes in Africa, I hope to cast some enlightenment and inspiration on the minds of the participants. This speech elaborates the impacts of economic crimes in a general perspective and distinguishes traditional from economic crime and the cost of the latter to society. It hints at the statistics of economic crime during the Covid-19 pandemic, the exacerbation of cybercrime in the digital spaces in the Covid-19 era. I then propose that we should question how Africa can rise to the occasion through international, regional, sub-regional and country-specific mechanisms to detect, prevent and prosecute economic crimes and cybercrimes.
Zhivete Model - a Hybrid of V Model and Agile Scrum for Product Development

The New Product Development Process (NPDP) is a systematic approach to product development, consisting of various stages from ideation to commercialization. The Waterfall model and the Iterative (Agile) model are two commonly used approaches to product development, with each having its own strengths and weaknesses. The V-model is a sequential development process used in regulated industries, emphasizing the importance of verification and validation in product development. Although the V-model is well-suited for regulated industries, it can be inflexible and not adaptable to changes in requirements or feedback. Therefore, a more agile model is needed, with continuous testing and validation throughout the development process. In this paper we are Proposing the Zhivete model - a hybrid of V model and Agile Scrum for product development. Offers structured approach with flexibility and adaptation, potentially reducing time and cost.
OPTIMIZATION IN PARAMETRIC DESIGN SPACE

Optimisation of complex design configurations in terms of performance is rarely achieved due to the computational effort required to explore multi-dimensional design space. Genetic algorithms (GAs) are currently being employed by many research groups for that purpose but tend to be configured as software systems which are independent of the CAD modelling system. This paper describes a CAD system where design for product performance, design for manufacturing and design for cost are accommodated within one integrated adaptive environment. One aspect of this system is covered in depth in this paper, the evolutionary optimisation of performance and cost using hybrid coevolving GAs.
Authenticated Ranked Keyword Search over Encrypted Data with Strong Privacy Guarantee

In the past decades, with the development of cloud computing, ranked keyword search, which devotes to find the most relevant results, has been extensively studied in outsourcing domain. However, due to a mass of sensitive information containing in the outsourced data, the issue of privacy has become the main brunt. Existing works primarily resort to searchable encryption to protect the privacy but do not consider the access pattern and search pattern, which can be used to infer the privacy information. Moreover, since the cloud server may be malicious, result integrity also needs to be considered. Therefore, in this paper, we study the problem of secure and authenticated ranked keyword search, called SARKS. Specifically, we first propose a framework that integrates d-differential privacy, erasure coding, and oblivious traverse to achieve access pattern and search pattern protection and meanwhile propose a scheme based on merkle hash tree to realize the correctness and completeness of the query results. To accelerate the performance, we further propose an improved scheme by adopting clustering method. Finally, the formal security analysis is conducted and the empirical evaluation over the real-world dataset has demonstrated the feasibility and practicability of our proposed schemes.
Does Discretionary Spending and Ethical Responsibility Guarantee Customer Loyalty

Customer loyalty is still a major concern to organizations despite the various activities of manufacturing firms in their various host communities. Given this, this paper examines the relationship between discretionary spending, ethical responsibility and customer loyalty. The descriptive survey research design was adopted in the study. The unit of analysis was International Breweries and a purposive sampling technique was used to select sample for the study from the target population. The stakeholder theory was the underpinning theory. The result revealed that there exists a strong significant positive relationship between discretionary spending, ethical responsibility and customer loyalty. Based on the findings, it was recommended that organizations should have a standing committee to oversee the proper implementation, monitoring, control, and evaluation of their discretionary spending and ethical responsibility activities.
Influence of the COVID-19 pandemic in the academic production of health researchers from public universities of the State of São Paulo, Brazil

Introduction: Knowledge about what may influence the academic output of researchers during the COVID-19 pandemic may help direct efforts to improve individual researchers’ capacity and minimize the impact of other aspects on their academic output. Aim: To evaluate the influence of the COVID-19 pandemic in the academic production of researchers from public universities in the health area in the State of São Paulo. Material and Methods: Cross-sectional, online survey. Health researchers from public universities in the state of São Paulo were evaluated anonymously. Questionnaires addressed sociodemographic and professional characterization of researchers, their academic production and the scientific production related to research projects on COVID-19. Data were collected using the SurveyMonkey® program. Chi-square tests and logistic regression analyses were performed using IBM-SPSS v.21.0 software; significance was determined by p&lt;0.05. Results: 568 researchers answered all questionnaires. Regarding research partnerships, 297 (50.51%) said they were negatively affected. 231 (39.29%) confirmed difficulty in obtaining financial support. The pandemic changed the way 91.1% of researchers deal with their work routine. The greatest workload of the research team was associated mainly with nurses (46.7%). Biologists have greater chance (OR=4.8) of encountering difficulties in relation to physicians, and researchers from UNIFESP (OR=2.75) are the ones who are more likely to encounter difficulties. FAPESP was the agency that most funded studies related to COVID. Conclusion: These findings reinforce the need to establish funding targets and research incentives, especially for early career researchers. Future studies are needed to determine the impact of the pandemic on researchers’ scientific output over time.
Role of Disruptive Technology in Efficiency of 21st Century Organisation

Disruptive technology is playing a bigger and bigger part in how effectively 21st-century enterprises operate in India. The country is undergoing a digital revolution as a result of the rapid use of new technologies including artificial intelligence (AI), cloud computing, and the Internet of Things (IoT). Traditional industries including banking, healthcare, and agriculture are being significantly transformed by disruptive technologies. With the government's goal for a cashless economy and the launch of UPI, digital payments have become widely used in India (Unified Payments Interface). For both enterprises and people, this has increased the efficiency and convenience of financial transactions. Telemedicine has become a revolutionary technology in the healthcare industry, enabling doctors to conduct distant consultations and enhancing access to healthcare for people living in rural locations. In a similar vein, precision agriculture has increased crop yields and decreased waste thanks to IoT sensors and data analytics. Organizations that use these technologies are expected to gain a competitive advantage, boost operational effectiveness, and provide better customer service as India continues to embrace disruptive technology.
Simulation Tools for Immersive Technology Services for People with Disabilities

The transfer of immersive technologies will soon be able to transform human life, allowing the implementation of H2M interactions to the fullest and with a new quality. People will be able to protect themselves from hazardous industries, learn faster, provide medical services to highly qualified specialists in the shortest possible time, and much more. The technology provides a paradigm shift towards wireless technology for real-time remote control. At the same time, the introduction of the Tactile Internet concept requires a revision of the principles for building existing and future communication networks. The article describes an experiment in the implementation of immersive technologies&#x0D; in fifth generation communication networks. In the article, a microservice application was created that simulates the movement of a human hand in space, which allows displaying sign language. Ways to reduce delays are also analyzed.
Handwritten Text Recognition System using Machine Learning

Handwritten character recognition is an ongoing research field that features machine learning, computer vision and pattern recognition. To do this, one scans a handwritten document and converts it into a simple text document. The basic Optical Character Recognition (OCR) process is to examine the text of a document and convert it into codes used for data processing. In this machine learning project, deep learning techniques were used to model a neural network that recognizes individual handwritten characters and handwritten numerals. To recognize them, a convolutional neural network (CNN) was built to train on alphabets and the digits datasets and further the predictions done by the trained model were visualized using OpenCV.