Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of insecticidal efficacy against maize leaf aphid [Rhopalosiphum maidis (Fitch)] under inner terai condition of Nepal

Field experiments were carried out at the National Maize Research Program in Rampur, Chitwan, with the objective of comparing the effectiveness of insecticides in the field for controlling maize leaf aphid (Rhopalosiphum maidis Fitch) during the winter season of 2019 and 2020. The design of the experiment was randomized complete block with four replications. The plot size was 6 rows of 5 m long with the spacing of 60cm × 25cm. Maize hybrid Rampur Hybrid-14 (RML-86/RML-96) was used as experimental variety. The efficacies of five insecticides viz., thiomethoxam 25% w/w 0.2g/L (T1), acetamiprid 20%WP 2g/L (T2), flonicamid 50% WG 0.5g/L (T3), neemix 3ml/L(T4), imidacloprid 0.5ml/L(T5) and untreated control (T6) were used as experimental treatments. The recommended dose of fertilizer was 180: 60: 40 N: P2O5: K2O kg/ ha with farm yard manure 10 t/ha and seed rate was 20kg/ha. Data on aphid incidence, severity, yield and yield components were recorded. All the tested insecticides significantly (P≤0.05) reduced the plant infestation caused by maize aphid, and thereafter increased the grain yield of maize compared to control. However, newer insecticide flonicamid 50% WG 0.5g/L was found as the most effective insecticide with lower aphid colony per plant (2.85), aphid score (2.63), aphid infested plant (7.33%) and higher crop yield (7904.79kg/ha). The application of insecticides prior to their severe infestations is necessary for the efficient control of the maize leaf aphid. The research findings will assist maize farmers in choosing and applying the best insecticide to ensure efficient management of maize leaf aphid with high yield.
A Robotic Aerial Platform with Functionally Anthropomorphic Arms designed for Physical Interaction

Frequently, ground robots are hampered by debris and objects on the ground, and safely surpassing them is not always trivial. On the contrary, a robot capable of flying is intrinsically immune to such obstacles and, therefore, greatly enhances the possibility of inspecting and intervening in adverse surroundings for humans. This work introduces a novel teleoperated aerial platform for inspection and intervention in unstructured environments. The robot is composed of an aerial base, two arms, and a two-degrees-of-freedom head that consent the access of human operators in any workplace in total safety. The arms are designed with a joint structure of tendons and are held by elastic components. This composition considerably improves the robustness by inserting softness and redistributing the weights to lessen the actions on the drone. Moreover, the aerial platform employs two soft hands capable of adapting to the shape of the objects under grasp, increasing the manipulation performance. We presented the mechanical and control design, a gazebo simulation employed to test the controllers, and a physical structure for the experimental validation of the system. The system is available as Open-Source material.
Hologram design for holographic laser machining inside transparent materials

A holographic laser machining can improve the processing efficiency. In the method, multiple light spots are generated at the processing positions by focusing a spatially phase modulated laser beam with a spatial light modulator. When this method is applied to bulk-laser machining, the accuracy of focusing positions is sometimes reduced because of the distortion of light focusing. In this paper, we present two calculation method of a phase hologram for reducing the distortion of light focusing. One method is based on the iterative Fourier transform method. In this method, the distortion of light focusing was reduced by fixing phase in light spots in the iterative process. The other is based on the optimal rotation angle method. In this method, the evaluation function was modified to make the light intensity at the positions of phase jump zero. In both two methods, we succeed to improve the quality of light spots. In addition, we show the application of these methods to writing of optical waveguides inside glasses.
Wideband Substrate Integrated Cavity-Backed Dielectric Resonator Antenna at Sub-6-GHz Band

The design and implementation of a wideband single-mode dielectric resonator antenna (DRA) with a substrate integrated cavity-backed structure is presented in this paper. Two short-circuited striplines with differential feeding are placed inside the substrate integrated cavity (SIC) for pure mode excitation of the DRA, providing stable radiation characteristics, and low cross polarization levels. The cavity is coupled to the cylindrical DRA through an annular slot. The fundamental ${\mathrm{ HEM}}_{11\delta }$ mode of the DRA is excited over a wide frequency band, as a result of the similar field distribution created on the coupling annular slot by two modes inside the cavity. Measurement results show a maximum gain of 8.2 dBi at 5.45 GHz. The gain is 7.8 dBi at the center frequency of 5.15 GHz and its 1-dB bandwidth is 23%. The measured 10-dB return loss bandwidth is 26% (centered at 5.15 GHz) which is the highest among the previously reported works based on cavity-backed DRAs and single-mode excited DRAs. The measured co-polarization and cross-polarization gain difference at the boresight direction is more than 26 dB. This antenna element can be used to cover a wide range of wireless standards in wireless access point and applications due to its stable wide band performance.
A Prototype of IoT-based Real-time Respiratory Rate Monitoring Using an Accelerometer Sensor

The emergence of the internet of things (IoT) has provided a dynamic change of healthcare system that enables interoperability and secure data transmission for a more effective healthcare delivery. This paper extends the implementation of IoT in combining medical devices and sensors with an IoT platform. Among various fundamental vital signs, respiratory rate is one of the most sensitive indicators of physiological conditions. Although it is deemed as a prognostic parameter that can predict miscellaneous diseases, respiratory rate monitoring remains frequently omitted and considered as time-consuming using manual counting methods. To address this problem, this paper presents a prototype of a respiratory rate monitoring device based on IoT and real-time systems. The device is equipped with an accelerometer sensor to measure abdominal movement generated by the inhale-exhale process. Butterworth filter and peak detection algorithm are applied to the signal to detect respiratory cycles. The proposed system uses the message queuing telemetry transport (MQTT) protocol that implements publish-subscribe systems. Respiratory rate results are featured on the Node-RED dashboard with motion and peak signals plotted during the test. Monitoring was carried out on five participants and the results were analyzed using paired t-test. Good performance was demonstrated by having no significant difference in respiratory rate using the proposed method compared to the actual values (p>0.05). Hence, this work provides a convenient technique for respiratory rate estimation and may further allow for the improvement of the effectiveness of remote patient monitoring.
The gravitational field of a spinning pencil of light

An exact solution of Einstein’s equations in a vacuum (outside of singularities), belonging to Kundt’s class and Petrov type N, is interpreted as the metric of a spinning pencil of light (a linear source infinitely extended in one direction and moving with the speed of light). It is shown that the gravitational fields of two parallel pencils of light do not interact with each other, i.e., the superposition of the metrics of two parallel pencils of light is an exact solution of Einstein’s equations in a vacuum.
AI and Stochastic Terrorism – Should it be done?

The use of Artificial Intelligence and Machine Learning technology may seem to be the tools needed to combat media-inspired “lone wolf attacks” by implementing the concept of “stochastic terrorism,” targeting harmful media influences. Machine Learning is in current use to sort through social media data to assess hate speech. Artificial Intelligence is in current use to interpret the data and trends processed by Machine Learning for tasks such as finding criminal networks. The question becomes “can stochastic terrorism be proven” and “should this be implemented. ” Labeling someone as a “terrorist,” regardless of any modifier for the term, tags the person or group for severe, potentially lethal, response by the government and the community. Criminal accusation cannot ethically be done casually or without sufficient cause. Due to documented problems with bias in all aspects of the issue, using these computational tools to establish legal causation between media statements by pundits, politicians, or others and the violence of “lone wolf” actors would not meet the requirements of US jurisprudence or the ethical principles for Artificial Intelligence of being explainable, transparent, and responsible.
FSM: Correspondenceless scan-matching of panoramic 2D range scans

Recent years have seen the introduction of more affordable but less accurate 2D range sensors whose field of view is $2\pi$. Scan-matching with these has been insufficiently researched, while being a challenge due to these sensors' increased measurement uncertainty. This paper proposes a real-time method for matching scans extracted from panoramic 2D LIDAR sensors. The method leverages properties of the Fourier transform which arise due to the periodicity of the range signal. Matching is performed in a correspondenceless manner. The proposed method outperforms established scan-matching methods in terms of pose accuracy and robustness in tests on public domain data, and over noise levels of commercially available sensors. The source code is available for download.
Development and Experimental Evaluation of a Novel Portable Haptic Robotic Exoskeleton Glove System for Patients with Brachial Plexus Injuries

This paper presents the development and experimental evaluation of a portable haptic exoskeleton glove system designed for people who suffer from brachial plexus injuries to restore their lost grasping functionality. The proposed glove system involves force perception, linkage-driven finger mechanism, and personalized voice control to achieve various grasping functionality requirements. The fully integrated system provides our wearable device with lightweight, portable, and comfortable characterization for grasping objects used in daily activities. Rigid articulated linkages powered by Series Elastic Actuators (SEAs) with slip detection on the fingertips provide stable and robust grasp for multiple objects. The passive abduction-adduction motion of each finger is also considered to provide better grasping flexibility for the user. The continuous voice control with bio-authentication also provides a hands-free user interface. The experiments with different objects verify the functionalities and capabilities of the proposed exoskeleton glove system in grasping objects with various shapes and weights used in activities of daily living (ADLs).
Learning Turn-Taking Behavior from Human Demonstrations for Social Human-Robot Interactions

Turn-taking is a fundamental behavior during human interactions and robots must be capable of turn-taking to interact with humans. Current state-of-the-art approaches in turn-taking focus on developing general models to predict the end of turn (EoT) across all contexts. This demands an all-inclusive verbal and non-verbal behavioral dataset from all possible contexts of interaction. Before robot deployment, gathering such a dataset may be infeasible and/or impractical. More importantly, a robot needs to predict the EoT and decide on the best time to take a turn (i.e, start speaking). In this research, we present a learning from demonstration (LfD) system for a robot to learn from demonstrations, after it has been deployed, to make decisions on the appropriate time for taking a turn within specific social interaction contexts. The system captures demonstrations of turn-taking during social interactions and uses these demonstrations to train a LSTM RNN based model to replicate the turn-taking behavior of the demonstrator. We evaluate the system for teaching the turn-taking behavior of an interviewer during a job interview context. Furthermore, we investigate the efficacy of verbal, prosodic, and gestural cues for deciding when to begin a turn.
Study of Interferents of a Plasmonic Sensor for Uremic Toxins

This work shows the response of a colorimetric sensor based on albumin bound to citrate-capped silver nanoparticles to interferents. The sensor capability of quantifying protein-bound uremic toxins, such as indoxyl sulfate, and uremic toxins that do not bind to proteins, such as creatinine and urea, is demonstrated. Furthermore, optimal sensor outputs were obtained independently of concentration of silver nanoparticles, indicating that smaller nanoparticles are possibly responsible for the sensing of below-uremic concentrations of uremic toxins.
Flexible Collision-free Platooning Method for Unmanned Surface Vehicle with Experimental Validations

This paper addresses the flexible formation problem for unmanned surface vehicles in the presence of obstacles. Building upon the leader-follower formation scheme, a hybrid line-of-sight based flexible platooning method is proposed for follower vehicle to keep tracking the leader ship. A fusion artificial potential field collision avoidance approach is tailored to generate optimal collision-free trajectories for the vehicle to track. To steer the vehicle towards and stay within the neighborhood of the generated collision-free trajectory, a nonlinear model predictive controller is designed. Experimental results are presented to validate the efficiency of proposed method, showing that the unmanned surface vehicle is able to track the leader ship without colliding with the surrounded static obstacles in the considered experiments.
Motion tracking with non-stationary camera based on area and level set weighted average of centroid shifting vectors

In this paper, we propose a new representation of the location of the target that aims for the application of object tracking with non-stationary cameras and non-rigid motion: the area weighted mean of the centroids corresponding to each color bin of the target. With this representation, the target localization in the next frame can be achieved by a direct one step computation. The tracking based on this representation has several advantages such as being possible to track in low-rate-frame environment, allowing partial occlusion and being fast due to the one step computation. We also propose a background feature elimination algorithm which is based on the level set based bimodal segmentation and is incorporated into the tracking scheme to increase the robustness of the scheme.
Hematological Toxicity in Carcinoma Cervix Patients Undergoing Concurrent Chemo Radiation Therapy or Radiation Therapy alone in a Tertiary Hospital of North East India: A Prospective Study

Introduction: The current standard of treatment for locally advanced cancer of cervix is cisplatin based concurrent chemo radiation therapy followed by brachytherapy. Concurrent chemo radiotherapy causes acute hematological toxicities, which in turn leads to prolongation of therapy. Objective: The main purpose of our study, was to analyze the acute hematological toxicity in cervical cancer patients undergoing radiation or chemo radiation therapy. Materials and Methods: This is a hospital based observational study done at department of Radiation Oncology, Assam medical college and hospital. All the biopsy proven cervical cancer patients attending the department, within the study period from September 2020 to May 2021 are included in the study. Results: Out of 26 patients in the study sample, grade 1 hematologicaltoxicity is seen in 2 patients (7.5%), grade 2 hematological toxicity seen in 20 patients (77%), grade 3 hematological toxicities in 4 patients (15.5%) and none of the patients developed grade 4 nor grade 5 hematological toxicity or any other life threatening complications. Conclusion: Hematological toxicity is more common with conventional external beam therapy in locally advanced carcinoma cervix patients. Compared to conventional external beam radiotherapy and 3DCRT, patients undergoing treatment with IMRT experiences significantly less acute grade ≥2 hematological toxicities.
A Novel Perceptive Robotic Cane with Haptic Navigation for Enabling Vision-Independent Participation in the Social Dynamics of Seat Choice

Goal-based navigation in public places is critical for independent mobility and for breaking barriers that exist for blind or visually impaired (BVI) people in a sight-centric society. Through this work we present a proof-of-concept system that autonomously leverages goal-based navigation assistance and perception to identify socially preferred seats and safely guide its user towards them in unknown indoor environments. The robotic system includes a camera, an IMU, vibrational motors, and a white cane, powered via a backpack-mounted laptop. The system combines techniques from computer vision, robotics, and motion planning with insights from psychology to perform 1) SLAM and object localization, 2) goal disambiguation and scoring, and 3) path planning and guidance. We introduce a novel 2-motor haptic feedback system on the cane's grip for navigation assistance. Through a pilot user study we show that the system is successful in classifying and providing haptic navigation guidance to socially preferred seats, while optimizing for users' convenience, privacy, and intimacy in addition to increasing their confidence in independent navigation. The implications are encouraging as this technology, with careful design guided by the BVI community, can be adopted and further developed to be used with medical devices enabling the BVI population to better independently engage in socially dynamic situations like seat choice.
SO-PFH: Semantic Object-based Point Feature Histogram for Global Localization in Parking Lot

Global localization is essential for autonomous mobile systems, especially indoor applications where the GPS signal is denied. Although the appearance-based methods have been successfully applied in various localization tasks, they face various challenges such as light variation, viewpoint changing, and dynamic interference. Additionally, the appearance-based methods usually require a visual feature point map, which increases the storage burden. This paper proposes a novel global localization solution that leverages sparse and repetitive semantic object information. The proposal can fulfill global localization based on object-level maps that are self-built or externally provided. In this solution, the semantic objects are firstly modeled with a point cloud. Then, the object's semantic information is embedded into the geometry of the corresponding point, and the Semantic Object-based Point Feature Histogram (SO-PFH) descriptors of the modeled point clouds are estimated. Finally, the global localization is executed by applying a Geometric Consistency Filter-based RANdom SAmple Consensus (GCF-RANSAC) method to match point clouds. Experiments and simulations are conducted in indoor parking lots. The results demonstrate the effectiveness of the proposed method.
Factors in Credit Decision-making and Related Research Gaps in Indonesia: A Literature Review

For wholesale and Small Medium Enterprise (SME) segments, the credit decision-making process in banking mainly uses a human approach. Although there is already automation in determining the credit system at the bank, the human side still dominates the final decisions. An analyst from the bank needs to understand the background and factors which influence effective decisions. This study aims to review the literature to identify determinants of credit decision-making—The authors used a systematic article search for empirical studies conducted from 1990 to 2021 about the topic. The results suggest three conclusions about factors in credit decision-making: decision-maker's external factors, decision-maker's internal factors, and related research gaps in Indonesia.
Man-In-The-Middle Attack Detection Using Ensemble Learning

Cyber security is one of the most vital demands about network infrastructure and hence very necessary to protect the information sent and received during data transmission against outside factors trying to intercept systems. Man-In-The-Middle (MITM) attacks can dramatically compromise the security of wireless fidelity network where an attacker eavesdrops and intercepts the communication medium over the wireless communication networks. This kind of attack aims to steal sensitive data such as credit card details, login accounts, and other important financial transactions. Even though, many detection techniques have been proposed to mitigate Man-In-The-Middle attacks, however, this attack still occurs and causes tremendous damages. In this study, we propose a systematic approach using Ensemble Learning to detect MITM attack packets in a network. Our Ensemble Learning approach proves to be a valid stratagem for the detection of MITM Attack Packets.
Behaviour Learning with Adaptive Motif Discovery and Interacting Multiple Model

We propose an approach that enables simultaneous interpretable learning of a high-level discrete behaviour and its low-level rhythmic sub-behaviour. We do this though a unified reward function, where a reward function that only describes low-level behaviour, with less impact on learning of other behaviours is recovered from few-shot motion demonstrations. To this end, we first extract local behaviour motifs from state-only human demonstrations and random driving samples using an adaptive motif discovery approach derived from the Matrix Profile algorithm. We then optimize parameters for motif discovery by maximizing the sum and entropy over motif sizes. Interacting Multiple Model (IMM) estimators are constructed on top of linear-Gaussian dynamics of discovered motifs, the cumulative distributions over motifs estimated by IMMs serve as the basis of the reward function. By combining the recovered reward with the terrain type signal gathered from the environment, we are able to train a dual-objective off-road vehicle controller that demonstrates both terrain selection and human-like driving behaviours. Compared with related approaches across 10 people, our rhythmic behaviour reward recovery approach enables the controller to produce higher preference over human driving demonstrations. In addition to performing more stable across different people with 87% less variance than the best baseline in rhythmic behaviour indicator, our method reduces the negative effects on higher-level behaviour learning while maintaining high interpretability at all stages of the algorithm.