Reviewerly/exHarmony huggingface dataset:
Development of Interactive Multimedia Based Lectora Inspire for Accounting Subjects on Adjustment Journal Materials

The use of learning media is one of the important aspects in the learning process to achieve learning objectives. However, there are still educators who only use conventional learning media so that students feel bored and do not focus on learning activities. From the validation results of material experts obtained 81%, from media experts 81% and 91.67% from the results of student response questionnaires. In conclusion, interactive multimedia adjustment journal material can be a very feasible medium so that it is expected to make it easier for students to understand adjustment journals made using Lectora Inspire.
Steganography Based on Pixel Intensity Value Decomposition

This paper focuses on steganography based on pixel intensity value decomposition. A number of existing schemes such as binary, Fibonacci, Prime, Natural, Lucas, and Catalan-Fibonacci (CF) are evaluated in terms of payload capacity and stego quality. A new technique based on a specific representation is used to decompose pixel intensity values into 16 (virtual) bit-planes suitable for embedding purposes. The new decomposition scheme has a desirable property whereby the sum of all bit-planes does not exceed the maximum pixel intensity value, i.e. 255. Experimental results demonstrate that the new decomposition scheme offers a better compromise between payload capacity and stego quality than other existing decomposition schemes used for embedding messages. However, embedding in the 6th bit-plane onwards, the proposed scheme offers better stego quality. In general, the new decomposition technique has less effect in terms of quality on pixel value when compared to most existing pixel intensity value decomposition techniques when embedding messages in higher bit-planes.
PEMANFAATAN MICROSOFT INOVATIVE EDUCATION FOLLOW OFFICE 365 PADA MASA PEMBELAJARAN JARAK JAUH (PJJ) DI SMPN 5 PALU

Office 365 is an online learning method that is considered appropriate to be applied in both partner schools as an additional method other than conventional. The e-learning method is an electronic learning system that utilizes internet facilities in the process, teachers can interact with students in online classes. SMPN 5 Palu, Central Sulawesi Province is one of the junior high schools which is strategically located, can be reached from three directions and transportation is smooth. This school has strengths, among others: having teaching staff who are highly committed to improving the quality of graduates, a large number of applicants, high student enthusiasm for learning, and adequate laboratories, including a multimedia laboratory. The learning method used by teachers at partner schools so far is the conventional method, namely lectures in class, there is no use of internet facilities in their learning because they do not know, even though the facilities and infrastructure owned by teachers and students are very supportive, because teachers and students most of the students already have their own laptops and in schools there are already available computer laboratory facilities. Likewise, telecommunications and internet network facilities have reached almost all areas of Palu City, so it is not difficult to implement online learning. Where the purpose of this training is that learning can run effectively even in PJJ conditions by adapting to Microsoft Office 365 so that students who are far outside the city of Palu can still carry out online learning. Assisting teachers in managing online classes such as uploading materials, creating online practice questions and assigning value to the questions, creating materials into interesting learning videos and so on. Microsoft Office 365 is one of the Cloud-based Office applications created by the Microsoft company that allows users to be able to access various online/online content with the help of the internet. The development of a digital class based on Microsoft 365 has just been trained for 30 teachers at SMP N 5 Palu.
Implementing Grover oracles for quantum key search on AES and LowMC

Grover's search algorithm gives a quantum attack against block ciphers by searching for a key that matches a small number of plaintext-ciphertext pairs. This attack uses $O(\sqrt{N})$ calls to the cipher to search a key space of size $N$. Previous work in the specific case of AES derived the full gate cost by analyzing quantum circuits for the cipher, but focused on minimizing the number of qubits. In contrast, we study the cost of quantum key search attacks under a depth restriction and introduce techniques that reduce the oracle depth, even if it requires more qubits. As cases in point, we design quantum circuits for the block ciphers AES and LowMC. Our circuits give a lower overall attack cost in both the gate count and depth-times-width cost models. In NIST's post-quantum cryptography standardization process, security categories are defined based on the concrete cost of quantum key search against AES. We present new, lower cost estimates for each category, so our work has immediate implications for the security assessment of post-quantum cryptography. As part of this work, we release Q# implementations of the full Grover oracle for AES-128, -192, -256 and for the three LowMC instantiations used in Picnic, including unit tests and code to reproduce our quantum resource estimates. To the best of our knowledge, these are the first two such full implementations and automatic resource estimations.
Students’ Reading Strategies During the Pandemic in Higher Education

The purpose of this article is to analyze the perceptions and experiences of undergraduate students in using strategies for reading texts during the Covid-19 pandemic. Researchers chose to use qualitative research in this study with a case study approach. The participants involved were 14 undergraduate students at the State Islamic University of Sultan Maulana Hasanuddin, Banten, West Java, Indonesia where participants were selected using the snowball sampling method. Data was collected through in-depth interviews which were then transcribed in which the names of participants were written pseudonymously. The collected data is then analyzed by the thematic analysis method. This study found that reading activities during the pandemic have their challenges and advantages and students have started to tend to get used to reading texts on a screen rather than reading in printout form, although some of them still prefer to read in printout form. The five main strategies in reading texts that are commonly used by students during the Covid-19 pandemic are memory association, retelling, previewing, identifying the main idea, and habituation. Researchers conclude that the effects of the Covid-19 pandemic have forced undergraduate students to get used to reading texts in on-screen form and reading strategies are still varied. Therefore, this research implies that the teaching and learning process should accommodate many strategies in reading texts based on proficiency in English and not dominate just one strategy.
A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which integrates scientific discipline wisdom in a scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR) methods. SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods. We test SciMED on five configurations of a settling sphere, with and without aerodynamic non-linear drag force, and with excessive noise in the measurements. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from the data, and demonstrate how the integration of domain knowledge enhances its performance. Our results indicate better performance on these tasks than the state-of-the-art SR software packages , even in cases where no knowledge is integrated. Moreover, we demonstrate how SciMED can alert the user about possible missing features, unlike the majority of current SR systems.
Ermittlung und Kommunikation von Anforderungen in etablierten UX-Prozessen

There is a strong overlap between requirements engineering (RE) and user experience (UX). Nevertheless, in practice both disciplines are often performed by separate roles and there are deficits in collaboration. In order to provide starting points for the further development of roles, activities and artifacts of the disciplines, the Requirements Engineering and User Experience Working Group (AK REUX) has been conducting a series of case studies since 2021, analyzing the UX processes of different companies from a RE perspective. We presented interim results of this investigation at the RE specialist group meeting in 2022 and compared them with the experiences of the participants.
Cluster-based multidimensional scaling embedding tool for data visualization

We present a new technique for visualizing high-dimensional data called cluster MDS (cl-MDS), which addresses a common difficulty of dimensionality reduction methods: preserving both local and global structures of the original sample in a single 2-dimensional visualization. Its algorithm combines the well-known multidimensional scaling (MDS) tool with the $k$-medoids data clustering technique, and enables hierarchical embedding, sparsification and estimation of 2-dimensional coordinates for additional points. While cl-MDS is a generally applicable tool, we also include specific recipes for atomic structure applications. We apply this method to non-linear data of increasing complexity where different layers of locality are relevant, showing a clear improvement in their retrieval and visualization quality.
The Implementation of ESP at the Faculty of Economy and Islamic Business UIN SMH Banten

The needs of English at the university level are not only focused on learning general English, but also on learning specific English that relates to academic studies or professional work. English for Specific Purposes is the term given to this particular English language (ESP). As a result, the goals of this study are to investigate the current implementation of English for Specific Purposes (ESP) at UIN Sultan Maulana Hasanuddin Banten's Faculty of Economy and Islamic Business and determine whether the current implementation of ESP meets the needs of the students. This study's participants are 47 students from three different departments: Islamic Banking, Islamic Economy, and Sharia Insurance. The information was gathered using a questionnaire and an interview. The data was then qualitatively analyzed. The findings revealed that the current implementation of ESP is equally well-applied in all three departments; however, there were some issues with course materials and time allocation. Furthermore, the implementation of English class has met the needs of the students, particularly in improving their vocabulary and speaking skills. The findings suggest that lecturers should receive ESP training and that the time allocation for the ESP course be considered.
Positive Politeness Used in the Interviews Conducted by Desi Anwar to Dewi Soekarno and Bill Gates

Interview is a popular means of gathering information in our society. However, there is little research on cross-cultural interviews which has been conducted. This research is a descriptive qualitative study which aims to examine Desi Anwar’s choice of positive politeness strategies in relation to her interviewees’ cultural backgrounds, i.e., Dewi Soekarno (Japanese) and Bill Gates (American). The interviews are transcribed and coded based on the positive politeness strategies used and the cultures of Japanese and American communication styles. The instrument used in analyzing the data is Excel Matrix. Findings show that the interview with Dewi Soekarno (Japanese interviewee) contains more positive politeness strategies than the interview with Bill Gates (American interviewee). The positive politeness strategies used in the interview with Dewi Soekarno are Exaggeration, Repetition, Including Both S and H, and also Compliments. This result is contradictory to Japanese and American communication styles, which Japanese do not prefer positive politeness; while Americans do. This study will shade some light on positive politeness used in cross-cultural interviews involving Japanese and American interviewees. Since both interviewees in this research are from different genders, it would be satisfactory if future research includes this aspect into consideration.
Rule-adhering synthetic data -- the lingua franca of learning

AI-generated synthetic data allows to distill the general patterns of existing data, that can then be shared safely as granular-level representative, yet novel data samples within the original semantics. In this work we explore approaches of incorporating domain expertise into the data synthesis, to have the statistical properties as well as pre-existing domain knowledge of rules be represented. The resulting synthetic data generator, that can be probed for any number of new samples, can then serve as a common source of intelligence, as a lingua franca of learning, consumable by humans and machines alike. We demonstrate the concept for a publicly available data set, and evaluate its benefits via descriptive analysis as well as a downstream ML model.
Learning state correspondence of reinforcement learning tasks for knowledge transfer

Deep reinforcement learning has shown an ability to achieve super-human performance in solving complex reinforcement learning (RL) tasks only from raw-pixels. However, it fails to reuse knowledge from previously learnt tasks to solve new, unseen ones. Generalizing and reusing knowledge are the fundamental requirements for creating a truly intelligent agent. This work proposes a general method for one-to-one transfer learning based on generative adversarial network model tailored to RL task.
A Fast and Simple Evolution Strategy with Covariance Matrix Estimation

With the rise of A.I. methods the demand for efficient optimization methods that are easy to implement and use increases.This paper introduces a simple optimization method for numerical blackbox optimization.It proposes to apply covariance matrix estimation for the (1+1)-ES with Rechenberg's step size control.Experiments on a small set of benchmark functions demonstrate that the approach outperforms its isotropic variant allowing competitive convergence on problems with scaled and correlated dimensions.
Orthogonality in Additive Echo State Networks

Reservoir computing (RC) is a state-of-the-art approach for efficient training in temporal domains.In this paper, we explore new RC architectures that generalise the popular leaky echo state network model (leaky-ESN) introducing an additive orthogonal term outside the nonlinear part of the ESN equation.We investigate the benefits of employing orthogonal matrices in ESNs both inside the nonlinearity and outside of it.We show empirically how to boost the memory capacity towards the theoretical maximum value while still preserving the power of nonlinear computations.Ergo, we optimise the compromise between computing with memory and computing with nonlinearity.The proposed model demonstrates to outperform both leaky-ESN and orthogonal reservoir ESN models on tasks requiring nonlinear computations with memory.
Recurrent Restricted Kernel Machines for Time-series Forecasting

In this paper, we propose a novel method for time-series modeling and forecasting.It is based on the temporal formulation of Restricted Kernel Machines leading to a dynamical equation in the latent-variables.Forecasting involves finding the next latent variable and then solving a pre-image problem to predict a new-point in the input space.Further, we benchmark our model on several standard data sets against other well-known time-series models.
Real-time capable Ensemble Estimation for 2D Object Detection

Deep neural networks tend to make overconfident predictions.Although ensemble methods improve the predictive performance by producing better calibrated confidences, they are computationally expensive.Thus, we propose a real-time capable ensemble method for object detection that significantly improves the performance with only a minor increase in runtime.Our method diversifies the prediction of the class probabilities on the anchor space using multiple classification heads.A regularization further increases the diversity of the heads, making ensemble distillation unnecessary.On the KITTI benchmark dataset, our approach increases the mean average precision of an SSD based network from 0.58 to 0.71.
Generalised hardness of approximation and the SCI hierarchy -- On determining the boundaries of training algorithms in AI

Hardness of approximation (HA) -- the phenomenon that, assuming P $\neq$ NP, one can easily compute an $\epsilon$-approximation to the solution of a discrete computational problem for $\epsilon > \epsilon_0 > 0$, but for $\epsilon < \epsilon_0$ it suddenly becomes intractable -- is a core phenomenon in the foundations of computations that has transformed computer science. In this paper we study the newly discovered phenomenon in the foundations of computational mathematics: generalised hardness of approximation (GHA) -- which in spirit is close to classical HA in computer science. However, GHA is typically independent of the P vs. NP question in many cases. Thus, it requires a new mathematical framework that we initiate in this paper. We demonstrate the hitherto undiscovered phenomenon that GHA happens when using AI techniques in order to train optimal neural networks (NNs). In particular, for any non-zero underdetermined linear problem the following phase transition may occur: One can prove the existence of optimal NNs for solving the problem but they can only be computed to a certain accuracy $\epsilon_0 > 0$. Below the approximation threshold $\epsilon_0$ -- not only does it become intractable to compute the NN -- it becomes impossible regardless of computing power, and no randomised algorithm can solve the problem with probability better than 1/2. In other cases, despite the existence of a stable optimal NN, any attempts of computing it below the approximation threshold $\epsilon_0$ will yield an unstable NN. Our results use and extend the current mathematical framework of the Solvability Complexity Index (SCI) hierarchy and facilitate a program for detecting the GHA phenomenon throughout computational mathematics and AI.
Collision-Free 6-DoF Trajectory Generation for Omnidirectional Multi-rotor Aerial Vehicle

As a kind of fully actuated system, omnidirectional multirotor aerial vehicles (OMAVs) has more flexible maneuverability than traditional underactuated multirotor aircraft, and it also has more significant advantages in obstacle avoidance flight in complex environments.However, there is almost no way to generate the full degrees of freedom trajectory that can play the OMAVs' potential.Due to the high dimensionality of configuration space, it is challenging to make the designed trajectory generation algorithm efficient and scalable.This paper aims to achieve obstacle avoidance planning of OMAV in complex environments. A 6-DoF trajectory generation framework for OMAVs was designed for the first time based on the geometrically constrained Minimum Control Effort (MINCO) trajectory generation framework.According to the safe regions represented by a series of convex polyhedra, combined with the aircraft's overall shape and dynamic constraints, the framework finally generates a collision-free optimal 6-DoF trajectory.The vehicle's attitude is parameterized into a 3D vector by stereographic projection.Simulation experiments based on Gazebo and PX4 Autopilot are conducted to verify the performance of the proposed framework.
Online Whole-body Motion Planning for Quadrotor using Multi-resolution Search

In this paper, we address the problem of online quadrotor whole-body motion planning (SE(3) planning) in unknown and unstructured environments. We propose a novel multi-resolution search method, which discovers narrow areas requiring full pose planning and normal areas requiring only position planning. As a consequence, a quadrotor planning problem is decomposed into several SE(3) (if necessary) and R^3 sub-problems. To fly through the discovered narrow areas, a carefully designed corridor generation strategy for narrow areas is proposed, which significantly increases the planning success rate. The overall problem decomposition and hierarchical planning framework substantially accelerate the planning process, making it possible to work online with fully onboard sensing and computation in unknown environments. Extensive simulation benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the state-of-the-art methods in computation time while maintaining high planning success rate. The proposed method is finally integrated into a LiDAR-based autonomous quadrotor, and various real-world experiments in unknown and unstructured environments are conducted to demonstrate the outstanding performance of the proposed method.
Decoupled Pronunciation and Prosody Modeling in Meta-Learning-Based Multilingual Speech Synthesis

This paper presents a method of decoupled pronunciation and prosody modeling to improve the performance of meta-learning-based multilingual speech synthesis. The baseline meta-learning synthesis method adopts a single text encoder with a parameter generator conditioned on language embeddings and a single decoder to predict mel-spectrograms for all languages. In contrast, our proposed method designs a two-stream model structure that contains two encoders and two decoders for pronunciation and prosody modeling, respectively, considering that the pronunciation knowledge and the prosody knowledge should be shared in different ways among languages. In our experiments, our proposed method effectively improved the intelligibility and naturalness of multilingual speech synthesis comparing with the baseline meta-learning synthesis method.
Depression, Self-Esteem and Daily Living Activities among Geriatric Home Residence People

and 84% of them had low function of daily living activities. The study also revealed a statistically
Policy Space Identification in Configurable Environments

We study the problem of identifying the policy space of a learning agent, having access to a set of demonstrations generated by its optimal policy. We introduce an approach based on statistical testing to identify the set of policy parameters the agent can control, within a larger parametric policy space. After presenting two identification rules (combinatorial and simplified), applicable under different assumptions on the policy space, we provide a probabilistic analysis of the simplified one in the case of linear policies belonging to the exponential family. To improve the performance of our identification rules, we frame the problem in the recently introduced framework of the Configurable Markov Decision Processes, exploiting the opportunity of configuring the environment to induce the agent revealing which parameters it can control. Finally, we provide an empirical evaluation, on both discrete and continuous domains, to prove the effectiveness of our identification rules.