Reviewerly/exHarmony huggingface dataset:
Visual Analysis on Adversarial Images

Adversarial Machine Learning (ML) is one of the biggest cyber threats to deep neural networks (DNN). Adversarial samples are crafted or created by an attacker to mislead a DNN model in making a decision. On the presence of adversarial samples in a dataset, an interest is to trace their patterns before designing an effective defense algorithm. In this paper, we propose a hybrid method, i.e. Convo t-SNE where convolution neural network (CNN) is used to extract the features of images and t-distributed Stochastic Neighborhood Embedding (t-SNE) is applied to reduce dimensions so that adversarial attack patterns can be visualized. In our work, three attack methods are applied on three public datasets (i.e., MNIST, CIFAR10 and CIFAR100). The graphical results indicate clearly the presence of attack patterns using Convo t-SNE. The proposed method is then hybrid with k-mean to further evaluate its effectiveness. Two evaluation metrics i.e. classification accuracy and a clustering normalized mutual information (ClustNMI, a metric to compare between two clustering algorithms, the higher the better) are used. The results are encouraging where Convo t-SNE improves the clustering algorithms with a higher classification accuracy and better ClustNMI than t-SNE.
On the Effectiveness of Heterogeneous Ensembles Combining Graph Neural Networks and Heuristics for Dynamic Link Prediction

With the ubiquity of complex networks in domains as diverse as social network analysis, recommender systems and epidemiology, improvements in link predictions have a potential for far-reaching impact. In machine learning, a well-known approach to improve classification performance is to combine several methods in an ensemble. It is also well established that, diversity or complementarity of the classifiers to be combined, is a prerequisite for a potential performance improvement of the formed ensemble. In this work, we combine link prediction heuristics (e.g. Common Neighbour, Adamic-Adar etc.), with static graph neural networks, discrete dynamic graph neural networks, and continuous dynamic graph neural networks. We analyze the importance of each of the methods and how complementary they are with respect to each other. We also use greedy searches to select better subsets of methods to combine and train the ensemble in a roll-forward manner to make it adapt to changes in the dynamic network over time. In the conducted experiments on a number of challenging link prediction tasks, the ensemble from the greedy search and the roll-forward ensemble shows on average a 55% and 61% improvement respectively, over the best single method.
Registration of Medical Image Sequences Using Auto-differentiation

This paper focuses on image registration using the automatic differentiation of deep learning frameworks. Specifically, a method for the registration of image sequences is proposed and tested on retinal video ophthalmoscopic data and brain DCE MR images. PyTorch auto-differentiation has been used as a core of an optimisation tool to find the optimal image transformation parameters. It allows us to easily design a loss function for our registration tasks. The image registration was achieved by simultaneous registration of all images using a global loss function without the need of the reference frame.
The Development of the Touchless Device for Capacitive Touch Panels

Due to the influence of the new coronavirus COVID-19, “social distancing” is necessary, and it is required to reduce contact with people and objects. Even with the widespread distribution of vaccines, “social distancing” and “new normal” have been continuing. On the other hand, at many restaurants and commercial facilities, tablets and touch panels are widely used with the advantage of saving labor costs. Users can select menus or input necessary information themselves. Since they are used by the general public, it is not considered hygienic and desirable from the viewpoint of “social distancing”. Demand for Human Machine Interfaces that users can operate without anxiety is increasing. Therefore, we have developed a touchless device that makes capacitive touch panels operate without using hands. This device realizes the touchless operation by applying the principle of the detective in the capacitive touch panel. This device is composed of an Infrared sensor and conductive, transparent sheets, and does not affect the operativity of using touch panels. In this presentation, we will explain the system overview and operating principle of this touchless device.
Proposal for Shoplifting Detection Using Skeletal Data Assuming Surveillance Camera Footage

Shoplifting causes a decrease in retail store profits and can impact business operations. However, implementing measures to prevent shoplifting, such as installing surveillance cameras and optimizing employee placement, incurs both financial and time costs. In this study, we propose a method to detect shoplifting behavior by obtaining skeletal information from videos resembling surveillance camera footage and applying machine learning using a fully connected neural network. By detecting shoplifting from surveillance camera footage, we can reduce the cost of introducing new cameras, as we can utilize existing ones for shoplifting detection.
Building an Effective API Strategy

An API strategy is about aligning APIs to meet business goals, what technology to use for building secure and scalable APIs, and how to build an engaging ecosystem and grow it with partners and developers using the APIs. Having an API strategy is necessary to reap the business benefits of APIs. This chapter looks at the need for an API strategy, the essential components of an effective API strategy, how to align it to the business objectives, and finally, an approach to build a string API strategy from scratch.
Perancangan Survival Horror Game Out Of Sight Menggunakan Unity 3D Engine

A game is the result of the creative expression of a game maker that has an element of entertainment, can be played and has an end goal. In horror games, it is still divided into several more types of games, one of which is a survival horror game that requires players to live against enemy attacks that come their way. This game was created to enrich horror-themed domestically made games. Designing games using the unity 3D engine with the process of applying the MDLC (Media Develpoment Life Cycle) model. A method suitable for game design, starting with the preparation, design, collection of materials, testing and distribution. Out Of Sight game has gameplay with a first-person perspective combined with the survival horror genre. Made with the C# programming language this game is able to run perfectly with various features. There is also a unique feature, namely slowmotion which serves to slow down time to make it easier for players to shoot enemies. This survival horror game has been successfully created and completed. This game conducts testing using black box testing where all features function normally except for the key that serves to open the door. Based on respondents 18.5% said that the door lock had a bug. This game was created to enrich horror-themed domestically made games.
The Penerapan Strategi Pembelajaran Reading Aloud dalam Meningkatkan Hasil Belajar pada Pembelajaran Tematik Muatan Bahasa Indonesia Siswa Kelas I B MI Negeri 2 Pangkal Pinang

This research is the result of studies and observations of the results of student learning by considering the learning process without using Reading Aloud Learning Strategy with learning using Reading Aloud Learning Strategy. This type of research is classroom action research (Classroom Action Research), using qualitative descriptive approach, this study aims to find out about implementation of Reading Aloud Learning Strategies, and improving learning outcomes students after using the Reading Aloud Learning Strategy on Thematic learning of Indonesian language content on students' reading texts in class I B MI Negeri 2 Pangkalpinang in Pangkalpinang. The data analysis is conducted by describing or explaining conditions associated with research objectives, how to calculate results the value obtained by students during the learning process, percentage and presentation of tables and figures related to variable conditions research such as the average value of students, the number of students who completed, the number of students who did not complete, the percentage of completeness was minimal, the activeness of students following lessons in each cycle. The results of the analysis present an increase in student learning outcomes in Thematic learning of Indonesian language content in reading texts and Cycles I and II using the Reading Aloud Learning Strategy. The results of this study indicate that the implementation of the Learning Strategy. Reading Aloud has been proven to improve student learning outcomes in learning Thematic content of Indonesian in the text reading material. It can be seen from the results of the average student learning, such as cycle I with an average of 65 (46.8 %), cycle II is 78.1 (96.8%).
Analisis Kata Kunci untuk Mendapatkan Konversi Tertinggi dari Platform Google dan Tokopedia

The business world is very closely related to advertising, advertisements in print, electronic and digital media, In advertising on digital media we need keywords as a reference for search engines to find what we want, Targeting the right keywords in articles is very important to help websites easy to find in search engines. However, in choosing these keywords it is often not appropriate or not in accordance with what is desired, where the inaccuracy of the keywords will make the ad not suitable for the site or product being marketed, so that it is not optimal. The first step is to determine the items to be advertised and then look for the right keywords by looking at the Click Through Rate (CTR), which is the ratio of the number of clicks to the number of impressions, then running ads based on the keywords that have been obtained for an item, then analyze the results of running ads. The results of the two platforms between Google Ads and Tokopedia get an increase of visits after running ads, running ads using keywords with high CTR is very influential on visits and sales. It was recorded that during the ads run there were 2 sales that entered Tokopedia with a total of 3 items sold. If the purpose of the ad is for Brand Awareness, it is better to use Google ads to run ads, because the number of impressions of Google ads is better, but if the purpose of the ad is to sell then it is better to use Tokopedia because the number of conversions is more than Google ads.
Title Pending 862

The 2023 Research Data Access and Preservation (RDAP) Summit, Building on Experience: Centering Communities in Data Creation and Access, focused on engagement with and building communities within data environments, including how data is being made more accessible for a wider range of communities. The 2023 RDAP Summit was a natural extension from the prior year’s theme of Envisioning an Inclusive Data Future, which highlighted the ways data service providers tailor their services to address specific needs. A selection of presentations from this year’s Summit were expanded into articles for this special issue covering topics on developing and maintaining communities that address aspects across the research data life cycle.
Pengukuran Indeks Literasi Digital di Perpustakaan Khusus Indonesia

The Ministry of Communication and Informatics has been measuring the level of digital literacy in Indonesia annually since 2020, but the measurement of the digital literacy index does not yet include the scope of libraries. This research focuses on developing a framework for the digital literacy index specifically for libraries, referring to the digital literacy frameworks of UNESCO, ITU, DigComp 2.1, and the DQ Institute. It also measures the digital literacy competencies of librarians and library users in specialized libraries. The objective of this research is to provide recommendations regarding the mapping of digital literacy competencies of librarians and library users based on the measurement results in order to improve the national digital literacy index. Specialized libraries were chosen because the majority of their services are provided digitally, especially after the COVID-19 pandemic. The method used in this study is a quantitative method through surveys conducted with library users and librarians in specialized libraries in Indonesia. The results of this study include a specialized digital literacy index framework for the library environment, as well as the mapping of digital literacy among librarians and library users based on the developed index framework. The recommendations based on the findings provided in the conclusion of this paper emphasize the importance of enhancing librarian competencies through training or workshops, and curriculum adjustments to support the improvement of the digital literacy index for library and information science programs at universities.
The people DataViz’s history ignores: a step forward to an intersectional history of Data Visualization

The history of Data Visualization, like history itself, has been told through the works of a narrow demographic group: white, cisgender men from high-income countries. While Florence Nightingale often features in such historical accounts and some recent literature has branched out to explore contributions from people with diverse gender and ethnical identities, a true feminist, intersectional, and decolonized History of Information Visualization is still missing. This paper attempts to jumpstart the effort of looking at women and people of color in Visualization’s past and examine the oppressive structures that hampered the deserved dissemination of their contributions.
Impact of Social Media on Academic Performance of Pupils. A Case Study of Chipepo Secondary School in Gwembe District

The study was the impact of social media on academic performance of pupils. A case study of Chipepo secondary school in Gwembe District in Southern Province, Zambia. The researcher used the descriptive quantitative research method supported partly by qualitative data. The population for the study consisted of 110 purposively selected grade 12 pupils in one (1) purposively selected secondary school (Chipepo) in Gwembe district in Zambia. A total sample of 40 grade 12 pupils were simple randomly selected using balloting from Chipepo secondary which was included in this study. The instrument used for the study was a thirty (31) item close ended questionnaire and an interview guide for research question 4. The data collected was coded and analyzed using descriptive statistics. Findings from the study revealed that male pupils used social media more than their counterparts the female pupils. Moderate time 61-90 minutes was spent by pupils on social media per day. The use of social media did not distract academic performance of pupils instead it helped them to improve academic performance. Pupils who used social media were middle aged to younger. Google was commonly used by pupils also WhatsApp and Facebook. It was therefore recommended that the Ministry of General Education and head teachers institute incentives that might help to encourage the use of social media in schools.
Quality of e-government services at the population and civil registration office Biak Numfor District

This study aims to examine the quality of e-government services provided by the Population and Civil Registration Office of Biak Numfor Regency. This research is expected to provide valuable input for local governments and can be used as a basis for improving and optimizing services. This research was conducted at the Population and Civil Registration Office of Biak Numfor Regency. The type of research used in this study is qualitative research with a descriptive approach. Data sources in this study are primary data sources and secondary data sources. The informants in this study were service users from the Population and Civil Registration Office of Biak Numfor Regency. Informants from the Population and Civil Registration Office of Biak Numfor Regency were chosen because they had an important role in the service process. As for community informants, service users are chosen because they have information that is about the research topic and can provide more accurate ones. The selection of informants is carried out through purposive sampling techniques.
Documon: an intelligent web platform to identify, recommend, and manage material relevance of research publications

Access to knowledge has been widely broadened and made easy via various new technologies compared to how it was 30 years ago. This includes various sources that provide online access to thousands of research publications. As convenient as it may, dealing with large numbers of these reference materials can be quite a hassle while managing them can be even more so. Even though a combination of utility tools and reference management tools can be used to tackle this issue, it involves an unnecessary amount of manual work and time. The main goal of this project is to take a smarter approach of creating a web application that can be used to provide an interface for researchers to identify the material relevance of a research publication without having to read through the entire thing while also providing a centralized way for managing reference materials via the application itself. This eliminates a very common practice of using different applications such as MS. Word, Excel, and Reference management tools. “Documon” differs from existing solutions including available Reference management tools given its simplicity, automated workflows, “Research on The Go” features as well as the overall application utilizing Natural Language Processing, Web scraping, content suggestions and Information extraction using document patterns in PDFs.
Cyber security and information: a systematic literature review

The present investigation examine the worldwide progression of Cyber Security as a subject of research and detects a recent decline in published research subsequent to a noteworthy surge in 2017. The findings of the study indicate that there is a need for scholars to undertake more extensive research on cybersecurity and leverage bibliometric data to obtain more profound understandings of Cyber Security. The concept of cybersecurity encompasses a range of strategies and techniques that are employed to protect sensitive data from unauthorised access, theft, or damage. These measures typically involve proactive measures to prevent attacks, as well as reactive measures to detect and respond to security breaches. The present investigation undertook an extensive review of the literature utilizing bibliometric analysis to examine the scholarly works pertaining to Cyber Security.
Interval graphs for Genome-Fragment combinations

The main purpose of this research work is to apply the theory of "interval graphs" to a specific viral genome, reconstruct the sequence from the unknown-ordered fragments for possible combinations for further studies. We use the restriction digestion method to cut the chromosome into many segments with several restriction enzymes. These cut segments can be separated by agarose gel electrophoresis according to their molecular size and are not in order of the original sequence. Besides, the segments cut out by each enzyme are different, so there will be intersections (sub-segments). The method of interval graph, suggested by14, attempts to use the information at the intersection to reorganize the original sequence. We selected our own patented insect viral gene sequence and conducted research on the above-mentioned interval graph method. More specifically, the isolated recombinant baculovirus, tentatively named ABM, and the widelyused prototype of baculovirus species, which is Autographa californica multiple nucleopolyhedrovirus (AcMNPV), were used as the model system. Although the length of the insect viral genome sequence we used is relatively short as compared with the genome of organisms, this study failed to achieve the purpose of sorting by the method of interval graph. The two main reasons can be summarized as follows: First, the enzyme experiment detected multiple short segments that did not intersect with other segments, and the interval graph method could not determine their positions in the original sequence. Secondly, any "graph” is made up of vertices and edges. Representing gene sequences with interval graphs means translating segments and intersecting segments with vertices and edges. Even if the improved method was adopted, the number of vertices and edges that can be loaded by the existing hardware makes this research unable to complete all the steps of the basic interval graph construction. This paper illustrates the experimental methods, mathematical procedures, relevant data and details, and provides experiences and references for scholars who are interested in researching this topic to seek breakthroughs in key steps.
An approach in applying software quality assurance in academic application: case study of student information system - International Student Module (ISM)

Software Quality Assurance (SQA) is a way to verify quality in the software. It is the set of activities which make certain processes, procedures as well as standards fit for the project and implemented appropriately. SQA is a process which works parallel to the development of software. It concentrates on improving the software development process so problems can be avoided. SQA is an activity that is applied throughout the software process, which will bring value to the project by saving money and time, implementing stable, competitive, and safe product, building constant process, helping to meet client expectations, and will create good developer’s reputation among the users. SQA can give significant impact to the software project in an academic environment with high defect removal efficiency which decreases the cost of software development and satisfy the users and target customers with respective to their expectations. Based on the implementation of SQA in International Student Module (ISM), the result showed that the error could be minimized beside reduced time effort in developing the software.
Deep learning approaches to SQL injection detection: evaluating ANNs, CNNs, and RNNs

In the digital era, SQL injection (SQLi) attacks on web applications pose significant threats to data integrity and security. While traditional methods such as signature-based and anomaly-based detections have some limitations, this research explores the application of neural networks in countering these attacks. Specifically, this research evaluates the performance of three primary neural network architectures: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for SQLi attack detection. The research methodology involves converting text-based SQL queries into numeric values suitable and compatible with the neural networks, using Term Frequency-Inverse Document Frequency (TF-IDF), tokenization, and padding. Results show that the CNN outperforms in almost all metrics, with RNNs following closely and ANNs achieving the lower results.