Reviewerly/exHarmony huggingface dataset:
A Logical Framework for Understanding Why

Epistemic logic pays barely any attention to the notion of understanding, which stands in total contrast to the current situation in epistemology and in philosophy of science. This paper studies understanding why in an epistemic-logic-style. It is generally acknowledged that understanding why moves beyond knowing why. Inspired by philosophical ideas, we consider whereas knowing why requires knowing horizontal explanations, understanding why additionally requires vertical explanations. Based on justification logic and existing logical work for knowing why, we build up a framework by introducing vertical explanations, and show it could accommodate different philosophical viewpoints via adding conditions to the models. A sound and complete axiomatization for the most general case is given.
Assessing the Effect of Text Type on the Choice of Linguistic Mechanisms in Scientific Publications

In this paper, we report a qualitative and quantitative evaluation of a hand-crafted set of discourse features and their interaction with different text types. To be more specific, we compared two distinct text types—scientific abstracts and their accompanying full texts—in terms of linguistic properties, which include, among others, sentence length, coreference information, noun density, self-mentions, noun phrase count, and noun phrase complexity. Our findings suggest that abstracts and full texts differ in three mechanisms which are size and purpose bound. In abstracts, nouns tend to be more densely distributed, which indicates that there is a smaller distance between noun occurrences to be observed because of the compact size of abstracts. Furthermore, in abstracts we find a higher frequency of personal and possessive pronouns which authors use to make references to themselves. In contrast, in full texts we observe a higher frequency of noun phrases. These findings are our first attempt to identify text type motivated linguistic features that can help us draw clearer text type boundaries. These features could be used as parameters during the construction of systems for writing evaluation that could assist both tutors and students in text analysis, or as guides in linguistically-controllable neural text generation systems.
Limited Symmetry

This paper aims to tackle some of the basic (a-)symmetries of presupposition projection in a pragmatic, bivalent, and incrementally-oriented framework. The main data point that we are trying to capture is that projection from the first conjunct of a conjunction is asymmetric, while projection from the first disjunct of a disjunction can be symmetric. We argue that a solution where there are effectively two filtering mechanisms, one symmetric and one asymmetric, [11], is not tenable given recent experimental evidence, [4, 7]. Instead, we propose a bivalent system, where at each point during the incremental interpretation of a sentence S, the comprehender is trying to compute the sets of worlds in the context where the truth value of S has already been determined. This computation plays out differently in the case of conjunction vs the case of disjunction, and coupled with appropriate definitions of the incremental interpretation process and of what it means for a presupposition to project in the current system, it leads to asymmetric conjunction, but symmetric disjunction.
Thermal analysis of electrolyte biopolymer degradation from brown seaweed (Turbinaria sp)

Abstract The biopolymer electrolyte can potentially develop solar cells, batteries and fuel cells. Biopolymer This electrolyte can be synthesized from alginate extracted from brown seaweed ( Turbinaria sp ). This study aims to analyze the thermal properties of biopolymer electrolyte degradation. The biopolymer electrolyte has been degraded through exposure to sunlight and UV light. The results of the TGA analysis showed a mass reduction of the biopolymer electrolyte exposure UV light by 7.15% at a temperature of 120 °C – 267 °C. This is due to oxidation-reduction reactions that cause the bonds to break in the polymer chain.
Development of low cost pyranometer IoT based solar irradiance measurement station

Abstract This paper presents the design of self-sustainable IoT pyranometer station using low cost BPW34 photodiode. The sustainable power of the station is achieved by a 20W solar panel charges a 7.2Ah lead acid battery through a charge controller. This station uses low cost BPW34 sensors which are connected to a 16-bit ADC module to convert the analog voltage into digital data. This module is connected to an ESP32 microcontroller that connects to Wi-Fi and sends sensor readings to ThingSpeak IoT platform to allow monitoring. This study collected over 36000 data points throughout February 2023 with an average daily data loss of 2.47%. Polynomial regression method was used to determine a best fit curve equation between the BPW34 sensor and the reference sensor. The result shows that the proposed BPW34 sensor achieve the highest error of ±80W/m 2 with approximate 3% of daily data. The summary, a second order polynomial equation is found best fit for the proposed low cost BPW34 sensor.
Modeling and analysis of rooftop solar photovoltaic system for educational institution in Malaysia

Abstract The education sector is one of the contributors to greenhouse gas emissions. Therefore, the education sector strongly supports the government’s aspirations to reduce carbon emissions. Energy consumption is related to the amount of carbon emission production—energy consumption information helps to forecast energy savings while creating carbon emission reduction. The research aims to propose a new modeling of rooftop solar photovoltaic (PV) by installing the Net Energy Metering (NEM) meter in the Politeknik Sultan Azlan Shah (PSAS) educational institution's ring distribution system. The energy demand at PSAS in 2022 is 3,916,534 kWh/year, equivalent to an energy cost of RM 1,429,535. The new model design is based on setting 20% of the total power supply in PSAS generated by solar. A feasibility analysis on the proposed modification of the distribution layout design found that in the next four years, it is predicted that the minimum energy savings that PSAS will obtain are 2,075,763 kWh, while the annual bill savings are RM 757,654. This energy saving leads to a reduction in gas emissions where PSAS can reduce the production of 1,526,620 kg of CO 2 to the environment while saving 38,165 mature trees from being cut down.
Water footprint of batik-making process: a case study on small enterprise in Laweyan, Surakarta City, Indonesia

Abstract Currently, water is a product of economic value because groundwater scarcity is starting to occur everywhere. Improving water quality and efficiency is one of the targets in the sustainable development goals (SDGs). Generally, the batik-making process by Small and Medium Enterprises (SMEs) uses groundwater in the process. It will cause negative environmental impacts. This research examines the water footprint of batik-making SMEs in Laweyan, Surakarta City, Indonesia, which use combination methods and synthetic dyes. Direct groundwater use for making batik cloth resulted a blue water footprint of 1.38 L/m 2 . Simultaneously, the grey water footprint is calculated by calculating the water needed to dilute COD from production, resulting in a value of 14.60 L/m 2 . The results showed that the water footprint of the batik-making process requires 15.94 L/m 2 of water. The 1 st washing requires more water than the other production stages. Reducing the water footprint in the batik industry can be done through proper management of water resources so that not only conserves water for humans and the ecosystem but also preserves batik as a cultural heritage.
Markov chain analysis to predict natural disasters in the Province of Bangka Belitung Islands as part of preventative measures to prevent environmental damage

Abstract Bangka Belitung Islands Province is a province which is famous for its beauty and natural wealth. In addition to its natural beauty and wealth, this province has disaster-prone areas. Based on the results of the 2021 Indonesian Disaster Risk Index (IRBI) measurement, the Bangka Belitung Islands Province has the second highest risk class in Indonesia with a value of 160.98. Therefore, a study is needed that aims to determine the prediction of natural disasters in the Bangka Belitung Islands Province as a preventive measure to prevent environmental damage. The analytical method used in this research is the Markov Chain method. The data used in this study is secondary data obtained from the official website of the National Disaster Management Agency (BNPB) from 2017 to 2022 with several types of disasters, including floods, landslides, tornadoes, forest and land fires, and natural disasters others (missing persons, house fires, drowning persons, etc.). Based on the results of calculations using the Markov Chain concept, it can be concluded that the possibility of flooding in 2023 is 31.46%, landslides are 7.73%, tornadoes are 43.81%, forest and land fires are 14.32%, and other disasters of 2.69% with a Mean Absolute Error (MAE) of 0.356 and a Mean Square Error (MSE) of 0.17.
QUIC website fingerprinting based on automated machine learning

Recently, QUIC for the secure and faster connections has standardized but it is unclear that QUIC can cope with website fingerprinting (WF), a technique to infer visited websites from network traffic, since most existing efforts targeted TCP-induced traffic. To this end, we propose a novel QUIC WF technique based on Automated Machine Learning (AutoML). In our approach, we revisit traffic features appeared in literature, but relies on an AutoML framework to achieve best practice without manual intervention. Through experiments, we show that our technique outperforms state-of-the-art WF techniques with an F1-score of 99.79% and a 20-precision of 92.60%.
Evaluation Of Drainage Canal Mitigation In Banjarmasin City Post Hydrometeorological Disaster

Abstract Data from the Environmental Health Risk Assessment (EHRA) study in 2012 stated that 52% of inundation in Banjarmasin City was caused by a problematic drainage system, the causes of which included siltation of the canals and the large number of weeds growing around the drainage canals, which would interfere with the function of the drainage canals. The data used are road canal cross-sectional dimensions, topographical data, canal alignment data, and land use data in North Banjarmasin Sub District. The calculation analysis method uses the Mononobe method. The results of these calculations will later be used for modeling which are indicated to exceed the water capacity, of course also by adding topographical parameters, land use, and canal alignment. Meanwhile, the planned discharge data from the results of the calculated rainfall and the measuring data of the channel cross-sectional dimensions are used for detailed modeling of the channel cross-section using HecRas. The condition of drainage channels is over-capacity, influenced by the presence of several channels covered with bushes and concrete, the amount of sediment that has not been dredged, the absence of an adequate catchment area system due to the characteristics of the soil in the form of peat soil.
Analysis of Flood-Vulnerable Areas Using Scoring and Weighting Methods Based on Geographic Information Systems (Case Study: Sidoarjo Regency)

Abstract Indonesia is a country that is very vulnerable to natural disasters, one of which is flooding. A flood is an event or situation in which an area or land is submerged due to increased water volume. According to the National Disaster Management Agency (BNPB) records, throughout 2021, the most frequent disasters were floods, totaling 1,298 incidents. Floods also occurred in Sidoarjo Regency, specifically on Sunday, 17 January 2021, from afternoon to evening, with high rainfall intensity submerging two sub-districts. The highest flood in Tanggulangin District was in Kedungbanteng Village. As a result, as many as ± 408 Heads of Families (KK) were affected. Furthermore, in Porong District, the highest flood was in Pesawahan Village. As many as ± 490 Heads of Families (KK) were affected. Therefore, further analysis is required regarding the causes of the flood. In this research, an analysis was carried out regarding flood-prone areas in Sidoarjo Regency using a scoring and weighting method based on geographic information systems. This research used six parameters comprising land elevation, slope, rainfall, soil type, river flow density and land cover, which were then given scores and weights for each parameter based on the size of the parameter. After scoring each parameter, a weighted overlay is performed to obtain a flood-vulnerable map. The results of the flood vulnerability level in Sidoarjo Regency are divided into three vulnerability classes, namely low class with an area of 1100.84 Ha (1.71%), medium class with an area of 58143.33 Ha (90.52%), and high class with an area of 4987. 06 Ha (7.76%) of the total area of Sidoarjo Regency.
Quantum JPEG

The joint photographic expert group algorithm compresses a digital image by filtering its high spatial-frequency components. Similarly, we introduce a quantum algorithm that uses the quantum Fourier transform to discard the high spatial-frequency qubits of an image, downsampling it to a lower resolution. This allows one to capture, compress, and send images even with limited quantum resources for storage and communication. We show under which conditions this protocol is advantageous with respect to its classical counterpart.
Assessing the Effectiveness of the Implementation of Cybercrimes Mitigation Strategies in Selected Commercial Banks in Tanzania

This study aimed to assess the effectiveness of implemented cybercrime mitigation strategies for commercial banks in Tanzania. Most financial sectors, like banks, are vulnerable to continuous attacks from external and internal cybercriminals such that the majority of banks spend their time updating and maintaining cybercrime mitigation strategies against cyber attacks. Despite the ongoing efforts to prevent cyber attacks the studies and experiences show that such attacks still occur regardless of the strong measures implemented against cyber attacks. It is articulated with different researchers that there is a gap to make a resilient and stronger systems against cybercrimes. This research assessed the effectiveness of cybercrime mitigation strategies by analyzing public awareness, budget allocation, support from management, and availability of skilled personnel. The study used a sample of 885 respondents from five biggest banks in Tanzania. The collected data were analyzed using descriptive statistical methods. The implications emanating from the study were discussed.
Antiference: New Concept for Evolutive Mitigation of RFI to GNSS

The past decade has shown a growing awareness of the dangers of intentional interference (especially jamming and spoofing) with GNSS signals. The Antiference project uses reconfigurable digital signal processing methods in the detection, classification, and mitigation of interference by employing machine learning techniques. The ML-based jamming classifier uses distinctive features of spectrograms for the differentiation of various jamming attacks. A residual neural net is used to map the spectrograms to the different jamming types. It relies on a fingerprinting architecture. Fingerprints summarize the characteristics of all the incoming signals, which are stored in and matched to a database of previously encountered interference types. To validate the implemented functionalities, a developed test-bed runs test scenarios and benchmarks the results against two state-of-the-art COTS receivers with interference mitigation capabilities.
SecHealth: Enhancing EHR Security in Digital Health Transformation

In the contemporary wave of digital transformation, the implementation of electronic health records (EHRs) has become a pivotal undertaking for numerous nations. However, amidst this technological advancement, a critical facet deserving heightened attention is the security and privacy of these electronic health systems. Regrettably, this crucial concern often finds itself eclipsed by other aspects of digitalization. Consequently, these oversight lapses create vulnerabilities within the EHR framework, leaving them open and exposed to an array of malicious cyber intrusions.
A Comparative Assessment of Threat Identification Methods in EHR Systems

This paper describes threat identification process for electronic health records systems. Identification of threats is the first step in developing more secure health care applications and it is especially useful if done in the initial phase of the application development. The recent increase in number and severity of attacks on health care applications show, how important is to focus on cyber security. The process of threat identification is explained with three different tools for threat identification: attack tree diagram, data flow diagram and process flow diagram. Then the three most common threat models are explained in relation with the electronic health records systems: STRIDE, CIA and LINDDUN. The comparison of the three treat identification tools is practically demonstrated on an use case scenario of an electronic health records system which is currently being piloted in Ghana and Indonesia. For data flow diagram, Microsoft Threat Modeling Tool is selected to automatically generate threats using the STRIDE model. Generated threats are then compared to the threats identified manually with the attack tree diagram. The process flow diagram is utilized to visualize users' interactions with the electronic health records system. Based on the identified threats, counter measures are suggested to limit the vulnerabilities of similar electronic health record systems.
Eye Direction Detection as Wheelchair Navigation Using Faster R-Cnn

In this life, many people with disabilities lose the opportunity to live better than others. especially people with physical disabilities who may not have mobility like us. Physical disability is caused by several things, some from birth or disease and also an accident. People with physical disabilities who have difficulty walking require a wheelchair to be able to move. However, hands and arms must be used to operate the wheelchair. We found a way to solve the problem. we plan to move the wheelchair with eyeball movement as navigation. this solution does not only solve the problem of physical disability. we can also reduce contact to often hold the wheelchair. To detect eyeball movement, we use the Faster R-CNN method. This method if we compare it with other methods has competitive accuracy. From the results we have, we get a mAP value of more than 0.853 from each class.
Feature Location Using Extraction of Code Documentation

Feature location is a set of procedures of how to seek and find between software artifacts in pieces of source code. Code documentation is the breadcrumb of a puzzle which could help to get the developer's actual intention of specific code purpose. The token documents are extracted from the class name, method name, and Javadoc using natural language processing. The class name, method name, and Javadoc combine as documents. The VSM Lucene employed to index the document. The noun or verb of the use case scenario was transformed using a generic-specific relationship or replacement using the same meaning code name which was used as a query to identify the feature location. Each token from the use case scenario is categorized as class, method, or Javadoc to determine what is the best field to match. The query tokens are also preprocessed using NLP. The number of use case scenarios was 15, and the number of queries was about 150. The number of relevant files was 93, while the number of retrieved were 259. The number of relevant and retrieved was 65 files. As a result, the research could get the average precision rate was 24%, the average recall was 70%. The best precision was 50% and the best recall was 100%.
Mutual Information for Learning Context Representation on RNN-Attention Based Models in Open Domain Generative Chatbot

Chatbot is an example of the application of Artificial Intelligence that can receive and answer questions automatically. Chatbots are widely used in various fields such as health, customer service, entertainment, education and others. There are two approaches to chatbot development, rule-based and generative. Rule-based chatbot has the advantage of being easy to develop and produces good answers but requires predefined rules that are defined manually. Generative chatbot can provide dynamic and natural answers and does not require predefined rules. However, the drawback of generative chatbot lies in the weak representation of sentence information and information bottleneck which results in loss of information or context. The main objective of this research is to get the best model for open domain generative chatbot in a predefined scenario and improve the performance of the model in terms of word information representation using SBERT Pretrained Word Embedding and reduce information loss in encoder bottleneck and output using Mutual Information. Based on the experimental results, LSTM with the addition of Bahdanau Attention achieved the best performance in all scenarios with the highest BLEU and BERT F1-Score. Whereas in the 50 and 100 (long) sequence scenarios, the addition of Mutual Information and SBERT can improve overall model performance for BLEU by 3.62% and 2.58% respectively and BERT Score by 3.16% and 5.10% respectively.
Enhancing the Efficiency of Navigation Systems: Dijkstra Algorithm Optimization Through Integration of Douglas-Peucker Line Simplification Technique

Smartphones have grown popular in the current digital era among people. Map-based applications are one of many that are used daily by people to acquire information about specific locations. Google Maps and Waze are two popular examples of map-based applications for travel and navigation, as they provide detailed information while the user navigates from one point to another. One of the most commonly used algorithms in map-based applications is the Dijkstra algorithm. Although the Dijkstra algorithm can determine the shortest route between two points, there is an issue when the selected route involves a large number of data points that need to be processed. The more data points involved, the longer it takes to do a route calculation in the Dijkstra algorithm. This can lead to a degradation in system performance, especially on smartphones with limited resources. To address this issue, the Douglas-Peucker algorithm is proposed as a solution to reduce the number of points involved in the calculation using the line simplification technique. Line simplification means reducing unnecessary points that are considered unimportant. This study suggested that integrating the Douglas-Peucker line simplification technique before Dijkstra could improve the system performance more than the system without integrating line simplification in terms of reducing both memory usage and the time required to compute the solution.