Reviewerly/exHarmony huggingface dataset:
Kajian Pelaksanaan Pendampingan Serta Pemeriksaan Pengelolaan Keuangan SMA/SMK Negeri Oleh Auditor Inspektorat Daerah Guna Meminimalisir Penyimpangan Pengelolaan Keuangan

The purpose of research to find out the implementation of mentoring and financial management audits for SMA/SMK Negeri in North Sulawesi Province by regional inspectorate auditors in order to minimize financial management irregularities. This research was conducted at the Provincial Government of North Sulawesi. The data analysis techniques are data reduction, data display and conclusion drawing/verification which are carried out interactively and take place continuously until complete, so that the data reaches saturation. The results of this study indicate that financial management assistance for SMA/SMK by the Regional Inspectorate auditor is sufficient to minimize financial management irregularities. With the assistance of the regional inspectorate auditor, the school gets additional knowledge that can be used in the implementation of school financial management. 2) Examination of SMA/SMK Financial Management by the Regional Inspectorate auditor is sufficient to minimize financial management irregularities. With the examination of SMA/SMK financial management from the regional Inspectorate auditor, the school gets suggestions for improvement that can be used to correct mistakes.
INTEGRATION OF COUNSELING AND TEACHING: A CREATIVE PEDAGOGICAL PRAXIS

The profile of teachers has undergone a change by the split up between advocacy and academics giving birth to counselling and teaching as separate professions. This sharp divide has serious economical and professional consequences specifically creating a gap between teachers and students. This paper is an evidence-based analysis for supporting the integration of teaching and counselling in the classroom situation. Qualitative data were drawn from theoretical views and models of accomplished teachers to emphasize that the role of a teacher is multifaceted which includes teaching, counselling, guidance, facilitating, and mentoring capable of rendering holistic support to the all-around development of students.
Investigating the Validity of the IELTS Listening Test

The International English Language Testing System (IELTS) has gained popularity in recent years and it is now accepted by many educational institutions worldwide. While IELTS offers a distinct academic version of the reading and writing test components, it uses the same listening module for the General Training and the Academic exam. The present article explores to what extent the listening subtest in the Academic IELTS test is a useful measure of test-takers listening ability and a predictor of their academic success. This study focusses on the test’s construct validity. A critical review of the major strengths and potential drawbacks of the listening test is followed by a comparison of the test scores with another traditionally accepted test of academic English. Conclusions about the validity of the IELTS listening test are drawn along with some suggestions for design improvement. Future research directions are also proposed.
Secondary Dispersion Halos of Platinum and Palladium in the Shorzha Dunite-Peridotite Massif of the Sevan Ophiolite Belt of Armenia

Distribution of platinoids within the massifs of Sevan ophiolitic belt has been very poorly studied. As a result of the research implemented, a negative assessment of the prospects for platinum content of dunite-harzburgite massifs of Sevan ophiolitic zone was given [1,2].
RANCANG BANGUN APLIKASI ADMINISTRASI DESA BERBASIS WEBSITE

Administration is an administrative activity carried out in the organization using the tools or facilities in the office concerned. Administration is a subsystem in an office organization that serves as the main support of efforts to achieve the organization's overall goals in the form of services into and out of the organization. The purpose of this study is to find out the population administration services carried out by Peucangpari Village to the community. The method carried out in this research is the waterfall method is very suitable in making applications needed in the creation of administrative applications in the current peucangpari village, where the stages of this method are started by analyzing data so that the system will be developed according to user needs. Waterfall method (waterfall model) which begins with research on the current system, design, coding, and maintenance of the system. The results obtained from this study are about village administration applications that can be applied and implemented to assist village parties in providing services to the community.
Reinforcement Learning: Playing Tic-Tac-Toe

Machine learning constructs computer systems that develop through experience. Applications surround disciplines in daily life ranging from malware filtering to image recognition. Recent research has shifted towards maximizing efficiency in decision-making, creating algorithms that quickly and accurately process patterns to generate insight. This research focuses on reinforcement learning, a paradigm of machine learning that makes decisions through maximizing reward. Specifically, we use Q-learning – a model-free reinforcement learning algorithm – to assign scores for different decisions given the unique states of the problem. Widyantoro et al. (2009) have studied the effect of Q-learning on learning to play Tic-Tac-Toe. However, the study yielded a win/tie rate of less than 50 percent. We believe that does not represent an effective algorithm to exploit the benefits of Q-learning fully. In the same environment, this research aims to close the gaps in the effectiveness of Q-learning while minimizing human input. Data were processed by setting the epsilon value as 0.9 to ensure randomness, then consecutively decrease with a constant rate as possible states increase. The program played 300,000 games against its previous version, eventually securing a win/tie rate of approximately 90 percent. Future directions include improving the efficiency of Q-learning algorithms and applying the research in practical fields.
Dynamic virtual assistance of I/O functionalities

With significant advancements being witnessed in the engineering industry daily, it has become increasingly vital for society to seek out particular new ways of interacting with computer technology and automation as their demand grows in society. Today, every device is developing the use of touch screen technology on its computer systems, although it is not cost-effective to use in all applications. A specialized system, similar to a virtual device, that provides object pursuit (tracking) and Gestures to let us engage; it might be an effective alternative to the standard touch screen and also the solid physical gadgets. The goal is to create an object pursuit (tracking) program that communicates with the computer system. This proposed model is a computer vision-based control system that involves hand movements taken from a digital camera via a hand detection technique implemented with OpenCV libraries. Our project applies gesture recognition as a topic that comes under two computer science fields augmented reality and human-computer interaction and we have created a virtual gesture system to elucidate human gestures through mathematical algorithms. Users can use simple finger or hand gestures to control or interact with the system without physically touching them and also included voice assistance to start and end the gesture controlling system. Gesture recognition can be viewed as a way for computers to begin to recognize human body language and signs, thus stuffing the void between computing systems and humans than the earliest text user interfaces or even graphical user interfaces, which still limit the majority of input to keyboard and mouse are may not be very efficient at all times. The algorithm is focused on deep learning for detecting the gestures. Hence, the proposed system will avoid the pandemic situation of COVID-19 spread by reducing the human interaction with the devices to control the system
Human-Drone Interaction

Drones are continually entering our daily lives by being used in a number of different applications. This creates a natural demand for better interaction ways between humans and drones. One of the possible applications that would benefit from improved interaction is the inspection of smoking in prohibited areas. We propose our own gesture of drone flight that we believe would deliver the message "not to smoke" better than the ready-made built-in gesture. To this end, we conducted a within-subject experiment involving 19 participants, where we evaluated the gestures on a drone operated through the Wizard-of-Oz interaction design. The results demonstrate that the proposed gesture was better at conveying the message compared to the built-in gesture.
Analytical and numerical bounds on entanglement delivery waiting times

The vision of a global network that enables quantum communications between any point on Earth is known as the quantum internet. One crucial element of this network is the use of quantum repeater chains, which have the potential to overcome transmission losses and implement entanglement or quantum key distribution protocols over extended distances. There are various proposals for quantum repeaters, but they can generally be evaluated based on two main figures of merit: the average time for end-to-end entanglement delivery and the associated average fidelity. However, characterizing these quantities can be difficult due to factors such as feedback loops, decoherence, entanglement generation being a probabilistic process, and the potential failure of subprotocols. In this talk, I will discuss algorithmic and analytical methods for computing these quantities for relevant families of protocols.
Quantum teleportation in high dimensions with spatially structured photons

Quantum teleportation allows protected information exchange between distant parties without the need for a physical link, a crucial re- source in future quantum networks. Using high-dimensional quantum states for teleportation offers the promise of higher information capacity channels, protection against optimal cloning machines and improved resilience to noise, but is limited by the commonly used linear optical detection requiring ancillary photons, where the number of entangled photons grows with the dimension to be teleported. Here we overcome this restriction with non-linear optical detection and demonstrate the teleportation of high-dimensional states with just two entangled photons as a quantum resource . Using photonic spatial modes as an example, we demonstrate a 15-dimensional teleportation channel, exceeding the state-of-the-art of three dimensions, offering a scaleable route to high-capacity quantum information transfer.
A novel privacy-aware global infrastructure for ecological footprint calculator (GIEFC) based on internet of things and blockchain

Abstract Climate change and global warming are the greatest harmful consequences of human activity. Indeed, Human technological development and demographic growth has been accompanied by globalization. In this new era, the logistics revolution has removed borders and industrial value chains have been distributed all over the world. It is becoming difficult to attribute responsibilities for ecological impacts accurately, since consumers are not necessarily in the same area as the countries of manufacture or extraction of raw materials. At the same time, a new paradigm called the Internet of Things has come to reshape our relationship to the world. The smallest everyday objects are connected to the Internet and allow acquisition of large amount of data, interact with cyberspace and in some cases act on the real world in an automated way. On the other hand, the last decade has also seen the rise of a new technology for storing transactions data in a reliable and consented way. Blockchain has changed the way operations are made based on the concept of distributed ledger giving a new breath to the economy. They have also dived into companies business by allowing private implementations with per-missioned blockhains. The aim of this work is to take advantage of the power of the two technologies combined – the Internet of Things provides wide possibilities when it comes to collecting information and establishing controls on the one hand, and blockchain provides very good means to ensure traceability while respecting confidentiality and privacy – in order to create a global architecture for a digital ecological impact calculator respecting privacy. In order to validate our proposal we first modeled it with a Petri network in order to verify its functioning, then we implemented it in order to measure average running time of each major step: for operations relative to certification authority we have obtained 0.342 second per operation, 0.342 second per operation concerning production and buying actions reporting and 0.299 second par scoring operation. Finally we used these outputs to build a queue model in order to check if the proposed architecture steady-state does not change over time. As results we showed that for the simplest form of queue model the servers of our architecture have a utilization rate that is close to 50% and that the overall waiting time remains below one minute, on the other hand with the Petri net we have proved from the marking graph that GIEFC performs the expected tasks according to the described specifications.
Bringing the Industry Partner to the Cybersecurity Education Table as an Active Participant

The University of Washington (UW) published their pedagogical model for Cybersecurity Education – the Kuzima-Bespalko-Popovsky (KBP) model. In 2005, the National Center for Academic Excellence in Cybersecurity certified the status of NCAE-CR based on the tight pairing to the Knowledge Units (KUs). The KBP model aided in maintaining the quality of the instruction. The original KBP model goals are reviewed here and the changes to those goals are explained. The model provided a solid public – private partnership engagement model. Industry professionals from the field taught the NIST / NICE KSAs and provided real-time experience, contributing to the curriculum’s resiliency. We modified our competency assessment from the World of Work Inventory (WOWI) assessment test to the CYBERGenius.IQ, integrated industry-based CERT material for scalability, and created a certified student collaboratory to manage continuing education post-graduation into the job.
Interactive Program Visualization to Teach Stack Smashing

This paper presents an experience report on using an interactive program visualization tool — Dynamic, Interactive Stack-Smashing Attack Visualization (DISSAV) — and a complementary active-learning exercise to teach stack smashing, a key software security attack. The visualization tool and active-learning exercise work synergistically to guide the student through challenging, abstract concepts in the advanced cybersecurity area. DISSAV and the exercise are deployed within the software security module of an undergraduate cybersecurity course that introduces a broad range of security topics. A study is designed that collects and evaluates student perceptions on the user interface of DISSAV and the effectiveness of the two resources in improving student learning and engagement. The study finds that over 80% of responses to user interface questions, 66% of responses to student learning questions and 64% of responses to student engagement questions are positive, suggesting that the resources improve student learning and engagement in general. The study does not find discernible patterns of difference in responses from students of different ages and varying levels of prior experience with stack smashing attacks, program visualization tools and C programming.
Security Mindset Fundamentals and Second Language Learning

Security mindsets can be said to engage elements of situational awareness and analytical, creative, and practical elements of adversarial thinking. Scholars have debated whether this is taught or fostered, but they have acknowledged that security mindsets are critical. Here, the argument is made that implicit features of language itself can be drawn on in everyday K12+ second language (L2) learning settings to introduce members of the general populace and, among them, potential future members of the cybersecurity workforce, to security thinking. Beyond the features of language itself, L2 lessons can also be adapted to familiarize students with explicit security-related topics and scenarios. By exploiting these novel connections between language learning and security thinking, L2 learning contexts can become a security mindset training ground for millions of U.S. students.
Addressing the Cybersecurity Issues and Needs of Rural Pennsylvania Nonprofit Organizations

The need for cybersecurity competence has become a strategic area for all types of organizations, be it large or small, for profit or nonprofit. This is an area of particular concern for smaller nonprofit organizations; and especially for those in rural areas with limited resources to address their cybersecurity risks. Cyber-attacks wreak havoc on the networks and systems for services provided to nonprofit consumers. The problems associated with various types of attacks, from outside nefarious individuals/groups or internal personnel, are particularly difficult for nonprofits in rural communities with limited resources for cybersecurity infrastructure and limited staff proficient in cybersecurity knowledge and skills. We have developed a cybersecurity assessment process to ascertain key needs and weaknesses with respect to cybersecurity for nonprofits in such rural communities in Pennsylvania. Additionally, this grant-sponsored work-in-progress research aims to provide guidance to rural nonprofits with “best practices” and related content that can be easily implemented despite their limitations.
Exploring Data Augmentation Methods on Social Media Corpora

Data augmentation has proven widely effective in computer vision. In Natural Language Processing (NLP) data augmentation remains an area of active research. There is no widely accepted augmentation technique that works well across tasks and model architectures. In this paper we explore data augmentation techniques in the context of text classification using two social media datasets. We explore popular varieties of data augmentation, starting with oversampling, Easy Data Augmentation (Wei and Zou, 2019) and Back-Translation (Sennrich et al., 2015). We also consider Greyscaling, a relatively unexplored data augmentation technique that seeks to mitigate the intensity of adjectives in examples. Finally, we consider a few-shot learning approach: Pattern-Exploiting Training (PET) (Schick et al., 2020). For the experiments we use a BERT transformer architecture. Results show that augmentation techniques provide only minimal and inconsistent improvements. Synonym replacement provided evidence of some performance improvement and adjective scales with Grayscaling is an area where further exploration would be valuable. Few-shot learning experiments show consistent improvement over supervised training, and seem very promising when classes are easily separable but further exploration would be valuable.
Codes with Weighted Poset Block Metrics

Weighted poset block metric is a generalization of weighted poset metric introduced by Panek et al. ([\ref{panek}]) and the metric for linear error-block codes introduced by Feng et al. ([\ref{FENG}]). This type of metrics includes many classical metrics such as Hamming metric, Lee metric, poset metric, pomset metric, poset block metric, pomset block metric and so on. In this work, we focus on constructing new codes under weighted poset block metric from given ones. Some basic properties such as minimum distance and covering radius are determined.
Exploring Self-Supervised Representation Learning For Low-Resource Medical Image Analysis

The success of self-supervised learning (SSL) has mostly been attributed to the availability of unlabeled yet large-scale datasets. However, in a specialized domain such as medical imaging which is a lot different from natural images, the assumption of data availability is unrealistic and impractical, as the data itself is scanty and found in small databases, collected for specific prognosis tasks. To this end, we seek to investigate the applicability of self-supervised learning algorithms on small-scale medical imaging datasets. In particular, we evaluate $4$ state-of-the-art SSL methods on three publicly accessible \emph{small} medical imaging datasets. Our investigation reveals that in-domain low-resource SSL pre-training can yield competitive performance to transfer learning from large-scale datasets (such as ImageNet). Furthermore, we extensively analyse our empirical findings to provide valuable insights that can motivate for further research towards circumventing the need for pre-training on a large image corpus. To the best of our knowledge, this is the first attempt to holistically explore self-supervision on low-resource medical datasets.
Learning to Influence Human Behavior with Offline Reinforcement Learning

When interacting with people, AI agents do not just influence the state of the world -- they also influence the actions people take in response to the agent, and even their underlying intentions and strategies. Accounting for and leveraging this influence has mostly been studied in settings where it is sufficient to assume that human behavior is near-optimal: competitive games, or general-sum settings like autonomous driving alongside human drivers. Instead, we focus on influence in settings where there is a need to capture human suboptimality. For instance, imagine a collaborative task in which, due either to cognitive biases or lack of information, people do not perform very well -- how could an agent influence them towards more optimal behavior? Assuming near-optimal human behavior will not work here, and so the agent needs to learn from real human data. But experimenting online with humans is potentially unsafe, and creating a high-fidelity simulator of the environment is often impractical. Hence, we focus on learning from an offline dataset of human-human interactions. Our observation is that offline reinforcement learning (RL) can learn to effectively influence suboptimal humans by extending and combining elements of observed human-human behavior. We demonstrate that offline RL can solve two challenges with effective influence. First, we show that by learning from a dataset of suboptimal human-human interaction on a variety of tasks -- none of which contains examples of successful influence -- an agent can learn influence strategies to steer humans towards better performance even on new tasks. Second, we show that by also modeling and conditioning on human behavior, offline RL can learn to affect not just the human's actions but also their underlying strategy, and adapt to changes in their strategy.