Reviewerly/exHarmony huggingface dataset:
Augmented Triplet Network for Individual Organism and Unique Object Classification for Reliable Monitoring of Ezoshika Deer

Wild Deer’s grazing in farmers and villager’s field has caused serious troubles for the residents and concerned authorities here in Northern Hokkaido of Japan. Furthermore, the number of deer in northern Japan is increasing rapidly leading to increment in road accidents which is also becoming a serious problem that needs to be handled. Several procedures have been followed to avoid or at least minimize the destruction caused by the white-tailed deer, but a solid solution is yet to be found. To this context, we are proposing a highly effective solutions to identify the individual wild deer and pinpoint the most trouble causing group of deer’s using augmented triplet network, a novel technique of computer vision. Initially the deer are recognized and classified individually which will help us track the deer and know the behavior of the deer in depth. Our research method identifies individual deer using several steps where we initially take video feed which is further divided into several frames. The extracted frames are then cleaned and processed for the training efficiency. The cleaned frames are then sent to the Convolution Network where the Nested triplet loss is calculated for the maximum accuracy.
PointSiamRCNN: Target-aware Voxel-based Siamese Tracker for Point Clouds

Currently, there have been many kinds of pointbased 3D trackers, while voxel-based methods are still underexplored. In this paper, we first propose a voxel-based tracker, named PointSiamRCNN, improving tracking performance by embedding target information into the search region. Our framework is composed of two parts for achieving proposal generation and proposal refinement, which fully releases the potential of the two-stage object tracking. Specifically, it takes advantage of efficient feature learning of the voxel-based Siamese network and high-quality proposal generation of the Siamese region proposal network head. In the search region, the groundtruth annotations are utilized to realize semantic segmentation, which leads to more discriminative feature learning with pointwise supervisions. Furthermore, we propose the Self and Cross Attention Module for embedding target information into the search region. Finally, the multi-scale RoI pooling module is proposed to obtain compact representations from target-aware features for proposal refinement. Exhaustive experiments on the KITTI tracking dataset demonstrate that our framework reaches the competitive performance with the state-of-the-art 3D tracking methods and achieves the state-of-the-art in terms of BEV tracking.
A population-based game-theoretic optimizer for the minimum weighted vertex cover

Toward higher solution efficiency and faster computation, this paper addresses the minimum weighted vertex cover (MWVC) problem by introducing game theory into iterated optimization and proposing a population-based game-theoretic optimizer (PGTO). A group of candidate solutions are iterated through the specially designed procedures of swarm evolution (SE), learning in games (LIG), and local search (LS) successively. Within the framework of potential game theory, we prove that LIG computes in finite time Nash equilibria that represent vertex cover solutions where no redundant nodes exist. Moreover, theoretical analysis is presented that by exchanging the actions of certain neighbours, LS is capable of generating better results upon the input Nash equilibrium. Through intensive numerical experiments, we show that while enlarging the population size could boost the global objective, a mutation probability between 0.05 and 0.2 is more likely to provide the best performance. Comparison experiments against the state of the art demonstrate the superiority of the presented methodology, both in terms of solution quality and computation speed.
Governance and tax revenue: does foreign aid matter?

This study seeks to explores the relationship among governance, foreign aid, and tax revenue. A common notion on governance is that it is a multifaceted factor, it may affect from other factor that is foreign aid. Foreign aid can hamper the governance situation and thus can reduce tax collection. To test these theoretical assumptions, we collected the numerical data from Asian economies for the years 2001–2019 and employed the panel FMOLS (fully modified ordinary least square) test to estimate the regression. The empirical findings first reveal that governance and foreign aid have a positive association with tax revenue in the long run. However, a negative trend in tax collection was observed following the interaction of foreign aid and governance. Foreign aid deteriorates the governance situation, which has a negative spillover impact on tax revenue. Our empirical analysis suggests that policy officials should focus on exercising governance and foreign aid effectiveness to meet the objective of more tax collection. Points for practitioners Policy officials should focus on better governance exercises. They should carefully decide on the volume of foreign aid receipts. However, if it becomes necessary to receive foreign aid, then they should focus on aid effectiveness. Such steps can benefit the practitioners to collect more tax.
Keypoint Description Using Statistical Descriptor with Similarity-Invariant Regions

This article presents a method for the description of key points using simple statistics for regions controlled by neighboring key points to remedy the gap in existing descriptors.Usually, the existent descriptors such as speeded up robust features (SURF), Kaze, binary robust invariant scalable keypoints (BRISK), features from accelerated segment test (FAST), and oriented FAST and rotated BRIEF (ORB) can competently detect, describe, and match images in the presence of some artifacts such as blur, compression, and illumination.However, the performance and reliability of these descriptors decrease for some imaging variations such as point of view, zoom (scale), and rotation.The introduced description method improves image matching in the event of such distortions.It utilizes a contourlet-based detector to detect the strongest key points within a specified window size.The selected key points and their neighbors control the size and orientation of the surrounding regions, which are mapped on rectangular shapes using polar transformation.The resulting rectangular matrices are subjected to two-directional statistical operations that involve calculating the mean and standard deviation.Consequently, the descriptor obtained is invariant (translation, rotation, and scale) because of the two methods; the extraction of the region and the polar transformation techniques used in this paper.The description method introduced in this article is tested against well-established and wellknown descriptors, such as SURF, Kaze, BRISK, FAST, and ORB, techniques using the standard OXFORD dataset.The presented methodology demonstrated its ability to improve the match between distorted images compared to other descriptors in the literature.
Recurrent Neural Networks and Morphological Features in Language Modeling for Serbian

This paper describes the current state-of-the-art language model for the Serbian language, and also a specific way of dealing with one of the issues that is present in Serbian automatic speech recognition systems. This issues arises from the fact that Serbian is a highly inflective language, which leads to recognition mistakes where the basic form of the word is guessed correctly, but the word ending is not. The mistaken word ending can indicate, for example, a wrong word case, grammatical gender or grammatical number. A way to solve this problem was to use additional lexical and morphological word features as neural network input while assessing the likelihoods of predicted words. The experiments have shown significant reduction in word error rates when employing certain combinations of these additional features.
Relative Precise Positioning based on Moving Baseline and the Effect of Uncommon Satellite Combination

For drone swarm flight, it is important that the drone&#x0027;s position is determined quickly and accurately. Therefore, in general, the single reference RTK(Real Time Kinematics) method using a ground reference station is used. However, in the RTK method using ground reference station, as the distance between the drone and reference station increases, the reception rate of the correction information may decrease or may not be able to receive it at all. It can cause problems in drone operation and control. Therefore, using the principle of single reference RTK, moving baseline RTK is proposed as a method that can do RTK without using a ground reference station. In this method, RTK is performed by designating one antibody participating in the swarm flight as a moving base and the dynamic of the moving base is compensated through its velocity compensation. Also, there is a possibility that an error may occur as a uncommon satellite combination is used in the velocity calculation because there is a difference in the visible satellites between the moving base and other antibodies. However, the change in satellite combination did not significantly affect the results.
A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions

A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed method is mainly designed to address two issues. The first one is the dilemma between the weak target detection and difficult computation originating from the high dimensions of measurement. The second issue is the suppression of inhomogeneous background clutter and various clutter regions. The extension experiment using synthetic data showcases that no false alarm track would be built in the clutter regions, and the detection rate of close targets exceeds 94%. The experiments using real 3D radar also prove that the method works well in tracking closely maneuvering extended targets even if a clutter region exists.
Synthetic Differential Privacy Data Generation for Revealing Bias Modelling Risks

Personally identifiable information (PII) continues to be used in predictive modeling by academic researchers and industry organizations. Most notably, the healthcare industry has been a popular testbed for innovative approaches from academia and institutions to address research using PII in predictive applications and synthetic data generation. The majority of these approaches that generate synthetic PII are based on actual data or obfuscating real data parts. Privacy leakage and ethical disclosure results continue to be among the largest issues that are difficult to avoid in synthetic PII generation techniques. In this analysis, we propose a novel method to generate synthetic, differential privacy data while avoiding the common pitfalls and capable of being leveraged broadly. Evidence is also shown that proves how our novel approach can maintain inference for modeling and potential risks tied to PII features. We conclude with a summarization of our findings and results and a short discussion on how using PII data may impact organizations interested in developing predictive applications.
Generalized network model for career guidance using emotional intelligence, self-assessment and attitudes toward the employer

Choosing the best practice and associated components which will guarantee successful selection and further career success of job applicants is a complex process. There are many components that can contribute in order to support the ultimate “right” decision of the recruiter. In the current paper, the role of emotional intelligence, company attractiveness, and the concept of “work-life balance” expressed by the job seeker is investigated. A generalized net model based on mentioned factors in order to support the selection of job applicants by human resources units is proposed. Modelling the evaluation process of job candidates can be used to find out the most appropriate candidates for achieving the goals of the organization.
Study on detection of breast cancer using Machine Learning

Breast cancer is one of the woman's most prominent cancer and most malignant of all cancers. It is the world's largest autopsy of cancer deaths for females and happens in about 3 from 10 people. This article includes numerous machine learning methods and data mining strategies to assess the early detection of breast cancer. Machine learning is used in clinical applications such as identification of cancer cells. The cancerous cells are categorized as Benign and Malignant. This paper analyzes the quality of numerous unsupervised, supervised and other methods for the integrity and prediction for breast cancer. This research could provide various methodologies to better understand early cancer detection. Early detection for breast cancer can be a potential benefit in the management of this condition, not only does early treatment make it possible to heal it, but it also prevent its recurrence.
Intensity-based nonrigid endomicroscopic image mosaicking incorporating texture relevance for compensation of tissue deformation

Image mosaicking has emerged as a universal technique to broaden the field-of-view of the probe-based confocal laser endomicroscopy (pCLE) imaging system. However, due to the influence of probe-tissue contact forces and optical components on imaging quality, existing mosaicking methods remain insufficient to deal with practical challenges. In this paper, we present the texture encoded sum of conditional variance (TESCV) as a novel similarity metric, and effectively incorporate it into a sequential mosaicking scheme to simultaneously correct rigid probe shift and nonrigid tissue deformation. TESCV combines both intensity dependency and texture relevance to quantify the differences between pCLE image frames, where a discriminative binary descriptor named fully cross-detected local derivative pattern (FCLDP) is designed to extract more detailed structural textures. Furthermore, we also analytically derive the closed-form gradient of TESCV with respect to the transformation variables. Experiments on the circular dataset highlighted the advantage of the TESCV metric in improving mosaicking performance compared with the other four recently published metrics. The comparison with the other four state-of-the-art mosaicking methods on the spiral and manual datasets indicated that the proposed TESCV-based method not only worked stably at different contact forces, but was also suitable for both low- and high-resolution imaging systems. With more accurate and delicate mosaics, the proposed method holds promises to meet clinical demands for intraoperative optical biopsy.
Learning and planning in partially observable environments without prior domain knowledge

Planning in stochastic and partially observable environments is a central problem in artificial intelligence. To address this issue, an accurate model or a black-box simulator of the environment is usually needed in the literature. Although some recent approaches have been proposed for learning optimal behaviors under model uncertainty, prior knowledge about the environment is still required to guarantee the performance of the proposed algorithms. With the benefits of the Predictive State Representations (PSRs) approach for state representation and model prediction, in this paper, we introduce an approach for planning under partial observability with no prior domain knowledge, where an offline PSR model is firstly learned and then combined with online Monte-Carlo tree search for planning under model uncertainty. Furthermore, we also showed that with the proposed framework, the PSR models learned via other techniques, e.g., the online PSR model learning approach, can be integrated straightforwardly. By comparing with the state-of-the-art approach of planning under model uncertainty, we demonstrated the effectiveness of the proposed approaches along with the proof of their convergence.
Coherence and quantum Fisher information in general single-qubit parameter estimation processes

Quantum metrology is superior to classical metrology in parameter estimation. This is because quantum states have coherence that, however, does not exist in classical states. Previous works were limited to the study of the relationship between quantum Fisher information (QFI) and coherence of the initial probe state only for a specific single-qubit parameter estimation process (e.g., a process described by a single-qubit spin angular momentum operator ${J}_{z}$). Here, we consider a more general case and study the relationship between QFI and coherence of the initial probe state for a single-qubit general parameter estimation process, which is described by a single-qubit spin angular momentum operator ${J}_{\stackrel{P\vec}{n}}$ with a general unit vector $\stackrel{P\vec}{n}$. We find that the QFI is proportional to the square of coherence of the initial probe state, when the single-qubit bases are eigenstates of the spin angular momentum operator ${J}_{\stackrel{P\vec}{n}}$ describing the parametrization process. We also design and conduct a linear optical experiment to support our theory. The experimental results are in good agreement with the theoretical ones. This work provides an important guideline for enhancing the precision of parameter estimation.
Device Fingerprinting with Magnetic Induction Signals Radiated by CPU Modules

With the widespread use of smart devices, device authentication has received much attention. One popular method for device authentication is to utilize internally measured device fingerprints, such as device ID, software or hardware-based characteristics. In this article, we propose DeMiCPU , a stimulation-response-based device fingerprinting technique that relies on externally measured information, i.e., magnetic induction (MI) signals emitted from the CPU module that consists of the CPU chip and its affiliated power-supply circuits. The key insight of DeMiCPU is that hardware discrepancies essentially exist among CPU modules and thus the corresponding MI signals make promising device fingerprints, which are difficult to be modified or mimicked. We design a stimulation and a discrepancy extraction scheme and evaluate them with 90 mobile devices, including 70 laptops (among which 30 are of totally identical CPU and operating system) and 20 smartphones. The results show that DeMiCPU can achieve 99.7% precision and recall on average, and 99.8% precision and recall for the 30 identical devices, with a fingerprinting time of 0.6~s. The performance can be further improved to 99.9% with multi-round fingerprinting. In addition, we implement a prototype of DeMiCPU docker, which can effectively reduce the requirement of test points and enlarge the fingerprinting area.
Forecasting of Wave Height Time Series Using AdaBoost and XGBoost, Case Study in Pangandaran, Indonesia

Wave height forecasting has a vital role in coastal activities. The prediction results help minimize losses on various ocean activities, such as sea transportation and designing coastal structures. The numerical method is usually utilized for wave height forecasting, but it requires high computational resources to solve its complexity. Moreover, the high-resolution grid resolution is also required to produce an accurate prediction. In this paper, we use XGBoost and AdaBoost methods to forecast the wave height accurately. Five years of data is used in this study, 4.5 years of data as training data, and six months of data as testing data. We use the testing data to forecast 7 days, 14 days, 30 days, 45 days, and 60 days ahead. We use wave data at the southern part of the Pangandaran region, Indonesia, as a study case. We compared the forecasting results by using XGBoost and AdaBoost to obtain the highest accuracy. Hyperparameter tuning for both methods is also used in this study to optimize the model's performance. We use the GridSearch algorithm for hyperparameter tuning to find the best parameters value combination for both models. The highest accuracy of forecasting results is 0.064 for RMSE and 0.987 for the coefficient correlation value.
Augmented Reality (AR) Technology-Based Learning: The Effect on Physics Learning during the COVID-19 Pandemic

The COVID-19 pandemic has significantly disrupted education and has instantaneously shifted education from being conducted predominately ‘face to face’ to being totally ‘online’. For most teachers, this unexpected teaching approach has impelled them into finding ways to provide the same quality of education to their students. One way of doing this is by adopting educational technologies in learning and teaching, including the use of augmented reality (AR) technology. AR technology has been integrated into the field of physics education. In this study, the effects of AR technology on understanding of the concepts of electricity in an online learning environment for 11th-grade students was investigated. Pretest and posttest were carried out in the control group and the experimental group. The results showed that AR technology improved understanding of electrical concepts for the students in the experimental group compared to the control group, with a very significant difference between both groups. This research contributes to the development of AR technology in education, especially in relation to the teaching and learning of abstract physics concepts.
Learning Environment based on an Interactive Projection Table for Children

This paper proposes a tabletop learning environment for children. Some physics-based phenomena such as reflection and refraction of light are difficult for children to understand. Our interactive projection significantly help children understand behaviors of light through watching simulated light particles and interacting with them by moving real objects on the table. Our system consists a PC, video projector, and depth sensor. Depth-based object detection is performed with each of captured frames at 60 FPS. In the prototype of our implemented system for learning behaviors of light, simulated light particles reacts in realtime with actual objects moved by a user hand, which would greatly help people including children understand behaviors of light such as propagation and reflection. Our future work includes to conduct a series of user experiments in local museums to evaluate usability of the proposed system. Development of other learning applications are also of high importance.
Dynamic Obstacle Avoidance of Multi-Rotor UAV using Chance Constrained MPC

Recently, in order to carry out tasks efficiently such as infrastructure inspection and goods transportation, operations using multi-rotor Unmanned Aerial Vehicles (UAVs) in formation flight are often considered. One of the main issues in motion planning among multiple UAVs is collision avoidance. Model Predictive Control (MPC) is characterized by its ability to consider collision avoidance in the framework of constrained optimization. For this reason, there have been many studies on collision avoidance using MPC, but few studies take into account the uncertainty that occurs in real environments. On the other hand, Chance constrained MPC (CCMPC) is considered to be more robust in collision avoidance due to the consideration of uncertainty. However, the structure of the collision probability constraint equation to be introduced into the evaluation function of MPC has not been sufficiently studied. In this study, the structure of equations for incorporating probability constraints into the evaluation function is examined. Moreover, by quantitatively comparing the equations with the same structure with deterministic constraints introduced into the evaluation function, the difference in collision avoidance is clarified.