Reviewerly/exHarmony huggingface dataset:
A Survey of Text Generation Models

Natural language processing (NLP)is a field of linguistics and computer science which focuses on the interaction of humans and computers.The main aim of natural language processing is to make sure that a computer can understand what a human says and possibly get key insights from the auditory data.Natural language text production is a well-known sub-part of NLP which focuses on converting auditory data from spoken languages into text.This survey aims to shed some light on crucial details about the past, present and the future of text production algorithms along with an aim to provide a comprehensive overview of how different machine learning techniques are being investigated and studied for different NLP applications.Finally, some important research gaps which were found out through the review are highlighted as the study is drawn to a close.This study also aims to synthesize a guide for beginners in this field and to point them towards related research and popular practices.
Spectral characteristics of two parameter fifth degree polynomial convolution kernel

In this paper, the spectral characteristic of a polynomial two parameter convolutional fifth-order interpolation kernel is determined. The spectral characteristic is determined as follows. First, the kernel is decomposed into components. After that, the spectral characteristics of each kernel component were calculated using the Fourier transform. Finally, the spectral characteristic of the interpolation two parameter kernel using a combination of the spectral components of the kernels and the kernel parameters, a and b, is formed. Through a numerical example and a graphical representation of the spectral characteristics of the one parameter and two parameter kernels, greater similarity of the spectral characteristics of the 2 P kernel, relative to the ideal box characteristic, is shown.
Bayesvl software package (Github Repository)

Bayesvl is R package for visually learning the graphical structures of Bayesian networks, and performing Hamiltonian Markov chain Monte Carlo (MCMC) with 'Stan'. ***** For more information, please visit: https://mindsponge.info/posts/14 *****
The Prospect of Rooftop Photovoltaic Development Considering Global Horizontal Irradiation Uncertainty and Government Policies: A Case of Java Island, Indonesia

Global horizontal irradiation uncertainty and government policy significantly affect the economics of rooftop photovoltaic development. Unfortunately, previous studies neglect these aspects. Therefore, it is necessary to undertake the economic analysis of rooftop photovoltaic development by considering its global horizontal irradiation uncertainty and government policies. This research creates a model that can be used to perform analysis and assess rooftop photovoltaic development prospects by considering irradiation uncertainty and government policies. The attractiveness of rooftop photovoltaic development is indicated by the value of the Levelized cost of electricity, net present value, and internal rate of return. Monte Carlo simulation models irradiation uncertainty. Java Island in Indonesia is used as a case study to show the performance of the proposed model. The research results show that the rooftop photovoltaic is not economically viable if built by the utility. Rooftop photovoltaic systems are only profitable when built by the private sector and is used for internal usage only. These rooftop photovoltaic investments provide an internal rate of return above 14%, which shows that investors with green capital funds and local investors are interested in investing. The emission reductions from a 100 m2 rooftop photovoltaic system vary from 214 to 384 tons of CO2 per year.
Location distribution of randomly acquired characteristics on a shoe sole

Abstract Footwear comparison is used to link between a suspect's shoe and a shoeprint found at a crime scene. Forensic examiners compare the two items, and the conclusion reached is based on class characteristics and randomly acquired characteristics (RACs), such as scratches or holes. An important question concerns the distribution of the location of RACs on shoe soles, which can serve as a benchmark for comparison. This study examines the probability of observing RACs in different areas of a shoe sole using a database of approximately 13,000 RACs observed on 386 outsoles. The analysis is somewhat complicated as the shoes are differentiated by shape and contact surface, and the RACs' locations are subject to measurement errors. A method that takes into account these challenges is presented. All impressions are normalized to a standardized axis to allow for inter‐comparison of RACs on outsoles of different sizes and contact areas, and RACs are localized to one of 14 subareas of the shoe sole. Expected frequencies in each region are assumed to be Poisson distributed with rate parameters that depend on the subarea and the contact surface. Three different estimation approaches are studied: a naive crude approach, a shoe‐specific random effects model, and an estimate that is based on conditional maximum likelihood. It is shown that the rate is not uniform across the shoe sole and that RACs are approximately twice as likely to appear at certain locations, corresponding to the foot's morphology. The results can guide investigators in determining a shoeprint's evidential value.
Threat Model-Agnostic Adversarial Defense using Diffusion Models

Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks. Following the discovery of this vulnerability in real-world imaging and vision applications, the associated safety concerns have attracted vast research attention, and many defense techniques have been developed. Most of these defense methods rely on adversarial training (AT) -- training the classification network on images perturbed according to a specific threat model, which defines the magnitude of the allowed modification. Although AT leads to promising results, training on a specific threat model fails to generalize to other types of perturbations. A different approach utilizes a preprocessing step to remove the adversarial perturbation from the attacked image. In this work, we follow the latter path and aim to develop a technique that leads to robust classifiers across various realizations of threat models. To this end, we harness the recent advances in stochastic generative modeling, and means to leverage these for sampling from conditional distributions. Our defense relies on an addition of Gaussian i.i.d noise to the attacked image, followed by a pretrained diffusion process -- an architecture that performs a stochastic iterative process over a denoising network, yielding a high perceptual quality denoised outcome. The obtained robustness with this stochastic preprocessing step is validated through extensive experiments on the CIFAR-10 dataset, showing that our method outperforms the leading defense methods under various threat models.
IMPLEMENTASI TRI HITA KARANA SEBAGAI STRATEGI PARIWISATA BALI BERBASIS ENVIRONMENTAL SECURITY

As a tourism area that has been worldwide, Bali certainly has various problems it faces. The rapid development of tourism in Bali has resulted in many impacts resulting from this industry. The impacts resulting from the tourism industry not only provide benefits for Bali, but also provide considerable losses. One of the aspects that suffer the most from the development of tourism is the environmental aspect. As a multidimensional industry, tourism will have many significant impacts on the environment, starting from the construction of facilities, the use of transportation equipment, and the operation of the tourism industry. Therefore, Bali must prevent any damage that may be caused by the tourism industry because Bali also offers natural beauty as a tourism attraction. The ideal strategy that can be applied by Bali is to implement the Tri Hita Karana concept which consists of parayangan, pawongan, and palemahan. The implementation of the Parahyangan can be done by protecting the environment around the temple which is also a tourist destination. The implementation of pawongan is carried out by collective action between the community, government, and tourism actors to jointly realize tourism based on environmental security. The application of palemahan can be done starting from small actions, namely not littering and being able to protect the environment properly. The application of the three elements that compose Tri Hita Karana will be able to create Bali tourism based on environmental security and be able to overcome various problems that threaten the environment.
The chimney tube in musical acoustics: A textbook-level formulation for students and musicians

The chimney tube, comprising connected body and chimney segments with respective diameters d1 and d2, is ubiquitous in musical acoustics and can be a useful analog for flute embouchure holes, woodwind toneholes, and organ pipes. Most treatments are complex and not readily accessible to lay audiences. Simple expressions for the input impedance of ideal chimney tubes are derived by two methods. Chimneys short compared to the overall length of the resonator are shown to have effective lengths larger than their physical length by the factor (d1/d2)2 when the chimney is open and smaller by the same factor when closed. Examples presented range from a simple cylindrical tube open at both ends to the classic Helmholtz resonator. Insights are provided about tonehole placement and the tradeoffs between a tonehole's position and diameter; how the resonance frequencies of an open-open chimney tube with a short chimney do not depend on which end is used as the input; and the interesting way in which a long chimney can change the spacing of the resonances from the 1:2:3 pattern of a cylinder open at both ends to the characteristic 1:3:5 pattern of a cylinder open at one end and closed at the other.
OrgAn: Organizational Anonymity with Low Latency

There is a growing demand for network-level anonymity for delegates at global organizations such as the UN and Red Cross. Numerous anonymous communication (AC) systems have been proposed over the last few decades to provide anonymity over the internet; however, they introduce high latency overhead, provide weaker anonymity guarantees, or are difficult to deploy at the organizational networks. Recently, the PriFi system introduced a client/relay/server model that suitably utilizes the organizational network topology and proposes a low-latency, strong-anonymity AC protocol. Using an efficient lattice-based (almost) keyhomomorphic pseudorandom function and Netwon’s power sums, we present a novel AC protocol OrgAn in this client/relay/server model that provides strong anonymity against a global adversary controlling the majority of the network. OrgAn’s cryptographic design allows it to overcome several major problems with any realistic PriFi instantiation: (a) unlike PriFi, OrgAn avoids frequent, interactive, slot-agreement protocol among the servers; (b) a PriFi relay has to receive frequent communication from the servers, which can not only become a latency bottleneck but also reveal the access pattern to the servers and increases the chance of server collusion/coercion, while OrgAn servers are absent from any real-time process. We demonstrate how to make this public-key cryptographic solution scale equally well as the symmetric-cryptographic PriFi with practical pre-computation and storage requirements. Through a prototype implementation, we show that OrgAn provides similar throughput and end-to-end latency guarantees as PriFi, while still discounting the setup challenges in PriFi.
Moby: A Blackout-Resistant Anonymity Network for Mobile Devices

Internet blackouts are challenging environments for anonymity and censorship resistance. Existing popular anonymity networks (e.g., Freenet, I2P, Tor) rely on Internet connectivity to function, making them impracticable during such blackouts. In such a setting, mobile ad-hoc networks can provide connectivity, but prior communication protocols for ad-hoc networks are not designed for anonymity and attack resilience. We address this need by designing, implementing, and evaluating Moby, a blackout-resistant anonymity network for mobile devices. Moby provides end-to-end encryption, forward secrecy and sender-receiver anonymity. It features a bi-modal design of operation, using Internet connectivity when available and ad-hoc networks during blackouts. During periods of Internet connectivity, Moby functions as a regular messaging application and bootstraps information that is later used in the absence of Internet connectivity to achieve secure anonymous communications. Moby incorporates a model of trust based on users’ contact lists, and a trust establishment protocol that mitigates flooding attacks. We perform an empirically informed simulation-based study based on cellphone traces of 268,596 users over the span of a week for a large cellular provider to determine Moby’s feasibility and present our findings. Last, we implement and evaluate the Moby client as an Android app.
On Defeating Graph Analysis of Anonymous Transactions

In a ring-signature-based anonymous cryptocurrency, signers of a transaction are hidden among a set of potential signers, called a ring, whose size is much smaller than the number of all users. The ringmembership relations specified by the sets of transactions thus induce bipartite transaction graphs, whose distribution is in turn induced by the ring sampler underlying the cryptocurrency. Since efficient graph analysis could be performed on transaction graphs to potentially deanonymise signers, it is crucial to understand the resistance of (the transaction graphs induced by) a ring sampler against graph analysis. Of particular interest is the class of partitioning ring samplers. Although previous works showed that they provide almost optimal local anonymity, their resistance against global, e.g. graph-based, attacks were unclear. In this work, we analyse transaction graphs induced by partitioning ring samplers. Specifically, we show (partly analytically and partly empirically) that, somewhat surprisingly, by setting the ring size to be at least logarithmic in the number of users, a graph-analysing adversary is no better than the one that performs random guessing in deanonymisation up to constant factor of 2.
Rainfall Characteristics for Irrigation of Pauh, Padang, West Sumatera Area for Irrigation

Abstract Rainfall data for Pauh area is evaluated to determine irrigation needs for plants. Precipitation data for 2021 is collected by the WeatherHawk Signature weather station every 20 minutes. Then the hourly, daily and monthly rainfall is calculated. Monthly rainfall is quite high with an average of 566 mm. However, the rainfall is not evenly distributed throughout the year. In each month the distribution of rainfall is also uneven. Several days of very heavy rainfall and consecutive days without rain are quite long with an average of 7 days. Even the longest days without rain reaches 16 days. From this evaluation it was concluded that sprinkler or micro irrigation is necessary to maintain crop productivity.
Sentiment Analysis of COVID data extracted via Twitter

Different types of social media sites exist, wherein some of them are LinkedIn, Twitter, Facebook, Instagram, WhatsApp, etc. As the number of social media users increases, the opportunity for the user to express their feelings also increases. Twitter is a choice of many users as it not only allows the users to express their thoughts but to interact with official accounts (PMO, Defense Ministry) which can be seen with a verified tick on the website. In this thesis titled ‘Sentiment Analysis of COVID data extracted via Twitter’, multiple machine learning and deep learning techniques have been researched and implemented to perform sentiment analysis. Moreover, a novel approach using deep learning architecture has been proposed. It is based on a combination of Bidirectional Long Short Term (BiLSTM) neural networks and Convolution Neural Networks (CNN). Prior to implementing the algorithms, the data is acquired by using web-scraping techniques and/or public APIs pertaining to Twitter. A comparative analysis of the efficiency and performance of the proposed technique along with other existing approaches discovered during the literature review phase is also presented. KEYWORDS: Sentiment analysis, machine learning, deep learning, Natural Language Processing
Manajemen Perpustakaan Sekolah Dasar Negeri 2 Rejang Lebong

This study aims to analyze how the implementation of library management in SD Negeri No. 2 Rejang Lebong Regency. Data analysis techniques used are interviews, observation and documentation. To analyze the data that has been obtained, the researchers analyzed qualitative data. The results showed that it was very visible that the students in terms of library administration organizing SDN No. 2 Curup has been implemented but is not ideal, there are still many shortcomings, both in terms of structure and foundations, the library office as far as the administration for library administration is very large, the variety of reading material centers according to the educational program concerned, not adequate and other supporting reference books, implying that the library of SDN No. 2 Curup cannot be expected to progress like the library used by various existing schools, on the grounds that the library of SDN No. 2 Curup is still inadequate with regards to assistance that encourages the best library, both in terms of administration and office other supporters. Therefore, the nature of the library must be improved again with the hope that this library can be utilized and utilized as well as possible by Curup Elementary School students.
CAPD: a context-aware, policy-driven framework for secure and resilient IoBT operations

The Internet of Battlefield Things (IoBT) will advance the operational effectiveness of infantry units. However, this requires autonomous assets such as sensors, drones, combat equipment, and uncrewed vehicles to collaborate, securely share information, and be resilient to adversary attacks in contested multi-domain operations. CAPD addresses this problem by providing a context-aware, policy-driven framework supporting data and knowledge exchange among autonomous entities in a battlespace. We propose an IoBT ontology that facilitates controlled information sharing to enable semantic interoperability between systems. Its key contributions include providing a knowledge graph with a shared semantic schema, integration with background knowledge, efficient mechanisms for enforcing data consistency and drawing inferences, and supporting attribute-based access control. The sensors in the IoBT provide data that create populated knowledge graphs based on the ontology. This paper describes using CAPD to detect and mitigate adversary actions. CAPD enables situational awareness using reasoning over the sensed data and SPARQL queries. For example, adversaries can cause sensor failure or hijacking and disrupt the tactical networks to degrade video surveillance. In such instances, CAPD uses an ontology-based reasoner to see how alternative approaches can still support the mission. Depending on bandwidth availability, the reasoner initiates the creation of a reduced frame rate grayscale video by active transcoding or transmits only still images. This ability to reason over the mission sensed environment, and attack context permits the autonomous IoBT system to exhibit resilience in contested conditions.
An Empirical Study on Bugs in PHP

PHP (Hypertext Preprocessor) is a scripting language that has been widely used in web development. This paper conducts an empirical study on bugs in PHP. By analyzing 35,921 bug reports, 6524 revisions, and root causes of randomly selected 500 bugs, we find that: (1) Among all the 385 versions involved in these bugs, there are the most bugs in PHP 4.0.4, PHP 4.0.6, and PHP 4.0.3; Documentation bugs are mainly distributed in PHP 4.y.z and PHP 5.y.z; Security bugs are distributed primarily in the relatively later normal versions of PHP 5.y.z. (2) Documentation, Compile, and Scripting Engine packages are greatly affected by bugs; 73.71% of documentation bugs affect documentation; PHAR, EXIF, and GD are more affected by security bugs. (3) It may be not difficult to repair most bugs since the number of modified lines of code and files are limited; However, nearly 11% of bugs need more than one year to repair; Compared with documentation bugs, security bugs are more difficult to be repaired; The duration of bugs in PHP 8.y.z is shorter than in other versions. (4) Semantic bugs and documentation bugs are the more common root causes of bugs than others. Besides, among semantic bugs, the “Missing Features” bugs and “Processing” bugs are more than others. These results could indicate some potential problems during the detecting and repairing of PHP’s bugs. These findings reveal some laws of bugs in PHP. It could assist developers of PHP in improving their development quality, assist maintainers of PHP in detecting and repairing bugs more effectively, and suggest users of PHP evade potential risks.
A Scalable Covert Communication Service For Coworkers

Both 5G Internet and COVID-19 pandemic have increasingly prompted thousands of companies and organizations to shift from a centralized office model to a distributed home model, which poses a new requirement: how to securely and rapidly share private data for coworkers on Internet. The covert communication systems are widely used to deliver private information because of the possibility of extending the system to Internet-scale size. However, most existing systems are inadequate to solve the requirement, since either the servers in centralized systems face the risk of being monitored and infiltrated, or the multi-hop routing schemes in decentralized systems lead to diverse attacks and high delivery latency. To this end, we proposed a scalable covert communication service for coworkers, called SC2. For security and hiddenness, we adopt the content slicing and multichannel routing to prevent adversary from monitoring and analyzing data. For scalability, we design a two-hop logic overlay to support low latency routing, and an adaptive channel selction technique to exploit the available bandwidth of the system. To evaluate the performance of SC2, we deploy the system in various IoT clouds and storage clouds. The experimental results demonstrate that SC2 is able to transmit both short messages and bulk content. Under various parameter settings, the delivery latency of SC2 linearly decreases with the number of channels, and SC2 takes full advantage of the available bandwidth with the growing number of users.
Pelatihan Pembuatan Tas Kain bagi Kelompok Usaha Bersama Maju Bersama Sejahtera sebagai Program Community Empowerment

Maranatha Christian University has a vision and mission of the Three Pillars of Higher Education based on ICE values: integrity, care, excellence. Community service activities are aimed at community groups assisted by Kota Baru Parahyangan (KBP), members of the Maju Bersama Sejahtera Joint Business Group (KUBE). The method carried out through mentoring and creative experimental practices using treated fabric with ecoprint technique and sewn into the cloth bag products. Through this training hoped could create a community that is independent and able to work or be a creative entrepreneur to produce bag products that have aesthetic and even economic value. The activities carried out offline with 4 FSRD UK. Maranatha lecturers, 2 FSRD students, and 20 KUBE Maju Bersama Sejahtera participants, assisted by 6 committee members from the KBP team. The final activity result is that participants are able to produce cloth bags that have been trained by the lecturers.