Miracle-dz/UniFAR-FTUs huggingface dataset:
Adjusting Indonesian Multiword Expression Annotation to the Penn Treebank Format

Multiword Expression (MWE) has been a pain in the neck, especially in determining its word-classes in syntactic treebank. Previous work had proposed annotation guidelines for Indonesian MWEs that align to the Penn Treebank (PTB) format. However, we think that their proposed annotation still needs improvements. Therefore, this study proposes a new annotation guideline in labeling Indonesian MWE that conforms to PTB format. Moreover, we also revised the MWE annotation of an existing Indonesian constituency treebank consisting of 1030 sentences to conform to the new guidelines. To evaluate the revised treebank’s quality, we built an Indonesian constituency parser model using the revised treebank and Stanford parser. The experiments show that the resulting parser has an F1-score of 69.97%.
Mixed Test Environment-based Vehicle-in-the-loop Validation - A New Testing Approach for Autonomous Vehicles

The current test of autonomous driving technology requires extensive experimental verification, whether in simulation or on real roads. Netherthless, how to test autonomous vehicles thoroughly in a safe and comprehensive manner remains a major challenge. To achieve safer and more effective autonomous driving testing, this paper proposes a novel mixed test environment-based validation method with vehicle in the loop(ViL) : (1) our method supports more realistic drive safety tests in mixed scenarios which integrate the synthetic and the real-world scenarios. Synthetic scenarios offer complex traffic simulation with diverse road conditions. The real-world scenarios introduce the real autonomous driving vehicle, the real sensor suite as well as the test field to the test loop, having further bridged the gap between Hardware-in-the-Loop(HiL) testing and real road tests than ViL; (2) virtual perceptional results are simulated directly and delivered to the real vehicle in Unified Fusion Data Format(UFDF), without rendering virtual detection data for reduced resource consumption; (3) diverse test scenarios are configurable and reproducible with OSM-based High Definition(HD) map, enabling the simulation to be decoupled from a specific test filed or traffic facilities. A series of experiments on the application of our method have been demonstrated, and our approach is proved to be a promising drive safety testing technique before actual road testing.
Sentences Similarity Based on Deep Structured Semantic Model and Semantic Role Labeling*

This paper proposes a method of sentences similarity based on deep semantic model and semantic role labeling. Deep semantic model is to learn word representation in a semantic space using deep learning technology, then we use generated vectors to calculate sentences similarity. At the same time, we label sentences with semantic roles and calculate similarity based on semantic roles. Finally, those two kinds of sentences similarity are linearly combined as final sentences similarity. In this paper, experiments are carried out on the SemEva1-2017 and SemEva1-2016 task 1, and Pearson correlation coefficients reach 85% and 63% respectively. Experimental results show that our proposed method outperforms existing methods demonstrating the effectiveness of our approach.
Bioscouring of Cotton Fiber with Polygalacturonase Induced in Sclerotium rolfsii using Cellulose and Glucose-pectin

Textile industrial scouring processes are currently based on the use of chemical reagents that cause an important environmental impact. For some years, several research groups have focused on obtaining enzymes that can carry out the scouring task in an effective way, allowing at the same time a decrease of the industrial costs of water and energy associated with the process. As part of this effort, bioscouring of cotton using enzymes produced in Sclerotium rolfsii has been successfully carried out in the present study. Enzyme production was induced by glucose, glucose-pectin or cellulose, and in all cases the enzyme activity corresponded to polygalacturonase activity. The weight loss after bioscouring was more efficient with enzymes induced by pectin because these have a specific activity for removing pectin content from the cotton fiber. The effectiveness of the enzymatic scouring was equivalent or better than that achieved by the conventional alkaline process.
Design and Realization of AMC-Based Tunable Multi-Band Microstrip Antenna

In this paper, design and realization of tunable multi-band microstrip antenna based on artificial magnetic conductor (AMC) is presented. The involvement of AMC structure on the design of microstrip antenna makes the dimension of antenna to be smaller, hence it is easy to use for portable communication devices. The tunable frequency response of proposed microstrip antenna can be achieved by incorporating external elements, namely varactor diode. Here, the varactor diodes of STMD3001 type are used with the reverse DC bias voltage of 0V, 3V, and 10V. The proposed multi-band microstrip antenna is design using two layers of 1.6mm thick FR4 epoxy dielectric substrate. There is a square patch on the top side of the first layer and 4 identical square patches arranged in a 2×2 array on the top side of the second layer. The results show that the proposed AMC-based microstrip antenna could produce the tunable frequency response in the frequency range of 1.575GHz to 2.75GHz along with the variation of reverse DC bias voltage.
On 3-Degree 4-Chordal Graphs

A graph G is a 3-degree 4-chordal graph if every cycle of length at least five has a chord and the degree of each vertex is at most three. In this paper, we investigate the structure of a 3-degree 4-chordal graphs, which is a subclass of 3-degree graphs. We present a structural characterization based on minimal vertex separators and bi-connected components. Further, using our structural results, we present a polynomial-time algorithm for the Hamiltonian cycle problem and other classical optimization problems such as minimum vertex cover, minimum connected vertex cover, longest cycle, feedback vertex set, treewidth and minimum fill-in.
Multimodal Medical Image Fusion Based on Weighted Local Energy Matching Measurement and Improved Spatial Frequency

Multimodal medical image fusion (MMIF) technology can effectively improve the efficiency and accuracy of doctors in clinical diagnosis and treatment by combining different modes of medical images into a medical image with rich information. To achieve an optimal balance between computational loss and fusion quality, this article presents a new MMIF method based on weighted local energy matching measurement (WLEMM) and improved spatial frequency (SF). First, the latent low-rank representation (LatLRR) is employed to learn a decomposition matrix that is applied to decompose a source image into its base part and saliency part. Then, in order to adaptively select a fusion strategy for the base part, we propose a novel fusion rule, namely WLEMM, which is constructed by calculating the matching degree between the corresponding pixels of the base parts. Meanwhile, after considering the characteristics of the main diagonal SF and the secondary diagonal SF, a fusion strategy based on improved SF and L2 norm is designed to merge saliency parts. Finally, the fused image is obtained by combining the fused base part and saliency part. Various experiments on multiple groups of medical images demonstrate that the proposed method has superior performance compared with the state-of-the-art fusion methods, in subjective visual and objective index evaluations. Furthermore, the research work of this article is helpful for further detection and segmentation of medical images.
Ensemble Model for COVID-19 detection from chest X-ray Scans using Image Segmentation, Fuzzy Color and Stacking Approaches

Coronavirus is a virus of RNA-type that can infect both humans and animal and causes a wide variety of respiratory infections. In humans, it also causes pneumonia. Since coronavirus has been declared a pandemic, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been the standard method for detection but is a time consuming operation and due to sudden surge in demand it has a high cost. In this study, coronavirus was detected from X-ray scans of chest using a deep learning model consisting of fuzzy image enhancement, offline data augmentation, image segmentation and classification through Convolutional Neural Network. For training and classification, an ensembeled model consisting of the features of VGG-16, ResNet-50 and MobileNetV2 was built and optimized with bayesian optimization. The proposed model achieved an overall accuracy of 96.34%. The precision, recall and F1-Score for COVID-19 class was 100%, 96% and 98% respectively.
VINS-PL-Vehicle: Points and Lines-based Monocular VINS Combined with Vehicle Kinematics for Indoor Garage

In this paper, we propose VINS-PL-Vehicle, a points and lines-based monocular visual-inertial navigation system(VINS) combined with vehicle kinematics for indoor garage. The indoor garage contains some texture-less regions. Thus, it is difficult to ensure the robustness of VINS only using point features. Therefore, in addition to point features, we also add line features on the pillars, parking slots, and top structures in the garage. Besides, we utilize the accurately known velocity and steering wheel angle from the vehicle chassis to form the kinematic constraints between image frames, which can not only solve the problem that the scale is not observable due to insufficient excitation of accelerometer when the vehicle is running at a constant speed, but also utilize its drift-free characteristics to improve the accuracy of system initialization and optimization. The vehicle tests show that our algorithm can achieve significantly higher positioning accuracy than VINS-Mono in the indoor garage.
A Unified Evaluation Framework for Autonomous Driving Vehicles

Automated Driving System (ADS) safety assessment is a crucial step before deployment on public roads. Despite the importance of ADS safety assurance to test ADS reliability, most of the existing work is strongly attached to a single testing data source (i.e. on-road collected testing data, simulation or test track). Each source has different fidelity levels and capabilities, therefore there is a lack of a solution that allows for all data sources to complement each other to enable agnostic end to end evaluation and contributes towards different testing goals. Evaluation of ADSs is considered as a mandatory step in the autonomous vehicle development life cycle, demanding a reliable and comprehensive method is important. Here, we propose a source-agnostic framework, which can perform ADS evaluation compatible with different testing sources. Our findings show that this comprehensive solution can save time, effort and money consumed in ADS evaluation.
Safety Verification of a Data-driven Adaptive Cruise Controller

Imitation learning provides a way to automatically construct a controller by mimicking human behavior from data. For safety-critical systems such as autonomous vehicles, it can be problematic to use controllers learned from data because they cannot be guaranteed to be collision-free. Recently, a method has been proposed for learning a multi-mode hybrid automaton cruise controller (MOHA). Besides being accurate, the logical nature of this model makes it suitable for formal verification. In this paper, we demonstrate this capability using the SpaceEx hybrid model checker as follows. We develop an automated tool to translate the automaton model into constraints and equations required by SpaceEx. We then verify that a pure MOHA controller is not collision-free. By adding a safety state based on headway in time, a rule that human drivers should follow anyway, we do obtain a provably safe cruise control. Moreover, the safe controller remains more humanlike than existing cruise controllers.
Joint Overlap Ratios and Power Allocation with User Fairness in Partial Non-Orthogonal Multiple Access (P-NOMA)

In power-domain non-orthogonal multiple access (NOMA), since many users are able to utilize a single frequency subcarrier with different power levels, user interference occurs if successive interference cancellation (SIC) at the receiver is not performed perfectly. To reduce the amount of interference among all the users, recently an alternative approach called partial-NOMA (P-NOMA) was proposed. In this approach, the system allows partially overlapped bandwidth of the users and enables them to use NOMA in the overlapped region while the users in the non-overlapped bandwidth are arranged to utilize orthogonal multiple access (OMA). What should be the amount of overlap ratios (OR)s and the corresponding power levels still needs further research. In this paper, we propose an algorithm for joint allocation of ORs and power in P-NOMA system, which is based on gradient search, with the maximum sum-rate objective and user fairness constraint. Simulation results show that the proposed algorithm, which has lesser complexity than exhaustive search, obtains the optimum ORs and power allocation (PA) coefficient such that the maximum sum-rate of the P-NOMA is higher than that of the conventional NOMA while the target user fairness based on Jain's index is achieved.
MitrApp: An Intelligent Recommendation System For Counselling

In modern era there has been a phenomenal increase of suicidal syndromes with teens and student population being susceptible the most. Since most of the teens and youth are active in social media, the framework serves more to predict the suicidal tendency of the user. The proposed App captures the face of the user to identify his emotional state and immediately play or encourage him to go through audio or video content that serves as an immediate counselling session with no human intervention. Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. This review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. this paper provides a brief review of researches in the field of FER conducted over the past decades. Deep-learning-based FER approaches using deep networks enabling “end-to-end” learning are then presented. This review primarly focuses on two datasets namely, KDEF(Karolinska Directed Emotional Faces) dataset and Kaggle FER2013 dataset. The proposed App captures the face of the user to identify his emotional state and immediately play or encourage him to go through audio or video content that serves as an immediate counselling session with no human intervention. This paper takes suggests instant counter measures such as recommending some anti-suicidal videos and some online expert support. Since people having suicidal tendency will post/ likes the photos/ images related to the suicide thoughts detection of suicide related objects like suicide rope will help in predicting suicidal behaviour more accurately. Since a large section of community(about 28%) are attempting suicides by means of suicidal rope, a model is trained on the most frequently used suicide rope images.
A Comparison of Three Different Parameterizations to Represent Planes For the Mapping Step of Direct Visual SLAM Approaches

In recent years, research in robotics and computer vision has been developed to improve the capacity to construct a more realistic representation of the environment through the use of images. Direct SLAM has been one of the lines of research that have advanced in this direction. The ability to extract information from the entire image allows generating increasingly impressive maps. The use of planes for environmental representation has been the option of several developed techniques. Planes are present in most scenes and can be mathematically represented in various ways. Thus, this article consists of a comparison of three distinct forms of plane parameterization to generate plane estimates using stereo images. For this, an optimization technique used in works on direct SLAM is adopted and experimental results are compared.
Reinforcement Learning for Physics-Based Competitive Games

Physics-based games are vast in terms of possible state spaces. There are many strategies that can be implemented in competitive games such as playing passively and waiting for the opponent to make a mistake, playing aggressively to force mistakes from the opponent, or even using environmental objects to an agent’s advantage. The vastness of possibilities makes it difficult for a programmer to account for all these situations and create a rule-based intelligent and believable hard-coded AI agent. This project seeks to take advantage of reinforcement learning to create agents that can adapt to dynamically changing physics-based environments such as the example of competitive vehicular soccer games. It seeks to produce believable agents that perform intrinsic behaviors such as defending their goal and attacking the ball using reward functions. Through trial-and-error, the reward function is modified to progressively form behavioral patterns that improve in performance. The performance tests prove that a reward function that considers different state space parameters can produce better performing agents compared to ones with a less defined reward function and state space. Moreover, the final agent trained through the experiments has proved to be believable and hard to distinguish from a human player.
AdversarialQR: An adversarial patch in QR code format

In this paper, we present a method to camouflage an attack on image recognition system by using an adversarial patch embedded on a scan-ready QR code. Adversarial patch refers to a class of a real-world attack on a machine learning system that adds a ‘patch’ onto the image. However, unlike existing methods, they are highly conspicuous to human perception. As these attacks are performed in the real world, they require users to manipulate the scene. However, not only the patch catches the attention of the classification system but also bystanders' attention as well. We believe that forcing the adversarial patch into the form of a scan-ready QR code can conceal its primary reason to exist in the scene. The main challenge of the research is the process of forcing an adversarial patch into a scan-ready QR code while trying to retain as much information for the patch to work as a real-world adversarial example. The experiments had been done to investigate trade-off compared to training the patch in different shapes.
Inverse Kinematics Determination and Trajectory Tracking Algorithm Development of a Robot with 7 Joints

The analyzed robot has 7 actuators and therefore it is a redundant robot whose position and orientation of the end-effector can generally be set in an infinite number of ways. Determination of joint angles that implement a set position and orientation of the end-effector is difficult for this robot. The paper presents the method of determining analytical formulas for the inverse kinematics problem of the analyzed robot and presents the idea of selection a particular solution. The basic idea of the trajectory tracking algorithm is that a slight change in the position and orientation of the end-effector must also be associated with a slight change in angular positions on individual joints. The paper shows the operation of the tracking algorithm of an exemplary trajectory, both simulation and for real KINOV A® Gen3 Ultra lightweight robot.
Performance-based Impedance Control of a One Degree-of-Freedom Finger Extensor for Hand Rehabilitation

Encouraging post-stroke patients to perform self-initiated movements is an essential requirement to achieve motor learning during rehabilitation. Control schemes are hence designed around the central idea of modulating assistance/resistance provided during therapy based on the patient's performance. This ensures patients are challenged appropriately as per their capability. This paper describes the design and implementation of a novel performance-based impedance control algorithm on a one degree-of-freedom hand rehabilitation device. The control scheme modifies resistance provided to patients depending upon the force exerted by them at each instant of the therapy session. Prior to implementation of the control program, a system model describing the kinematics and dynamics of the device is developed. The control architecture and force flow between the controller and the device is described. Preliminary experiments have been performed on a healthy subject to evaluate the performance of the developed controller. Results reveal the efficiency of the controller in tailoring hand rehabilitation to each individual patient's level of impairment.
A Multi-Task Reinforcement Learning Approach for Navigating Unsignalized Intersections

Navigating through unsignalized intersections is one of the most challenging problems in urban environments for autonomous vehicles. Existing methods need to train specific policy models to deal with different tasks including going straight, turning left and turning right. In this paper we formulate intersection navigation as a multi-task reinforcement learning problem and propose a unified learning framework for all three navigation tasks at the intersections. We propose to represent multiple tasks with a unified four-dimensional vector, which elements mean a common sub-task and three specific target sub-tasks respectively. Meanwhile, we design a vectorized reward function combining with deep Q-networks (DQN) to learn to handle multiple intersection navigation tasks concurrently. We train the agent to navigate through intersections by adjusting the speed of the ego vehicle under given route. Experimental results in both simulation and realworld vehicle test demonstrate that the proposed multi-task DQN algorithm outperforms baselines for all three navigation tasks in several different intersection scenarios.
A 0.14-to-0.29-pJ/bit 14-GBaud/s Trimodal (NRZ/PAM-4/PAM-8) Half-Rate Bang-Bang Clock and Data Recovery (BBCDR) Circuit in 28-nm CMOS

This paper reports a half-rate bang-bang clock and data recovery (BBCDR) circuit supporting the trimodal (NRZ/PAM-4/PAM-8) operation. The observation of their crossover- points distribution at the transitions introduces the single-loop phase tracking technique. In addition, low-power techniques at both the architecture and circuit levels are employed to greatly improve the overall energy efficiency and multiply data throughput by increasing the number of levels on the magnitude. Fabricated in 28-nm CMOS, our BBCDR prototype scores a 0.29/0.17/0.14 pJ/bit efficiency at 14.4/28.8/43.2 Gb/s under NRZ/PAM-4/PAM-8 modes, respectively. The jitter is < 0.53 ps (integrated from 100 Hz to 1 GHz) with approximately-equivalent constant loop bandwidth, and we achieve at least 1-UIpp jitter tolerance up to 10 MHz for all the three modes.
Graph-based Approach for Crowdsourced Mapping: Evaluation through Field Experiments

Automotive players have recently shown an increasing interest in high-precision mapping, with the aim of enhancing vehicles safety and autonomy. Nevertheless, the acquisiton, processing and updates of accurate maps remains an economic challenge. Collaborative mapping through vehicles crowdsourcing represents a promising solution to tackle this problem. However, the potential accuracy provided by such an approach has yet to be assessed. In this paper, we evaluate the performances of crowdsourced mapping in real conditions. We build a map of geolocalized landmarks by crowdsourcing observations from multiple vehicles, and applying several successive map updates. We evaluate the map quality through a field-test with multiple vehicle passings. Furthermore, we study the benefits of crowdsourced mapping for vehicles positioning.