Reviewerly/exHarmony huggingface dataset:
Real-Time Stereo Disparity Prediction Based on Patch-Embedded Extraction and Depthwise Hierarchical Refinement for 3-D Sensing of Autonomous Vehicles on Energy-Efficient Edge Computing Devices

Sensing and communications are dispensable for autonomous vehicles and IoT. One key task in autonomous driving is the sensing of 3D information surrounding a vehicle. Most existing stereo disparity prediction networks pursue accurate disparity maps on high-performance GPUs with high energy consumption. While very few stereo networks achieving real-time on resource-constrained edge devices are hardly satisfactory in terms of accuracy. To tackle this, we propose a lightweight and efficient three-stage stereo disparity prediction network named HRSNet for real-time stereo matching on energy-efficient edge devices with limited resource, reducing energy consumption and requirement on high-performance computing resources. The network consists of a serial patch-embedded feature extractor with short shortcut connections and a depthwise hierarchical refinement. In the stage of refining disparity maps, we employ a novel 2D aggregation network containing expanded depthwise separable convolutions with residual connections to regularize 3D group-wise cost volumes, resulting in accuracy improvement and inference acceleration. By evaluating on KITTI 2015, the proposed network achieves approaching results to state-of-the-art metrics on high-end GPU, and frame-rate@D1-all results of 64.6 FPS@6.16%, 41.7 FPS@3.49%, 32.2 FPS@2.29% in Stages 1-3 on Jetson Nano with ultra-low average power consumption of 6.3 W after tensorRT optimization.
VL-NMS: Breaking Proposal Bottlenecks in Two-stage Visual-language Matching

The prevailing framework for matching multimodal inputs is based on a two-stage process: (1) detecting proposals with an object detector and (2) matching text queries with proposals. Existing two-stage solutions mostly focus on the matching step. In this article, we argue that these methods overlook an obvious mismatch between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i.e., query-agnostic), hoping that the proposals contain all instances mentioned in the text query (i.e., query-aware). Due to this mismatch, chances are that proposals relevant to the text query are suppressed during the filtering process, which in turn bounds the matching performance. To this end, we propose VL-NMS, which is the first method to yield query-aware proposals at the first stage. VL-NMS regards all mentioned instances as critical objects and introduces a lightweight module to predict a score for aligning each proposal with a critical object. These scores can guide the NMS operation to filter out proposals irrelevant to the text query, increasing the recall of critical objects, and resulting in a significantly improved matching performance. Since VL-NMS is agnostic to the matching step, it can be easily integrated into any state-of-the-art two-stage matching method. We validate the effectiveness of VL-NMS on three multimodal matching tasks, namely referring expression grounding, phrase grounding, and image-text matching. Extensive ablation studies on several baselines and benchmarks consistently demonstrate the superiority of VL-NMS.
Adaptation Diffusion Strategy Over Wireless Fading Channels

Federated learning has introduced the solution to train machine learning model with data distributed over network nodes, where the training data remains stored on the nodes, which may be phones or network sensors [1]. With federated learning the nodes don't exchange the data which may be of large volumes and may be considered too sensitive but exchange just the parameters of the models, thereby ensuring a basic level of privacy [2]. In this work, we introduce new strategy for federated learning over nodes of wireless network. Since FEDAVG algorithm is centralized (uses the structure client-server), we distribute the learning over the graph nodes using the concept of diffusion strategy, where we suppose that the graph is adapted in time. We show how this strategy overcomes heterogeneity and converges in fewer rounds of communication. The main goal is to employ diffusion strategy in a decentralized manner to improve the convergence time without increasing the communication rounds. We compare the performance of the new approach with the consensus algorithm for the client's IID data (IID-Independent and Identically Distributed) and non-IID client data.
A Unified Framework From Face Image Restoration to Data Augmentation Using Generative Prior

In the domain of data enhancement, image restoration and data augmentation are two tasks gaining increasing attention.Current image restoration models focus on improving clarity using pre-trained generative models, and data augmentation methods try to generate new samples with the help of generative models.These two related topics have long been studied completely separately.We propose a downstream-friendly restoration framework based on pre-trained generative models with the capability of data augmentation for face images.We carefully design our framework to achieve high fidelity when inheriting the generation ability from the pre-trained generator.To achieve this goal, we use a modified U-Net to predict the biases of latent codes and feature maps to guide the generator.We further propose to adopt linear interpolation as an approach to enriching the datasets for downstream tasks, especially for classimbalanced tasks.Effectiveness of our method is demonstrated through experiments on three datasets and one downstream task.
Semantic Fusion Enhanced Event Detection via Multi-Graph Attention Network with Skip Connection

The target of event detection (ED) is to recognize and categorize event triggers in a given text. Because dependency trees transmit extensive structural information that has been proven effective, most contemporary ED approaches rely primarily on syntactic information. The majority of these methods, however, are ineffective due to two issues: a) how to mine and use both first-order and multi-order syntactic relations, and b) loss of information due to external dependency analysis toolbox. In this paper, we overcome the aforementioned two challenges by proposing the Semantic Fusion Enhanced Event Detection via Multi-Graph Attention Network with Skip Connection (SMGED) model. To solve the first problem mentioned above, SMGED constructs a multi-layer graph attention network with skip connections to capture syntactic relations with different orders and avoid information over-propagation. Then to solve the second problem, an information fusion module is proposed to fuse the contextual information and syntactic relations of each word to compensate for the lost information. We carried out extensive experiments and compared the proposed SMGED to existing methods. The comparison results validated the proposed model's effectiveness and superiority.
A novel unsupervised method for root cause analysis of anomalies using sparse optimization techniques

Anomaly detection is an imperative problem associated with emerging fields such as the Internet of Things, Telecommunication and Manufacturing Industries. Timely detection of anomalies and attribution to its sources enables to enforce preventive measures so as to maintain optimal process operation and avoid undesirable down-times. In this work, we propose an unsupervised method which enables detection, quantification and diagnosis of amplitude anomalies from multivariate data. The proposed method utilizes the ideas of sparse optimization to effectively decompose the data contributions into with and without anomalies for the purpose of anomaly detection and quantification. Further, the ideas of the directed graph are implemented on the estimated anomaly-free data to determine the root cause of anomaly to enable preventive maintenance. The efficacy of the proposed method is demonstrated on two case studies comprising of synthetic and real-time datasets.
Teachers’ Talk and Students’ Attitude in Learning English at Secondary Schools in Indonesia: A Correlational Analysis

Teachers’ talk is a key tool for engaging students in learning. This research is carried out to examine the correlation between students’ attitude and teachers’ talk in learning English at secondary schools in Indonesia in which English is taught as a foreign language. These two aspects are substantial when learning objectives are to be achieved. Unlike the previous studies that focused more on the nature of teachers’ talk and its effect on the development of specific language skills, this study scrutinizes the interrelation between teachers’ talk and one of the individual differences possessed by language learners, i.e., attitude. Two research questions are formulated to be answered by this study. First, what types of attitude possessed by the learners and how they are classified? Second, how does teachers’ talk correlate with the learners’ attitude in the attempt of achieving the learning goals? A quantitative approach is applied in this study involving learners of two secondary schools, namely, SMAN 6 and SMKs Harapan Bangsa as research population in Sidrap, a district in South Sulawesi Province, Indonesia. The researcher used purposive sampling technique in choosing 160 research samples from these two secondary schools. The data were collected using questionnaire and a set of hypotheses testing is utilized to measure the normality, linearity/regression, validity, and the reliability of the instrument. Pearson correlation value formula by Pearson was used to determine whether correlation existed between teachers’ talk and students’ attitude in learning English. The result of the analysis shows the correlation coefficient between teachers’ talk (X) and students’ attitude (Y), which confirms that both variables have correlation, which was interpreted as fair. The Pearson correlation value, which in this case is 0.350, shows a tenuous positive correlation. The implication of this study lies on the importance of teachers’ talk in shaping the learners’ attitude, which in turn leads to the success of language learning.
Bravely Say I Don’t Know: Relational Question-Schema Graph for Text-to-SQL Answerability Classification

Recently, the Text-to-SQL task has received much attention. Many sophisticated neural models have been invented that achieve significant results. Most current work assumes that all the inputs are legal and the model should generate an SQL query for any input. However, in the real scenario, users are allowed to enter the arbitrary text that may not be answered by an SQL query. In this article, we focus on the issue–answerability classification for the Text-to-SQL system, which aims to distinguish the answerability of the question according to the given database schema. Existing methods concatenate the question and the database schema into a sentence, then fine-tune the pre-trained language model on the answerability classification task. In this way, the database schema is regarded as sequence text that may ignore the intrinsic structure relationship of the schema data, and the attention that represents the correlation between the question token and the database schema items is not well designed. To this end, we propose a relational Question-Schema graph framework that can effectively model the attention and relation between question and schema. In addition, a conditional layer normalization mechanism is employed to modulate the pre-trained language model to generate better question representation. Experiments demonstrate that the proposed framework outperforms all existing models by large margins, achieving new state of the art on the benchmark TRIAGESQL. Specifically, the model attains 88.41%, 78.24%, and 75.98% in Precision, Recall, and F1, respectively. Additionally, it outperforms the baseline by approximately 4.05% in Precision, 6.96% in Recall, and 6.01% in F1.
D-S Augmentation: Density-Semantics Augmentation for 3-D Object Detection

Cameras and light detection and ranging (LiDAR) sensors are commonly used together in autonomous vehicles to provide rich color and texture information and information on the location of objects, respectively. However, fusing image and point-cloud information remains a key challenge. In this article, we propose D-S augmentation as a 3-D object detection method based on point-cloud density and semantic augmentation. Our proposed approach first performs 2-D bounding box detection and instance segmentation on an image. Then, a LiDAR point cloud is projected onto an instance segmentation mask, and a fixed number of random points are generated. Finally, a global <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> -nearest neighbor clustering is used to associate random and projected points to give depth to virtual points and complete point-cloud density augmentation (P-DA). Then, point-cloud semantic augmentation (P-SA) is performed, in which the instance segmentation mask of an object is used to associate it with the point cloud. The instance-segmented class labels and segmentation scores are assigned to the projected cloud, and the projection points added with 1-D features are inversely mapped to the point-cloud space to obtain a semantically augmented point cloud. We conducted extensive experiments on the nuScenes (Caesar et al., 2020) and KITTI (Geiger et al., 2012) datasets. The results demonstrate the effectiveness and efficiency of our proposed method. Notably, D-S augmentation outperformed a LiDAR-only baseline detector by +7.9% in terms of mean average precision (mAP) and +5.1% in terms of nuScenes detection score (NDS) and outperformed the state-of-the-art multimodal fusion-based methods. We also present the results of ablation studies to show that the fusion module improved the performance of a baseline detector.
Fundamental trade-off between the speed of light and the Fano factor of photon current in three-level lambda systems

Electromagnetically induced slow-light medium is a promising system for quantum memory devices, but controlling its noise level remains a major challenge to overcome. This work considers the simplest model for such medium, comprised of three-level $\Lambda$-systems interacting with bosonic bath, and provides a new fundamental trade-off relation in light-matter interaction between the group velocity of light and the Fano factor of photon current due to radiative transitions. Considering the steady state limits of a newly derived Lindblad-type equation, we find that the Fano factor of the photon current maximizes to 3 at the minimal group velocity of light, which holds true universally regardless of detailed values of parameters characterizing the medium.
Effects of Heuristic Type, User Interaction Level, and Evaluator’s Characteristics on Usability Metrics of Augmented Reality (AR) User Interfaces

Heuristic evaluation is a famous usability inspection method often used in the assessment of Augmented Reality (AR) systems. Usability heuristics can be characterized as general or specific to a domain. During comparative studies between a new and a traditional set of heuristics, the focus is often around performance metrics (number of problems, time, errors), and little attention is given to the experience itself (i.e., evaluator’s overall satisfaction toward utilization of the set of heuristics). In addition, limited research has been done on the application of heuristics to evaluate interfaces with different levels of interaction, along with how a longer set of low-level heuristics compares against a traditionally-sized set of high-level heuristics. In this study, recruited participants (mostly university students) performed a heuristic evaluation using either a set of 110 specific heuristics (SH) or a set of 23 general heuristics (GH) to evaluate two versions of an AR application, designed with a low and a high level of interactivity. Big Five personality traits, prior experience with AR or Virtual Reality systems, and various usability data (e.g., subjective ratings related to the usability of heuristics and AR application, number of unique problems, and severity ratings) were collected through questionnaires and utilized during statistical analysis. Results show that even though the employment of the set of GH produced higher overall satisfaction of evaluators, the set of SH resulted in a significantly larger number of unique usability problems. Furthermore, the more interactive version of the AR application was perceived by evaluators as the harder version to inspect, regardless of the set of heuristics utilized. Neuroticism and agreeableness traits were found with a significant impact on subjective usability ratings.
Design of K-means method segmentation for visitor characteristics of Bangkalan cultural heritage tourism

Tourism is a travel activity that is carried out temporarily to fulfill curiosity, spend time off, or leisure and other purposes. Indonesia has a lot of potential and natural resources to be developed into an attractive tourist attraction. And some of these natural resources have been utilized and developed into some very interesting tourist objects, especially in Bangkalan Madura. And the tourism sector is one of the sectors of economic development. Therefore, in this study, it is necessary to strive optimally in developing tourism objects in the segmentation field. The segmentation that will be made is a mapping system that can help classify the number of tourist attraction entrance tickets into three classes, namely high, medium and low classes. The system that will be made is web-based to make it easier for tourism object managers to analyze tourism potential in classes. The importance of analysis and modeling in the problems in the development of this mapping system, so that it can be used in the analysis for the development of tourism potential in Bangkalan, Madura. In this study, we propose a modeling stage using the K-Means method. The contribution of the system design is that the segmentation of tourist attraction visitors using the K-Means method can help the tourism object manager in developing existing tourist attraction facilities so as to increase the number of visitors. To strengthen this contribution, the design of this system is carried out in the stages of problem identification and survey, data collection, literature study, design, and implementation. The results of testing the effectiveness of this system using a Likert scale of 1 to 5 with 63 respondents and resulting in 74.60%. This shows that the design of the mapping system of visitors who come to Bangkalan, Madura tourism objects, is very influential in developing facilities and infrastructure according to the needs of market segments.
The analysis of test items for student's creative and critical thinking assessment: Towards mobile application development

This research aims at developing the assessment of creative and critical thinking altogether in one package. It is developed by (1) determining the subtest, (2) composing the test questions, (3) validating the content to the experts, (4) instrument tryout to limited subject, as well as (5) analyzing the validity, reliability, and the difficulty rate of each question item. The findings present 6 sub tests with different type of obective test, those are C1 Parse with selection questions, C2. Deviate with mutiple choices questions, C3. Modify with matching questions, C4. Verify with truefalse questions, C5. Compare with order-sorting questions, and C6. Conclude with grouping questions. Among 240 question items, after validity and reliability test is conducted, 169 items are selected as the final results. Each question items is equipped with timer and score according to the rate of difficulty.
Modeling the PISA’s score of Indonesian students using multivariate generalized linear model

The Multivariate Generalized Linear Model applies a lot in data science. However, this model is still less discussed in education, specifically in data analysis in the Program for International Student Assessment (PISA), which has complex structure data. This study aims to analyze the factors that influence the PISA's scores of Indonesian students simultaneously covering the three subjects of the PISA assessment, namely mathematics literacy, science literacy, and reading literacy. The complexity of PISA data which involves multivariate response variables which assumes a correlation between response variables, adds to the complexity of the analysis. One approach is the Multivariate Generalized Linear Model with the Quasi Likelihood estimation method. Took the data sources from the PISA survey was conducted by Organization for Economic Cooperation and Development in 2018. This study indicates that the factors that influence the PISA's scores of Indonesian students simultaneously are the class taken, parental education, facilities at home, student discipline, teacher feedback during learning, age of entering kindergarten, and failing a grade during elementary school. Based on the model diagnostic, it can conclude that Multivariate Generalized Linear Model produces a model that fits in modeling the PISA's scores of Indonesian students.
Forecasting the number of demam berdarah dengue (DBD) patients using the fuzzy method at the Siwalankerto public health center

Dengue Hemorrhagic Fever or what weoften hear with DHF is a disease that we often encounter in our environment, even now cases of DHF have spread in 472 districts or cities in 34 provinces. Fuzzy logic is a method that has a vague or unclear meaning. So, fuzzy logic is logic that is fuzzy, or contains elements of uncertainty. This theory is usedin the fields of control theory, decision making, etc.There are three variables in this study population density, frequency of drainage, and cases of DHF. The dataused are 2015-2020 data and the data to be tested for the output of this research is data in 2020 where the data for this year is data that has never been tested before. The forecasting results are based on research on 72 input data and 12 data used to validate the forecasting results using the Mamdani fuzzy method to get an accuracy of about 83%.