In debatable topics, people use evidence to reason for a claim. The claim conveys a stance toward a particular aspect of the evidence. Existing studies mainly focus on identifying the claim stance; which is determined by its relevant evidence; however, the task to get a factual claim if the claim is non-factual is not considered. We thus study the question to what extent a false claim can be reconstructed from its premises to be true, either by generating a new factual claim from relevant premises or by determining the positions for the misleading information in the false claim and modifying it concerning the evidence. To address such an issue, we introduce a factual claim-making task, a new task to predict the factuality of the claim that is associated with evidence that supports or refutes the given claim. If the claim is non-factual, we propose two different models to get a factual claim. In the generator model, we generate a factual claim by applying the generation model. In the modifier model, we depend on the sequence operation model to modify the misleading information. The experimental results on the Perspectum dataset show the effectiveness of our models. The performances of the proposed system achieved 76.84% and 78.36% of F1 scores for the generator and modifier mode, respectively.

This study designs an electronic smart control system to replace thermal and electromagnetic circuit breakers, as well as a traditional energy meter. With voltage and current sensors, several values can be calculated. RMS values of voltage and current are the first result, followed by power factor, load, and energy consumption. This system produced 3-channel line output that can be controlled by means of current and energy consumption. According to all testing, it is known that the device developed based on the system can perform measurements with acceptable accuracy. The RMS value of voltage measured is as accurate within 0.5% of its maximum capacity. On the other side, all channels have current RMS value accuracy of 0.3%. Both parameters reach such accuracy thanks to the 1-millisecond resolution of sensors acquisition. The power factor can also be measured as accurately as 2% according to the cos phi meter with a resolution of 0.01. The combination of those accuracy results in a current limiter and energy consumption limiter within a very small resolution. For energy consumption average error, it displays 2% as the accuracy. The current limiter can be achieved automatically and dynamically with a response time under 50 milliseconds. The energy consumption limiter operated in resolution below 0.001 kilowatt-hours. All testing result indicates that the goal of the research is achieved.

During the Covid 19 pandemic, large-scale social restrictions (PSBB) were imposed in Ambon city. With the imposition of large-scale social restrictions, the learning process conducted at SD Neg 80 Halong was closed. All students are encouraged to stay at home from March until now. This of course causes losses on the part of the students because they cannot get their rights as students to enjoy education and socialize with their peers. For teachers, they cannot communicate face-to-face with students and the lessons that should be given to students are hindered by pandemic situations and conditions. Problems or constraints experienced by students, parents and teachers can be overcome by learning online by using several applications that support the teaching and learning process, such as Google Classroom, Zoom and Google Meet. This application can be a solution for the learning process of teachers and students.