Reviewerly/exHarmony huggingface dataset:
A Set of Usability Constructs and Indicators for UI/UX Research on Mobile Food Ordering Applications in India

The m-commerce industry has seen a significant growth in the recent times in India. One particular area is the mobile food delivery ecosystem in India. The appearance and usability of the interface for these mobile food ordering applications (MFOAs) play a crucial role in driving their usage. A successful UX research and usability testing of mobile applications ensure better business outcomes. In this study, UX/UI usability constructs along with a set of indicators that define each of them specific to MFOAs, are proposed, tested and validated for usage in usability testing. By conducting extensive surveys, the constructs and indicators have been tested and validated for their reliability. They may serve as a general guideline for designing user interfaces for MFOAs and conducting user experience research.
Unveiling the Landscape of Operating System Vulnerabilities

Operating systems play a crucial role in computer systems, serving as the fundamental infrastructure that supports a wide range of applications and services. However, they are also prime targets for malicious actors seeking to exploit vulnerabilities and compromise system security. This is a crucial area that requires active research; however, OS vulnerabilities have not been actively studied in recent years. Therefore, we conduct a comprehensive analysis of OS vulnerabilities, aiming to enhance the understanding of their trends, severity, and common weaknesses. Our research methodology encompasses data preparation, sampling of vulnerable OS categories and versions, and an in-depth analysis of trends, severity levels, and types of OS vulnerabilities. We scrape the high-level data from reliable and recognized sources to generate two refined OS vulnerability datasets: one for OS categories and another for OS versions. Our study reveals the susceptibility of popular operating systems such as Windows, Windows Server, Debian Linux, and Mac OS. Specifically, Windows 10, Windows 11, Android (v11.0, v12.0, v13.0), Windows Server 2012, Debian Linux (v10.0, v11.0), Fedora 37, and HarmonyOS 2, are identified as the most vulnerable OS versions in recent years (2021–2022). Notably, these vulnerabilities exhibit a high severity, with maximum CVSS scores falling into the 7–8 and 9–10 range. Common vulnerability types, including CWE-119, CWE-20, CWE-200, and CWE-787, are prevalent in these OSs and require specific attention from OS vendors. The findings on trends, severity, and types of OS vulnerabilities from this research will serve as a valuable resource for vendors, security professionals, and end-users, empowering them to enhance OS security measures, prioritize vulnerability management efforts, and make informed decisions to mitigate risks associated with these vulnerabilities.
The Effect of Open Innovation on Firm Performance: Research Gaps

Open innovation can be considered as the critical branch of innovation management. It is an emerging field of research that is important for achieving sustainable competitive advantages through enhancing firm performance. This study aims to identify the research gaps in the effect of open innovation and firm performance based on the systematic literature review process. Depending on the systematic approach current study expects to find out research gaps and it targets to develop a conceptual framework to minimize the identified research gaps in the effect of open innovation and firm performance for future empirical investigations. An extensive literature review was implemented to achieve the research objectives through the adaptation of the desk research strategy. The systematic literature review process explored the effect of open innovation and firm performance was not entirely positive. Further, it discovered the three research gaps between open innovation and firm performance mainly. A conceptual framework was developed to minimize the identified research gaps by identifying a new variable to the established relationship. The literature survey and logical arguments provide a solid foundation to build up the conceptual framework. It was proposed to be tested empirically in the research context of listed companies in Sri Lanka by future researchers.
Extrema Graphs: Fitness Landscape Analysis to the Extreme!

Fitness landscape analysis often relies on visual tools to provide insight to a search space, allowing for reasoning before optimisation. Currently, the dominant approach for visualisation is the local optima network, where the local structure around a potential global optimum is visualised using a network with the nodes as local minima and the edges as transitions between those minima through an optimiser. In this paper, we present an approach based on extrema graphs, originally used for isosurface extraction in volume visualisation, where transitions are captured between both maxima and minima embedded in two dimensions through dimensionality reduction techniques (multidimensional scaling in our prototype). These diagrams enable evolutionary computation practitioners to understand the entire search space by incorporating global information describing the spatial relationships between extrema. We demonstrate the approach on a number of continuous benchmark problems from the literature and highlight that the resulting visualisations enable the observation of known problem features, leading to the conclusion that extrema graphs are a suitable tool for extracting global information about problem landscapes.
Suspicious Object Tracking by Frame Differencing with Backdrop Subtraction

In order to prevent terrorism, tracking of objects by stationary surveillance cameras is frequently employed for security in public spaces including railway stations, airports, parking lots, and public transit. Many applications for accurate object detection in visual scenes may be found utilizing various vision algorithms. In this paper, we describe a model for monitoring many objects simultaneously with the identification of unclaimed luggage in a real-time setting. I recreated the backdrop scene from the original frame in this model. After that, we detected and followed moving items like people and parcels using a background-subtracted motion model. The suggested approach additionally records the past positions of moving items and then employs frame differentiation methods to track down past packages and identify any that were dropped by people The suggested system was tested in several indoor and outdoor situations with diverse illumination conditions using the PETS 2006 and PETS2007 datasets as well as. The real-time system was also run on a platform called MATLAB Simulink.
Voice Deepfake Detection Using the Self-Supervised Pre-Training Model HuBERT

In recent years, voice deepfake technology has developed rapidly, but current detection methods have the problems of insufficient detection generalization and insufficient feature extraction for unknown attacks. This paper presents a forged speech detection method (HuRawNet2_modified) based on a self-supervised pre-trained model (HuBERT) to improve detection (and address the above problems). A combination of impulsive signal-dependent additive noise and additive white Gaussian noise was adopted for data boosting and augmentation, and the HuBERT model was fine-tuned on different language databases. On this basis, the size of the extracted feature maps was modified independently by the α-feature map scaling (α-FMS) method, with a modified end-to-end method using the RawNet2 model as the backbone structure. The results showed that the HuBERT model could extract features more comprehensively and accurately. The best evaluation indicators were an equal error rate (EER) of 2.89% and a minimum tandem detection cost function (min t-DCF) of 0.2182 on the database of the ASVspoof2021 LA challenge, which verified the effectiveness of the detection method proposed in this paper. Compared with the baseline systems in databases of the ASVspoof 2021 LA challenge and the FMFCC-A, the values of EER and min t-DCF decreased. The results also showed that the self-supervised pre-trained model with fine-tuning can extract acoustic features across languages. And the detection can be slightly improved when the languages of the pre-trained database, and the fine-tuned and tested database are the same.
Human Detection and Tracking Based on YOLOv3 and DeepSORT

This paper proposes an enhanced object tracking architecture based on YOLOv3 and DeepSORT. YOLOv3 is used for object detection, but in this case, we have only selected the human class. For object tracking, the DeepSORT, Kalman filter, and Hungarian algorithm are used. The occlusion issue is solved using the Kalman filter. The parallel approach is used in the suggested architecture to increase the base method's speed. In a parallel approach, the input frame is fed simultaneously to the object detector and the object tracker, enabling the tracker to begin following a person as soon as the object detector recognizes them. Here, occlusion and speed are the two issues that we are concentrating on. The experimental outcomes using specific data demonstrate positive outcomes with faster operation.
Embeddings-Based Parallel Corpus Creation for English-Manipuri

Lack of parallel corpus is one of the main roadblocks for neural machine translation in Low-Resource languages. Collecting parallel sentences corpus from web are noisy and requires alignment. This work attempts to create an English-Manipuri parallel corpus crawled from ‘Maan Ki Baat’ translated data by devising an efficient English-Manipuri automatic sentence aligner based on embeddings. It helped reduce the manual effort of aligning noisy parallel corpus by 47.72%. The work also studies the sentence length correlation between the two languages. As a final output, an English-Manipuri parallel corpus is created which is ready to use for neural machine translation works.
Penjadwalan Mata Pelajaran Menggunakan Algoritma Particle Swarm Optimization (PSO) Pada SMPIT Mufidatul Ilmi

Scheduling has a division of time based on a work sequence arrangement plan in the form of a list or table of activities or an activity plan with a detailed division of implementation time which is very necessary in carrying out institutional/company business processes. It is important to note the complexity of the process in scheduling appropriate subjects from various perspectives, both teachers, students and classrooms. Provision of teacher teaching schedules based on abilities in the field of subjects, suitable time each semester is very important to consider for very complex schedule arrangements, the number of classrooms that can be used in teaching activities is relatively small, and preventing teacher teaching conflicts so that the need for optimization of eye scheduling lesson to be made. Furthermore, at the stage of application development using the Waterfall method. The purpose of this research is to build a lesson scheduling application at SMPIT Mufidatul Ilmi by applying the particle swarm optimization (PSO) algorithm to compile lesson schedules. Particle Swarm Optimization is a population-based algorithm that exploits individuals in search. In PSO the population is called a swarm and individuals are called particles. Each particle moves at a speed adapted from the search area and stores it as the best position ever achieved. Design analysis includes Use Case Diagrams, Activity Diagrams, Class Diagrams, Sequence Diagrams, Entity Relationship Diagrams (ERD). The results of this study provide several primary data (service) features, especially features to provide scheduling results from processing with the PSO algorithm
AI-Driven Bilingual Talkbot for Academic Counselling

Traditional technologies are transforming how businesses interact with their customers, creating demands for prompt responses & continuous accessibility. Students frequently have questions about college & university policies & procedures, academic process, extracurricular, as well as other aspects of educational career. Actually, there is a lack of service satisfaction because the educational advisers as well as the student affairs staff are overloaded with inquiries & unable to respond to them right away. In our work, we develop a multilingual talkbot that converses among learners in both English & Arabic using Artificial Intelligence (AI) & Natural Language Processing (NLP) technologies. The dialog bot was created in Python & is intended for use by students to answer questions about advice. We utilizing a intent built domain-specific compilation as the knowledge source for the talkbots, which is made up of the typical queries students ask advisers & their answers. By analysing the inputs, the talktbot engine ascertains the client's needs and, with an efficiency of 80.00 % in English and 75.00 % in Arabic, finds the most reasonable answer that meets the intention. We also tested the software solution as well as the talkbot's precision using field tests with pupils to see how well it responded to live input.
A Study Using OWASP On Secure Open Banking Architecture

Consumers have had greater control over their personal financial data since the Data Protection Rule of the European Union was put into effect in 2015. This occurred in 2015. As a direct result of this, "Open Banking" has developed, which provides 3rdparty organizations and software designers with Usage of a banking applications programming interfaces (APIs). This has resulted in an abundance of new approaches to the economic industry. However, a range of cybersecurity vulnerabilities exist when a bank makes its data available to 3rdparty vendors via an application programming interface (API). This paper presents a technology stack that consists of the segments and sub-Flask, the cloud-based system Heroku, and the continuous digital storage layer. These components work together to ensure that there is a proper division of responsibilities and that the procedure of integrating using Nordea's New Financial services APIs (sandbox edition) is as smooth as possible. We investigate the website service vulnerabilities by using the OWASP Top 10, which is a a ranking of the 10 biggest severe cyber risks. Based on our findings, we determine whether the technological framework provides sufficient strong security. Our findings not only helped in the description and creation of Nordea's APIs, but they may also help future developers and businesses that are working on web applications for modern banking improve their level of protection by enabling them to select the appropriate frameworks and considering the most significant threats. This would allow them to improve the level of protection offered by their applications.
Multi-Scale Feature Residual Feedback Network for Super-Resolution Reconstruction of the Vertical Structure of the Radar Echo

The vertical structure of radar echo is crucial for understanding complex microphysical processes of clouds and precipitation, and for providing essential data support for the study of low-level wind shear and turbulence formation, evolution, and dissipation. Therefore, finding methods to improve the vertical data resolution of the existing radar network is crucial. Existing algorithms for improving image resolution usually focus on increasing the width and height of images. However, improving the vertical data resolution of weather radar requires a focus on improving the elevation angle resolution while maintaining distance resolution. To address this challenge, we propose a network for super-resolution reconstruction of weather radar echo vertical structures. The network is based on a multi-scale residual feedback network (MR-FBN) and uses new multi-scale feature residual blocks (MSRB) to effectively extract and utilize data features at different scales. The feedback network gradually generates the final high-resolution vertical structure data. In addition, we propose an elevation upsampling layer (EUL) specifically for this task, replacing the traditional image subpixel convolution layer. Experimental results show that the proposed method can effectively improve the elevation angle resolution of weather radar echo vertical structure data, providing valuable help for atmospheric detection.
Social Media Content Recommendation System using Knn Algorithm

Online streaming platforms for YouTuber short films are now widely used. Sites like YouTube Shorts provide an excellent platform for content creators to share their knowledge, ideas, and interesting information with their audience. There are no limits to creativity in the world of short videos. Billions of people create, edit, watch and share short videos on YouTube Shorts. Whether you want to share your voice with the world or create fun videos for your friends, all you need is your phone and your ideas. Machine learning algorithms are used to analyze large amounts of user behavior data such as: B. Search, watch videos, like, comment, subscribe. By analyzing this data, recommendation systems can predict user preferences and recommend videos that users are likely to like. Companies are using social media to grow their business, and data mining and analytics are very important today. This article describes trend analysis for short videos on YouTube. Creators are encouraged to create compelling video content. These user characteristics such as views, comments, likes and dislikes are used for analytics. Analytics can use this algorithm as a supervised algorithm to classify other machine learning models and filter based on content.
Improving Writing a Descriptive Paragraph of Elementary School Students through Picture-Based Mind Mapping

The skill of writing descriptive paragraphs is one of the lessons that are difficult for elementary school students to understand. This study aims to analyze the effect of media image-based mind mapping on the skills of writing descriptive paragraphs of elementary school students. This study used quantitative research with a quasi-experimental design method with a Non-equivalent control group type. In this study, two groups were not randomly selected. Both groups were given a pretest to determine the initial state. The experimental group was treated with image media-based mind mapping, while the control group was image-based. Data collection techniques using observation and test sheets writing descriptive paragraphs. Assessment of the quality of writing descriptive paragraphs is based on focus, development, unity, coherence, and correctness. The collected data will be analyzed through two analyzes, namely descriptive statistical analysis and inferential statistical analysis. The results of the study show that image media-based mind mapping has a significant effect on improving the skills of writing descriptive paragraphs in elementary school students. Image media-based mind mapping inspires students to write because it can develop and describe the ideas they want to write in a clearer and well-structured manner.
The Traditional Game of Throwing Sandals in Learning Narrative Writing for Elementary School Students

Writing is a very important language skill in school. However, there are still many students who need help with writing. This study analyzes learning to write narratives by applying the traditional game of throwing sandals. The research method used is descriptive qualitative. The research subjects were 18 elementary school students. Data were collected through observation, questionnaires, and story-writing skill tests; After that, the data was evaluated using data reduction, data display, and verification methods. The results showed that learning to write narratives using the traditional game "Throw Sandals" impacted student activity, namely an average score of 3.7 and above, and students were enthusiastic about learning. Judging from the student's response to the traditional game of throwing sandals, an average score of 3.8 was obtained, and the average score for students' narrative writing ability was 82.4. These findings conclude that learning to write narratives using the traditional sandal-throwing game method can be successful, and students can easily express ideas from the experience gained. The traditional game of throwing sandals makes children interact with each other and can help shape and maintain a child's social spirit. Traditional games can encourage students' thinking to be more creative and increase self-confidence. Based on the research results, it can have implications for the quality of learning and the introduction of Indonesian culture to students.
Android-Based Interactive Learning Multimedia: Social Studies Material for Fourth Grade Elementary School Students

Today's learning media have yet to facilitate students' involvement in the learning process fully. This study aims to develop and analyze the effectiveness of Android-based interactive learning multimedia on economic activity material and its relationship to various fields of work and social and cultural life in the environment around elementary school students. The development model used is the Borg and Gall model with 10 development steps. The research subjects used 86 students and 4 grade IV elementary school teachers. The trial was conducted twice, and the operational or effectiveness test used a quasi-experimental design with a nonequivalent control-group design. Data was collected using interviews, questionnaires, scales, and tests. The data analysis technique used is qualitative and quantitative using the T-test. The results of this development research show that the developed Android-based interactive learning multimedia meets the eligibility criteria of "very feasible" with a score of 91 from the material expert and a score of 102 from the media expert, through the results of field trials fulfilling the practicality criteria of "very practical". The t-test analysis results show a significant difference in social studies learning outcomes of students who use Android-based interactive multimedia learning. It was concluded that multimedia based on Android is feasible, practical, and effective for fourth-grade elementary school students' social studies learning.
Bilateral Adversarial Patch Generating Network for the Object Tracking Algorithm

Deep learning-based algorithms for single object tracking (SOT) have shown impressive performance but remain susceptible to adversarial patch attacks. However, existing adversarial patch generation methods primarily focus on generating patches within the search region, neglecting the incorporation of template information, which limits their effectiveness in carrying out successful attacks. There is also a lack of evaluation metrics to assess the patch’s adversarial abilities. In this study, we propose a bilateral adversarial patch-generating network to address these limitations and advance the field of adversarial patch generation for SOT networks. Our network leverages a Focus structure that effectively integrates both template and search region information, generating separate adversarial patches for each branch. We also introduce the DeFocus structure to solve the size discrepancy between the template and search region of the tracking network. To effectively mislead the tracking network, we have designed adversarial object loss and adversarial regression loss functions tailored to the network’s output. Moreover, we propose a comprehensive evaluation metric that measures the patch’s adversarial ability by establishing a relationship between the relative patch size and attack performance. As UAV view data often constitute small objects requiring smaller patches, we evaluate our approach on the UAV123 and UAVDT datasets. Our evaluation encompasses not only the overall attack performance but also the effectiveness of our strategy and the transferability of the attacks. Experimental results demonstrate that our algorithm generates patches with higher attack efficiency compared to existing methods.