Reviewerly/exHarmony huggingface dataset:
Embedded System Real-Time Vehicle Detection based on Improved YOLO Network

Real-time detection of the pavement environment is an important part of autonomous driving technology. This paper presents a real-time vehicle detection system based on embedded devices. Based on the existing yolov3-tiny neural network structure, this paper proposes a new neural network structure - the YOLO v3-live. Tailoring the network layer structure of YOLOv3-tiny, and quantify the network parameters in the network. Reducing the complexity of computing in embedded devices, making the proposed neural network structure more suitable for embedded devices. The new structure is tested, before quantization the YOLO v3-live's detection precision can achieve 87.79% mAP on the test set, after quantization of network parameters can achieve 69.79% mAP and detection speed can achieve 28 FPS.
Geometry-Aware Cell Detection with Deep Learning

Analyzing cells and tissues under a microscope is a cornerstone of biological research and clinical practice. However, the challenge faced by conventional microscopy image analysis is the fact that cell recognition through a microscope is still time-consuming and lacks both accuracy and consistency. Despite enormous progress in computer-aided microscopy cell detection, especially with recent deep-learning-based techniques, it is still difficult to translate an established method directly to a new cell target without extensive modification. The morphology of a cell is complex and highly varied, but it has long been known that cells show a nonrandom geometrical order in which a distinct and defined shape can be formed in a given type of cell. Thus, we have proposed a geometry-aware deep-learning method, geometric-feature spectrum ExtremeNet (GFS-ExtremeNet), for cell detection. GFS-ExtremeNet is built on the framework of ExtremeNet with a collection of geometric features, resulting in the accurate detection of any given cell target. We obtained promising detection results with microscopic images of publicly available mammalian cell nuclei and newly collected protozoa, whose cell shapes and sizes varied. Even more striking, our method was able to detect unicellular parasites within red blood cells without misdiagnosis of each other.IMPORTANCE Automated diagnostic microscopy powered by deep learning is useful, particularly in rural areas. However, there is no general method for object detection of different cells. In this study, we developed GFS-ExtremeNet, a geometry-aware deep-learning method which is based on the detection of four extreme key points for each object (topmost, bottommost, rightmost, and leftmost) and its center point. A postprocessing step, namely, adjacency spectrum, was employed to measure whether the distances between the key points were below a certain threshold for a particular cell candidate. Our newly proposed geometry-aware deep-learning method outperformed other conventional object detection methods and could be applied to any type of cell with a certain geometrical order. Our GFS-ExtremeNet approach opens a new window for the development of an automated cell detection system.
Anomaly pattern detection for streaming data

Outlier detection aims to find a data sample that is different from most other data samples. While outlier detection is performed at an individual instance level, anomaly pattern detection on a data stream means detecting a time point where a pattern to generate data is unusual and significantly different from normal behavior. Beyond predicting the outlierness of individual data samples in a data stream, it can be very useful to detect the occurrence of anomalous patterns in real time. In this paper, we propose a method for anomaly pattern detection in a data stream based on binary classification for outliers and statistical tests on a data stream of binary labels of normal or an outlier. In the first step, by applying the clustering-based outlier detection method, we transform a data stream into a stream of binary values where 0 stands for the prediction as normal data and 1 for outlier prediction. In the second step, anomaly pattern detection is performed on a stream of binary values by two approaches: testing the equality of parameters in the binomial distributions of a reference window and a detection window, and using control charts for the fraction defective. The proposed method obtained the average true positive detection rate of 94% in simulated experiments using real and artificial data. The experimental results also show that anomaly pattern occurrence can be detected reliably even when outlier detection performance is relatively low.
ADCM: attention dropout convolutional module

Network architecture design plays an important role in boosting the performance of models in various applications. In this work, we design a general and lightweight module named the attention dropout convolutional module (ADCM). It consists of two submodules, channel attention dropout (CAD) and position attention dropout (PAD), and each submodule integrates both attention and dropout mechanisms. The attention mechanism emphasizes the meaningful information and suppresses unnecessary noise. The dropout-channel in the CAD submodule filters the channel based on its channel attention, while the dropout-region in the PAD submodule filters the region consisting of the spatially correlated features according to its position attention. The two dropout methods we designed allow the baseline network to learn more robust features and further boost its performance. Finally, we deploy the ADCM in consecutive layers of classical convolutional neural networks and evaluate its performance on multiple benchmark datasets. The experimental results demonstrate that the ADCM brings significant improvements to the performance of the baseline models at negligible computational cost and with less complexity.
Music Creation by Example

Short online videos have become the dominating media on social platforms. However, finding suitable music to accompany videos can be a challenging task to some video creators, due to copyright constraints, limitations in search engines, and required audio-editing expertise. One possible solution to these problems is to use AI music generation. In this paper we present a user interface (UI) paradigm that allows users to input a song to an AI engine and then interactively regenerate and mix AI-generated music. To arrive at this design, we conducted user studies with a total of 104 video creators at several stages of our design and development process. User studies supported the effectiveness of our approach and provided valuable insights about human-AI interaction as well as the design and evaluation of mixed-initiative interfaces in creative practice.
Plausibility-promoting generative adversarial network for abstractive text summarization with multi-task constraint

Abstractive text summarization is an essential task in natural language processing, which aims to generate concise and condensed summaries retaining the salient information of the input document. Despite the progress of previous work, generating summaries, which are informative, grammatically correct and diverse, remains challenging in practice. In this paper, we present a Plausibility-promoting Generative Adversarial Network for Abstractive Text Summarization with Multi-Task constraint (PGAN-ATSMT), which shows promising performance for generating informative, grammatically correct, and novel summaries. First, PGAN-ATSMT adopts a plausibility-promoting generative adversarial network, which jointly trains a discriminative model D and a generative model G via adversarial learning. The generative model G employs the sequence-to-sequence architecture as its backbone, taking as input the original text and generating a corresponding summary. A novel language model based discriminator D is proposed to distinguish the generated summaries by G from the ground truth summaries without the saturation issue in the previous binary classifier discriminator. The generative model G and the discriminative model D are learned with a minimax two-player game, thus this adversarial process can eventually adjust G to produce high-quality and plausible summaries. Second, we propose two extended regularizations for the generative model G using the multi-task learning, sharing its LSTM encoder and LSTM decoder with text categorization task and syntax annotation task, respectively. The auxiliary tasks help to improve the quality of locating salient information of a document and generate high-quality summaries from language modeling perspective alleviating the issues of incomplete sentences and duplicated words. Experimental results on two benchmark datasets illustrate that PGAN-ATSMT achieves better performance than the state-of-the-art baseline methods in terms of both quantitative and qualitative evaluations.
AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy

Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. For colorectal cancer, the best screening test available is the colonoscopy. During a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a video of the internal mucosa of the colon. The video data are displayed on a monitor for the physician to examine the lining of the entire colon and check for colorectal polyps. Detection and removal of colorectal polyps are associated with a reduction in mortality from colorectal cancer. However, the miss rate of polyp detection during colonoscopy procedure is often high even for very experienced physicians. The reason lies in the high variation of polyp in terms of shape, size, textural, color and illumination. Though challenging, with the great advances in object detection techniques, automated polyp detection still demonstrates a great potential in reducing the false negative rate while maintaining a high precision. In this paper, we propose a novel anchor free polyp detector that can localize polyps without using predefined anchor boxes. To further strengthen the model, we leverage a Context Enhancement Module and Cosine Ground truth Projection. Our approach can respond in real time while achieving state-of-the-art performance with 99.36% precision and 96.44% recall.
Non-Interactive Multi-Client Searchable Encryption: Realization and Implementation

In this article, we introduce a new mechanism for constructing multi-client searchable encryption (SE). By tactfully leveraging the RSA-function, we propose the first multi-client SE protocol that successfully avoids per-query interaction between data owner and client. Therefore, our approach significantly reduces the communication cost by eliminating the need for data owner to authorize client queries at all times. To be compatible with the RSA-based approach, we also present a deterministic and memory-efficient &#x2018;keyword to prime&#x2019; hash function, which may be of independent interest. Further, to improve efficiency, we put forward a more generic construction from set-constrained PRFs. The construction not only inherits the merits of our first protocol, but also achieves an enhanced security (against untrusted clients), where colluding attack among clients is also taken into account. Both protocols are instantiated via the recent representative SE protocol by Cash <i>et al.</i> with the support of boolean queries. At last, we implement our proposed protocols and comprehensively evaluate their performance to demonstrate their practicability and scalability.
Video semantic segmentation via feature propagation with holistic attention

Since the frames of a video are inherently contiguous, information redundancy is ubiquitous. Unlike previous works densely process each frame of a video, in this paper we present a novel method to focus on efficient feature propagation across frames to tackle the challenging video semantic segmentation task. Firstly, we propose a Light, Efficient and Real-time network (denoted as LERNet) as a strong backbone network for per-frame processing. Then we mine rich features within a key frame and propagate the across-frame consistency information by calculating a temporal holistic attention with the following non-key frame. Each element of the attention matrix represents the global correlation between pixels of a non-key frame and the previous key frame. Concretely, we propose a brand-new attention module to capture the spatial consistency on low-level features along temporal dimension. Then we employ the attention weights as a spatial transition guidance for directly generating high-level features of the current non-key frame from the weighted corresponding key frame. Finally, we efficiently fuse the hierarchical features of the non-key frame and obtain the final segmentation result. Extensive experiments on two popular datasets, i.e. the CityScapes and the CamVid, demonstrate that the proposed approach achieves a remarkable balance between inference speed and accuracy.
Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for traffic classification and show good performances. However, they need a large amount of labeled data to train a model, while labeled data is hard to obtain. Aiming at solving this problem, this paper proposes a method to train a model based on the K-nearest neighbor (KNN) algorithm, which only needs a small amount of data. Due to the fact that the importance of different traffic features varies, and traditional KNN does not highlight the importance of different features, this study introduces the concept of feature weight and proposes the weighted feature KNN (WKNN) algorithm. Furthermore, to obtain the optimal feature set and the corresponding feature weight set, a feature selection and feature weight self-adaptive algorithm for WKNN is proposed. In addition, a three-layer classification framework for encrypted network flows is established. Based on the improved KNN and the framework, this study finally presents a method for fine-grained classification of encrypted network flows, which can identify the encryption status, application type and content type of encrypted network flows with high accuracies of 99.3%, 92.4%, and 97.0%, respectively.
Face Recognition with Convolutional Neural Network and Transfer Learning

Face recognition finds its major applications in biometrical security, health care and marketing. Among many biometrics like finger print, iris, voice, hand geometry, signature etc., face recognition has attained the most popular research orientation because of high recognition rate, uniqueness and large number of features. Some of the challenges in face recognition task include the differences in illumination, poses, expressions and background. Now-a-days deep learning methods are widely employed and they ensure promising results for image recognition and classification problems. Convolutional Neural Networks automatically learn features at multiple levels of abstraction through back propagation with convolution layers, pooling layers and fully connected layers. In this work, an automated face recognition method using Convolutional Neural Network (CNN) with transfer learning approach is proposed. The CNN with weights learned from pre-trained model VGG-16 on huge ImageNet database is used to train the images from the face database. The extracted features are fed as input to the Fully connected layer and softmax activation for classification. Two publicly available databases of face images - Yale and AT&T are used to test the performance of the proposed method. Experimental results verify that the method gives better recognition results compared to other methods.
Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network

In the diagnosis of neurodegenerative disorders, F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is used for its ability to detect functional changes at early stages of disease process. However, anatomical information from another modality (CT or MRI) is still needed to properly interpret and localize the radiotracer uptake due to its low spatial resolution. Lack of structural information limits segmentation and accurate quantification of the 18F-FDG PET/CT. The correct segmentation of the brain compartment in 18F-FDG PET/CT will enable the quantitative analysis of the 18F-FDG PET/CT scan alone. In this paper, we propose a method to segment white matter in 18F-FDG PET/CT images using generative adversarial network (GAN). The segmentation result of GAN model was evaluated using evaluation parameters such as dice, AUC-PR, precision, and recall. It was also compared with other deep learning methods. As a result, the proposed method achieves superior segmentation accuracy and reliability compared with other deep learning methods.
Cross-spectrum thermal to visible face recognition based on cascaded image synthesis

Face synthesis from thermal to visible spectrum is fundamental to perform cross-spectrum face recognition as it simplifies its integration in existing commercial face recognition systems and enables manual face verification. In this paper, a new solution based on cascaded refinement networks is proposed. This method generates visible-like colored images of high visual quality without requiring large amounts of training data. By employing a contextual loss function during training, the proposed network is inherently scale and rotation invariant. We discuss the visual perception of the generated visible-like faces in comparison with recent works. We also provide an objective evaluation in terms of cross-spectrum face recognition, where the generated faces were compared against a gallery in visible spectrum using two state-of-the-art deep learning based face recognition systems. When compared to the recently published TV-GAN solution, the performance of the face recognition systems, OpenFace and LightCNN, was improved by a 42.48% (i.e. from 10.76% to 15.37%) and a 71.43% (i.e. from 33.606% to 57.612%), respectively.
Geothermal resource potential assessment of Fujian Province, China, based on geographic information system (GIS) -supported models

Potential geothermal areas were identified and classified according to geothermal, geological and geophysical spatial associations in Fujian province, China. Five publicly available datasets were used in this study: seismic activity, geomagnetism, fault distribution, intrusive rock distribution, and Bouguer gravity anomaly, which were digitally processed into five impact factors maps: Gutenberg-Richter b-value, magnetic anomaly, distance to faults, distance to intrusive rock and distance to major grabens, respectively. Based on the geographic information system supported weight-of-evidence model and fuzzy logic model, the geothermal prediction maps were established using the impact factors maps. The prediction maps divide the potential geothermal areas of Fujian province into four grades, where the extremely high potential geothermal area covers 14,000 km2, accounting for 11.54% of the total area and the developed geothermal area is in good agreement with the extremely high potential area. The results reveal that the weight-of-evidence model is more accurate, and the application of each fuzzy operator in the process of fuzzy synthesis of the fuzzy logic model has a certain subjectivity, which requires higher professional knowledge to drive. It is anticipated that this paper will guide further investigations in Fujian province and guide the study of delineating potential geothermal regions with limited information.
Gold deposition and resource potential of the Linglong gold deposit, Jiaodong Peninsula: Geochemical comparison of ore fluids

The Linglong gold deposit is a super large deposit in the Linglong goldfield, northern Jiaodong Peninsula. The orebodies consist of hundreds of auriferous quartz veins that surrounded by relatively narrow K-feldspar alteration halos. Four mineralization stages have been identified on the basis of cross-cutting relationships, and mineralogical and textural characteristics: milky quartz–pyrite (stage 1), grey-white quartz–pyrite (stage 2), quartz–pyrite–base-metal sulfide (stage 3), and quartz–carbonate (stage 4). Native gold was mainly deposited in stages 2 and 3. Petrographical and microthermometric studies on quartz from stages 2 and 3 ores reveal three types of fluid inclusions associated with gold mineralization: Type 1 H2O–CO2–NaCl ± CH4 inclusion that consisting FIA 1 and represent a homogeneous fluid trapped at the same pressure and temperature; coexisting Type 2a H2O-rich H2O–CO2–NaCl ± CH4 and Type 2b CO2-rich H2O–CO2–NaCl ± CH4 inclusions that constitute FIA 2 and are interpreted to be products of a heterogeneously trapped fluid. The ore-forming fluids belong to a H2O–NaCl–CO2 ± CH4 system with medium temperatures (~290–340 °C), low salinity (~4–7 wt% NaCl eq.), and are CO2-rich (~15% mol) with minor CH4 (~2–5% mol). The δD–δ18O values of ore-forming fluids vary from −63 to −57‰ and 6.0‰ to 9.3‰, respectively, overlap both the magmatic and metamorphic fluid fields. The δ34S values of hydrothermal pyrite have a narrow range from 5.6‰ to 7.9‰, and are within the ranges of Neoarchean Jiaodong Group, and Mesozoic Linglong granite, Guojialing granitoid and intermediate–basic dikes. Combined with H–O–S isotope data, fluid inclusions compositions, and regional geological events, the ore-forming fluids may have a metamorphic-dominate origin, but a mantle or crustal contribution cannot be excluded. Fluid immiscibility played a major role, and fluid-wallrock interaction may be a subordinate reason for gold deposition in Linglong. The stage 2 quartz grains contain more FIAs 2 and volatiles than those in stage 3 quartz, indicating a more extensive fluid-immiscibility process which has a positive-correlated higher gold grade. Based on the data of FIAs 2, the P–T conditions of gold depositions generally range from 242 to 54 MPa and 348 to 269 °C, with a depth of metallogenesis of 5.4–9.0 km. Considering the depth of regional post-ore exhumation has been calculated as about 5–7 km, the space with deep gold exploration is expectable at Linglong.
Know Your Customer (KYC) Implementation with Smart Contracts on a Privacy-Oriented Decentralized Architecture

Enterprise blockchain solutions attempt to solve the crucial matter of user privacy, albeit that blockchain was initially directed towards full transparency. In the context of Know Your Customer (KYC) standardization, a decentralized schema that enables user privacy protection on enterprise blockchains is proposed with two types of developed smart contracts. Through the public KYC smart contract, a user registers and uploads their KYC information to the exploited IPFS storage, actions interpreted in blockchain transactions on the permissioned blockchain of Alastria Network. Furthermore, through the public KYC smart contract, an admin user approves or rejects the validity and expiration date of the initial user’s KYC documents. Inside the private KYC smart contract, CRUD (Create, read, update and delete) operations for the KYC file repository occur. The presented system introduces effectiveness and time efficiency of operations through its schema simplicity and smart integration of the different technology modules and components. This developed scheme focuses on blockchain technology as the most important and critical part of the architecture and tends to accomplish an optimal schema clarity.