Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on UAV Multi-Obstacle Detection Algorithm based on Stereo Vision

the UAV has the advantages of small size, low cost and flexibility, and has a wide application prospect in military, civilian and scientific research. While applying the drone to perform the mission, it is of great significance to ensure that the drone detects obstacles during the flight. Due to the high maneuverability of UAVs, detection algorithms based on single-target often miss other potential obstacles, resulting in insufficient reliability. This paper proposes multi-obstacle detection algorithm based on stereo vision. The dual-camera platform is established and the image computational hardware is applied to run the algorithm. After 3D reconstruction and stereo matching, the depth map of the scene is obtained. After that, a series of image processing algorithms are applied to find out five subtle dangerous objects and give them bounding boxes. At last, this paper establishes the hardware system and carries out several tests outsides. According to the multi-obstacle detection algorithm proposed in this paper, the results show that the algorithm can detect at most five obstacles in 15m and has practical application value.
Binary and ternary convolutional neural network acceleration by in-nonvolatile memory computing with Voltage Control Spintronics Memory (VoCSM)

We report a novel convolutional neural network (CNN) accelerator utilizing “voltage control spintronics memory” (VoCSM). High throughput processing is achieved by high speed in-“nonvolatile memory”-computation using high density VoCSM array. Since VoCSM has largest endurance even in short write pulse of all nonvolatile memories, write access speed can be increased. Also, density of processing element is higher than conventional SRAM based one, throughput is further increased. These technique reduces latency of CNN by 46% (ternary) and 73% (binary) compared to conventional CMOS processor with 8bit integer (INT8) for CIFAR-10 classification task. Also, energy is reduced by 72% (ternary) and 86% (binary).
Phased Array Antenna with Key Shaped Elements for 60 GHz mmWave Communications

The main focus of this paper revolves around analyzing and simulating an array of antenna with changing phases. Creating the array of antennas helped with increasing the gain and directivity as well as reducing side lobes compared to a single element antenna. The results indicate that steering the beam of the antenna array in a particular direction with the help of phase shifters is much more efficient in comparison to the conventional method of using mechanical modes. On a final note, instead of using a conventional microstrip square patch array element, a new and unique key shaped antenna element has been developed that gives substantially better performance than the original conventional array. The design was targeted to operate at 60 GHz for the purpose of vehicular application.
THE TWO-EVENT CONSTRUCT OF POSTINJURY MULTIPLE ORGAN FAILURE

The two-event construct of postinjury multiple organ failure (MOF) is based on the fundamental concept that injury primes the innate immune system such that a second insult, during this vulnerable window, provokes unbridled systemic inflammation, resulting in organ dysfunction (Fig. 1) (1, 2). Because the neutrophil (PMN) is a pivotal early effector cell in the pathogenesis of postinjury MOF (3–5), PMN priming/activation is typically studied as the in vitro surrogate of the two-event phenomenon (6). Priming is defined as an enhanced response to a stimulus that is from previous exposure of the cell to a different agonist (7). PMN priming for cytotoxicity encompasses a wide range of physiologic responses, including superoxide anion generation, degranulation of enzymes, cytokine (IL8) and lipid mediator (LTB4) production, enhanced integrin expression (CD11b/CD18), decreased selectin expression (L selectin), reduced deformability, cellular elongation, and delayed apoptosis (8). Physiologically relevant priming agents are diverse and include platelet-activating factor (PAF), LTB4, C5a, substance P, TNF, IL8, granulocyte-macrophage-colonystimulating factor, interferon, lipopolysaccharide (LPS), influenza virus, and L selectin cross-linking and CD18 crosslinking. The objectives of this brief overview are to provide some historic perspective on the two-event hypothesis and to present supporting evidence from the clinical arena as well as the bench.
Enhancing the Physical Layer Security of Cooperative NOMA System

In the future wireless communication networks, the key technique such as NOMA of 5G becomes prevailing. This paper analyzes the physical layer security of cooperative NOMA system. In analysis, this system which adopts decode-and-forward (DF) consists of one base station (BS), M relays and two users. In particular, a two-stage relay scheme is proposed, and analytical results are developed to demonstrate that this two-stage relay scheme can achieve the lower outage probability among all possible relay schemes, and realize the full diversity gain. The closed-form expression on outage probability is derived, as well as the asymptotic expression on outage probability is derived in the high SNR. Meanwhile, Monte-Carlo simulations are provided to demonstrate that cooperative NOMA with this two-stage relay scheme outperforms that based on the conventional max-min approach.
The trusted online platform of cross-border e-commerce: a managerial model

With the rapidly development and change of e-commerce, online platforms have become increasingly important. In order to promote the global digital economic, a trusted cross-border online marketplace is expected. This paper focused on the e-commerce platform and then proposed a concept model of trusted e-commerce platform from managerial perspective with three dimensions: the external environment of platform, the self-regulation of platform, and the internal management of the platform. Detailed framework was discussed for each dimension to explain how to establish a trusted online platform and the positive impacts on the e-commerce transaction were finally summarized.
Ship Extraction using Post CNN from High Resolution Optical Remotely Sensed Images

A post convolutional neural networks (CNN) method is proposed to extract ships from high resolution optical remotely sensed images. It consist of two parts: ship proposal detection and ship extraction based on CNN. The first part aims to locate possible ships through classification of water and no-water, seawater area extraction using mathematical morphology, and ship proposal extraction. Ships are extracted in the second part by implementing a trained CNN on the ship proposals. Experimental results on a high resolution optical image of San Francisco Bay show the efficiency and robustness of the proposed post CNN method.
Dynamic Set kNN Self-Join

In many applications, data objects can be represented as sets. For example, in video on-demand and social network services, the user data consists of a set of movies that have been watched and a set of users (friends), respectively, and they can be used for recommendation and information extraction. The problem of set similarity self-join hence has been studied extensively. Existing studies assume that sets are static, but in the above applications, sets are dynamically updated, and this requires continuous updating the join result. In this paper, we study a novel problem, dynamic set kNN self-join, i.e., for each set, we continuously compute its k nearest neighbor sets. Our problem poses a challenge for the efficiency of computation, because just an element insertion (deletion) into (from) a set may affect the kNN results of many sets. To address this challenge, we first investigate the property of the dynamic set kNN self-join problem to observe the search space derived from a set update. Then, based on this observation, we propose an efficient algorithm. This algorithm employs an indexing technique that enables incremental similarity computation and prunes unnecessary similarity computation. Our empirical studies using real datasets show the efficiency and scalability of our algorithm.
Recognizing Biomedical Named Entities by Integrating Domain Contextual Relevance Measurement and Active Learning

Named entity recognition is a basic and core task of biomedical text mining. Comparing with other named entity recognition methods, methods based on domain relevance measurement need the smaller training corpora and entity samples and are appropriate for recognizing narrow-domain entities, which belong to a subdivision and small semantic class. However, how to obtain the high-quality target corpus set become a key issue. This paper propose a biomedicine named entity recognition method by integrating domain contextual relevance measurement and active learning. Firstly, binding with densitybased clustering and semantic distance measurement, the representative and informative contexts are selected to construct the target corpus set by an active learning approach. Secondly, the domain contextual relevance of candidate entities is calculated by using Domain the discrimination degree and domain dependence function for recognizing the target entities. Experimental results show that the proposed method can effectively reduce training time and improve the accuracy of entity recognition.
K-way Fast Approximate Spectral Clustering

In recent years, k-way spectral clustering is one of the most popular clustering algorithms. Due to its computational complexity of $O(n^{3})$, with n the number of data points it is a challenging task to apply traditional spectral clustering algorithms for large-scale data sets. In this paper, we propose a novel algorithm called k-way Fast Spectral Clustering (KFSC) based on kd-tree. This algorithm is based on a theoretical analysis that provides a statistical characterization of the effect of local distortion on the mis-clustering rate. The computational complexity is $O(ndl)+O(m^{3})+O(m^{2})$, where l is the depth of kd-tree, m is the number of representative sets (set of similar data points) and d is the dimension of data. Extensive experimental results on real data sets and synthetic data sets demonstrate that the proposed methodology outperforms Normalized cut (Ncut) in terms of computation, while achieving comparable clustering accuracy.
MemFuzz: Using Memory Accesses to Guide Fuzzing

Fuzzing is a form of random testing that is widely used for finding bugs and vulnerabilities. State of the art approaches commonly leverage information about the control flow of prior executions of the program under test to decide which inputs to mutate further. By relying solely on control flow information to characterize executions, such approaches may miss relevant differences. We propose augmenting evolutionary fuzzing by additionally leveraging information about memory accesses performed by the target program. The resulting approach can leverage more sophisticated information about the execution of the target program, enhancing the effectiveness of the evolutionary fuzzing. We implement our approach as a modification of the widely used AFL fuzzer and evaluate our implementation on three widely used target applications. We find distinct crashes from those detected by AFL for all three targets in our evaluation.
Method of Maximum Permitted Learning Rate Calculation for Neural Controller of Balancing Robot

This research is to solve a problem of sustainability of a balancing robot controlled by an artificial neural network. The mentioned network acts as a regulator and calculates at its output layer a control action for the plant. Online training of such a network is necessary to improve the quality of the robot control since it changes its parameters or a mode of functioning in the course of operation. Implementing such training, the question of the learning rate limitation arises sharply. It is directly related to the assessment of sustainability of the control system under consideration. That is why a method based on the second Lyapunov approach is proposed to calculate the upper allowable limit of the online learning rate for the neural network controller under various conditions at each moment of its functioning. This method does not require the plant mathematical model. The efficiency of the approach is proved by experiments with a real balancing robot based on the EV3 platform.
A Safe, Open Source, 4G Connected Self-Flying Plane With 1 Hour Flight Time and All Up Weight (AUW) <300 g: Towards a New Class of Internet Enabled UAVs

Here, we present an open-source cloud (4G) connected and controlled self-flying airplane based on a secure, encrypted flight stack with all up weight of <300 g as an example of a new class of unmanned aerial vehicles (UAVs). The avionics portion (including the flight controller, 4G cellular data modem, and on-board companion Linux computer) weighs only 40 g, which is about 10 times lighter than previous avionics package achieving the same mission: 4G secure encrypted robust self-healing Internet connectivity for video and telemetry, autonomous flight capability without the need for a pilot in local proximity to the UAV, and GPS positioning. With a flight time of 1 h, this represents the lightest, longest flight time 4G UAV ever demonstrated. Enabled by the convergence of advances in multiple hardware and software systems and disciplines, this effectively defines a new class of the UAVs, distinct from larger multirotors for aerial surveys and photography and smaller nano-quadcopters with short flight times and ranges, defined by the characteristics of inherently stable aerodynamic flight in case of hardware failure (such as motor failure), inherently long flight times, inherently long range due to the efficiency of a wing over quadrotor, and Internet connectivity for control from any point on earth to virtually any point on earth.
Effect of Sub-10nm Fin-widths on the Analog Performance of FinFETs

This work experimentally investigates the effect of fm-width $(W_{fin})$ scaling in sub-10nm regime on the analog performance of nFinFETs. It is shown that the device trans-conductance $(g_{m})$ degrades and output conductance $(g_{ds})$ improves with reduction in $W_{fin}. $ Various sources affecting the variability of $g_{m}$ and gds in sub-10nm $W_{fin}$ regime are also explored. Through different analog performance metrics, it is shown that the analog performance of thinner fin FinFETs can be further increased by properly optimizing the S/D resistance and gate dielectric.
Complexity Reduction for two Human-like Ambidextrous Robotic Hands Control

In this paper, task-oriented grasp analysis was made concentrating on the reduction of complexity to achieve adaptable and stable grasps. The hand orientation normal was used for the alignment between the hand and the object. A Varying Pulse Width Form was used to represent the object, where the values of 3D model orientation angles were converted into pulse width values (VPWF) of the objects, and an object matching loop (OML) was used to choose the most suitable hand shape, using defined splines of the overall 3D model of the object, and a curvature adaptation algorithm. The experiments showed that we can analyze the gestures of grasping regardless of grasping power, and showed that there is a strong relation among finger joints. The experiments also showed that coordinating between the two hands based on the Normal Inclination method offers a more generic, speedy, and precise solution for a wide range of robotic applications. One of the important results was in deriving a relation of the precision of the grasp due to the object angle of orientation and brightness. The final aim of this research is to integrate the control of both arms and hands as one entity, avoid collision, coordinate between them, and use them in any application where labor involves a specific series of tasks: for rescuing, manufacturing, making surgeries, and for artificial limbs.
Bird Image Retrieval and Recognition Using a Deep Learning Platform

Birdwatching is a common hobby but to identify their species requires the assistance of bird books. To provide birdwatchers a handy tool to admire the beauty of birds, we developed a deep learning platform to assist users in recognizing 27 species of birds endemic to Taiwan using a mobile app named the Internet of Birds (IoB). Bird images were learned by a convolutional neural network (CNN) to localize prominent features in the images. First, we established and generated a bounded region of interest to refine the shapes and colors of the object granularities and subsequently balanced the distribution of bird species. Then, a skip connection method was used to linearly combine the outputs of the previous and current layers to improve feature extraction. Finally, we applied the softmax function to obtain a probability distribution of bird features. The learned parameters of bird features were used to identify pictures uploaded by mobile users. The proposed CNN model with skip connections achieved higher accuracy of 99.00 % compared with the 93.98% from a CNN and 89.00% from the SVM for the training images. As for the test dataset, the average sensitivity, specificity, and accuracy were 93.79%, 96.11%, and 95.37%, respectively.
Intra‐abdomen Ewing's sarcoma

An 18-year-old male adolescent presented with sudden onset, colicky epigastric pain. On physical examination, tenderness was over right upper quadrant and Murphy’s sign was positive. No loco-regional swelling or tenderness was noted in the extremities. His medical history was otherwise unremarkable. Preliminary laboratory investigations revealed leukocytosis of 14.02 10/mL (normal range, 5–10 10/mL), serum lactate dehydrogenase of 235 U/L (normal range, 45–90 U/L), serum bilirubin of 1.5 mg/dL (normal range, 0.4–2.0 mg/dL) and alkaline phosphatase of 88 U/L (normal range, 60–120 U/L). Serum alanine transaminase and r-glutamyl transpeptidase values were normal. Viral serology results for hepatitis B and hepatitis C were negative. Transabdominal ultrasound revealed heterogenic hepatic tumour 10.4 ¥ 5.7 cm in size and moderate ascites. Contrast-enhanced computed tomography revealed a huge lobulated solid tumour 14 ¥ 11 ¥ 10 cm in size over segments 5 and 6 of the right lobe of liver. There is fluid accumulation in the subhepatic space. Normal appearance of gall bladder without stone formation and compression of the pancreatic head by the mass were noted as well (Fig. 1). Cystadenocarcinoma of gall bladder versus hepato-cellular carcinoma was the preoperative differential diagnosis. Intra-operative findings revealed hemoperitoneum and an intra-abdominal tumour with rupture over subhepatic space, connecting to the gall bladder and liver. During dissection, the tumour was found to be partially encapsulated and easily dissected from the retro-peritoneum, adrenal gland and kidney (Fig. 2). As the frozen section reported malignancy, the patient underwent cholecystectomy, total resection of the tumour, hepato-duodenal lymph node dissection and omentectomy. His postoperative recovery was uneventful and was discharged on the 7th post-operative day. Pathology report: grossly, the tumour measured 13 ¥ 8 ¥ 7 cm in size, grey and mildly firm in consistency, focal areas revealed massive necrosis (Fig. 3). On microscopic examination, the tumour composed of hypercellular small blue round cell tumour cells. Ewing’s sarcoma was confirmed by periodic acid-schiff (PAS) staining (+), neuron-specific enolase (NSE) (+) (Fig. 4) and reverse transcription polymerase chain reaction (RT-PCR) result of translocation t (11; 22) (q24; q12). There was no extraskeletal lesion found on bone scan. Subsequent adjuvant combination chemotherapy was received at the oncology department.
A Review on Machine Learning Classification Techniques for Plant Disease Detection

In India, Agriculture plays an essential role because of the rapid growth of population and increased in demand for food. Therefore, it needs to increase in crop yield. One major effect on low crop yield is disease caused by bacteria, virus and fungus. It can be prevented by using plant diseases detection techniques. Machine learning methods can be used for diseases identification because it mainly apply on data themselves and gives priority to outcomes of certain task. This paper presents the stages of general plant diseases detection system and comparative study on machine learning classification techniques for plant disease detection. In this survey it observed that Convolutional Neural Network gives high accuracy and detects more number of diseases of multiple crops.
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting

In the field of ophthalmology, retinal images are essential for the diagnosis of many diseases. These images are acquired with a device called the retinal camera. However, often small dust particles in the sensor produce image artifacts that can be confused with small lesions, such as micro-aneurysms. The digital removal of artifacts can be understood as an inpainting process in which a set of pixels are replaced with a value obtained from the surrounding area. In this paper, we propose a methodology based on the sparse representations and dictionary learning for the removal of artifacts in retinal images. We test our method on real retinal images coming from the clinical setting with actual dust artifacts. We compare our restoration results with a diffusion-based inpainting technique. Encouraging experimental results show that our method can successfully remove the artifacts, while assuring the continuity of the retinal structures, like blood vessels.
2ED: An Efficient Entity Extraction Algorithm Using Two-Level Edit-Distance

Entity extraction is fundamental to many text mining tasks such as organisation name recognition. A popular approach to entity extraction is based on string matching against a dictionary of known entities. For approximate entity extraction from free text, considering solely character-based or solely token-based similarity cannot simultaneously deal with minor name variations at token-level and typos at character-level. Moreover, the tolerance of mismatch in character-level may be different from that in token-level, and the tolerance thresholds of the two levels should be able to be customised individually. In this paper, we propose an efficient character-level and token-level edit-distance based algorithm called FuzzyED. To improve the efficiency of FuzzyED, we develop various novel techniques including (i) a spanning-based candidate sub-string producing technique, (ii) a lower bound dissimilarity to determine the boundaries of candidate sub-strings, (iii) a core token based technique that makes use of the importance of tokens to reduce the number of unpromising candidate sub-strings, and (iv) a shrinking technique to reuse computation. Empirical results on real world datasets show that FuzzyED can efficiently extract entities and produce a high F1 score in the range of [0.91, 0.97].