Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Continuous Object Tracking and Boundary Detection Schemes in IoT Assisted Wireless Sensor Networks

With the new age of data innovation, the Internet of Things (IoT) proliferation has drawn enormous thought and has applied to help applications in different fields i.e., natural assurance, military observing, and industrial applications. WSNs are the essential segment of IoT for monitoring as well as tracking. The most preeminent applications provide confinement and identification of continuous objects i.e. wildfire, toxic gas, bio synthetics concoctions, and so forth. In the case of continuous objects such as fire and toxic gases are detected to identify the boundary of damage and alert teams for rescue efforts. It is also helpful in identifying safe paths for rescue. We have investigated various existing surveys that carried out different concepts associated with continuous object tracking and find out the deficit of boundary detection of object. In order to replete the present cleft of analysis, we have inspected various current state-of-the-art works on boundary detection of a continuous object that has yet not been added to the current writing. This paper presents an extensive overview of different continuous object tracking schemes which involve energy efficiency, boundary detection, communication, data aggregation, and network structural design in literature with the aid of featuring taxonomy. We summarized, compared, and classified these schemes along with their analysis and performance. Moreover, for further evaluation mechanism, strengths and weaknesses of these schemes are presented. Finally, various state-of-the-art open research challenges are identified. Moreover, there is a need to overcome these challenges through novel and reliable arrangements by the researchers.
Object Detection Using Color Segmentation, Radial Symmetry Detector, and Hough Method

Currently, unmanned control systems for various vehicles, including cars, are widely used. Driving an unmanned vehicle involves solving tasks related to recognizing road objects. An improved algorithm for solving the problem of detection and recognition of round traffic light signals is proposed. To solve this problem, we use: fast radial symmetry transformation, Hough transform, and color segmentation. A special feature of the algorithm is that the areas where the light signals are located they are preliminarily determined based on the color attribute, followed by specifying the shape and position of objects. At the final stage, the solution is refined using the Hough method. The proposed algorithm has shown operability, it can be expanded in terms of the types of recognized signals and applied as part of unmanned vehicle control systems, as well as part of driver assistance systems for solving problems related to the prevention of dangerous and emergency situations.
A Transformed Coprime Array With Reduced Mutual Coupling for DOA Estimation of Non-Circular Signals

Recently, the design of sparse linear array for direction of arrival (DOA) estimation of non-circular (NC) signals has attracted great attention because the difference and sum co-array offered by non-circularity increases the aperture of virtual linear array. In this paper, we present a coprime array with shifted and flipped sub-array for the DOA of non-circular signals. By shifting one sub-array, the proposed array structure achieves a higher number of consecutive lags than the prototype coprime array with the same number of sensors. Then, through flipping the shifted sub-array with the zero point as the symmetry point, the number of sensor pairs with small separation is significantly reduced, making the resulting structure much sparser. For the proposed array structure, we derive the closed-form expression for the number of consecutive lags, the optimal distribution of two sub-arrays with a given number of sensors and the weight function. Numerical simulations are conducted to verify the superiority of the proposed array over the existing sparse arrays.
Modified Ant Colony Algorithm for Placement of Electronic Elements on the Printed Circuit Board

This paper presents the formulation of an optimization problem of elements placement on the printed circuit board. The choice of problem constraints and optimization criteria based on the structural, thermal and electromagnetic parameters of the PCB design is established. The description of the ant colony algorithm main procedures for solving the placement problem is given. The results of computational experiments on the ant colony algorithm effectiveness for solving the problem of elements placement on a printed circuit board are presented.
Combined Nonlinear Control Algorithm for Structural Undefined Non-Affine Input Delayed Plant

The article is devoted to solving the problem of the combined control law synthesis for one class of non-affine plants with a delay in the input variable. The considered plant operates under conditions of a priori parametric and structural uncertainties in the presence of permanent external noise. Only the scalar plant output is available for direct measurement. The structure of the control system includes a reference predictor and dynamic filter-corrector. The synthesis of the combined regulator is based on the use of the hyperstability criterion, and the required operation quality of the obtained system is ensured at the stage of simulation.
Polymer/oxide bilayer dielectric for hysteresis-minimized 1 V operating 2D TMD transistors

Despite their huge impact on future electronics, two-dimensional (2D) dichalcogenide semiconductor (TMD) based transistors suffer from the hysteretic characteristics induced by the defect traps located at the dielectric/TMD channel interface. Here, we introduce a hydroxyl-group free organic dielectric divinyl-tetramethyldisiloxane-bis (benzocyclobutene) (BCB) between the channel and conventional SiO2 dielectric, to practically resolve such issues. Our results demonstrate that the electrical hysteresis in the n-channel MoS2 and p-channel MoTe2 transistors were significantly reduced to less than ∼20% of initial value after being treated with hydrophobic BCB dielectric while their mobilities increased by factor of two. Such improvements are certainly attributed to the use of the hydroxyl-group free organic dielectric, since high density interface traps are related to hydroxyl-groups located on hydrophilic SiO2. This concept of interface trap reduction is extended to stable low voltage operation in 2D MoTe2 FET with 30 nm BCB/10 nm Al2O3 bilayer dielectric, which operates well at 1 V. We conclude that the interface engineering employing the BCB dielectric offers practical benefits for the high performance and stable operation of TMD-based transistors brightening the future of 2D TMD electronics.
Question Classification of CoQA (QCoC) Dataset

This paper proposes a new dataset for question classification process. Named QCoC (Question Classification of CoQA), this dataset is created based on Stanford’s CoQA (A Conversational Question Answering Challenge) dataset. The total of QCoC datapoint is 116630 (total of combined question-answer pairs in CoQA training and evaluation dataset). Common question classification datasets are classifying question based on its paired answer’s knowledge (the semantic of answer’s context). For QCoC, classification is done differently that is per answer’s feature (semantic and syntactic of answer’s type). This paper discusses the question classification datasets, QA datasets, and justification of CoQA as selected base for QCoC. Then QCoC specification is discussed with class definition, classification method and result subsections. To the author’s knowledge, such dataset is still nonexistent to date. This paper suggests that this type of dataset is useful in solving abstractive answers issue in Question-Answering (QA) system. While factual answers can be directly produced by regular QA system, abstractive answers need some additional components. Although it is a recognizable issue, lack of suitable dataset perhaps is the reason why such direction is not being pursued. With QCoC dataset made publicly available1, hopefully such direction is open for further exploration.
ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation

Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of video analysis and understanding. Despite the great achievement in TAPG, most existing works ignore the human perception of interaction between agents and the surrounding environment by applying a deep learning model as a black-box to the untrimmed videos to extract video visual representation. Therefore, it is beneficial and potentially improves the performance of TAPG if we can capture these interactions between agents and the environment. In this paper, we propose a novel framework named Agent-Aware Boundary Network (ABN), which consists of two sub-networks: (1) an Agent-Aware Representation Network to obtain both agent-agent and agents-environment relationships in the video representation; and (2) a Boundary Generation Network to estimate the confidence score of temporal intervals. In the Agent-Aware Representation Network, the interactions between agents are expressed through local pathway, which operates at a local level to focus on the motions of agents whereas the overall perception of the surroundings are expressed through global pathway, which operates at a global level to perceive the effects of agents-environment. Comprehensive evaluations on 20-action THUMOS-14 and 200-action ActivityNet-1.3 datasets with different backbone networks (i.e C3D, SlowFast and Two-Stream) show that our proposed ABN robustly outperforms state-of-the-art methods regardless of the employed backbone network on TAPG. We further examine the proposal quality by leveraging proposals generated by our method onto temporal action detection (TAD) frameworks and evaluate their detection performances.
A Conformal, Optimized 3D Printed Kneepad With Deformation Sensing

Additive manufactured smart wearables are establishing an increasingly firm foothold within the monitoring of physical activities. A tiler was the use case proposed by this study, which seeks to monitor changes in their typically kneeling work position with the goal of tracking behaviour and alleviating long-term damage due to incorrect posture. This paper has described the development process of a conformal kneepad with embedded sensing, obtained by means of interposing flexible tactile sensors between 3D printed elastomeric layers. Topology optimization is employed towards material reduction in the context of tailoring the kneepad design for the differentiated loading which the case study imposes. Considerations on the inherent challenges of overmolding flexible geometries are provided along with a design-to-fabrication process description which visits 3D printing, post-processing and 3D scanning. The use of the latter enables the creation of a fully conformal design, optimized for the perfect fit on each individual wearing the kneepad. The topology optimization is validated numerically and the ability of the kneepad to react to different pressure stimuli is tested with a robot arm, capable of simulating different contact angles and load cases. By means of dedicated electronics, the sensed information is modulestreamed over WiFi and tactile images are formed on a user-friendly terminal. The kneepad concept ultimately serves as a case study for extended additive manufactured smart wearables, with potential to be extended towards autonomous feedback and posture correction systems.
Performance Analysis on Denial of Service attack using UNSW-NB15 Dataset

With the advancement of network technology, users can now easily gain access to and benefit from networks. However, the number of network violations is increasing. The main issue with this violation is that irresponsible individuals are infiltrating the network. Network intrusion can be interpreted in a variety of ways, including cyber criminals forcibly attempting to disrupt network connections, gaining unauthorized access to valuable data, and then stealing, corrupting, or destroying the data. There are already numerous systems in place to detect network intrusion. However, the systems continue to fall short in detecting and counter-attacking network intrusion attacks. This research aims to enhance the detection of Denial of service (DoS) by identifying significant features and identifying abnormal network activities more accurately. To accomplish this goal, the study proposes an Intrusion Analysis System for detecting Denial of service (DoS) network attacks using machine learning. The accuracy rate of the proposed method using random forest was demonstrated in our experimental results. It was discovered that the accuracy rate with each dataset is greater than 98.8 percent when compared to traditional approaches. Furthermore, when features are selected, the detection time is significantly reduced.
The Probe: Design and Development of a Large Hexapod Robot

A hexapod robot has six legs; as such it can always walk in a statically stable way. In addition, people do not often see large-legged robots in real life. For these reasons, we design and develop a large hexapod robot to impress people at retail stores. The developed hexapod robot, named Probe, has a height of 2.1m, width of 3.3m, and mass of 480kg. The actuator is customized with a frameless BLDC motor, a harmonic drive gear, an electromagnetic brake, a multi-turn absolute magnetic encoder, heat sinks, etc. To firmly secure the frameless motor, we use a clamping ring and suggest the design of a tapered shaft hub and insert. We design an interface circuit board to acquire sensor data and a brake switch board to engage and release brake via an Elmo driver’s signal. Additionally, an analytic solution to the kinematic equations of Probe’s legs is described. After development of the Probe, Two Probes are deployed at two different stores in South Korea and they conduct scenario motions during business hours. In future, the design of the foot tip will be modified because the urethane cover beneath foot tip wears while the Probe is walking.
A Review: Static Analysis of Android Malware and Detection Technique

Android malware has become more widespread in recent years due to the growing popularity of Android mobile. Android malware is installed without the user’s consent on a mobile device and exhibits significant risks to users, including personal information disclosure and fraud. Furthermore, some malware may be hidden inside the mobile device using various obfuscation techniques and cause harm. To mitigate these risks, the researcher recommended a variety of detection techniques. Unfortunately, it remains challenging as Android malware continues to increase. Signature-based detection is currently used to detect malware, but unknown malware and the newest malware are not recognised in this approach. This demonstrates that developing an effective malware detection method is crucial, with an enormous gap in recent studies and methods. This study aims to present researchers with a review of Android malware detection methods and empirical findings, focusing on static analysis. The research discussed the trends of Android malware, Android vulnerabilities, static analysis approaches, and a summary of recent studies in static analysis. Additionally, future research guidelines in the static analysis are suggested to optimise the functionality of Android malware detection.
Partner fidelity and environmental filtering preserve stage‐specific turtle ant gut symbioses for over 40 million years

Sustaining beneficial gut symbioses presents a major challenge for animals, including holometabolous insects. Social insects may meet such challenges through partner fidelity, aided by behavioral symbiont transfer and transgenerational inheritance through colony founders. We address such potential through colony-wide explorations across 13 eusocial, holometabolous insect species in the ant genus Cephalotes . Through amplicon sequencing, we show that previously characterized worker microbiomes are conserved in sol-dier castes, that adult microbiomes exhibit trends of phylosymbiosis
Hobotnica: exploring molecular signature quality

A Molecular Features Set (MFS), is a result of vast diversity of bioinformatics pipelines. In case when MFS is used for further analysis to distinguish between phenotypes, it is often referred to as a signature. Lack of the “gold standard” for most experimental data modalities makes it hard to provide valid estimation for a particular MFS’s quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrix (DM) and their power to distinguish between phenotypes. The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS’s quality. Here we propose Hobotnica, an approach to estimate MFS’s quality by their ability to stratify data, and assign them significance scores, that allows for collating various signatures and comparing their quality for contrasting groups.
The Effects of Incline Level on Optimized Lower-Limb Exoskeleton Assistance

For exoskeletons to be successful in real-world settings, they will need to be effective across a variety of terrains, including on inclines. While some single-joint exoskeletons have assisted incline walking, recent successes in level-ground assistance suggest that greater improvements may be possible by optimizing assistance of the whole leg. To understand how exoskeleton assistance should change with incline, we used human-in-the-loop optimization to find whole-leg exoskeleton assistance torques that minimized metabolic cost on a range of grades. We optimized assistance for three expert, able-bodied participants on 5 degree, 10 degree and 15 degree inclines using a hip-knee-ankle exoskeleton emulator. For all assisted conditions, the cost of transport was reduced by at least 50% relative to walking in the device with no assistance, a large improvement to walking that is comparable to the benefits of whole-leg assistance on level-ground. This corresponds to large absolute reductions in metabolic cost, with the most strenuous conditions reduced by 4.9 W/kg, more than twice the entire energy cost of level walking. Optimized extension torque magnitudes and exoskeleton power increased with incline, with hip extension, knee extension and ankle plantarflexion often growing as large as allowed by comfort-based limits. Applied powers on steep inclines were double the powers applied during level-ground walking, indicating that larger exoskeleton power may be optimal in scenarios where biological powers and costs are higher. Future exoskeleton devices can be expected to deliver large improvements in walking performance across a range of inclines, if they have sufficient torque and power capabilities.
Stable breast cancer prognosis

Predicting breast cancer prognosis helps improve the treatment and management of the disease. In the last decades, many prediction models have been developed for breast cancer prognosis based on transcriptomic data. A common assumption made by these models is that the test and training data follow the same distribution. However, in practice, due to the heterogeneity of breast cancer and the different environments (e.g. hospitals) where data are collected, the distribution of the test data may shift from that of the training data. For example, new patients likely have different breast cancer stage distribution from those in the training dataset. Thus these existing methods may not provide stable prediction performance for breast cancer prognosis in situations with the shift of data distribution. In this paper, we present a novel stable prediction method for reliable breast cancer prognosis under data distribution shift. Our model, known as Deep Global Balancing Cox regression (DGBCox), is based on the causal inference theory. In DGBCox, firstly high-dimensional gene expression data is transferred to latent network-based representations by a deep auto-encoder neural network. Then after balancing the latent representations using a proposed causality-based approach, causal latent features are selected for breast cancer prognosis. Causal features have persistent relationships with survival outcomes even under distribution shift across different environments according to the causal inference theory. Therefore, the proposed DGBCox method is robust and stable for breast cancer prognosis. We apply DGBCox to 12 test datasets from different breast cancer studies. The results show that DGBCox outperforms benchmark methods in terms of both prediction accuracy and stability. We also propose a permutation importance algorithm to rank the genes in the DGBCox model. The top 50 ranked genes suggest that the cell cycle and the organelle organisation could be the most relevant biological processes for stable breast cancer prognosis. Author summary Various prediction models have been proposed for breast cancer prognosis. The prediction models usually train on a dataset and predict the survival outcomes of patients in new test datasets. The majority of these models share a common assumption that the test and training data follow the same distribution. However, as breast cancer is a heterogeneous disease, the assumption may be violated in practice. In this study, we propose a novel method for reliable breast cancer prognosis when the test data distribution shifts from that of the training data. The proposed model has been trained on one dataset and applied to twelve test datasets from different breast cancer studies. In comparison with the benchmark methods in breast cancer prognosis, our model shows better prediction accuracy and stability. The top 50 important genes in our model provide clues to the relationship between several biological mechanisms and clinical outcomes of breast cancer. Our proposed method in breast cancer can potentially be adapted to apply to other cancer types.
Testing for heterogeneous rates of discrete character evolution on phylogenies

Many hypotheses in the field of phylogenetic comparative biology involve specific changes in the rate or process of trait evolution. We present a method designed to test whether the rate of evolution of a discrete character has changed in one or more clades, lineages, or time periods. This method differs from other related approaches (such as the ‘covarion’ model) in that the ‘regimes’ in which the rate or process is postulated to have changed are specified a priori by the user, rather than inferred from the data. Similarly, it differs from methods designed to model a correlation between two binary traits in that the regimes mapped onto the tree are fixed. We apply our method to investigate the rate of dewlap color and/or caudal vertebra number evolution in Caribbean and mainland clades of the diverse lizard genus Anolis. We find little evidence to support any difference between mainland and island evolution in either character. We also examine the statistical properties of the method more generally and show that it has acceptable type I error, parameter estimation, and power. Finally, we discuss the relationship of our method to existing models of heterogeneity in the rate of discrete character evolution on phylogenies.