Reviewerly/exHarmony huggingface dataset:
SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs

In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).
Hessian matrix non-decomposition theorem and its application to nonlinear filtering

In 1983, Roger Brockett gave his invited lecture at the International Congress of Mathematics about the classification of finite dimensional estimation algebra (FDEA), which plays a vitally important role in the classification of finite dimensional filters (FDFs). The Hessian matrix non-decomposition (HMND) theorem was one of the key steps for the complete classification of FDEAs with maximal rank of the arbitrary state space dimension (Yau, 2003; Yau and Hu, 2005). In (Yau et al., 1999), the HMND theorem for homogeneous polynomials of degree 4 was proven and at the same time, it is firstly proposed that there exists a HMND conjecture for the general polynomials of degree 2d with d≥2. In this paper, we consider the Hessian matrix of a nontrivial real homogeneous polynomial at degree 6. For the state space dimension n≤4, we prove such matrix cannot be decomposed in the form Q(x)Q(x)T, where Q(x) is a skew-symmetric matrix whose all elements are homogeneous polynomials of degree 2, and Q(x)T is the transpose of Q(x). The fundamental strategy we use in this paper is that we generalized the techniques proposed by Yau et al. (1999) for the case of degree 4 and derived identities of coefficient matrices for the case of degree 6. For the arbitrary state space dimension n, the HMND theorem is demonstrated under several conditions by certain basic linear algebra techniques. These results can be closely related to the classification of FDEAs with non-maximal rank in the filtering theory.
Multi-view triangulation without correspondences

We introduce a novel method to solve the joint problem of correspondence and triangulation of points from multiple calibrated perspective views and show its application to counting the number of leaves present on plants photographed from multiple angles in phenotyping facilities. Assuming there is a set of n calibrated views of an object, the input to our algorithm is a set of 2D points (e.g., leaf tips, fruits, flowers) detected in each of the n views, and the output is a set of 3D points corresponding to the 2D points when re-projected on views. Our approach is robust to noise and occlusion. In particular, it is not required for all points to be visible from one of the views, as our algorithm can infer that a point is occluded by reasoning on the 3D geometry of the scene. Our algorithm is suitable for many points (approx. thousands) reconstructed from a reduced set of views (up to 15). For example, our implementation finds the correspondence of 20 points captured by six cameras in about one second on consumer hardware. We evaluate the performance on synthetic data as well as real examples. We show that the accuracy of leaf counting from multi-view images is drastically improved by our algorithm.
Diverse and consistent multi-view networks for semi-supervised regression

Label collection is costly in many applications, which poses the need for label-efficient learning. In this work, we present Diverse and Consistent Multi-view Networks (DiCoM)—a novel semi-supervised regression technique based on a multi-view learning framework. DiCoM combines diversity with consistency—two seemingly opposing yet complementary principles of multi-view learning—based on underlying probabilistic graphical assumptions. Given multiple deep views of the same input, DiCoM encourages a negative correlation among the views’ predictions on labeled data, while simultaneously enforces their agreement on unlabeled data. DiCoM can utilize either multi-network or multi-branch architectures to make a trade-off between computational cost and modeling performance. Under realistic evaluation setups, DiCoM outperforms competing methods on tabular, time series and image data. Our ablation studies confirm the importance of having both consistency and diversity.
Rural Folk Dance Movement Recognition Based on an Improved MCM-SVM Model in Wireless Sensing Environment

In order to improve the accuracy and timeliness of folk dance movement recognition, this paper proposes an improved MCM-SVM recognition model to recognize the lower limb human motion of ethnic dance in rural areas based on sensors. In order to recognize these actions, the SVM algorithm is used to identify the current action, and the MCM is used to optimize the recognition result. The experimental results show that the proposed improved model achieves higher recognition rate compared to the SVM algorithm for the recognition of different dance moves. The average recognition rate exceeds 93%, and the average recognition time is about 0.6 ms, which verifies the effectiveness of the proposed model. The proposed model will provide guidance and practicality for the design and construction of future dance movement recognition systems.
ANALISIS PENERAPAN SISTEM INFORMASI APLIKASI PEGADAIAN DIGITAL SERVICE DALAM MEMUDAHKAN NASABAH BERTRANSAKSI (STUDI KASUS PEGADAIAN UNIT BETUN)

The PDS (Pegadaian Digital Service) application is an innovation and new breakthrough by PT. Pegadaian (Persero). The Betun Unit Pawnshop Office is currently in the process of socializing the PDS Application to all pawnshop office customers. The features in the PDS Application apart from offering a gold savings feature, Pegadaian Digital also offers online pawning features, installment/billing information, maturity information, and several other interesting features. This feature offers several advantages to users. As a new application for customers, of course it will take time for the acceptance process in terms of features and usability. Even the employees of PT. Pegadaian (Persero) has the right to assess and evaluate the content offered on the application. Making this application aims to make it easier for customers and prospective customers to interact indirectly with Pegadaian, namely by knowing the up-to-date gold price, checking bills, making payments, and others. With the Digital Pegadaian application, customers no longer need to come to the outlet to make payments, they only need to go through the application and then pay according to the bank that has been determined by Pegadaian or through ATM, SMS Banking, and M-Banking. The purpose of this study is to evaluate the PDS (Pegadaian Digital Service) application in terms of usability using the Website Usability Evaluation (WEBUSE) method and create a table of recommended solutions from a list of usability problems. The WEBUSE method is used as a tool to measure the usability level of the PDS (Pegadaian Digital Service) application based on the perception of the end user (in this case the management of PT. Pegadaian and its customers). Data collection is done by distributing questionnaires to all employees and customers who come to make transactions or in the process of the PDS Application socialization agenda. The results of this study are expected to be input for the Management of PT. Pegadaian, especially the Betun Unit Pawnshop Office in an effort to implement the Digital Service Pawnshop Application.
Requirements on Technical Debt: Dare to Specify Them!

Could maintainability requirements help organizations prioritize technical debt management? The new standard ISO/IEC 5055 Automated Source Code Quality Measures can be used to set targets. We also look at a pragmatic way to turn CodeScene’s code health metric into a quality gate for pull requests.
In the name of the nobility of the cause, what I did is right

Abstract Discourse-focused analyses of political communication show a complex interplay between narration and argumentation. Yet, current analytical tools fall short of accounting for the multifarious ways in which narratives perform as arguments. This paper adopts the notion narrative argument, developed in argumentation theory, to examine the ways the ‘hero-protector’ narrative serves as argument. The paper analyzes four speeches given by Donald Trump and Joe Biden, whereby the use of force on foreign grounds is justified via the ‘hero-protector’ narrative. The analytical framework combines the argumentation strategies of the Discourse-Historical Approach (DHA) with pragma-dialectics’ argumentation schemes. The analysis shows that each narrative sequence constituting the ‘hero-protector’ narrative constructs specific argument schemes, and the logical connections between these sequences link arguments in chains to collectively justify the rightness of claims. The paper, thus, seeks to illustrate the possibility of conceptualizing narrative discourse as an effective way to argue for or against a claim.
Iterative Fusion and Dual Enhancement for Accurate and Efficient Object Detection

Single Shot Multibox Detector (SSD) uses multi-scale feature maps to detect and recognize objects, which considers the advantages of both accuracy and speed, but it is still limited to detecting small-sized objects. Many researchers design new detectors to improve the accuracy by changing the structure of the multi-scale feature pyramid which has proved very useful. But most of them only simply merge several feature maps without making full use of the close connection between features with different scales. In contrast, a novel feature fusion module and an effective feature enhancement module is proposed, which can significantly improve the performance of the original SSD. In the feature fusion module, the feature pyramid is produced through iteratively fusing three feature maps with different receptive fields to obtain contextual information. In the feature enhancement module, the features are enhanced along the channel and spatial dimensions at the same time to improve their expression ability. Our network can achieve 82.5% mean Average Precision (mAP) on the VOC 2007 [Formula: see text], 81.4% mAP on the VOC 2012 [Formula: see text] and 34.8% mAP on COCO [Formula: see text]-[Formula: see text]2017, respectively, with the input size [Formula: see text]. Comparative experiments prove that our method outperforms many state-of-the-art detectors in both aspects of accuracy and speed.
Substorm Onset Prediction Using Machine Learning Classified Auroral Images

Abstract We classify all sky images from four seasons, transform the classification results into time‐series data to include information about the evolution of images and combine these with information on the onset of geomagnetic substorms. We train a lightweight classifier on this data set to predict the onset of substorms within a 15 min interval after being shown information of 30 min of aurora. The best classifier achieves a balanced accuracy of 59% with a recall rate of 39% and false positive rate of 20%. We show that the classifier is limited by the strong imbalance in the data set of approximately 50:1 between negative and positive events. All software and results are open source and freely available.
Video Representation Learning for Decoupled Deep Reinforcement Learning Applied to Autonomous Driving

This work focuses on using Deep Reinforcement Learning (DRL) to control an autonomous vehicle in the hyper-realistic urban simulation LGSVL. Classical control systems such as MPC maneuver vehicles based on a given trajectory, current velocity, position, distances, and more. Our approach does not pass this information to the DRL agent but only images provided by the camera. Current DRL efforts also exploit similar approaches for autonomous driving, but they are only suitable for small, simple tasks using simple simulations. Our approach consists of two differently trained neural networks (NN), a perceptual NN for representation learning and an actor NN for selecting the correct action. The perception NN will be trained via representation and self-supervised learning to strengthen our DRL agent's understanding of the scene. It can recognize temporal information and the dynamics of a complex environment. This work shows the importance of decoupling the perception and decision (actor) model for autonomous driving. All in all, we could drive autonomously in a hyper-realistic urban simulation using our modular DRL framework. Moreover, our approach also provides a solution for other similar tasks in the field of robotics based on images.
Obstacle Avoidance with Zigzag Tentacles for Multirotor UAVs

In this paper, we present an extended tentacle-based obstacle avoidance method for multirotor unmanned aerial vehicles (UAVs). Our objective is to achieve a rapid and safe autonomous flight in narrow and complex spaces. Traditional tentacle-based obstacle avoidance methods generate only simple-shape candidate paths. Consequently, paths that bypass obstacles cannot be found and the moving speed of a UAV is needed to be decreased when navigating narrow spaces because the tentacles cannot be sufficiently extended. In the presented method, zigzag tentacles are generated. A UAV can fly through narrow spaces without decreasing its speed because the zigzag tentacles can find bypass paths. We conducted experiments with simulated and actual multirotor UAVs. The results show that the presented method achieved a rapid and safe autonomous flight in both experiments. In addition, we compare the presented method with the dynamic window approach which is a traditional tentacle-based method. This comparison shows the effectiveness of the zigzag tentacles, i.e., the presented method can achieve autonomous flight whereas the traditional method causes the UAV to become stuck.
End-to-End Motion Planners Through Multi-Task Learning for Mobile Robots with 2D LiDAR

In order for mobile robots to move toward a destination autonomously while avoiding obstacles, motion planning is an essential capability. We have thus far proposed motion planners based on deep neural networks, DNNs, for a robot equipped with a 2D LiDAR. A multilayer perceptron, MLP, was used as the DNN. In this paper, convolutional neural network, CNN, is also used as the DNN. Policies of these so-called end-to-end motion planners represented by MLP and CNN are trained through imitation learning. However, imitation learning sometimes causes a problem of generalization ability of the motion planners. In consequence, it might be difficult for a robot to plan suitable motions in unknown environments. For this challenge, we introduce an auxiliary task into the output layer in addition to the main task to determine the linear and angular velocities as the motion output. As the auxiliary task, the motion planners estimate the destination angle. Through multi-task learning, the accuracy of the main task is increased by the auxiliary task. In the navigation experiments, we show that MLP is more effective than CNN for improving the generalization ability of the motion planners. Finally, the robot based on the motion planner with MLP successfully moves toward a destination while avoiding obstacles even in unknown environment.
ReAT: Long Text Encoder Representations with Attention Tokens

BERT is a Transformer-based language model with a self-attention mechanism and has made many advances in natural language processing. However, it is difficult for BERT to process long texts more than 1,000 words, and it is possible to process them only when reducing the number of sentences. In this paper, a novel language model, ReAT, is proposed and experimented. The authors aim at developing a language model which is as accurate as or more accurate than BERT when processing long texts without reducing the number of sentences. ReAT supports long texts by dividing a given long text into dozens of sentence units as its preprocessing. In order to verify effectiveness of ReAT, we conducted simple experiments using QQP and MRPC after pre-training. As their results, ReAT achieved 89.7 percent accuracy by the QQP test and 73.0 percent accuracy by the MRPC test, respectively, at a stage when pre-training had not been completed. ReAT achieved 32-times longer text processing than BERT (from 512 words to 16384 ones). The amount of computing the self-attention was reduced successfully. In addition, our proposed language model achieved to extract feature based on sentence-by-sentence.
Reduction of training computation by network optimization of Integration Neural Network approximator

In constructing approximators for simulations, such as the finite element method using machine learning, there is a conflict between reducing training data generation time and improving approximation accuracy. To solve this problem, we proposed a Hybrid Neural Network and Integration Neural Network as an approximator for simulations with high accuracy, even with a small number of data. This method combines a simple perceptron approximator that mimics multiple regression analysis created based on deductive knowledge (linear approximator) and a neural network approximator created based on inductive knowledge (nonlinear approximator). This combination is based on Weierstrass' approximation theorem. In this study, by applying the approximator theorem one step further, we investigated the reduction of learning computational complexity by simplifying the network structure of the Integration Neural Network and improving the network structure. As a result, we found that approximators with almost the same accuracy can be constructed, and the number of weight updates in the learning process can be reduced to about 5%.
Proposal of Duct Cleaning Unit Using Planetary Gear Mechanism for Kitchen Ventilation Ducts and Design of Grease Cleaning Brush for Square Ducts: Model Design and Simulation of Effective Cleaning Range

In this paper, we propose a kitchen ventilation ducts cleaning robot that can clean ducts of various shapes and sizes, such as round and square ducts. To reduce the risk of duct fires, this study aims to develop a cleaning robot that cleans grease in kitchen ventilation ducts. In our previous studies, we focused on a planetary gear mechanism as a cleaning unit using brushes. The cleaning performance of a prototype was evaluated by a cleaning experiment of round ducts with grease. However, to clean square ducts, it was necessary to develop brush modules according to the shape of a square duct considering the characteristics of grease. This paper proposes a cleaning unit to clean both round and square ducts and a modeling method of brush module shape and brushes arrangement for square ducts based on the grease characteristics. Furthermore, by simulating the effective cleaning range of the brushes fixed to the brush module, it was found that proposed brush module can clean square duct while exerting an effective pushing force for cleaning. Therefore, the proposed modeling method of a brush module is expected to reduce the torque required for cleaning and improve the grease adsorption performance. In addition, the results of the simulation show the feasibility of cleaning square ducts by a duct cleaning unit using a planetary gear mechanism.
Fast and Light Object Classification from Aerial Drone Image

This paper proposes a fast and solid method to classify objects from the aerial drone image with different altitudes and perspective angles. To classify objects from drone datasets with various characteristics, there are two difficulties. The first difficulty is that the object size is too small. The second difficulty is that the object has many various characteristics. Therefore, we proposed a simple convolution blocks and shortcut blocks with a dilate convolution. Since the drone image is a 4k <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(3840\mathrm{x}2160)$</tex> in size, thus a very high resolution, neighboring pixels for a pixel of interest are similar to each other, so it is advantageous in terms of speed to use the skipped pixels. However, as the network goes deeper, the feature map size becomes smaller and there is a possibility that the surrounding pixel values become much different. Therefore, the normal convolution layer was also arranged and constructed. In addition, since the objects in the drone image have a small size because taken at a high altitude, it loses more features as the network gets deeper. Therefore, the features extracted from the original image were supplemented by periodically calculating each pixel by using the shortcut technique. It records fewer parameters than other networks, thus it executes faster and more accurately. The number of parameters is 1,032,054, which is lower than MobileNet(3,504,872), ShuffleNet(1,366,792), and SqueezeNet(1,248,424). Also, the proposed network outperformed with an accuracy of 98.18%, which is higher than MobileNet(97.28%), ShuffieNet(95.88%), and SqueezeNet(94.32%). when classifying images with 4 GTX3090s, it took in a speed of 1,102 FPS. Therefore, it was proved that fast and accurate network configuration is possible when classification experiments of Aerial Drone Images with various features are conducted using a simple convolution block and shortcut block.
Development and Examination of Secure Quadcopter Control System with Partially Homomorphic Encryption

This study developed an encrypted flight control system for a quadcopter and also examined the performance of the developed quadcopter through experiments. In this study, a commercial field-programmable gate array board (Zybo Z70-10) was used to build an experimental platform quadcopter with control parameters encrypted by a partially homomorphic public-key encryption scheme. The flight controller consists of a feedback controller for roll actions and a feedforward controller for thrust. The resulting data obtained from implementation and thrust tests using the developed quadcopter are presented, and then, it confirms the performance of the encrypted flight controller of the quadcopter.