Reviewerly/exHarmony huggingface dataset:
Pengembangan E-Modul Membaca Siswa Kelas 3 SDN 029 Pekanbaru

This development research was carried out based on the teacher's difficulty in presenting complete and interesting teaching materials so that students were less enthusiastic when the learning process took place. This study aims to test the feasibility of teaching materials in the form of e-modules on theme 2 sub-theme 3 for third grade elementary school. This research is a development research using the ADDIE model. The stages of the ADDIE model to develop this e-module were modified into three stages, namely analysis, design and development. The data collected in this study are quantitative data and qualitative data. The methods used to collect data are: observation, interviews, and questionnaires. The subjects involved in this study were 2 design experts, 2 material experts and 2 linguists. The data analysis technique used is quantitative and qualitative descriptive data analysis techniques. The results showed that the validation of design experts obtained an average of 92.31% in the "Valid" category, material experts obtained an average of 87.49% in the "Valid" category, and linguists obtained an average of 82.04 % with the category "Valid". So that the average value obtained from the development of this reading e-module is 87.28% with the "Valid" category.
Dialogue Strategy Adaptation to New Action Sets Using Multi-Dimensional Modelling

A major bottleneck for building statistical spoken dialogue systems for new domains and applications is the need for large amounts of training data. To address this problem, we adopt the multi-dimensional approach to dialogue management and evaluate its potential for transfer learning. Specifically, we exploit pre-trained task-independent policies to speed up training for an extended task-specific action set, in which the single summary action for requesting a slot is replaced by multiple slot-specific request actions. Policy optimisation and evaluation experiments using an agenda-based user simulator show that with limited training data, much better performance levels can be achieved when using the proposed multi-dimensional adaptation method. We confirm this improvement in a crowd-sourced human user evaluation of our spoken dialogue system, comparing partially trained policies. The multi-dimensional system (with adaptation on limited training data in the target scenario) outperforms the one-dimensional baseline (without adaptation on the same amount of training data) by 7% perceived success rate.
Recognition of Overlapped Objects via Weakly-Supervised Methods with Bag of Alphabets

Weakly supervised learning can adjust model parameters by exploring image-level annotations, thus significantly reducing the cost of labeling samples. Although many efforts have been made to reduce the performance gap compared to supervised learning with expensive annotations, this article focuses on overlapped objects' adverse effects in recognizing overlapped objects via weakly supervised methods. In order to identify overlapped objects by weak supervision method, the current Bag-of-Words (BOW) framework for non-overlapped objects is modified to Bag-of-Alphabets (BOA), the words of objects corresponding to the alphabets of its parts. As a result, the un-covered factors of objects can significantly contribute to object recognition even when others may cover them. So, compared to BOW, a novel method, BOA is created to combine alphabets of parts into words of objects.
Dual Formulation for Chance Constrained Stochastic Shortest Path with Application to Autonomous Vehicle Behavior Planning

Autonomous vehicles face the problem of optimizing the expected performance of subsequent maneuvers while bounding the risk of collision with surrounding dynamic obstacles. These obstacles, such as agent vehicles, often exhibit stochastic transitions that should be accounted for in a timely and safe manner. The Constrained Stochastic Shortest Path problem (C-SSP) is a formalism for planning in stochastic environments under certain types of operating constraints. While C-SSP allows specifying constraints in the planning problem, it does not allow for bounding the probability of constraint violation, which is desired in safety-critical applications. This work’s first contribution is an exact integer linear programming formulation for Chance-constrained SSP (CCSSP) that attains deterministic policies. Second, a randomized rounding procedure is presented for stochastic policies. Third, we show that the CC-SSP formalism can be generalized to account for constraints that span through multiple time steps. Evaluation results show the usefulness of our approach in benchmark problems compared to existing approaches.
Detecting Human-to-Human-or-Object (H<sup>2</sup>O) Interactions with DIABOLO

Detecting human interactions is crucial for human behavior analysis. Many methods have been proposed to deal with Human-to-Object Interaction (HOI) detection, i.e., detecting in an image which person and object interact together and classifying the type of interaction. However, Human-to-Human Interactions, such as social and violent interactions, are generally not considered in available HOI training datasets. As we think these types of interactions cannot be ignored and decorrelated from HOI when analyzing human behavior, we propose a new interaction dataset to deal with both types of human interactions: Human-to-Human-or-Object (H2O). In addition, we introduce a novel taxonomy of verbs, intended to be closer to a description of human body attitude in relation to the surrounding targets of interaction, and more independent of the environment. Unlike some existing datasets, we strive to avoid defining synonymous verbs when their use highly depends on the target type or requires a high level of semantic interpretation. As H2O dataset includes V-COCO images annotated with this new taxonomy, images obviously contain more interactions. This can be an issue for HOI detection methods whose complexity depends on the number of people, targets or interactions. Thus, we propose DIABOLO (Detecting InterActions By Only Looking Once), an efficient subject-centric single-shot method to detect all interactions in one forward pass, with constant inference time independent of image content. In addition, this multi-task network simultaneously detects all people and objects. We show how sharing a network for these tasks does not only save computation resource but also improves performance collaboratively. Finally, DIABOLO is a strong baseline for the new proposed challenge of H2O Interaction detection, as it outperforms all state-of-the-art methods when trained and evaluated on HOI dataset V-COCO.
Research on self-balancing unmanned bicycle based on cascade PID control

Bicycle is a kind of underactuated and unstable zero dynamic system. In order to realize the balance control and automatic tracking of unmanned bicycle, Lagrange method is used to model the bicycle system and analyze its dynamic model. At the same time, combined with the skills and experience of driving bicycle, a cascade PID control method is proposed. This method can couple the balance control and line inspection control, avoid the conflict between balance and steering, and improve the system response speed and control accuracy. The steering data of the electromagnetic sensor is taken as the output variable of the outer loop control, and then the inclination angle and angular velocity of the bicycle are fused by the Kalman filter algorithm to obtain stable and accurate body attitude angle data, which is used as the inner loop feedback of PID control and cascade control with the output of the outer loop. The effectiveness and practicability of the cascade PID control method are verified by the self-made experimental prototype. The experimental data of the prototype show that the wheel deflection angle converges quickly and returns to the balance position after a short oscillation under the condition of active disturbance balance, which shows strong robustness. The scheme can provide reference for the engineering implementation of the bicycle without tracking.
Cloud K-SVD for Image Denoising

To remove Additive White Gaussian Noise (AWGN) from images using Cloud K-SVD, a collaborative dictionary learning algorithm. Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce a mutual dictionary to represent low-dimensional geometric structures in image data. We present a novel application of the algorithm as we use it to recover both noiseless and noisy images from overlapping patches. We implement a node network in Kubernetes using Docker containers to facilitate Cloud K-SVD. Results show that Cloud K-SVD can recover images approximately and remove quantifiable amounts of noise from benchmark gray-scaled images without sacrificing accuracy in recovery; we achieve an SSIM index of 0.88, 0.91 and 0.95 between noiseless and recovered images for noise levels ( $$\mu$$ = 0, $$\sigma ^{2}$$ = 0.01, 0.005, 0.001), respectively, which is on a par with SOTA in the field. Cloud K-SVD is evidently able to learn a mutual dictionary across multiple nodes and remove AWGN from images. The mutual dictionary can be used to recover a specific image at any of the nodes in the network.
Variational secure cloud quantum computing

Variational quantum algorithms (VQAs) have been considered to be useful applications of noisy intermediate-scale quantum (NISQ) devices. Typically, in VQAs, a parametrized ansatz circuit is used to generate a trial wave function, and the parameters are optimized to minimize a cost function. On the other hand, blind quantum computing (BQC) has been studied in order to provide a quantum algorithm with security by using cloud networks. A client with a limited ability to perform quantum operations hopes to have access to a quantum computer of a server, and BQC allows the client to use the server's computer without leakage of the client's information (such as input, running quantum algorithms, and output) to the server. However, BQC is designed for fault-tolerant quantum computing, and this requires many ancillary qubits, which may not be suitable for NISQ devices. Here, we propose an efficient way to implement the NISQ computing with guaranteed security for the client. In our architecture, only $N+1$ qubits are required, under an assumption that the form of ans\"atze is known to the server, where $N$ denotes the necessary number of the qubits in the original NISQ algorithms. The client only performs single-qubit measurements on an ancillary qubit sent from the server, and the measurement angles can specify the parameters for the ans\"atze of the NISQ algorithms. The no-signaling principle guarantees that neither parameters chosen by the client nor the outputs of the algorithm are leaked to the server. This work paves the way for new applications of NISQ devices.
Violation of high-dimensional Bell inequality using narrowband orbital-angular-momentum entanglement from warm atomic vapor

Narrowband biphotons from an atomic ensemble are an extraordinary source for quantum information science. The generation of biphotons in the warm atomic system has many applicable prospects for its simplicity, stability, and compactness. In this article we experimentally demonstrate the generation of biphotons from the spontaneous four-wave mixing (SFWM) process in a warm rubidium vapor cell. By performing high-dimensional Bell-type measurements and mutually unbiased measurements, we maximally obtain a three-dimensional orbital-angular-momentum discrete-variable (DV) entangled state. The violation of high-dimensional Bell inequalities shows the great potential of warm atomic systems in generating DV high-dimensional entanglement. Such narrowband photon pairs may find applications in quantum-memory-based quantum communication networks. Through integration with the continuous-variable (CV) entanglement already existing in the same SFWM platform, our results here pave the way to construct atomic-ensemble-based DV-CV hybrid quantum information protocols.
Evaluating the performance of clone detection tools in detecting cloned co-change candidates

Co-change candidates are the group of code fragments that require a change if any of these fragments experience a modification in a commit operation during software evolution. The cloned co-change candidates are a subset of the co-change candidates, and the members in this subset are clones of one another. The cloned co-change candidates are usually created by reusing existing code fragments in a software system. Detecting cloned co-change candidates is essential for clone-tracking, and studies have shown that we can use clone detection tools to find cloned co-change candidates. However, although several studies evaluate clone detection tools for their accuracy in detecting cloned fragments, we found no study that evaluates clone detection tools for detecting cloned co-change candidates. In this study, we explore the dimension of code clone research for detecting cloned co-change candidates. We compare the performance of 12 different configurations of nine promising clone detection tools in identifying cloned co-change candidates from eight open-source C and Java-based subject systems of various sizes and application domains. A ranked list and analysis of the results provides valuable insights and guidelines into selecting and configuring a clone detection tool for identifying co-change candidates and leads to a new dimension of code clone research into change impact analysis.
An Optimal System on Data Challenge with Distributed Data Management on Cloud, Fog and Edge Computing

As the Internet of All (IoE) is growing rapidly, there are increasing numbers of smart devices connected to the Internet that will lead to large-scale data, creating problems for traditional cloud computing models; for instance, bandwidth loading, slow response times, poor safety and poor privacy. Traditional cloud computing is no longer sufficient to satisfy today's intelligent society's diverse data processing needs, and advanced computing technologies are emerging. It is a brand-new paradigm of computing on the edge of the network. Edge and fog computings are a solution that supports industrial CPS growth in a decentralized, multi-level data analysis computing environment. It's not a simple task, however, since a number of barriers and new approaches and technologies need to be developed including complete architecture of edge computing, keyword technology, security and privacy and ultimately the edge of application computers.
Explainable AI framework for the financial rating models

As artificial intelligence has developed and applied over many industries, demands for explaining the results have been increased respectably. Recently EU created a new privacy regulation to guarantee the right of customers to have an explanation on the prediction model. Due to increasing demands on transparency for AI model, the techniques of explaining artificial intelligence (XAI) have been the center of attention. Heavy regularized industries, such as finance, have introduced many artificial intelligence powered software and models, and have faced huge pressures as well as demands to provide enough explanations from both customers and the authorities. There have been many XAI algorithms to interpret the model as well as the outcome, but there is little work done to explain the outcomes in comparison. In real world, it is often demanded to explain the reasons for alterations by comparison against previous ones such as changing credit rates over time which can be an essential factor for an individual’s life. In this paper, we suggest a comparison explanation framework using two outputs which can be financial ratings [11]. We have demonstrated the explanation framework on real world data with various ML algorithms and local surrogate models. We evaluate the quality of the explanation with the domain expert’s survey. The suggested framework received positive feedbacks from the experts and proves that it may applied to domain specialized areas. Also, the research contributes an alternative view of XAI in terms of practical usage at the field of finance.
Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning

Life and physical sciences have always been quick to adopt the latest advances in machine learning to accelerate scientific discovery. Examples of this are cell segmentation or cancer detection. Nevertheless, these exceptional results are based on mining previously created datasets to discover patterns or trends. Recent advances in AI have been demonstrated in real-time scenarios like self-driving cars or playing video games. However, these new techniques have not seen widespread adoption in life or physical sciences because experimentation can be slow. To tackle this limitation, this work aims to adapt generative learning algorithms to model scientific experiments and accelerate their discovery using in-silico simulations. We particularly focused on real-time experiments, aiming to model how they react to user inputs. To achieve this, here we present an encoder-decoder architecture based on the Transformer model to simulate real-time scientific experimentation, predict its future behaviour and manipulate it on a step-by-step basis. As a proof of concept, this architecture was trained to map a set of mechanical inputs to the oscillations generated by a chemical reaction. The model was paired with a Reinforcement Learning controller to show how the simulated chemistry can be manipulated in real-time towards user-defined behaviours. Our results demonstrate how generative learning can model real-time scientific experimentation to track how it changes through time as the user manipulates it, and how the trained models can be paired with optimisation algorithms to discover new phenomena beyond the physical limitations of lab experimentation. This work paves the way towards building surrogate systems where physical experimentation interacts with machine learning on a step-by-step basis.
Mission to a Metal World: In August, NASA will Launch a Probe to Study a Strange Metallic Asteroid Called Psyche

In August 2022, NASA will launch a probe to study a strange metallic asteroid called Psyche. The Psyche spacecraft will reach its destination in early 2026, where it will commence its two-yearlong scientific survey of the asteroid. What will it find there? Astronomers think we might see enormous surface faults from the contraction of freezing metal, glittering cliffs of green crystalline mantle minerals, frozen flows of sulfur lava, and vast fields of metal shards scattered over the surface from millennia of high-speed impacts. There will no doubt be plenty of surprises, too.
JellyBoard: A Flexible Breadboard for Electronic Prototyping

In product design and engineering, breadboard has made prototyping electronics faster and easier. It removes the process of soldering required in any circuit assembly with arrays of pinholes that are electrically connected with metal strips embedded underneath. However, the current design of the common breadboard is rigid, unsatisfactory in situations that favor the flexibility of components. In this study, we present a new fabrication method for flexible breadboard. It can be made with readily available materials, which are essential for most rapid electronic prototyping. It can freely deform when exerting force and can play a major role in the development of soft interfaces.
Designing Sustainable Urban Blocks: An Effort to Optimizing 3D Form and Achieving Maximum Amount of Solar Radiation

Although the needs of the world's population in different fields can be satisfied using solar energy, use of solar radiation energy is not often employed due to the requirement for complex calculations and lesser-known techniques in designing urban spaces, especially at the scale of urban blocks. Therefore, the present study was aimed to design and use optimization algorithms in planning urban blocks towards increasing absorption of solar radiation at different levels. For this purpose, a parametric simulation technique was performed for facilitating planning in urban blocks using Grasshopper plug-ins and their analysis in the Ladybug plug-in to calculate solar radiations for different types of urban blocks in this study. Then, optimizing solar radiations were investigated to absorb the maximum amount of solar energy based on the variables of urban block kinds by optimization algorithms in one-objective continuous functions. Also, this study tried to increase the absorbed solar radiation energy by 20 to 60% in the optimized urban blocks by employing the optimization of urban block kinds in the existing states and using a genetic algorithm in three parameters of height, scale, and orientation.
A Novel Pooling Method for Regularization of Deep Convolutional Neural Networks and Application to Image Classification

Dropout has been introduced as a powerful regularization approach to preventing overfitting problem in deep neural networks, particularly in deep convolutional neural networks (DCNNs). Recently, Various approaches of Dropout extension have been designed to avoid overfitting problems, as well as to enhance the generalization capability of deep neural networks. These methods include spectral dropout which achieves improved and avoids overfitting by eliminating noisy and weak Fourier domain coefficients of the neural network activations. Although spectral dropout has shown powerful results on using it in different convolutional neural network layers, its actual effect on the pooling layer has not been thoroughly explored. Furthermore, a pooling process plays a crucial role in deep convolutional neural networks, which serves to reduce the dimensionality of processed data for decreasing computational cost as well as for avoiding overfitting and enhancing the generalization capability of the network. For this reason, we focus on the pooling layer, and we propose a novel pooling method based on applying the Spectral dropout technique in the pooling region, in order to avoid overfitting problem, as well as to enhance the generalization ability of DCNNs. Experimental results on several image benchmarks show that our proposed technique outperforms the existing pooling methods in classification performance as well as is effective for improving the generalization ability of DCNNs. Moreover, we show that our proposed technique combined with other regularization methods, such as batch normalization, is competitive with other existing methods in classification performance.
Survey on Loss Function for Face Recognition

Significant progress in the development of new loss functions has led to the improvement in the discriminative power of learned face features. Generally, loss function is used to find the difference between the correct output and the predicted output of face features from a convolutional neural network model. Greater differences between the two outputs lead to higher loss function which indicates poor performance. On the other hand, for two outputs that are identical or near similarity, the loss function is zero or low respectively implying better performance. In other words, the model is trained iteratively until the loss function or error is minimized. As a result, loss function encourages the minimization of intra-class variations and the maximization of inter-class differences. Note that the loss function is only implemented during the training phase and is discarded in the testing phase. In this paper, different types of loss functions for face recognition are reviewed. These loss functions are categorized into four groups, namely Metric Learning, Classification Loss, Mining-based Loss and Balancing-based Loss.