Reviewerly/exHarmony huggingface dataset:
साइबर सिद्धान्तका आधारमा ‘आमालाई इमेल’ कविताको विश्लेषण {An Analysis of the Poem 'Aamalai Email' Based on Cyber Theory}

प्रस्तुत समालोचनात्मक लेख आमालाई इमेल कविताको विश्लेषणमा केन्द्रित रहेको छ । प्रस्तुत कविता रमेश गौतमको चिताको आगो साँच्चै तातो हुँदोरहेछ कवितासब्ग्रहमा समाविष्ट छ । प्रस्तुत सब्ग्रहमा समाविष्ट चउन्नवटा कवितामध्येको आमालाई इमेल कविता तेह्रौँ क्रममा समाविष्ट छ । विषयगत रूपमा विविधता रहेका प्रस्तुत सब्ग्रहका कविताहरूमध्ये यस कविताले वर्तमान समयमा निकै महत्त्वपूर्ण मानिएको साइबर जगतलाई विषय बनाएको पाइन्छ । साइबर जगतको प्रभाव साहित्यमा पर्न थालेपछि त्यससम्बन्धी अध्ययन गर्न साइबर समालोचनाको विकास भएको देखिन्छ । यसले साइबर साहित्यको अध्ययन गर्छ । प्रस्तुत अनुसन्धेय लेखमा आमालाई इमेल कविता उत्तरआधुनिक बहुलवादी समालोचनाअन्तर्गतको साइबर समालोचनाका दृष्टिले कस्तो छ ? भन्ने मुख्य शोध्य समस्यामूलक प्रश्नमा केन्द्रित रही अध्ययनलाई अगाडि बढाइएको छ । साइबर समालोचनाका दृष्टिबाट विश्लेषण गरी मूल्य निरूपण गर्ने उद्देश्य राखिएको प्रस्तुत लेखमा पहिचानात्मक तथा विश्लेषणात्मक विधिको प्रयोग गरी प्रस्तुत आमालाई इमेल कविता उत्तरआधुनिक साइबर साहित्यका दृष्टिले सबल रहेको निष्कर्ष निकालिएको छ । {The presented critical article focuses on the analysis of the poem Email to Mother. The poem presented by Ramesh Gautam is included in the collection of poems The fire of the pyre is really hot. Among the fourteen poems included in the presented collection, the email poem to the mother is included in the thirteenth order. Among the poems in the presented collection, which are diverse in terms of subject matter, this poem deals with the cyber world, which is considered very important nowadays. It seems that cyber criticism has been developed to study the impact of the cyber world on the literature. It studies cyber literature. How is the email poem to mother in the presented research article from the point of view of cyber criticism under postmodern pluralistic criticism? The study has been carried forward by focusing on the main solvable problematic question. In this article, which aims to analyze and value from the point of view of cyber criticism, it has been concluded that the email poem presented to the mother is strong from the point of view of postmodern cyber literature.}
Geophysical Evaluation of Agricultural Potential of Orlu and Environs Using Landsat Imagery

The scarcity of food afflicting third-world countries, particularly Nigeria, case study Orlu zone, Imo State, Nigeria, is intolerable, given the high rate of environmental degradation in the form of erosions, nation’s poor economic state, insecurity, and extremely low per capita income of citizens, motivated this research. This research is tailored to a possible approach to combating the threat of food insecurity via geophysical investigation of agricultural potential areas and as well help in managing food insecurity ravaging the area, particularly in this post-COVID lockdown era. In this research, a geophysical approach—Landsat imagery and interpretation—was used to identify areas with high agricultural yielding potentials and how to exploit them for bumper agricultural harvests to sustain livelihood and alleviate the food crisis and food inflation ravaging the zone. Within the study area, the following data were collected: Normalized Difference Vegetation Index (NDVI) map, lineament map and drainage pattern map. They were interpreted, and areas with high agricultural yield potentials were mapped. Band ratios (3/4, 4/2, 3/1, 5/4) were generated to reduce the effects of shadowing and as well improve the features present. The NDVI values that indicate soil viability, generated within the study area range from –0.22 to 0.51.
A wavelength-dependent extinction coefficient model for multispectral image defogging

Most of the state-of-the-art defogging models presented in the literature assume that the attenuation coefficient of all spectral channels is a constant, which inevitably leads to spectral distortion and information bias. To address this issue, this paper proposes a novel defogging method that takes into account the difference between the extinction coefficients of multispectral channels of light traveling through fog. Then the spatially distributed transmission map of each spectral channel is reconstructed to restore the fog-degraded images. The experimental results of various realistic complex scenes show that the proposed method has outstanding advantages in restoring lost detail, compensating for degraded spectral information, and recognizing targets hidden in dense fog. In addition, this work provides a rare method to characterize the intrinsic property of fog expressed as multispectral relative extinction coefficients, which act as a fundament for further reconstruction of multispectral information.
Nonreciprocal photonic quantum router via synthetic magnetism

Nonreciprocal photon transmissions at the single-quantum level play crucial roles in optical information processing. Here, we propose to develop a nonreciprocal photonic quantum router via synthetic magnetism, which can route photons of an input quantum state in one direction but block them in the other direction. Our model is based on a superconducting circuit of linearly coupled microwave cavities, and the Lorentz reciprocity is broken by synthesizing an effective magnetic field for photons. As a result, an input quantum signal from a given direction can be delivered on-demand to either of the two output ports, but it from the opposite direction is completely absorbed. Our scheme does not involve strong static magnetic fields and optical nonlinearity for generating the desired optical nonreciprocity. It is, therefore, expected to be a key ingredient for the construction of on-chip quantum networks.
Effects of Body Weight Group Exercise on Changes in Body Shape and Blood Stress Factors in Obese Female College Students

This study was conducted to find out how it affects changes in body shape and blood stress factors by having obese female college students perform body weight exercise for 8 weeks. The study subjects were divided into 10 body weight group and 10 control group based on 20 obese female college students. Two-way repeated ANOVA was conducted to see the significance and interaction effect between the two groups based on the data obtained through two pre- and post-measurements through 8 weeks of body weight exercise, and based on the above results, the following conclusions were drawn. derived. First, in the effect of improving the body shape of obese female college students through body weight exercise, there was a significant difference in weight (measurement period) and body fat percentage (measurement period, group), and it was found that there was an interaction effect. Second, in the effect of blood stress of obese female college students through body weight exercise, there was a significant difference in blood active oxygen (measurement period) and blood lactic acid (measurement period), and it was found that there was an interaction effect. Recently, various exercise methods have been proposed for diet and health promotion. The body weight exercise presented in this study has also been shown to make positive changes in eliminating obesity, and for modern people who lack time, if performed regularly at home training or a place where only space is used, obesity can be prevented. It will relieve blood pressure and reduce blood stress, leading to a healthy college life and healthy adulthood and middle age.
A Procedural Generation Platform to Create Randomized Gaming Maps using 2D Model and Machine Learning

As a video game developer, the most difficult problem I ran into was creating a map for the game, as it was difficult to create non repetitive and original gameplay [1]. My project proposes a solution to this problem as I use Answer Set Programming to create a program to procedurally generate maps for a video game [2]. In order to test its reliability, I allowed it to generate around 10,000 maps, stored the data of each of the maps, and used the common trends I find in the data to find problems with the program and fix it in the future. In developing a video game, level creation consumes a major portion of the development total time and level procedural generation techniques can potentially mitigate this problem. This research focused on developing a VVVVVV style level generator using Answer set programming for the game Mem.experiment which was developed at the same time. VVVVVV is a 2D puzzle platformer that uses changes in direction of gravity instead of jumping for the player's vertical movement [3]. During the development of the level generator, 10,000 levels were created. I found out that the average total time it took between generations is 45 seconds, and the average time for ASP to generate a map is 12 seconds [4]. This means that the process of displaying the generation took between 2x - 3x longer than generating the ASP solution.
Efficiently Navigating Information Overload: Developing a Machine-Learning-Based Application for Summarizing Academic Videos and Extracting Key Topics

The internet is a vast treasure trove of information on any subject that has allowed students and scientists alikeunprecedented access to the world's collective knowledge [1]. Unfortunately, a lot of it is buried in hours longseminars, talks and other videos on video sharing websites like YouTube [2]. When researching a topic for anacademic essay or paper, one might rightly be shocked by the massive time investment required to dig uptherelevant data or citations from these monolithic recordings [3]. This paper develops an application that aims toapply new machine-learning-based transcription and keyword extraction methods to cut these videos into small, digestible chunks, which are labeled with their most important topics in order to allow us only to have to manuallyanalyze the parts of the video that are relevant to our research without losing valuable context details [4]. Weapplied our program to instructional videos on YouTube, in order to test how well we can rearrange videodocuments for a more convenient view of its contents and conducted a qualitative evaluation of the approach. Theresults show that the application works as expected to provide video clips titled with its central topic for the user todownload in a reasonable amount of time via a simple browser-based extension [5].
A Mobile Application for Creating Dance Choreography according to Musicality of Inputted Audio using Machine Learning

Even the best writers in history were not blessed enough to have a constant surge of inspiration, a never-endingflowof ink on paper, or fingers flying across keyboards. These blocks in creativity are commonly known as writer's block, and many people experience this, since communication is important in every subject. Less commonly knownisdancer's block, which is when a choreographer experiences a stop in inspiration while choreographing [1]. Theseblocks can continue for days or even weeks, before an idea strikes the dancer, setting back the ef iciency of choreographing. When due dates come into play, such as choreographing for a production, show, or assignment, adancer cannot put forth their best choreography, and will be left feeling unsatisfied with the quality of their work. When a choreographer faces a challenge towards creating original choreography, how can one gain inspirationtoovercome this block [2]? How can we ensure that the inspiration given is appropriate for the song and style whichthe choreographer is designing the dance to? This paper develops an application to choreograph visuals inacreative manner, while assessing the musicality of the audio in order to reflect the same emotion in the movement. We applied our application to a jazz piece to be performed as part of a local high school dance show and conducteda qualitative evaluation of the approach. The results show that with the software application, dancers will be abletofind inspiration to continue choreographing, pushing them past a barrier of creativity, and allowing themto finishtheir dance with a quality of choreography that they can be proud to present [3].
Need Analysis Survey of Designing and Developing a Mobile Augmented Reality Application for Tourism English Learning in Higher Vocational Education, China

The English proficiency of Tourism Management students is underneath the average of college level.Providentially, the progression in technology has opened another possibility for researchers to explore the full potential of technology by integrating it into teaching and learning process in English.The combination of mobile learning and Augmented Reality (AR) is a creative and effective way for Tourism English learning, because AR presents virtual 3D content on the basis of real world and brings students directly to the real scene and working environment of Tourism, and mobile learning enables students to learn without time or space constraints.Although AR and Mobile Augmented Reality (MAR) are believed to benefit education, so far, there is limited published research that addresses MAR's benefits in higher vocational education, especially for Tourism English learning.Therefore, the objective of this research is to determine the need to develop a MAR application, namely Mobile Augmented Reality Tourism English Learning (MARTEL), for Tourism Management students in Higher Vocational College (HVC).This study surveyed the needs from 169 students from 1 st and 2 nd grades of Tourism Management program and data were analysed by descriptive statistics.In addition, 6 Tourism English teachers were interviewed and the text of the conversations was transcribed thematically.Findings revealed that both teachers and students expressed the lack of English proficiency, the insufficient effect of existing technological teaching tools, and feedback quite positively on MAR technology and MARTEL, indicating that the demand of MARTEL is considerable.It is foreseeable that as an advanced and interesting teaching tool, MARTEL application will be able to improve teaching and learning efficiency, make Tourism English an innovative and high-quality course, and even to a certain extent, change the teaching ecology of domestic Tourism English courses.
The Position of Points, Lines and Planes in a Mosque Construction for Geometrical Visualisation

The purpose of this qualitative descriptive study was to identify the concepts of the positions of points, lines, and planes in the building elements of the Kauman Gedhe Mosque foyers such as pillars, ceilings, crossbars and the roof of the building. The research data was obtained from direct observation which was documented using a cellphone camera and visualized with the help of GeoGebra. The results of the study show that there are on the building elements of the Mosque concepts of the position of a point to a line (a point is inside the line and a point is not on the line), a point to a plane (a point is in the plane and a point is outside the plane), a line to a line (two parallel lines, two perpendicular intersecting lines, two non-perpendicular intersecting lines, two coincident lines, and two intersecting lines), a line to a plane (parallel lines, a plane perpendicular, and a plane coincident with a plane), and a plane to a plane (two parallel planes, two non-perpendicular intersecting planes, two perpendicularly intersecting planes, and two coincident planes). Based on the results of this study, it is concluded that there are 16 mathematical concepts found in the Gedhe Kauman Mosque. Visualization of abstract mathematical objects can easily be imagined through cultural sites. This is able to lighten cognitive loads so that it is easy to understand and students are encouraged to construct their own understanding based on the experience they have gained.
PENINGKATAN KUALITAS PEMBELAJARAN MENYIMAK CERPEN DENGAN MENGGUNAKAN MEDIA REKAMAN PEMBACAAN CERPEN

Improving the Quality of Learning to Listen to Short Stories by Using Short Stories Recorded Media. This research is classified as Classroom Action Research because it aims to improve the quality of learning in the classroom, both the process and learning outcomes. The subjects of this study were students of class XII IPA 2 UPT SMA Negeri 16 Bone with a total of 35 students. This research data obtained in the form of data on planning, implementation, and evaluation of research results. The technique used to collect data in this research is observation (observation), documentation, interviews and tests. The data collected were analyzed qualitatively and quantitatively. The results showed that there was an increase in the ability to listen to readings using reading reading media in students of XII IPA 2 UPT SMA Negeri 16 Bone after learning to listen to poetry using poetry reading class media in the first cycle which achieved learning completeness of 42.86% with an average score of 42.86%. average 64.86. These results increased in the second cycle by 97.14% with an average value of 87.00.
Cross-Domain Toxic Spans Detection

Given the dynamic nature of toxic language use, automated methods for detecting toxic spans are likely to encounter distributional shift. To explore this phenomenon, we evaluate three approaches for detecting toxic spans under cross-domain conditions: lexicon-based, rationale extraction, and fine-tuned language models. Our findings indicate that a simple method using off-the-shelf lexicons performs best in the cross-domain setup. The cross-domain error analysis suggests that (1) rationale extraction methods are prone to false negatives, while (2) language models, despite performing best for the in-domain case, recall fewer explicitly toxic words than lexicons and are prone to certain types of false positives. Our code is publicly available at: https://github.com/sfschouten/toxic-cross-domain .
Data Security in Cloud Computing

Due to the growing use of cloud services and the possible hazards involved with storing and processing sensitive data in remote locations, data security is a crucial concern in cloud computing. This literature review analyses the state of the art in data security research for cloud computing and emphasises the major discoveries, difficulties, and recommendations for the future. Encryption, access control, network security, physical security, security monitoring, data segregation, backup and disaster recovery, compliance, and safe development practises are just a few of the data security-related topics included in the assessment. The study notes that a problem is the size and quantity of the body of literature as well as the requirement to stay up with the continuously changing area. The need for thorough coverage, evaluation of source quality and reliability, and overcoming language and accessibility challenges are also mentioned as drawbacks. The review goes into further detail on issues including time limits, subjectivity in selection and interpretation, information overload, and integration and synthesis of findings. It is recommended that future research in data security for cloud computing put a particular emphasis on advanced encryption methods, secure data sharing and collaboration, privacy-preserving methods, cloud forensics, threat intelligence, machine learning, security assurance and auditing, blockchain and distributed ledger technologies, as well as user awareness and education. Data security in cloud computing may be further reinforced to secure sensitive data and guarantee the reliability of cloud services by solving these issues and making progress in the sector.
A Review on Different Techniques for Mining Frequent Patterns in Unordered Trees

Numerous fields, such as bioinformatics, web mining, and social network analysis, now require frequent pattern mining in unordered trees. Finding repeating patterns and substructures inside a collection of unordered trees is the key to unlocking this technique's potential to provide important information about the underlying data. This paper gives a thorough overview of various methods for mining frequent patterns in unordered trees, highlighting their advantages, disadvantages, and practical uses.The review starts out by defining the basic terms and concepts related to frequent pattern mining in unordered trees. The discussion then moves on to a number of widely used algorithms in this setting, such as graph-based strategies, bottom-up tree traversal techniques, and depth-first search-based techniques. Each approach is thoroughly explained, including its underlying concepts, computational complexity, and applicability to different kinds of tree datasets.The paper also examines recent developments in the subject, including distributed frameworks and scalable parallel algorithms for mining common patterns in big unordered tree collections. In order to improve the mining process and the calibre of patterns found, it also looks at the incorporation of extra constraints and measurements like weighted support and tree edit distance.The paper also talks about recent developments in the subject, namely scalable parallel algorithms and distributed frameworks for finding common patterns in enormous unordered tree collections. In order to strengthen the mining process and raise the calibre of patterns found, it also looks at the incorporation of extra restrictions and metrics like tree edit distance and weighted support.
BERT Algorithm used in Google Search

Search engines are now a need for obtaining information due to the internet's explosive expansion in digital material. One of the most widely used search engines, Google, works hard to improve its search functionality. Google has recently used cutting-edge natural language processing (NLP) methods to enhance search results. The Bidirectional Encoder Representations from Transformers (BERT) method is one such ground-breaking invention. This study seeks to offer a thorough evaluation of the BERT algorithm and its use in Google Search. We examine BERT's design, training procedure, and salient characteristics, emphasising its capacity to comprehend the subtleties and context of real language. We also talk about BERT's effects on user experience and search engine optimisation (SEO), as well as potential future advances and difficulties.
Tampering Detection and Content Authentication Using Encrypted Perceptual Hash for Image Database

To detect and locate unapproved alterations or modifications made to digital pictures, tampering detection in images is a crucial study topic in the field of image forensics. As sophisticated picture altering tools become more widely available, it has become more difficult to guarantee the integrity and authenticity of digital visual output. This overview of the literature explores methodology, findings, and contributions in key tampering detection research studies. The breakthroughs in deep learning techniques and steganalysis are highlighted together with more conventional approaches like statistical analysis and error level analysis. The difficulties tampering detection faces are discussed, including generalisation, dataset accessibility, resilience against adversarial assaults, computing effectiveness, and moral and legal issues. With the goal of advancing the creation of precise, reliable, and effective algorithms for maintaining the integrity and authenticity of digital pictures, the paper offers insights into the current state of the art, limits, and future research paths in tampering detection.