Reviewerly/exHarmony huggingface dataset:
Multi-view unsupervised dimensionality reduction with probabilistic neighbors

Multi-view Unsupervised Dimensionality Reduction (MUDR) aims to find an optimal low-dimensional subspace to the original unlabeled high-dimensional multi-view data. Different views of multi-view data describe specific aspects that are independent or complementary with each other. How to make full use of the information from multiple views remains an open issue, especially in the absence of labels. In this study, we propose a novel model referred to as Multi-view unsupervised Dimensionality reduction with Probabilistic Neighbors (MDPN). It learns a multi-view consensus similarity matrix by adaptively weighing each view and properly assigning optimal neighbors for each sample. The consensus matrix has probability attributes and it encodes the probability of sample pairs to become neighbors. During the procedure, a projection matrix and the consensus matrix negotiate with each other to find the optimal subspace that preserves the structure/invariances of the original data. The projection matrix best serves dimensionality reduction. Moreover, considering the imbalance of dimensionality in multiple views, we extend this model to achieve flexible MUDR by projecting data from different views into subspaces of various dimensions. Two two-step iterative algorithms are developed to efficiently optimize the resultant objective problems. Extensive experimental results on synthetic and benchmark datasets demonstrate the superiorities of our models.
Stacked Generalization of Random Forest and Decision Tree Techniques for Library Data Visualization

The huge amount of library data stored in our modern research and statistic centers of organizations is springing up on daily bases. These databases grow exponentially in size with respect to time, it becomes exceptionally difficult to easily understand the behavior and interpret data with the relationships that exist between attributes. This exponential growth of data poses new organizational challenges like the conventional record management system infrastructure could no longer cope to give precise and detailed information about the behavior data over time. There is confusion and novel concern in selecting tools that can support and handle big data visualization that deals with multi-dimension. Viewing all related data at once in a database is a problem that has attracted the interest of data professionals with machine learning skills. This is a lingering issue in the data industry because the existing techniques cannot be used to remove or filter noise from relevant data and pad up missing values in order to get the required information. The aim is to develop a stacked generalization model that combines the functionality of random forest and decision tree to visualization library database visualization. In this paper, the random forest and decision tree techniques were employed to effectively visualize large amounts of school library data. The proposed system was implemented with a few lines of Python code to create visualizations that can help users at a glance understand and interpret the behavior of data and its relationships. The model was trained and tested to learn and extract hidden patterns of data with a cross-validation test. It combined the functionalities of both models to form a stacked generalization model that performed better than the individual techniques. The stacked model produced 95% followed by the RF which produced a 95% accuracy rate and 0.223600 RMSE error value in comparison with the DT which recorded an 80.00% success rate and 0.15990 RMSE value.
Smart Sensorization Using Propositional Dynamic Logic

The current high energy prices pose a serious challenge, especially in the domestic economy. In this respect, one of the main problems is obtaining domestic hot water. For this reason, this article develops a heating system applied to a conventional water tank in such a way as to minimize the necessary energy supply by converting it, under certain circumstances, into atmospheric. For this purpose, the domotic system has been equipped with sensors that automate the pressurization of the compartment and solenoid valves that regulate the external water supply. This design, to which different level sensors are applied, sends the information in real time to an artificial intelligence system, by means of deductive control, which recognizes the states of the system. This work shows the introduction of an extension of propositional dynamic logic in the field of energy efficiency. Thanks to this formalism, a qualitative control of the program variables is achieved by incorporating qualitative reasoning tools. On the other hand, it solves preventive maintenance systems through the early detection of faults in the installation. This research has led to the patenting of an intelligent domestic hot water system that considerably reduces energy consumption by setting disjointed heating intervals that, powered by renewable or non-renewable sources, are controlled by a propositional dynamic logic.
Building Multiclass Classification Model of Logistic Regression and Decision Tree Using the Chi-Square Test for Variable Selection Method

The growth and development of children under five (toddlers) affect their health conditions. Each region uniquely identifies the main factors influencing the toddler's health condition. The status of toddlers is generally categorized into two classes, namely normal and abnormal, so it is often found that the condition of toddler status is in the form of multi-response variables. Combining the two binary classes' response variables will form a multiclass response variable requiring different model development techniques and performance measurements. This study aims to determine the main factors that affect toddlers' health conditions in Malang, Indonesia, build multiclass logistic regression and decision tree classification models, and measure the model's performance. The Chi-square test selected predictor features as the input of multiclass logistic regression and decision tree models. From the feature selection, four main factors influence the status of toddlers' health conditions in Malang: the mother's history of diabetes before pregnancy, the father's blood pressure, psychological condition, and drinking water quality. The decision tree model performs better than the logistic regression model on the various performance measures used.
CGP-based Logic Flow: Optimizing Accuracy and Size of Approximate Circuits

Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Machine learning techniques show high performance in solving specific problems, being an attractive option to improve electronic design tools. We explore Cartesian Genetic Programming (CGP) for logic optimization of exact or approximate Boolean functions in our work. The proposed CGP-based flow receives the expected circuit behavior as a truth-table and either performs the synthesis starting from random circuits or optimizes a circuit description provided in the format of an AND-Inverter Graph. The optimization flow improves solutions found by other techniques, using them for bootstrapping the evolutionary process. We use two metrics to evaluate our CGP-based flow: (i) the number of AIG nodes or (ii) the circuit accuracy. The results obtained showed that the CGP-based flow provided at least 22.6% superior results when considering the trade-off between accuracy and size compared with two other methods that brought the best accuracy and size outcomes, respectively.
Finding Leisure through Improvisation at Home: Self-Sustainment during COVID-19

During tumultuous, shaky, and uncertain times such as COVID-19, understandings of and life at home have shifted dramatically, especially amidst lockdown or on-and-off stay-at-home orders. While staying at home made me feel a sense of safety and protection, lockdown was a whole other challenge. Staying at home from morning till dawn, doing the exact same things over and over again, and relying on technology more than ever to fuel my social needs, encompassed my lockdown routine; a routine developed out of desires for normalcy and desperation to feel a sense of stability and so-called productivity. In light of my sanity and survival, I had to make do. I had to improvise. I had to find ‘leisure’ that worked for me while the world moved cautiously amidst COVID. In the details of everyday pandemic life, I found leisure in improvisation. In some cases such as my lockdown experience, leisure can be found in improvisation; not just in one thing or activity per se, but also in a series of pursuits that can help us make do, pass time, keep sane, and even experience temporary bliss and enjoyment amidst an unsteady and unpredictable environment.
Jupyter Notebooks for Introducing Data Science to Novice Users

Data science is a bridge discipline involving computer science, statistics, and knowledge of the health field. We developed a Jupyter Notebook to enable novice users to easily and autonomously analyze data from social networks. We conducted an experimentation with non-programmer students. They had to adapt a R Notebook and complete 14 questions and to perform descriptive analyses. The average rate of correct answers was 90.7. Jupyter Notebook enabled novice users to easily and autonomously analyze data from Twitter.
Deep Learning Approaches for Multi-Label Incidents Classification from Twitter Textual Information

Background: Twitter is one of the most used social media, with 310 million active users monthly and 500 million tweets per day. Twitter is not only used to talk about trending topics but also to share information about accidents, fires, traffic jams, etc. People often find these updates useful to minimize the impact. Objective: The current study compares the effectiveness of three deep learning methods (CNN, RCNN, CLSTM) combined with neuroNER in classifying multi-label incidents. Methods: NeuroNER is paired with different deep learning classification methods (CNN, RCNN, CLSTM). Results: CNN paired with NeuroNER yield the best results for multi-label classification compared to CLSTM and RCNN. Conclusion: CNN was proven to be more effective with an average precision value of 88.54% for multi-label incidents classification. This is because the data we used for the classification resulted from NER, which was in the form of entity labels. CNN immediately distinguishes important information, namely the NER labels. CLSTM generates the worst result because it is more suitable for sequential data. Future research will benefit from changing the classification parameters and test scenarios on a different number of labels with more diverse data. Keywords: CLSTM, CNN, Incident Classification, Multi-label Classification, RCNN
Tighter monogamy relations in multiparty quantum systems

We investigate tight monogamy relations of multiparty quantum entanglement for any quantum state in this paper. First, we obtain a class of lower bounds for multiparty quantum systems which improve the previous results. Next, we establish a class of tighter monogamy relations in tripartite quantum systems by means of the new inequality. Furthermore, we generalize this relations to multiparty quantum systems. And then we prove the lower bounds we obtained are larger than the existing ones. Detailed examples are provided at last.
Mapping Ghana by GMT and R scripting: advanced cartographic approaches to visualize correlations between the topography, climate and environmental setting

The applications of the machine learning and programming approaches in cartography has been increasing in recent years.This paper presents a case study of the scripting techniques used for cartographic mapping using Generic Mapping Tools (GMT) and R language (raster and tmaps packages).The aim of the study is environmental mapping of Ghana.The materials include high-resolution raster grids: topography by the General Bathymetric Chart of the Oceans (GEBCO), climate and environmental datasets (TerraClimate) and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) for geomorphometric analysis (slope, aspect, hillshade and elevations).The methodology includes code snippets commented and explained with details of scripts.It is argued that using consolebased scripting tools for mapping is effective for cartographic workflow due to the logical structure and repeatability of scripts.The results include eight new thematic maps of Ghana performed using scripting approach in GMT scripting toolset and R language for quantitative and qualitative environmental assessment.Maps show correlations between the landforms of Ghana and certain environmental variables (drought index and soil moisture) showing the effects of the topographic relief on the distribution of the continuous geographic fields.These varied in several geographically distinct regions of Ghana: Ashanti (Kumasi), Volta, Savannah, coastal and northern regions.Demonstrated maps show that scripting method works effectively on a wide range of geosciences including environmental, topographic and climate studies.In such a way, this paper contributes both to the regional studies of Ghana and development of cartographic techniques.
Early Recognition of Untrimmed Handwritten Gestures with Spatio-Temporal 3D CNN

Early recognition of untrimmed handwritten gestures is the task of recognizing as soon as possible gestures drawn in a continuous stream, one after another. This is particularly challenging for multi-touch gestures because it is impossible to know when the gesture has started and finished. For mono-stroke gestures, in an application context where the finger is never removed from the device between gestures, the recognition is even more complex. In this work we present an extension of the Online Long-Term Convolutional 3D (OLT-C3D) network to address the task of early recognition of untrimmed gestures which have been addressed by very few works. To evaluate our approach, we created two synthetic datasets using freely available benchmarks, MTGSetB and ILGDB, simulating the streaming data in two different application scenarios. Furthermore, we propose a new evaluation metric for this specific task. Our approach achieves good performances on the two new datasets and will be a baseline for future works on this challenging task.
Where are the Large N Studies in Education?

Research, especially in Education, is hampered by scale and our aim is to help shift this dynamic by tracking relevant studies from major scientific venues. The current version of our dataset considers top conferences and journals from the domain (N = 33,531). Several heuristics using advanced text searches and regular expressions, together with NLP techniques ranging from part of speech tagging, syntactic dependency parsing, to semantic models, are employed to extract relevant studies. Our method achieves an F1 score of .633 when employed on a manually annotated subset of 1,000 articles. When applied to the entire dataset, the total number of articles with large N was around 10%, with a positive trend in the last years. [email protected] was by far the venue with the highest density of identified articles, thus arguing for its emphasis on scale. Further filtering of the articles is required when focusing only on learning, as the articles span across multiple domains and have multiple interests. Nonetheless, the order of magnitude raises current problems, namely that these studies are scarce and future endeavors should emphasize the importance of scale. Our dataset, including the validation subset, and the corresponding code for data crawling, PDF processing, and large N extraction mechanisms, have been open-sourced to further support the initiative and stimulate new analyses.
IMG-SMP: Algorithm and Hardware Co-Design for Real-time Energy-efficient Neural Motion Planning

Motion planning is a fundamental and critical task in modern autonomous systems. Conventionally, motion planning is built on uniform sampling that causes long planning procedure. Recently, built upon the powerful learning and representation abilities of deep neural network (DNN), neural motion planners have attracted a lot of attention because of the better biased sampling strategy learned from data. However, the existing NN-based motion planners are facing several limitations, especially the insufficient exploit of critical spatial information and the high computational cost incurred by neural network models. To overcome these limitations, in this paper we propose IMG-SMP, an algorithm and hardware co-design framework for neural sampling-based motion planner. At the algorithm level, IMG-SMP is an end-to-end neural network that can efficiently capture and process the critical spatial correlation to ensure high planning performance. At the hardware level, by properly rescheduling the computing scheme, the dataflow of IMG-SMP architecture can eliminate the unnecessary computations without affecting planning quality. The IMG-SMP hardware accelerator is implemented and synthesized using CMOS 28nm technology. Evaluation results across different planning tasks show that our proposed hardware design achieves order-of-magnitude improvement over CPU and GPU solutions with respect to planning speed, area efficiency and energy efficiency.
Enhancing distance transform computation by leveraging the discrete nature of images

This paper presents a major reformulation of a widely used solution for computing the exact Euclidean distance transform of n-dimensional discrete binary shapes. Initially proposed by Hirata, the original algorithm is linear in time, separable, and easy to implement. Furthermore, it accounts for the fastest existing solutions, leading to its widespread use in the state of the art, especially in real-time applications. In particular, we focus on the second step of this algorithm, where the lower envelope of a set of parabolas has to be computed. By leveraging the discrete nature of images, we show that some of those parabolas can be merged into line segments. It reduces the computational cost of the algorithm by about 20% in most practical cases, while maintaining its exactness. To evaluate the proposed improvement on different cases, two state-of-the art benchmarks are implemented and discussed.
The Analysis of Test Instrument Needs for Indonesian Language Lesson

This research describes the learners' needs toward HOTS test instrument development for the seventh-grade learners of a Public Junior High School Palembang. Test instrument has important roles in finding out the learners' skills in understanding the materials. The stage of this research is part of research and development by Gall Borg. It is a stage of collecting data or information to determine the needs of a developed product. The findings showed the students’ needs for the HOTS-based developed instrument from the discussion of the questionnaire and interview. On the other hand, the teacher realized that HOTS-based questions and learning for students had not answered their needs. Therefore, the teacher had to revise. It could be done by adjusting the materials needed by learners with those considered relevant to HOTS.
3-D path planning system for autonomous vehicle considering the rollover and path length

In the 3-D path planning, the undulation of terrain have a significant impact on the roll angle of autonomous vehicle (AV). However, the existing 3-D path planning methods rarely consider the roll angle, which may lead to the rollover of AV. To solve this problem, a 3-D path planning system considering the rollover and path length (3DPPS-CRPL) is presented in this work. The 3DPPS-CRPL can plan a shorter path on the basis of inhibiting the roll angle of AV. In order to inhibit the roll angle, a novel roll angle evaluation model based on the undulation of terrain is developed, and the amount of computation is also greatly reduced because complex dynamics is avoided. In order to optimize the path length, both 2-D and 3-D path length are evaluated synthetically by a path length evaluation model. In order to optimize the parameters of 3DPPS-CRPL, a fuzzy-based optimizer is proposed to determine the weight of roll angle and path length. The simulation results prove that 3DPPS-CRPL has excellent performance for inhibiting roll angle and optimizing the path length in 3-D environment.
Towards an Image Utility Assessment Framework for Machine Perception

In real-world applications, images and videos used in computer vision algorithms are often distorted due, e.g., to compression and transmission. As a result, they may lose relevant information content, or they may deviate significantly from the original data distribution used to train the machine task, rendering the visual content practically useless with respect to its initial purpose. Evaluating the utility of an image for machine tasks has received little attention so far in the literature. This concept of utility is substantially different from the visual quality typically used in image/video compression, as the latter is related to the perception of the human visual system. In this paper, we propose a definition of utility as the degree of confidence by which a machine task is able to take a decision. In this context, we propose a full-reference utility loss measure: we assume that the decision on the pristine image is correct (reference), and we measure the utility loss as the confidence reduction in the decision due to a noisy input with respect to this reference. We apply this general definition on two specific tasks, classification and object detection, and we study practical solutions to predict utility, as well as the ability of our utility measure to generalize across tasks.
Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning

For those seeking healthcare advice online, AI based dialogue agents capable of interacting with patients to perform automatic disease diagnosis are a viable option. This application necessitates efficient inquiry of relevant disease symptoms in order to make accurate diagnosis recommendations. This can be formulated as a problem of sequential feature (symptom) selection and classification for which reinforcement learning (RL) approaches have been proposed as a natural solution. They perform well when the feature space is small, that is, the number of symptoms and diagnosable disease categories is limited, but they frequently fail in assignments with a large number of features. To address this challenge, we propose a Multi-Model-Fused Actor-Critic (MMF-AC) RL framework that consists of a generative actor network and a diagnostic critic network. The actor incorporates a Variational AutoEncoder (VAE) to model the uncertainty induced by partial observations of features, thereby facilitating in making appropriate inquiries. In the critic network, a supervised diagnosis model for disease predictions is involved to precisely estimate the state-value function. Furthermore, inspired by the medical concept of differential diagnosis, we combine the generative and diagnosis models to create a novel reward shaping mechanism to address the sparse reward problem in large search spaces. We conduct extensive experiments on both synthetic and real-world datasets for empirical evaluations. The results demonstrate that our approach outperforms state-of-the-art methods in terms of diagnostic accuracy and interaction efficiency while also being more effectively scalable to large search spaces. Besides, our method is adaptable to both categorical and continuous features, making it ideal for online applications.