Miracle-dz/UniFAR-FTUs huggingface dataset:
HLA class I allele lineages and haplotype frequencies in Arabs of the United Arab Emirates

The high degree of polymorphism of the HLA system provides suitable genetic markers to study the diversity and migration of different world populations and is beneficial for forensic identification, anthropology, transplantation and disease associations. Although the United Arab Emirates (UAE) population of about nine million people is heterogeneous, information is limited for the HLA class I allele and haplotype frequencies of the Bedouin ethnic group. We performed low‐resolution PCR‐SSP genotyping of three HLA class I loci at HLA‐A, ‐B and ‐C for 95 unrelated healthy Bedouins from the cities of Al Ain and Abu Dhabi in the UAE. A total of 54 HLA allele lineages were detected; the most frequent low‐resolution allele lineages at each HLA locus were A*02 (0.268), B*51 (0.163) and C*07 (0.216). The inferred estimates for the two most frequent HLA‐A and HLA‐B haplotypes were HLA‐A*02 ~ HLA‐B*50 (0.070) and HLA‐A*02 ~ HLA‐B*51 (0.051), and the most frequent 3‐locus haplotype was HLA‐A*02 ~ HLA‐B*50 ~ HLA‐C*06 (0.068). The HLA allele lineage frequencies of the UAE Arabs were compared to those previously reported for 70 other world populations, and a strong genetic similarity was detected between the UAE Arabs and the Saudi Arabians from the west with evidence of a limited gene flow between the UAE Arabs and Pakistani across the Gulf from the east, and the UAE Arabs and Omani from the south of the Gulf Peninsula.
Adaptive Global Time Sequence Averaging Method Using Dynamic Time Warping

Time sequence averaging under dynamic time warping (DTW) is a non-trivial problem, and its exact solution requires unrealistic computational complexity in practice. The DTW barycenter averaging (DBA) method is one of the most effective iterative approximation solutions to date. However, there are still a few drawbacks in the DBA method. First, the length of the resulting average sequence depends on the selected initial average sequence; second, the discrepancy distance between the resulting average sequence and target sequence set is highly sensitive to the initialization, as we have demonstrated through the experiments described here. In this study, we propose an adaptive DBA (ADBA) algorithm to address these drawbacks. We define a scaling coefficient based on the DTW alignments such that the temporal aberrations between the average sequence and target sequence set can be qualitatively captured. The algorithm is realized by an iterative process. For each iteration, the temporary average sequence and target sequence set are partitioned into several aligned subsequence sets according to the variation in the signs of the scaling coefficients. Then, these partitioned average subsequences are adaptively compressed or stretched such that the average discrepancy distance and overall temporal aberration can be locally minimized. The comparison experiments carried out on the standard datasets illustrate that the proposed algorithm achieves lower average discrepancy distance, overall temporal aberration, and higher robustness than the available methods. Additionally, the proposed algorithm is verified by an accelerometer-based hand gesture recognition system, where ADBA produces more effective gesture templates.
Decision Controller for Object Tracking With Deep Reinforcement Learning

There are many decisions which are usually made heuristically both in single object tracking (SOT) and multiple object tracking (MOT). Existing methods focus on tackling decision-making problems on special tasks in tracking without a unified framework. In this paper, we propose a decision controller (DC) which is generally applicable to both SOT and MOT tasks. The controller learns an optimal decision-making policy with a deep reinforcement learning algorithm that maximizes long term tracking performance without supervision. To prove the generalization ability of DC, we apply it to the challenging ensemble problem in SOT and tracker-detector switching problem in MOT. In the tracker ensemble experiment, our ensemble-based tracker can achieve leading performance in VOT2016 challenge and the light version can also get a state-of-the-art result at 50 FPS. In the MOT experiment, we utilize the tracker-detector switching controller to enable real-time online tracking with competitive performance and ${10\times }$ speed up.
Security Issues in IIoT: A Comprehensive Survey of Attacks on IIoT and Its Countermeasures

Industrial Internet of Things (IIoT) applications connect machines, sensors and actuators in high-stake manufacturing industries. Industrial systems are using the potential of IoT to reduce the unnecessary operational cost and increase the usability and reliability of the industrial assets to achieve more profits. However, such smart Industries need connectivity and interoperability to enhance performance which makes them susceptible to various attacks. Recent attacks on Cyber-physical systems raise a strong security concern as such attacks causes a huge property loss and may also lead to life threatening situations. In this paper we discuss the potential security threats to the Industries adapting to IIoT and study the various attacks that are possible on the components in the layered IIoT architecture and some of the preventive measures. Finally, we propose IIoT attack taxonomy which would help in mitigating the risks of the attacks.
Geometry-constrained Car Recognition Using a 3D Perspective Network

We present a novel learning framework for vehicle recognition from a single RGB image. Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our work learns a novel 3D perspective feature representation of a vehicle, which is then fused with 2D appearance feature to predict the category. The framework is composed of a global network (GN), a 3D perspective network (3DPN), and a fusion network. The GN is used to locate the region of interest (RoI) and generate the 2D global feature. With the assistance of the RoI, the 3DPN estimates the 3D bounding box under the guidance of the proposed vanishing point loss, which provides a perspective geometry constraint. Then the proposed 3D representation is generated by eliminating the viewpoint variance of the 3D bounding box using perspective transformation. Finally, the 3D and 2D feature are fused to predict the category of the vehicle. We present qualitative and quantitative results on the vehicle classification and verification tasks in the BoxCars dataset. The results demonstrate that, by learning such a concise 3D representation, we can achieve superior performance to methods that only use 2D information while retain 3D meaningful information without the challenge of requiring a 3D CAD model.
Selective Electroless Copper Deposition by Using Photolithographic Polymer/Ag Nanocomposite

This paper presents a novel, direct, selective, vacuum-free, and low-cost method of electroless copper deposition, allowing additive patterning of nonconductive surfaces. Ag nanoparticles (NPs) synthesized inside a photosensitive polymer are acting as seeds for electroless copper deposition. The resulting copper film surface morphology was studied with scanning electron microscopy. Copper films were shown to display a rough grainlike structure, covering substrate uniformly with good metal-substrate adhesion. Copper thickness was studied as a function of the plating time, temperature, and Ag NPs seed concentration. A maximal copper thickness of <inline-formula> <tex-math notation="LaTeX">${0.44}\,\pm \,{0.05}~\mu \text{m}$ </tex-math></inline-formula> was achieved when plated at 30 °C with 0.4 M Ag(I). The minimum feature resolution of copper patterns, grown with 0.025- and 0.1-M silver salt, is attained down to <inline-formula> <tex-math notation="LaTeX">${10}~\mu \text{m}$ </tex-math></inline-formula>. The maximum electrical conductivity of the copper film prepared with 0.025-, 0.1-, and 0.4-M Ag(I) approaches (<inline-formula> <tex-math notation="LaTeX">${0.8}\,\pm \,0.1) \times {10}^{{7}}$ </tex-math></inline-formula> S/m, (<inline-formula> <tex-math notation="LaTeX">${1.1}\,\pm \,{0.1}) \times {10}^{{7}}$ </tex-math></inline-formula> S/m and (<inline-formula> <tex-math notation="LaTeX">${1.6}\,\pm \, {0.4}{)} \times {10}^{{7}}$ </tex-math></inline-formula> S/m, respe- ctively. Electroless copper interconnections and LED circuit on glass substrate were fabricated as a proof of concept demonstrators.
Robust Localization for Cognitive IoT via the Mobile Anchor Node Based on the Diameter-Varying Spiral Line

Research on IoT that merely aims at connecting and communicating is about to past. Thereafter, general objects should have the capability to learn, think, and understand both physical and social areas by themselves. Cognitive Internet of Things (CIoT) attempts to empower the current IoT with a “brain” for high-level intelligence, requiring networks to have the ability to bridge the physical and social worlds. This attempt means matching equipment and resources with people and their behavior. Therefore, accurate location information is crucial for equipment connecting to CIoT. This endeavor sets a higher requirement for the localization technology of wireless sensor networks in terms of accuracy, energy, and efficiency compared with that in the past. In this paper, we propose an efficient and accurate mobile anchor node assisted localization algorithm for WSNs based on diameter-varying spiral line (LDVSL), which broadcasts coordinates of the anchor node to assist localizing unknown sensor nodes. The proposed algorithm has two main innovations. First, we obtain the mobile anchor node position through a time and angle mechanism instead of GPS, given the unique characteristics of the diameter-varying spiral line. Second, the linear fitting method is adapted to select the key virtual node, which has the real maximum received signal strength indicator. Simulations indicate that the proposed LDVSL algorithm outperforms other similar algorithms in terms of average localization error and positionable node ratio. The simulations also show that the LDVSL is not affected by obstacles seriously and has good robustness. The LDVSL has a wide prospect of application in CIoT.
Cepstral (MFCC) Feature and Spectral (Timbral) Features Analysis for Musical Instrument Sounds

In this paper, Cepstral i.e. MFCC feature extraction of Musical Instrument sound is done. 13 MFCC coefficients are extracted & analyzed and found the appropriate coefficient required for Musical Instrument Identification. Similarly, Spectral (timbral) features of Musical Instrument Sounds are extracted using Music Information Retrieval (MIR) toolbox 1.6. After extracting the features, they are analyzed and found appropriate timbral features required for Musical Instrument Identification.
Developing Walking Assistants for Visually Impaired People: A Review

The development of walking assistants for visually impaired people has become a prominent research area due to the rapid growth of these individuals in recent decades. Although numerous frameworks have been developed to aid visually impaired people, a considerable portion of these is limited in their scopes. In this review, we exhibit a similar review of walking assistants for visually impaired people to demonstrate the advancement of such technologies. This review discusses the recent innovative technologies developed for the visually impaired to aid them in walking with their merits and demerits. With the help of this review, a schema is drawn for upcoming development in the field of sensors, computer vision, and smartphone-based walking assistants. This review aims to present the majority of the issues of such frameworks to serve as a basis for different researchers to develop walking assistants that ensure movability and safety of visually impaired people.
Wavelet Method for Automatic Detection of Eye-Movement Behaviors

With the rapid development of eye tracking technology, eye movements have become more and more important in human–computer interaction. Generally, eye movements are classified into fixation, saccade, and smooth pursuit. Since the eye movements are natural and fast, contain important cues for human cognitive state and visual attention, the eye movement behaviors are difficult to detect and classify. In this paper, the novel eye-movement data filtering and eye-movement classification algorithm are proposed. The nonlinear wavelet threshold denoising method was used to smooth the eye-movement data and detect saccades in the presence of smooth pursuit movements, according to different eye-movement behaviors related to the different characteristics of wavelet detail coefficients. Experiments were conducted to compare the eye-movement signal analyzing algorithm based on wavelet with other algorithms. The results showed that the eye-movement data filtering algorithm based on wavelet performed better than the other eye-movement filters. Moreover, the classification algorithm based on wavelet can classify different eye-movement behaviors more accurately. Then, we used an eye tracking technology to record and analyze the user’s eye movement during the test, so as to get the user’s psychological and cognitive state.
EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

A wearable based long-term lifelogging system is desirable for the purpose of reviewing and improving users lifestyle habits. Energy harvesting (EH) is a promising means for realizing sustainable lifelogging. However, present EH technologies suffer from instability of the generated electricity caused by changes of environment, e.g., the output of a solar cell varies based on its material, light intensity, and light wavelength. In this paper, we leverage this instability of EH technologies for other purposes, in addition to its use as an energy source. Specifically, we propose to determine the variation of generated electricity as a sensor for recognizing "places" where the user visits, which is important information in the lifelogging system. First, we investigate the amount of generated electricity of selected energy harvesting elements in various environments. Second, we design a system called EHAAS (Energy Harvesters As A Sensor) where energy harvesting elements are used as a sensor. With EHAAS, we propose a place recognition method based on machine-learning and implement a prototype wearable system. Our prototype evaluation confirms that EHAAS achieves a place recognition accuracy of 88.5% F-value for nine different indoor and outdoor places. This result is better than the results of existing sensors (3-axis accelerometer and brightness). We also clarify that only two types of solar cells are required for recognizing a place with 86.2% accuracy.
Interactive segmentation of medical images through fully convolutional neural networks

Image segmentation plays an essential role in medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fully-automated. Manual segmentation offers full control over the quality of the results, but is tedious, time consuming and prone to operator bias. Fully automated methods require no human effort, but often deliver sub-optimal results without providing users with the means to make corrections. Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction. In this paper we present a deep learning (DL) based semi-automated segmentation approach that aims to be a "smart" interactive tool for region of interest delineation in medical images. We demonstrate its use for segmenting multiple organs on computed tomography (CT) of the abdomen. Our approach solves some of the most pressing clinical challenges: (i) it requires only one to a few user clicks to deliver excellent 2D segmentations in a fast and reliable fashion; (ii) it can generalize to previously unseen structures and "corner cases"; (iii) it delivers results that can be corrected quickly in a smart and intuitive way up to an arbitrary degree of precision chosen by the user and (iv) ensures high accuracy. We present our approach and compare it to other techniques and previous work to show the advantages brought by our method.
Reinforcing Classical Planning for Adversary Driving Scenarios

Adversary scenarios in driving, where the other vehicles may make mistakes or have a competing or malicious intent, have to be studied not only for our safety but also for addressing the concerns from public in order to push the technology forward. Classical planning solutions for adversary driving do not exist so far, especially when the vehicles do not communicate their intent. Given recent success in solving hard problems in artificial intelligence (AI), it is worth studying the potential of reinforcement learning for safety driving in adversary settings. In most recent reinforcement learning applications, there is a deep neural networks that maps an input state to an optimal policy over primitive actions. However, learning a policy over primitive actions is very difficult and inefficient. On the other hand, the knowledge already learned in classical planning methods should be inherited and reused. In order to take advantage of reinforcement learning good at exploring the action space for safety and classical planning skill models good at handling most driving scenarios, we propose to learn a policy over an action space of primitive actions augmented with classical planning methods. We show two advantages by doing so. First, training this reinforcement learning agent is easier and faster than training the primitive-action agent. Second, our new agent outperforms the primitive-action reinforcement learning agent, human testers as well as the classical planning methods that our agent queries as skills.
Cross-Task Weakly Supervised Learning From Instructional Videos

In this paper we investigate learning visual models for the steps of ordinary tasks using weak supervision via instructional narrations and an ordered list of steps instead of strong supervision via temporal annotations. At the heart of our approach is the observation that weakly supervised learning may be easier if a model shares components while learning different steps: ``pour egg'' should be trained jointly with other tasks involving ``pour'' and ``egg''. We formalize this in a component model for recognizing steps and a weakly supervised learning framework that can learn this model under temporal constraints from narration and the list of steps. Past data does not permit systematic studying of sharing and so we also gather a new dataset aimed at assessing cross-task sharing. Our experiments demonstrate that sharing across tasks improves performance, especially when done at the component level and that our component model can parse previously unseen tasks by virtue of its compositionality.
Simulation-Based Study of High-Density SRAM Voltage Scaling Enabled by Inserted-Oxide FinFET Technology

A scheme for precisely adjusting the drive strength of an inserted-oxide FinFET (iFinFET) comprising two nanowire (NW) channel regions that are separated by a thin oxide layer, to enhance the manufacturing yield of a minimally sized six-transistor static random access memory (6T-SRAM) cell, is investigated in this paper. The 3-D process simulations show that the upper NW channel region can be selectively rendered nonconducting by dopant ion implantation followed by thermal annealing so that its threshold voltage is greater than the supply voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {DD}}$ </tex-math></inline-formula>). Furthermore, the position of the inserted-oxide layer can be adjusted to balance the tradeoff between the read stability and write-ability to achieve the lowest minimum cell operating voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {min}}$ </tex-math></inline-formula>). Using a compact transistor model calibrated to 3-D device simulations, doped iFinFET technology is projected to enable <inline-formula> <tex-math notation="LaTeX">${V}_{\text {min}}$ </tex-math></inline-formula> of a minimally sized 6T-SRAM cell to be substantially lower than <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DD}}$ </tex-math></inline-formula>, eliminating the need for write-assist circuitry and lowering power consumption.
Non-Contact Voltage Monitoring of HVDC Transmission Lines Based on Electromagnetic Fields

The high-voltage direct current (HVDC) transmission network is envisioned to advance further as a supplement to AC transmission in a new era of renewable energy and smart grid development. Voltage monitoring of HVDC transmission lines is critical for evaluating the power quality in power delivery and realizing the stability control of the power system. Unfortunately, the traditional potential transformers can only work with AC voltage while the other existing voltage sensors (e.g., Hall-effect voltage sensors, and optical-fiber voltage sensors) are still not practical for large-scale deployment for monitoring the HVDC transmission lines, owing to the fact that they are either invasive or expensive. In this paper, a non-contact electric-coupling-based voltage sensing with the assistance of magnetic-field sensing for monitoring the voltages of HVDC transmission lines is proposed. The voltages of HVDC transmission lines can be reconstructed from measuring the induced voltage of induction bars, and their correlation coefficients are attained by the magnetic sensing with an optimization program which determines their relative spatial positions. The sensitivity and stability of the proposed platform, and the corona effect of the HVDC transmission lines are discussed. The performance of the proposed sensing technique was investigated by simulation on a ±100 kV HVDC transmission line system, and was further experimentally validated on a scaled HVDC transmission-line testbed in the lab. This sensing technique can be implemented at low cost by adopting copper induction bars and magnetoresistive sensors. Considering its non-contact nature and cost-effectiveness, this technique is suitable for a large-scale deployment over HVDC transmission networks for wide-area monitoring.
IEEE 802.11bd & 5G NR V2X: Evolution of Radio Access Technologies for V2X Communications

With the rising interest in autonomous vehicles, developing radio access technologies (RATs) that enable reliable and low-latency vehicular communications has become of paramount importance. Dedicated short-range communications (DSRCs) and cellular V2X (C-V2X) are two present-day technologies that are capable of supporting day-1 vehicular applications. However, these RATs fall short of supporting communication requirements of many advanced vehicular applications, which are believed to be critical in enabling fully autonomous vehicles. Both the DSRC and C-V2X are undergoing extensive enhancements in order to support advanced vehicular applications that are characterized by high reliability, low latency, and high throughput requirements. These RAT evolutions—the IEEE 802.11bd for the DSRC and NR V2X for C-V2X—can supplement today’s vehicular sensors in enabling autonomous driving. In this paper, we survey the latest developments in the standardization of 802.11bd and NR V2X. We begin with a brief description of the two present-day vehicular RATs. In doing so, we highlight their inability to guarantee the quality of service requirements of many advanced vehicular applications. We then look at the two RAT evolutions, i.e., the IEEE 802.11bd and NR V2X, outline their objectives, describe their salient features, and provide an in-depth description of key mechanisms that enable these features. While both, the IEEE 802.11bd and NR V2X, are in their initial stages of development, we shed light on their preliminary performance projections and compare and contrast the two evolutionary RATs with their respective predecessors.