Reviewerly/exHarmony huggingface dataset:
Designing Quizizz as Media to Assess the Mastery of Simple Past Tense

This research study aimed to design Quizizz as media to assess the mastery of simple past tense to year-8 students of SMP Kristen Immanuel 1 Pontianak. The form of this research study was development research. The research method was design and development research. As a procedure, the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) model was applied. However, due to the COVID-19 pandemic, the researcher did three phases in this study: Analyze, Design, and Develop (ADD). In the analysis phase, the researcher interviewed the English teacher to find out the actual conditions of the students, the teacher, and the school. In the designing phase, the researcher observed the textbook and the curriculum applied in the school to design a significant product of Quizizz. During the development phase, the researcher used the Quizizz platform to develop the learning media for the students by combining multimedia such as images, GIFs, videos, and sounds. Then, the designed product was completed after having two internal evaluations to ensure that the product was usable and fulfilled all the assessment criteria. In conclusion, the Quizizz multimedia assessment assesses the mastery of simple past tense for year-8 students of SMP Immanuel 1 Pontianak.Keywords: Designing, Quizizz, Simple past tense
Android â€“ Based Nearby Location App For Orphanage Searches Using Google Place API Technology With Agile System Development Method

At this time the public is already aware of the value of sharing with the needy, so the search for orphanages is increasing drastically. Similarly, the use of maps on smartphones is increasing because people are aware of technology and use it in every day. But often people are troubled to know the location of the orphanage in around, as well as the condition of the orphanage which includes the number of children, the number of caretakers, and what are the needs of the orphanage without coming to the location of the orphanage. In addition, there are often overlapping scheduling problems that often make people who want to hold activities experience unilateral cancellation and there is also the issue of donations where people are confused how to give a little from their income to the orphanage without having to come to the location. With agile development systems method designed nearby location application using Google place, which is obtained the core of the problem is the difficulty of finding information orphanages and not easy to find orphanages around. Therefore, with this nearby location application is expected to help the public to find information on orphanages closest to their location, as well as be able to schedule activities that can be monitored in real time. So there is no overlap back on the activity. In addition, there is a function of giving donations, so that people can donate without having to come to the orphanage.
Development Of Android-Based Digital Maps for Learning the Mapking Process

Educational media is an important component in education for making the Jambi Province Map which must be presented in an innovative way. Media that is currently known is a digital map that is not easy to read and the position and location or color errors, this situation will lead to errors in meaning and purpose. This study aims to make a product in the form of a digital map and test its validity based on media experts. This study uses the ADDIE model development procedure which is modified into 4 stages, namely analysis, design, development and implementation. The Likert scale method was used as an analysis to retrieve information from 20 class VII students of SMPN I Jambi City as respondents, while the validation method was used as an analysis to test product validity. The results of the development in this study are in the form of digital map images designed through the Android-based UMapper and WhatsApp Plus Aero applications. Test results from two material experts show that this product gets a score of 47 and 46 out of a maximum score of 50 which means very good. This shows that the development of this media is very good in meeting the needs of students in the field of education for making Jambi Province maps, being creative and innovating in using media in the form of android-based applications.
MSLE: An ontology for materials science laboratory equipment – Large-scale devices for materials characterization

This paper introduces a new ontology for Materials Science Laboratory Equipment, termed MSLE. A fundamental issue with materials science laboratory (hereafter lab) equipment in the real world is that scientists work with various types of equipment with multiple specifications. For example, there are many electron microscopes with different parameters in chemical and physical labs. A critical development to unify the description is to build an equipment domain ontology as basic semantic knowledge and to guide the user to work with the equipment appropriately. Here, we propose to develop a consistent ontology for equipment, the MSLE ontology. In the MSLE, two main existing ontologies, the Semantic Sensor Network (SSN) and the Material Vocabulary (MatVoc), have been integrated into the MSLE core to build a coherent ontology. Since various acronyms and terms have been used for equipment, this paper proposes an approach to use a Simple Knowledge Organization System (SKOS) to represent the hierarchical structure of equipment terms. Equipment terms were collected in various languages and abbreviations and coded into the MSLE using the SKOS model. The ontology development was conducted in close collaboration with domain experts and focused on the large-scale devices for materials characterization available in our research group. Competency questions are expected to be addressed through the MSLE ontology. Constraints are modeled in the Shapes Query Language (SHACL); a prototype is shown and validated to show the value of the modeling constraints.
Optimization of Reversible Logic Networks with Gate Sharing

Logic synthesis for quantum computing aims to transform a Boolean logic network into a quantum circuit. A conventional two-stage flow first synthesizes the given Boolean logic network into a reversible logic network composed of reversible logic gates. Then, it maps each reversible logic gate into quantum gates to generate a quantum circuit. The state-of-the-art method for the first stage takes advantage of the lookup-table (LUT) mapping technology for FPGAs to decompose the given Boolean logic network into sub-networks, and then maps the sub-networks into reversible logic networks. Although every sub-network is well synthesized, we observe that the reversible logic networks could be further optimized by sharing the reversible logic gates belonging to different sub-networks. Thus, in this paper, we propose a new optimization method for the reversible logic networks by sharing gates. We translate the problem of extracting shareable gates to the exclusive-sums-of-product term optimization problem. The experimental results show that the proposed method successfully optimizes the reversible logic networks generated by the LUT-based method. It is able to reduce an average of approximately 4% of quantum gate cost without increasing the number of ancilla lines for a set of IWLS 2005 benchmarks.
An SMT-Solver-Based Synthesis of NNA-Compliant Quantum Circuits Consisting of CNOT, H and T Gates

It is natural to assume that we can perform quantum operations between only two adjacent physical qubits (quantum bits) to realize a quantum computer for both the current and possible future technologies. This restriction is called the Nearest Neighbor Architecture (NNA) restriction. This paper proposes an SMT-solver-based synthesis of quantum circuits consisting of CNOT, H, and T gates to satisfy the NNA restriction. Although the existing SMT-solver-based synthesis cannot treat H and T gates directly, our method treats the functionality of quantum-specific T and H gates carefully so that we can utilize an SMT-solver to minimize the number of CNOT gates; unlike the existing SMT-solver-based methods, our method considers "Don't Care" conditions in intermediate points of a quantum circuit by exploiting the property of T gates to reduce CNOT gates. Experimental results show that our approach can reduce the number of CNOT gates by 58.11% on average compared to the naive application of the existing method which does not consider the "Don't Care" condition.
FPGA Based Countermeasures against Side Channel Attacks on Block Ciphers

Field Programmable Gate Arrays (FPGAs) are increasingly ubiquitous. FPGAs enable hardware acceleration and reconfigurability. Any security breach or attack on critical computations occurring on an FPGA can lead to devastating consequences. Side-channel attacks have the ability to reveal secret information, such as secret keys from cryptographic circuits running on FPGAs. Power dissipation (PA), Electromagnetic (EM) radiation, fault injection (FI) and remote power dissipation (RPA) attacks are the most compelling and noninvasive side-channel attacks demonstrated on FPGAs. This paper discusses two PA attack countermeasures (QuadSeal and RFTC) and one RPA attack countermeasure (UCloD) in detail to protect FPGAs.
Multi-Wavelength Parallel Training and Quantization-Aware Tuning for WDM-Based Optical Convolutional Neural Networks Considering Wavelength-Relative Deviations

Wavelength Division Multiplexing (WDM)-based Mach-Zehnder Interferometer Optical Convolutional Neural Networks (MZI-OCNNs) have emerged as a promising platform to accelerate convolutions that cost most computing sources in neural networks. However, the wavelength-relative imperfect split ratios and actual phase shifts in MZIs and quantization errors from the electronic configuration module will degrade the inference accuracy of WDM-based MZI-OCNNs and thus render them unusable in practice. In this paper, we propose a framework that models the split ratios and phase shifts under different wavelengths, incorporates them into OCNN training, and introduces quantization-aware tuning to maintain inference accuracy and reduce electronic module complexity. Consequently, the framework can improve the inference accuracy by 49%, 76%, and 76%, respectively, for LeNet5, VGG7, and VGG8 implemented with multi-wavelength parallel computing. And instead of using Float 32/64 quantization resolutions, only 5,6, and 4 bits are needed and fewer quantization levels are utilized for configuration signals.
Latent Weight-Based Pruning for Small Binary Neural Networks

Binary neural networks (BNNs) substitute complex arithmetic operations with simple bit-wise operations. The binarized weights and activations in BNNs can drastically reduce memory requirement and energy consumption, making it attractive for edge ML applications with limited resources. However, the severe memory capacity and energy constraints of low-power edge devices call for further reduction of BNN models beyond binarization. Weight pruning is a proven solution for reducing the size of many neural network (NN) models, but the binary nature of BNN weights make it difficult to identify insignificant weights to remove.
Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics

Data quantization is an effective method to accelerate neural network training and reduce power consumption. However, it is challenging to perform low-bit quantized training: the conventional equal-precision quantization will lead to either high accuracy loss or limited bit-width reduction, while existing mixed-precision methods offer high compression potential but failed to perform accurate and efficient bit-width assignment. In this work, we propose DYNASTY, a block-wise dynamic-precision neural network training framework. DYNASTY provides accurate data sensitivity information through fast online analytics, and maintains stable training convergence with an adaptive bit-width map generator. Network training experiments on CIFAR-100 and ImageNet dataset are carried out, and compared to 8-bit quantization baseline, DYNASTY brings up to 5.1× speedup and 4.7× energy consumption reduction with no accuracy drop and negligible hardware overhead.
Knowledge Distillation in Quantum Neural Network Using Approximate Synthesis

Recent assertions of a potential advantage of Quantum Neural Network (QNN) for specific Machine Learning (ML) tasks have sparked the curiosity of a sizable number of application researchers. The parameterized quantum circuit (PQC), a major building block of a QNN, consists of several layers of single-qubit rotations and multi-qubit entanglement operations. The optimum number of PQC layers for a particular ML task is generally unknown. A larger network often provides better performance in noiseless simulations. However, it may perform poorly on hardware compared to a shallower network. Because the amount of noise varies amongst quantum devices, the optimal depth of PQC can vary significantly. Additionally, the gates chosen for the PQC may be suitable for one type of hardware but not for another due to compilation overhead. This makes it difficult to generalize a QNN design to wide range of hardware and noise levels. An alternate approach is to build and train multiple QNN models targeted for each hardware which can be expensive. To circumvent these issues, we introduce the concept of knowledge distillation in QNN using approximate synthesis. The proposed approach will create a new QNN network with (i) a reduced number of layers or (ii) a different gate set without having to train it from scratch. Training the new network for a few epochs can compensate for the loss caused by approximation error. Through empirical analysis, we demonstrate ≈71.4% reduction in circuit layers, and still achieve ≈16.2% better accuracy under noise.
Semantic Guided Fine-Grained Point Cloud Quantization Framework for 3D Object Detection

Unlike the grid-paced RGB images, network compression, i.e.pruning and quantization, for the irregular and sparse 3D point cloud face more challenges. Traditional quantization ignores the unbalanced semantic distribution in 3D point cloud. In this work, we propose a semantic-guided adaptive quantization framework for 3D point cloud. Different from traditional quantization methods that adopt a static and uniform quantization scheme, our proposed framework can adaptively locate the semantic-rich foreground points in the feature maps to allocate a higher bitwidth for these "important" points. Since the foreground points are in a low proportion in the sparse 3D point cloud, such adaptive quantization can achieve higher accuracy than uniform compression under a similar compression rate. Furthermore, we adopt a block-wise fine-grained compression scheme in the proposed framework to fit the larger dynamic range in the point cloud. Moreover, a 3D point cloud based software and hardware co-evaluation process is proposed to evaluate the effectiveness of the proposed adaptive quantization in actual hardware devices. Based on the nuScenes dataset, we achieve 12.52% precision improvement under average 2-bit quantization. Compared with 8-bit quantization, we can achieve 3.11× energy efficiency based on co-evaluation results.
An Error Analysis Method by Extracting Error Time Transition Patterns in Photovoltaic Power Forecasting

Information about forecasting errors of photovoltaic power generation (PV) output is useful in daily supply and demand operation of power systems for large-scale integration of PV. In the conventional error analyses, average is generally used which implies the summary of errors in a certain period. It does not represent actual transitions of errors that occurred every day and hence likelihood of future transitions with time cannot be grasped in the forecasting error. In this paper, we propose an error analysis method for PV output forecasting to grasp characteristics of error transitions such as the shapes and their occurrence probabilities. We enable such an analysis by extracting representative patterns in shape of error transitions in the following way. All the days are classified to some clusters by using a clustering method based on a similarity of shape of the error transition in that day, and then representative patterns are obtained by averaging the error transitions for each cluster. The numerical result shows that the k-means method, which is one of the clustering methods, is suitable for helping understand annual trends of error transitions. We also clarify how we should estimate the appropriate number of patterns of error time transitions.
A Comparative Study of Data Collection Periods for Just-In-Time Defect Prediction Using the Automatic Machine Learning Method

This paper focuses on the “data collection period” for training a better Just-In-Time (JIT) defect prediction model — the early commit data vs. the recent one —, and conducts a large-scale comparative study to explore an appropriate data collection period. Since there are many possible machine learning algorithms for training defect prediction models, the selection of machine learning algorithms can become a threat to validity. Hence, this study adopts the automatic machine learning method to mitigate the selection bias in the comparative study. The empirical results using 122 open-source software projects prove the trend that the dataset composed of the recent commits would become a better training set for JIT defect prediction models.
Study on distribution characteristics of underground space alteration information of drill core using hyperspectral technology

Alteration information is an important prospecting element in the exploration of deep earth resources. Hyperspectral technology has unique advantages in fine detection and inversion of altered mineral information. Drill core spectral scanning based on hyperspectral technology can identify mineral information in cores nondestructively, rapidly and in batches, which provides an effective means for revealing deep alteration information and its distribution characteristics. In this paper, we use this technology to study the alteration types and combination characteristics of the underground space of the typical deposit in the Kalatag ore collection area, as well as the distribution characteristics of the three-dimensional space of alteration information, and obtain the alteration mineral types and their relative content cataloguing curves of the 10,000 meter drilled cores in different exploration lines. The drill core in the mining area can be divided into three main alteration zones from the shallow part to the deep part, combined with the analysis of the geological core catalogue data. The upper zone is dominated by iron oxide, gypsum and aluminum-sericite. The middle zone is mainly composed of medium aluminum sericite, chlorite, and montmorillonite, with a small amount of high aluminum sericite and iron oxide compounds. In the lower zone, the altered minerals are dominated by high-alumina sericite and chlorite, with a small amount of medium-aluminum sericite and carbonate, as well as sericite and chlorite show obvious negative correlation. There are significantly different types of altered minerals, mineral assemblage characteristics and alteration scale in each alteration zone and between the zones, which reflects that the mineral generation environment has obvious differences with the change of drilling depth. Comprehensive research based on the differences of mineral formation environments can provide important support for deep exploration of underground alteration spatial characteristics, redox zoning, inversion of hydrothermal fluid characteristics and comprehensive analysis of metallogenic environments.
A Simple and Interactive System for Modeling Typical Japanese Castles

We present an interactive modeling system for Japanese castles. We develop an user interface that can generate the fundamental structure of the castle tower consisting of stone walls, turrets, and roofs. By clicking on the screen displaying the 3D space with the mouse, relevant parameters are calculated automatically to generate 3D models of Japanese-style castles. We use characteristic curves that often appear in ancient Japanese architecture for the realistic modeling of the castles. We evaluate the effectiveness of our method by comparing the castle generated by our method with a commercially-available 3D mode of a castle.
Ensemble-Based Method for Correcting Global Explanation of Prediction Model

Explainable artificial intelligence (AI) technology enables us to quantitatively analyze the whole prediction logic of AI as a global explanation. However, unwanted relationships learned by AI due to data sparsity, high dimensionality, and noise are also visualized in the explanation, which deteriorates confidence in the AI. Thus, methods for correcting those unwanted relationships in explanation has been developed. However, since these methods are applicable only to differentiable machine learning (ML) models but not to non-differentiable models such as tree-based models, they are insufficient for covering a wide range of ML technology. Since these methods also require re-training of the model for correcting its explanation (i.e., in-processing method), they cannot be applied to black-box models provided by third parties. Therefore, we propose a method called ensemble-based explanation correction (EBEC) as a post-processing method for correcting the global explanation of a prediction model in a model-agnostic manner by using the Rashomon effect of statistics. We evaluated the performance of EBEC with three different tasks and analyzed its function in more detail. The evaluation results indicate that EBEC can correct global explanation of the model so that the explanation aligns with the domain knowledge given by the user while maintaining its accuracy. EBEC can be extended in various ways and combined with any method to improve correction performance since it is a post-processing-type correction method. Hence, EBEC would contribute to high-productivity ML modeling as a new type of explanation-correction method.
Spatial-Temporal Aggregated Shuffle Attention for Video Instance Segmentation of Traffic Scene

Aiming at the problem of spatial focus regions distribution dispersion and dislocation in feature pyramid networks and insufficient feature dependency acquisition in both spatial and channel dimensions, this paper proposes a spatial-temporal aggregated shuffle attention for video instance segmentation (STASA-VIS). First, an mixed subsampling (MS) module to embed activating features from the low-level target area of feature pyramid into the high-level is designed, so as to aggregate spatial information on target area. Taking advantage of the coherent information in video frames, STASA-VIS uses the first ones of every 5 video frames as the key-frames and then propagates the keyframe feature maps of the pyramid layers forward in the time domain, and fuses with the non-keyframe mixed subsampled features to achieve time-domain consistent feature aggregation. Finally, STASA-VIS embeds shuffle attention in the backbone to capture the pixel-level pairwise relationship and dimensional dependencies among the channels and reduce the computation. Experimental results show that the segmentation accuracy of STASA-VIS reaches 41.2%, and the test speed reaches 34FPS, which is better than the state-of-the-art one stage video instance segmentation (VIS) methods in accuracy and achieves real-time segmentation.