Reviewerly/exHarmony huggingface dataset:
Virtual Reality Design and Realization of Interactive Garden Landscape

It is very important to study and explore the application of virtual reality technology in landscape garden design, especially in the current environment of triple network integration and Internet of Things construction, to promote and facilitate the rapid development of digital landscape garden design in China. In this paper, we study the implementation method of virtual landscape gardening system and establish a virtual environment based on the ancient city of Yangcheng. On the computer platform, we study and realize a virtual roaming system of medium complexity with more complete roaming functions. Using the Quest3D software platform, a desktop-type virtual garden simulation system was developed, focusing on the virtual reality modeling technology method and virtual system implementation. The experimental results show that the GPU-accelerated drawing method based on GLSL can significantly improve the drawing frame rate of 3D garden landscape vegetation scenes with a small amount of scene data and has a certain feasibility. Based on the OpenSceneGraph (OSG) graphics rendering engine, the visualization of various types of 3D landscape models is realized, and the spatial layout of various types of landscape with parametric control is realized through digital vector layers, which flexibly manage and organize various garden elements and reasonably organize the spatial topological relationship between various types of landscape in 3D space. By integrating cross-platform ArcGISEngine components, the basic data of garden scenes including terrain data and vector data are managed. Through scene view cropping and hierarchical detail modeling technologies, the drawing efficiency and rendering real time of the garden landscape are improved. It realizes interactive 3D scene browsing and provides a six-degree-of-freedom all-round display of the overall landscape.
Hierarchical Relational Inference

Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics. The notion of an abstract object, however, encompasses a wide variety of physical objects that differ greatly in terms of the complex behaviors they support. To address this, we propose a novel approach to physical reasoning that models objects as hierarchies of parts that may locally behave separately, but also act more globally as a single whole. Unlike prior approaches, our method learns in an unsupervised fashion directly from raw visual images to discover objects, parts, and their relations. It explicitly distinguishes multiple levels of abstraction and improves over a strong baseline at modeling synthetic and real-world videos.
PID-Based Approach to Adversarial Attacks

Adversarial attack can misguide the deep neural networks (DNNs) with adding small-magnitude perturbations to normal examples, which is mainly determined by the gradient of the loss function with respect to inputs. Previously, various strategies have been proposed to enhance the performance of adversarial attacks. However, all these methods only utilize the gradients in the present and past to generate adversarial examples. Until now, the trend of gradient change in the future (i.e., the derivative of gradient) has not been considered yet. Inspired by the classic proportional-integral-derivative (PID) controller in the field of automatic control, we propose a new PID-based approach for generating adversarial examples. The gradients in the present and past, and the derivative of gradient are considered in our method, which correspond to the components of P, I and D in the PID controller, respectively. Extensive experiments consistently demonstrate that our method can achieve higher attack success rates and exhibit better transferability compared with the state-of-the-art gradient-based adversarial attacks. Furthermore, our method possesses good extensibility and can be applied to almost all available gradient-based adversarial attacks.
EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following

The rapid rise of accessibility of unmanned aerial vehicles or drones pose a threat to general security and confidentiality. Most of the commercially available or custom-built drones are multi-rotors and are comprised of multiple propellers. Since these propellers rotate at a high-speed, they are generally the fastest moving parts of an image and cannot be directly “seen’’ by a classical camera without severe motion blur. We utilize a class of sensors that are particularly suitable for such scenarios called event cameras, which have a high temporal resolution, low-latency, and high dynamic range. In this paper, we model the geometry of a propeller and use it to generate simulated events which are used to train a deep neural network called EVPropNet to detect propellers from the data of an event camera. EVPropNet directly transfers to the real world without any fine-tuning or retraining. We present two applications of our network: (a) tracking and following an unmarked drone and (b) landing on a near-hover drone. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different propeller shapes and sizes. Our network can detect propellers at a rate of 85.1% even when 60% of the propeller is occluded and can run at upto 35Hz on a 2W power budget. To our knowledge, this is the first deep learning-based solution for detecting propellers (to detect drones). Finally, our applications also show an impressive success rate of 92% and 90% for the tracking and landing tasks respectively.
Nearest Neighbor Classifier Embedded Network for Active Learning

Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectiveness, the generalization ability of the discriminative classifier (the softmax classifier) is questionable when there is a significant distribution bias between the labeled set and the unlabeled set. In this paper, we attempt to replace the softmax classifier in deep neural network with a nearest neighbor classifier, considering its progressive generalization ability within the unknown sub-space. Our proposed active learning approach, termed nearest Neighbor Classifier Embedded network (NCE-Net), targets at reducing the risk of over-estimating unlabeled samples while improving the opportunity to query informative samples. NCE-Net is conceptually simple but surprisingly powerful, as justified from the perspective of the subset information, which defines a metric to quantify model generalization ability in active learning. Experimental results show that, with simple selection based on rejection or confusion confidence, NCE-Net improves state-of-the-arts on image classification and object detection tasks with significant margins.
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval

Deep quantization methods have shown high efficiency on large-scale image retrieval. However, current models heavily rely on ground-truth information, hindering the application of quantization in label-hungry scenarios. A more realistic demand is to learn from inexhaustible uploaded images that are associated with informal tags provided by amateur users. Though such sketchy tags do not obviously reveal the labels, they actually contain useful semantic information for supervising deep quantization. To this end, we propose Weakly-Supervised Deep Hyperspherical Quantization (WSDHQ), which is the first work to learn deep quantization from weakly tagged images. Specifically, 1) we use word embeddings to represent the tags and enhance their semantic information based on a tag correlation graph. 2) To better preserve semantic information in quantization codes and reduce quantization error, we jointly learn semantics-preserving embeddings and supervised quantizer on hypersphere by employing a well-designed fusion layer and tailor-made loss functions. Extensive experiments show that WSDHQ can achieve state-of-art performance in weakly-supervised compact coding.
GTM: A Generative Triple-wise Model for Conversational Question Generation

Generating some appealing questions in opendomain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction.To avoid dull or deviated questions, some researchers tried to utilize answer, the "future" information, to guide question generation.However, they separate a post-questionanswer (PQA) triple into two parts: postquestion (PQ) and question-answer (QA) pairs, which may hurt the overall coherence.Besides, the QA relationship is modeled as a one-to-one mapping that is not reasonable in open-domain conversations.To tackle these problems, we propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG).Latent variables in three hierarchies are used to represent the shared background of a triple and one-to-many semantic mappings in both PQ and QA pairs.Experimental results on a largescale CQG dataset show that our method significantly improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.Joint work with Pattern Recognition Center, WeChat AI, Tencent Inc, China.* Yang Feng is the corresponding author.
Automatic Counting Shrimp Larvae Based You Only Look Once (YOLO)

Shrimp farming activities can be divided into hatchery and rearing. During the hatchery process, there is an activity of counting shrimp larvae. Currently, the method of counting larvae was still done manually using the sampling method by taking a full cup of shrimp larvae and counting them manually. This process was timewasting and can make the result in miscalculations due to human error. Therefore, this study proposed the You Only Look Once (YOLOv3) algorithm to perform automatic counting of the quantity of shrimp larvae. The results of the YOLOv3 final model show a good performance with a mean Average Precision (mAP) value of 96.83% and an average accuracy value of 76.48%. This YOLO algorithm can calculate shrimp larvae with hight precision.
Automatic Emotion Recognition from Facial Expressions when Wearing a Mask

People communicate emotions through several nonverbal channels and facial expressions play an important part in this communicative process. Automatic Facial Expression Recognition (FER) is a very hot topic that has attracted a lot of interest in the last years. Most FER systems try to recognize emotions from the entire face of a person. Unfortunately, due to pandemic situation, people wear a mask most of the time, thus their faces are not fully visible. In our study, we investigate the effectiveness of a FER system in recognizing emotions only from the eyes region, which is the sole visible region when wearing a mask by comparing the results of the same approach when applied to the entire face. The proposed pipeline involves several steps: detecting a face in an image, detecting a mask on a face, extracting the eyes region, and recognize the emotion expressed on the basis of such region. As it was expected, emotions that are related mainly to the mouth region (e.g. disgust) are not recognized at all and positive emotions are the ones that are better determined by considering only the region of the eyes.
Privacy-Preserving Approximate <i>k</i>-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns

Abstract We study the problem of privacy-preserving approximate kNN search in an outsourced environment — the client sends the encrypted data to an untrusted server and later can perform secure approximate kNN search and updates. We design a security model and propose a generic construction based on locality-sensitive hashing, symmetric encryption, and an oblivious map. The construction provides very strong security guarantees, not only hiding the information about the data, but also the access, query, and volume patterns. We implement, evaluate efficiency, and compare the performance of two concrete schemes based on an oblivious AVL tree and an oblivious BSkiplist.
CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

Abstract Classifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.
Review of System Development Life Cycle (SDLC) Models for Effective Application Delivery

There are different system development models, tools, and applications that have been designed and developed before the use of computers for processing of information, and there is an increase in demand of software with cheaper cost, having more functionality, faster delivery, and of high quality than how it was previously. Therefore, there is need to know the methods and their applications that would conform to the organizational requirements for successful system deployment. Each method has constructive criticisms with various advantages and disadvantages to the system that are of importance for deploying software in a manner such that it helps in good decision making on a chosen method for delivery within deadline and proper quality. This paper explained four commonly used system development methods namely; waterfall, iterative, agile, and rapid application development (RAD). It thereafter categorically assesses these methods on a comprehensive set of features and made an alternative analysis of the applicability of the methods based on system development life cycle tools which are requirement, design, implementation, and testing. This paper describes the system development life cycle (SDLC) tools and applications for successful system deployment and the constructive comparison that should serve as a tool in model selection for system development.
Heterogeneous Face Frontalization via Domain Agnostic Learning

Recent advances in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on thermal to visible face synthesis and matching problems. However, current DCNN-based synthesis models do not perform well on thermal faces with large pose variations. In order to deal with this problem, heterogeneous face frontalization methods are needed in which a model takes a thermal profile face image and generates a frontal visible face. This is an extremely difficult problem due to the large domain as well as large pose discrepancies between the two modalities. Despite its applications in biometrics and surveillance, this problem is relatively unexplored in the literature. We propose a domain agnostic learning-based generative adversarial network (DAL-GAN) which can synthesize frontal views in the visible domain from thermal faces with pose variations. DAL-GAN consists of a generator with an auxiliary classifier and two discriminators which capture both local and global texture discriminations for better synthesis. A contrastive constraint is enforced in the latent space of the generator with the help of a dual-path training strategy, which improves the feature vector discrimination. Finally, a multi-purpose loss function is utilized to guide the network in synthesizing identity preserving cross-domain frontalization. Extensive experimental results demonstrate that DAL-GAN can generate better quality frontal views compared to the other baseline methods.
Cooperative People Tracking by Distributed Cameras Network

In the application of video surveillance, reliable people detection and tracking are always challenging tasks. The conventional single-camera surveillance system may encounter difficulties such as narrow-angle of view and dead space. In this paper, we proposed multi-cameras network architecture with an inter-camera hand-off protocol for cooperative people tracking. We use the YOLO model to detect multiple people in the video scene and incorporate the particle swarm optimization algorithm to track the person movement. When a person leaves the area covered by a camera and enters an area covered by another camera, these cameras can exchange relevant information for uninterrupted tracking. The motion smoothness (MS) metrics is proposed for evaluating the tracking quality of multi-camera networking system. We used a three-camera system for two persons tracking in overlapping scene for experimental evaluation. Most tracking person offsets at different frames were lower than 30 pixels. Only 0.15% of the frames showed abrupt increases in offsets pixel. The experiment results reveal that our multi-camera system achieves robust, smooth tracking performance.
Evaluating the success of the tele-pathology system in governmental hospitals in Kuwait: an explanatory sequential mixed methods design

Abstract Background Telepathology is the practice of reviewing and exchanging pathological images through telecommunication systems to obtain diagnoses remotely. Studying the factors that make such a system successful and favourable is important to ensure the merits of its implementation in clinical practice. Objective This study aims to evaluate the success of a telepathology system from the users’ perspectives, using specific evaluation criteria, namely: system quality, information quality, technical service quality, user satisfaction, and benefits. Methods A sequential explanatory mixed methods design was adopted in this study, which consists of two phases. Initially, a questionnaire was distributed via WhatsApp to all of the pathologists (total: 45) working at governmental hospitals in Kuwait. Followed by, semi-structured interviews with ten senior pathologists. Results Forty pathologists responded to the questionnaire, giving an 89% response rate. There were 42.5% of the respondents aged between 35–44 years old, and 52.5% were male. The quantitative results reveal that most of the respondents were satisfied with the quality of the telepathology system with a mean of 2.6025 (Standard Deviation (SD) = 0.47176), whereas they were dissatisfied with the quality of the information with a mean of 2.4100 (SD = 1.580) and the technical support services with a mean of 2.2750 (SD = 0.99535). In addition, there was disagreement on the benefits of telepathology in clinical practice among the pathologists with a mean of 2.4667 (SD = 0.77552). The qualitative results indicate that the lack of interest in and little experience with using the system were behind the general dissatisfaction of most of the respondents. All of the interviewees were satisfied with the performance of the telepathology system and considered it successful; however, the quality of the technical support services, including training workshops, was deemed deficient. Conclusion This study concluded that telepathology system in Kuwait is functioning well and has been successful in its implementation; however, pathologists are dissatisfied with it, mainly due to the deficient quality of the technical support services provided. In addition, the successful implementation of such advanced technologies requires careful steps to be taken on multiple levels: technical, organisational, and managerial. Recommendations were suggested.
Analysis of Serverless Computing Techniques in Cloud Software Framework

This paper describes briefly about the concept of serverless cloud computing model, its usage in IT industries and its benefits. In the traditional model the developer is responsible for resource allocation, managing servers and owning of servers, and it included three models based upon the service such as IaaS, PaaS and SaaS. In IaaS (Infrastructure as a Service) the content storage and accessing of network is carried out by the cloud provider, SaaS (Software as a Service) here different software’s are provided to the user as a service, PaaS (Platform as a Service), the developer gets access to certain services for carrying out organizing process and run it accordingly. In serverless cloud computing, the developer need not worry about owning, management, and maintenance of servers as it is carried out by the cloud service provider. Hence by using this model, the time that is needed for a system to reach the market is very much reduced and is cost effective. Serverless architecture includes three categories namely, AWS Lambda, Azure, and Google cloud. It also includes certain challenges such as it cannot be used in the case where a process takes longer time to run and it is discussed below in this paper.
Representation of binary classification trees with binary features by quantum circuits

We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. By using the quantum computer as a processor for probability distributions, a probabilistic traversal of the decision tree can be realized via measurements of a quantum circuit. We describe how tree inductions and the prediction of class labels of query data can be integrated into this framework. An on-demand sampling method enables predictions with a constant number of classical memory slots, independent of the tree depth. We experimentally study our approach using both a quantum computing simulator and actual IBM quantum hardware. To our knowledge, this is the first realization of a decision tree classifier on a quantum device.
The Role of Reading in English Language Classrooms

Being able to read well is crucially important for language learners. Successful performance at elementary and high school level, as well as on higher levels of academic education, is partly dependent on the ability to read. It is believed that good learners are those who are proficient in reading. However, building such a connection between a reader and the written information is complex for both English as a second language (ESL) and English as a foreign language (EFL) students, and requires the application of various reading strategies. The present study aimed to investigate EFL Bosnian learners' awareness of the use of various reading strategies. Furthermore, the study investigated students' reading habits, favorite reading materials, and the purpose of reading. It was conducted among fifty-seven ninth grade elementary school students. The findings proved that these students are aware of the importance of reading and development of reading skills, as well as of the importance of using appropriate reading strategies in order to be proficient readers and eventually to be more successful in the overall process of learning.