Reviewerly/exHarmony huggingface dataset:
An Insight into Image Steganography for Medical Informatics

Information security has become one of the major challenges in the field of medical informatics where a minute tempering in the digital footprint can cost a patient's life. Image steganography is one of the important techniques used. It is superior to cryptography since the unauthorized user is ignorant of the existence of confidential messages encapsulated in cover media and thus augmenting the concept of security. This chapter overviews the challenges in the data-hiding paradigm, the methodologies needing to be overcome, and the comprehensive comparative analysis of various algorithms in the field of medical image steganography.
The effect of discovery learning based Google Classroom on information literacy for 11th grade student high school in biology learning during a pandemic

Information literacy is important in biology learning, especially during the current pandemic. The purpose of this study, among others, is to determine the effect of Discovery Learning-based Google Classroom on the biological information literacy of 11th grade student high school during the pandemic. The research method uses a quasi-experimental research design with a non-equivalent control group design. The research subjects were students of 11th grade student MIPA 3 (control class) and 11th grade student MIPA 5 (experimental class) at Senior High School 1 Gambiran. Data was collected by using pretest and posttest in both classes. Students' information literacy was measured through a pretest and posttest which was measured using the AACU's information literacy assessment rubric. The results showed that there was an effect of Discovery Learning based Google Classroom on biological information literacy. It can be seen in the increase in information literacy in the experimental class which is taught by discovery learning based on Google Classroom by 72.59% and in the control class which is taught by conventional learning models (lectures) which is 33.57%.
Empirical analysis of webometric ranking in Nigeria polytechnics education sector

The January 2022 edition of webometric ranking placed Yaba College of Technology as number one from 152 polytechnics in Nigeria. The ranking weight is 66 for country ranking, 8162 world ranking, Impact, Openness, and Excellence of 9698, 4558, and 7190 respectively. The negative variation and low webometric ranking of Yaba College of Technology that happened to be the first higher institution of learning in Nigeria with the slogan the first and still the best is a point of concern and motivates this research work. This research work collected data to evaluate the indicators for webometric ranking among the students and staff of Yaba College of Technology, a total of 346 were sampled students 44.51 % and Staff 55.49 %. The discussion and analysis of data obtained revealed that the poor webometric ranking is due to inadequacy of the necessary ICT infrastructure to encourage robust web presence; non-availability of up-to-date and scanty content on the Polytechnics website; Non-frequent usage of the Polytechnic website by the staff and students of the Polytechnic; the inadequate number of external networks (subnets) links with Polytechnic website; insufficient number of the top-cited publications in high impact Journals from the staff of the Yaba College of Technology; and Scanty number of the profile of staff from the Polytechnic on Google Scholar and ResearchGate, etc. among others. This research work opined that low webometric ranking could result in the following negative impact on the polytechnics lowering the esteem of the Polytechnic in the eyes of stakeholders, potential students and funding agencies, academic exchange with reputable institutions from other parts of the world for teaching, learning and research may writhe. The consequence of our findings recommendations was made to improve webometric ranking in future.
Studying the Impact of Administrative Empowerment As a Mediating Variable in the Relationship between Organizational Commitment and Administrative Creativity: A Field Study in the Jordanian Pharmaceutical Companies in Light of Covid - 19

Managerial creativity enhances the distinction of any organization compared to other organizations due to its ability to find suitable solutions for the problems facing the organization and due to its ability to improve new products and services or through improving working methods or styles in it. Therefore, this topic has occupied a large area of interest in literature review. However, there are some aspects which are still ambiguous about the impact of each of the organizational commitment and administrative empowerment on one hand and administrative creativity on the other hand, especially in light of the COVID-19 pandemic which the world is suffering from nowadays. Accordingly, this study aims to investigate the impact of organizational commitment, with its various dimensions, on administrative creativity within the Jordanian pharmaceutical companies with the existence of administrative empowerment as a mediating variable. Data of the study was collected using a simple random sample which consisted of (201) employees working in the Jordanian pharmaceutical companies. Structural equation modeling (SEM) analysis was conducted using AMOS.20. &#x0D; The study found that there are strong correlation relationships between most of the study variables in addition to the existence of a significant impact of administrative empowerment on administrative creativity as a dependent variable. The (direct and indirect) results of the total impact analysis revealed that there is an impact of all dimensions of organizational commitment on administrative creativity with the existence of administrative empowerment as a mediating variable. Based on these results, the study recommended that Jordanian pharmaceutical companies conduct workshops, seminars and training programs to teach their employees the importance of organizational commitment and occupational empowerment and their role in enhancing administrative creativity. In addition, the investigated companies need to pay attention to involving their employees in setting goals, formulating policies and programs, participating in decision-making processes and spreading a culture of teamwork by providing incentives of various kinds, which contributes to enhancing the levels of organizational commitment and administrative creativity alike, especially in light of Covid-19 pandemic.
ANALISIS PEMAHAMAN GURU DAN SISWA PADA KELAS RENDAH TERHADAP PEMBELAJARAN STEAM DI SD NEGERI SODONG 02 BATANG

The purpose of this study was to find out how the understanding of teachers and low class students towards STEAM learning at SD Negeri Sodong 02 Batang. The data obtained were processed using qualitative descriptive techniques using data triangulation consisting of interviews, observations, and questionnaires. The understanding of teachers and low class students towards STEAM learning at SD Negeri Sodong 02 Batang has been carried out well, but has not been maximized in student questionnaire activities. The results of the observation that the average percentage of teachers' understanding of STEAM learning is 93.75% in the very good category, and students in the low class is 75% in the good category. Then, the results of the questionnaire, the average percentage of teachers' understanding of STEAM learning is 91.6% in the very good category, and students in the low class is 42.55% in the sufficient category. Based on data analysis and discussion that STEAM (Science, Technology, Engineering, Art, and Mathematics) learning is able to increase student activity in teaching and learning activities.
D-mapping: A knowledge-based method for requirements mining and concept design

To support automatic requirements mining and concept design for engineering designers, we proposed a knowledge-based method called D-mapping (Design Mapping). In our method, the design problem-solving processes are represented as mappings between functional and physical domains. For requirements mining, the earliest process of concept design, mappings take place inside the functional domain, and thus it is a self-mapping process. For physical architecture design, the later process of concept design, mappings take place between the functional and physical domains, and thus it is a cross-domain mapping process. We built a knowledge-based expert system to assist designers in those processes. The knowledge base of our expert system is based on amounts of Chinese patent texts and built using natural language processing technologies. The knowledge inference engine of our expert system is constructed based on various machine learning algorithms. The proposed method and the system can be proved to be novel and effective in case studies.
EmbeddingROUGE: Malay News Headline Similarity Evaluation

The evaluation metric is an essential part to evaluate text generation tasks such as news headline generation. The ROUGE evaluation metric is still the standard evaluation metric for most text summarization tasks. Nevertheless, this metric has its weakness due to its nature of measuring exact lexical overlapping between the candidate text and reference text. In this paper, we would like to attempt to propose a word embedding-based ROUGE named EmbeddingROUGE. The proposed design of this embedding-based evaluation metric aim to overcome the weakness of the ROUGE metric. Generally, the experiment shows that the EmbeddingROUGE demonstrates superior evaluation score over the original ROUGE.
Dolphin: A Cellular Voice Based Internet Shutdown Resistance System

Traditional censorship revolves around blocking access to some websites (or services) over the Internet. However, recently there has been a rise in the events of an extreme form of censorship viz., deliberate Internet shutdown, leading to complete Internet disconnection, severely impacting lives in such regions. Naturally, these shutdowns render all existing circumvention schemes unusable. Thus, we present Dolphin, a first of its kind system that can provide access to lightweight and delay tolerant Internet applications (email, tweets, news snippets, etc.) during Internet shutdowns. Dolphin uses the cellular voice channel to transmit data bits. A user in the shutdown region (who wishes to access these applications) requires a peer in non-shutdown region to send and retrieve content on its behalf. The data bits between the peers are sent by first encoding them into audio and then transmitting them over a cellular voice call. We overcome multiple challenges while designing and implementing Dolphin. E.g., the cellular voice channel is inherently lossy and unreliable. But the Internet applications need reliable transfers. Thus, in Dolphin we develop a TCP-style reliability layer to overcome the losses that works atop any underlying encoding and modulation scheme. Further, to evade eavesdroppers over the insecure voice channel, we provide end-to-end confidentiality. Also, Dolphin can function even without human intervention, by using cellular voice automation services. We experimentally show that Dolphin works for Internet applications, by testing it for sending email, tweets and accessing news snippets. All these applications take a few minutes to be accessed (e.g., a 500 character email was received in under 2 minutes).
PELATIHAN ENGLISH SPEAKING DAN RETORIKA KOMUNIKASI MELALUI SPEECH DAN DEBATE BAGI SISWA DI SMK

SMK Bisa, SMK Hebat, and SMK Siap Kerja are the mottos that are often heard today as motto in SMK. Vocational High Schools are specifically directed to master a vocational from an early age so that Vocational High School graduates have specific abilities and can be competitive in the world of work. The current world of work also does not only master on hard skills, communication plays an important role for these students to support the vocational soft skill abilities. English as a medium of communication is also the most important role in the world of work. Moving on from the need to master communication with English media, the team of this community training took the initiative to go down to school to share their knowledge, especially in terms of English speaking. In communication skills are also needed to build an interesting atmosphere. Therefore, the rhetorical communication is needed as a way to build interesting atmosphere in speaking. Moving on from these problems the community team collaborated with language and social science experts implemented this activity by sharing knowledge toward vocational school in Batam. This training activity applied the community education with training and practice course technique. The training provided is oriented towards improving speaking and communication rhetoric skills through speech and debate methods. The results of this activity showed that students begin to enjoy practicing communication as speech speakers or debate speakers. Through this method, students felt more confident to speak English because they talk about contemporary topics and are close to the world of teenagers themselves.
Automatic Sequential Stitching of High-Resolution Panorama for Android Devices Using Precapture Feature Detection and the Orientation Sensor

Image processing on smartphones, which are resource-limited devices, is challenging. Panorama generation on modern mobile phones is a requirement of most mobile phone users. This paper presents an automatic sequential image stitching algorithm with high-resolution panorama generation and addresses the issue of stitching failure on smartphone devices. A robust method is used to automatically control the events involved in panorama generation from image capture to image stitching on Android operating systems. The image frames are taken in a firm spatial interval using the orientation sensor included in smartphone devices. The features-based stitching algorithm is used for panorama generation, with a novel modification to address the issue of stitching failure (inability to find local features causes this issue) when performing sequential stitching over mobile devices. We also address the issue of distortion in sequential stitching. Ultimately, in this study, we built an Android application that can construct a high-resolution panorama sequentially with automatic frame capture based on an orientation sensor and device rotation. We present a novel research methodology (called "Sense-Panorama") for panorama construction along with a development guide for smartphone developers. Based on our experiments, performed by Samsung Galaxy SM-N960N, which carries system on chip (SoC) as Qualcomm Snapdragon 845 and a CPU of 4 × 2.8 GHz Kyro 385, our method can generate a high-resolution panorama. Compared to the existing methods, the results show improvement in visual quality for both subjective and objective evaluation.
PACKAGE RECEIVER BOX BASED ON IOT USING FUZZY MAMDANI AND MOBILE APPLICATION

The development of information technology is currently growing, where now everything has been transformed digitally. Consumers today to shop no longer need to come to the market. Consumers shop through e-commerce applications and just wait for the goods between the couriers from the expedition to arrive at their doorstep. Based on a survey conducted on March 10, 2021, with 100 respondents, it was concluded that 62% of the problems experienced were the process of receiving packages and security risks during package delivery, where the courier did not find the owner of the package at home. Answering the IUI problem requires an IoT-based package receiving box that helps make it easier for couriers to send goods and helps package owners to monitor packages that have been delivered by couriers. This package recipient box is accompanied by an ESP32 Cam camera module to scan the QR code receipt number, ESP8266 helps connectivity and android-based mobile applications with a firebase real-time database to make it easier for package recipients to monitor packages. In helping to facilitate decision making, Mamdani fuzzy logic is used. From the research experiment, it was found that the system can carry out the package storage process with an average time of 11 seconds and the time required to update to firebase is around an average of ±3 seconds.
Detecting Hand Gestures Using Machine Learning Techniques

The hand gesture recognition concept has recently been recognized as an essential part of the human-computer interaction (HCI) concept.Detecting and interpreting hand gestures is a very important topic.This is due to the intense desire to make communication between humans and the calculator or other device natural, away from wires, mouse, keyboards, and others.This recognition makes it possible for computers to capture and understand hand motions.Hand gestures are an important kind of nonverbal communication for a variety of reasons, including their usage in a variety of medical applications, communication between people who are hearing impaired, and robot control.Given the importance of applications for hand gesture recognition and technological progress in today's world, the purpose of the research is to shed light on the most important stage in hand gesture recognition, which is the process of detection and identifying hand gestures in the general sense; segmenting the image to obtain hand gestures before entering them into the feature extraction stages and classification.Six commonly used image segmentation methods were tested on a set of American Sign Language images in a variety of lighting conditions.When compared to the clustering and Otsu methods, the best segmenting results in terms of accuracy were obtained using the Canny and HSV color spaces.
Object based Forgery Detection in Surveillance Videos using Optimized CNN

The mass consumption of digital multimedia content is the most accepted and user-friendly presently. This digital interactive environment is based upon images, text, video, audio, etc. The advancement in technology also raises the risk of the fabrication of original content and generating fake content. A video is considered the most trust-able source for an individual, but advanced tools can easily forge the video content. To maintain the authenticity of the video, a scientific approach is required. In this paper, we proposed a pixel-based motion residual technique to uncover the traces of forgery in surveillance videos, followed by an optimized CNN with 92% accuracy on testing. The novelty of proposed network is the optimization in terms of trainable parameters are reduced, network’s complexity is very much reduced, fast operating and computationally cheap without compromising the accuracy.
Detect Your Fingerprint in Your Photographs: Photography-based Multi-Feature Sybil Detection

A darknet market is an online marketplace typically implemented over Tor, where vendors sell illegal products or criminal services. Due to dramatic growth in the popularity of such markets, there is a recognized need for automatic investigation of the market’s ecosystem and identification of anonymous vendors. However, as they often create multiple accounts (or Sybil accounts) within or across different marketplaces, detecting Sybil accounts becomes the key to understanding the ecosystem of darknet markets and identifying the actual relationship between the vendors. This study presents a novel Sybil detection method that extracts multiple features of vendors from photographs in a fine-grained level (e.g., image similarity, main category, subcategory, and text data), and reveals the multiple Sybil accounts of them simultaneously. Each feature is extracted from multiple rich sources using an image hash algorithm, Deep Neural Network (DNN) classifier, image restoration, and text recognition tool; and merged using a weighted feature embedding model. The matching score of each vendor is then calculated to identify not only the exact Sybil accounts, but multiple potential accounts suspected of being associated to a single operator. We evaluate the efficacy of our method using real-world datasets from four large darknet markets (i.e., SilkRoad2, Agora, Evolution, Alphabay) from 2014 to 2015. Because of the anonymity of darknet market, we construct the ground-truth of Sybil accounts by randomly splitting the dataset of vendors into two even parts. We used the first set to train the model, and linked the second set to the original vendor in the first set to evaluate performance. Our experimental results demonstrated that the proposed method outperforms the existing photography-based system with an accuracy of 98%, identifying up to 700% more candidate Sybil accounts than prior work. Additionally, our method detects multiple Sybil accounts for 90% of evaluated test cases, presenting a very different picture of darknet marketplace dynamics than methods that can only detect a single Sybil account for each target vendor. Due to its fine-grained multiple feature extraction from photographs, our method can be more generically applied to various darknet markets for Sybil detection regardless of their languages or categories of items.
Removing dependencies from large software projects: are you really sure?

When developing and maintaining large software systems, a great deal of effort goes into dependency management. During the whole lifecycle of a software project, the set of dependencies keeps changing to accommodate the addition of new features or changes in the running environment. Package management tools are quite popular to automate this process, making it fairly easy to automate the addition of new dependencies and respective versions. However, over the years, a software project might evolve in a way that no longer needs a particular technology or dependency. But the choice of removing that dependency is far from trivial: one cannot be entirely sure that the dependency is not used in any part of the project. Hence, developers have a hard time confidently removing dependencies and trusting that it will not break the system in production. In this paper, we propose a decision framework to improve the detection of unused dependencies. Our approach builds on top of the existing dependency analysis tool DepClean. We start by improving the support of Java dynamic features in DepClean. We do so by augmenting the analysis with the state-of-the-art call graph generation tool OPAL. Then, we analyze the potentially unused dependencies detected by classifying their logical relationship with the other components to decide on follow-up steps, which we provide in the form of a decision diagram. Results show that developers can focus their efforts on maintaining bloated dependencies by following the recommendations of our decision framework. When applying our approach to a large industrial software project, we can reduce one-third of false positives when compared to the state-of-the-art. We also validate our approach by analyzing dependencies that were removed in the history of open-source projects. Results show consistency between our approach and the decisions taken by open-source developers.
Preface

2022 IEEE the 10th International Conference on Information, Communication and Networks (ICICN 2022), which was held in Zhangye, Gansu, China during August 19-22, 2022. The conference is co-sponsored by Lanzhou University of Technology, IEEE and IEEE-CCSS (IEEE China Council Sensors Council and Systems Council Joint Chapter), co-organized by Northwestern Polytechnical University, Beijing University of Posts and Telecommunications and Lanzhou City University, partons with Xi'an University of Technology, Changchun University of Science and Technology, Xi'an Technological University, Macau University of Science and Technology, Shaanxi Normal University, Xi'an Jiaotong University, The Asia-Pacific Signal and Information Processing Association (APSIPA).