Reviewerly/exHarmony huggingface dataset:
Comprehensive Treatment of Urban Landscape Water Environment Based on Aquatic Plants Purification and Restoration

Water is the source of life and the foundation for the growth of all things. Water is also an important part of the urban ecological structure. A healthy water environment is the basis for human survival and development, and it is also one of the important conditions for the harmonious development of cities. With the rapid development of the economy and the improvement of the level of urbanization, the water environment pollution in the urban landscape has become more and more serious, such as serious water eutrophication and rapid algae reproduction in the water environment. As plants that can survive in water, aquatic plants have always been an important main body of the water ecological environment. It plays an unparalleled role in water environment purification and comprehensive treatment of water pollution. This paper aims to study the comprehensive management of urban landscape water environment based on the purification and restoration of aquatic plants. It combines the purification and restoration ability of aquatic plants and genetic algorithm to carry out the comprehensive management experiment of urban landscape water environment. The conclusion shows that after the purification and restoration of aquatic plants and comprehensive pollution control, the comprehensive eutrophic substance content of the urban landscape pond water body decreased by 21.7 mg, and the water body transparency increased by 20 cm.
Forecasting the future clinical events of a patient through contrastive learning

Deep learning models for clinical event forecasting (CEF) based on a patient's medical history have improved significantly over the past decade. However, their transition into practice has been limited, particularly for diseases with very low prevalence. In this paper, we introduce CEF-CL, a novel method based on contrastive learning to forecast in the face of a limited number of positive training instances.CEF-CL consists of two primary components: (1) unsupervised contrastive learning for patient representation and (2) supervised transfer learning over the derived representation. We evaluate the new method along with state-of-the-art model architectures trained in a supervised manner with electronic health records data from Vanderbilt University Medical Center and the All of Us Research Program, covering 48 000 and 16 000 patients, respectively. We assess forecasting for over 100 diagnosis codes with respect to their area under the receiver operator characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). We investigate the correlation between forecasting performance improvement and code prevalence via a Wald Test.CEF-CL achieved an average AUROC and AUPRC performance improvement over the state-of-the-art of 8.0%-9.3% and 11.7%-32.0%, respectively. The improvement in AUROC was negatively correlated with the number of positive training instances (P < .001).This investigation indicates that clinical event forecasting can be improved significantly through contrastive representation learning, especially when the number of positive training instances is small.
IoT Device Fingerprinting on Commodity Switches

IoT devices such as wearables, voice assistants and home appliances are becoming an integral part of our lives. However, these devices still represent a security and privacy risk with large-scale coordinated attacks often populating the news. The ability to tell which IoT devices are where in a network (i.e., to fingerprint them) can help administrators to mitigate such attacks at the earliest stages. While fingerprinting solutions exist, they often work offline, depend on sampled data or rely on payload information to work. In this paper, we propose PoirIoT, a high-speed in-network system for fingerprinting IoT devices. PoirIoT is based only on packet metadata (e.g., length and direction) and can detect a device as soon as it exchanges its first packets. We implement a prototype of PoirIoT on a Tofino-based programmable switch and show it can detect all possible IoT devices on a publicly available dataset. Moreover, PoirIoT runs at line rate and incurs minimal resource overhead on the programmable switch ASIC.
Efficient Object Detection and Classification Approach Using HTYOLOV4 and M2RFO-CNN

Object detection and classification are the trending research topics in the field of computer vision because of their applications like visual surveillance.However, the vision-based objects detection and classification methods still suffer from detecting smaller objects and dense objects in the complex dynamic environment with high accuracy and precision.The present paper proposes a novel enhanced method to detect and classify objects using Hyperbolic Tangent based You Only Look Once V4 with a Modified Manta-Ray Foraging Optimizationbased Convolution Neural Network.Initially, in the pre-processing, the video data was converted into image sequences and Polynomial Adaptive Edge was applied to preserve the Algorithm method for image resizing and noise removal.The noiseless resized image sequences contrast was enhanced using Contrast Limited Adaptive Edge Preserving Algorithm.And, with the contrast-enhanced image sequences, the Hyperbolic Tangent based You Only Look Once V4 was trained for object detection.Additionally, to detect smaller objects with high accuracy, Grasp configuration was observed for every detected object.Finally, the Modified Manta-Ray Foraging Optimization-based Convolution Neural Network method was carried out for the detection and the classification of objects.Comparative experiments were conducted on various benchmark datasets and methods that showed improved accurate detection and classification results.
Optical Dispersion Compensation through a Nonlinear-optical Loop Mirror (NOLM)-based Optical Reservoir

We propose a novel dispersion compensation method through a nonlinear-optical loop mirror (NOLM)-based optical reservoir (OR) computing scheme, utilizing the nonlinear mapping behavior happened in a highly nonlinear fiber (HNLF). The proposed scheme introduces the all-optical NOLM structure into the reservoir computing (RC) concept, enabling the acceleration through the nonlinear nature from an optical device. We carry out the joint optimization of the parameters from both the RC and the NOLM to achieve the function of the dispersion compensation. The detail results from the transmission scenario with the 20 ~ 200 km are investigated when only dispersion effect considered. A maximum transmission reach of 140km is obtained for a fixed OR accelerator subsystem used in the dispersion compensation, which significantly reduces the training cost because of the NOLM-based OR accelerator only optimizing the parameters once.
Implementation of Missing Data Imputation Schemes in Face Recognition Algorithm under Partial Occlusion

Face detection and recognition algorithms usually assume an image captured from a controlled environment. However, this is not always the case, especially in crowd control under surveillance or footage from a crime scene, where partial occlusions are unavoidable. Unfortunately, these occlusions have an adverse effect on the performance of these classical recognition algorithms. In this study, the performance of some selected data imputation schemes is evaluated on SVD/PCA frontal face recognition algorithm. The experiment was done on two datasets: Jaffe and MIT-CBCL, with immediate confirmation of the adverse effect of occlusion on the facial algorithm without implementing the imputation scheme. Further experimentation shows that IA is an ideal missing data imputation scheme that works best with the SVD/PCA facial recognition algorithm.
Millimeter Wave and Free-space-optics for Future Dual-connectivity 6DOF Mobile Multi-user VR Streaming

Dual-connectivity streaming is a key enabler of next-generation six Degrees Of Freedom (6DOF) Virtual Reality (VR) scene immersion. Indeed, using conventional sub-6 GHz WiFi only allows to reliably stream a low-quality baseline representation of the VR content, while emerging high-frequency communication technologies allow to stream in parallel a high-quality user viewport-specific enhancement representation that synergistically integrates with the baseline representation to deliver high-quality VR immersion. We investigate holistically as part of an entire future VR streaming system two such candidate emerging technologies, Free Space Optics (FSO) and millimeter-Wave (mmWave), that benefit from a large available spectrum to deliver unprecedented data rates. We analytically characterize the key components of the envisioned dual-connectivity 6DOF VR streaming system that integrates in addition edge computing and scalable 360° video tiling, and we formulate an optimization problem to maximize the immersion fidelity delivered by the system, given the WiFi and mmWave/FSO link rates, and the computing capabilities of the edge server and the users’ VR headsets. This optimization problem is mixed integer programming of high complexity and we formulate a geometric programming framework to compute the optimal solution at low complexity. We carry out simulation experiments to assess the performance of the proposed system using actual 6DOF navigation traces from multiple mobile VR users that we collected. Our results demonstrate that our system considerably advances the traditional state of the art and enables streaming of 8K-120 frames-per-second (fps) 6DOF content at high fidelity.
Recognition of Handwritten Bengali Characters using Low Cost Convolutional Neural Network

Handwritten character recognition of Bangla Script is one of the most difficult and complex tasks in pattern recognition, because of the complicated alignment and similarity in the characters. This paper aims to explore the usage of a Convolutional Neural Network to recognize Handwritten Bangla characters. The classification stages and feature extraction stages of any pattern recognition task are responsible for accurately recognizing the patterns. The paper proposes a novel low-cost CNN architecture for Bengali Character Recognition over present well-known datasets CMATERdb, BanglaLekha-Isolated, Ekush. Using Convolutional Neural Network, the proposed model achieves good accuracy and well generalizes over multiple datasets. Overall Recognition accuracy obtained for datasets such as CMATERdb, BanglaLekha-Isolated, Ekush is 87%, 89.6%, 83.1% respectively.
Diffusion Affine Projection Sign Least Mean Fourth Algorithm for Distributed Estimation

The problem of distributed estimation is considered for distributed network. A diffusion model on adaptive affine projection sign algorithm based on least mean fourth algorithm is introduced according to an Adapt-Then-Combine (ATC) design for the distributed framework. Least mean fourth (LMF) algorithm is defined by taking the fourth power of error to against the non-Gaussian environment. An adaptive affine projection sign algorithm based on LMF algorithm is proposed firstly. Then, an ATC diffusion model on adaptive affine projection sign algorithm based on LMF algorithm is presented in terms of the low complexity of sign approach with the order of adaptation and combination, named as ATC-DAPS-LMF. Numerical simulations verify that the proposed algorithm can perform significantly to the conventional affine projection sign algorithm.
Real-Time Facial Components Detection Using Haar Classifiers

Face detection is the process of identifying a face in an image with the help of facial characteristics. Detection of the face with other facial characteristics such as eyes, nose, and smile can help in solving many real-world problems such as Product rating, patient monitoring in hospitals as well as image Capturing, conference videos, etc. The application of real-time smile detection could also be integrated with facial recognition to make it more viable and effective for use. To detect a face and its components the system uses Haar features. Haar features have a resemblance to the convolution kernel, which is used to detect the presence of a particular feature in an image. The Difference Between the values present under the Dark region and the pixels present under the White region results in a single Value. The value is then compared to the threshold, If the value is Greater than the threshold then a human face is detected. Here, The integral image also plays a very important role in detecting a face and its features, it is used to calculate the sum of all pixels inside a rectangle by just using the corner values of a rectangle. Adaboost is also used to remove irrelevant features from a given set of features, Leaving behind only the relevant set of features. At last cascading is used to group the strong classifiers into stages. So, Each stage consists of a set of features that are very crucial in detecting whether a given image is a face or not. If Not, the image is discarded immediately. The Proposed Model detects a face by Capturing the Frames in real-time from a Webcam Feed, Converts the Captured Frames into Grayscale as it Requires Less Processing to be Done. The Haar Cascade Xml Files are used which Consist of a Necessary set of Haar Features required for face detection in a frame. There are Separate Xml Files that Consist of a Necessary set of features that Could be used for the detection of Other Body Parts. To Detect Facial Components such as Eyes and Nose or even a Smile, Haar Cascade Xml Files are used. A complete Facial Detection System with Real-Time Smile Detection Has Numerous Applications in Medical Sectors such as Emotion Detection, Sentiment Analysis, Etc., It can be Even used in the Improvement of a Business Model Enhancing the Feedback System which in turn Improves the Overall Customer Experience, Leading to Growth in Business.
Improved real-time three-dimensional stereo matching with local consistency

Existing stereo matching algorithms are unable to meet the real-time and high-performance dual requirements in practical applications. To address this problem, a novel stereo network is proposed, which utilizes the prior of local disparity consistency to improve the performance of real-time disparity estimation. Based on the initial disparity estimation by the light-weight pyramid matching network, novel spatial consistency refinement (SCR) module and time consistency refinement (TCR) module are designed for further disparity refinement. SCR module propagates the neighborhood high-confidence predictions of sparse sampling to unreliable regions for disparity refinement. A single-layer Dynamic Local Filter (DLF) is designed to realize the content-adaptive propagation, which effectively improves the disparity quality without significantly increasing the burden of computation and memory. For real-time disparity estimation of consecutive frames, novel TCR module is further proposed to refine the disparity estimation based on the time local consistency of disparity. The proposed method is evaluated on the Scene Flow and KITTI 2015 datasets with comprehensive experiments. Experimental results demonstrated that our method can achieve high-accuracy disparity estimation and real-time running speed of over 40 FPS, which significantly outperforms the compared networks with similar runtimes.
2D and 3D representation of objects in architectural and heritage studies: in search of gaze pattern similarities

Abstract The idea of combining an eye tracker and VR goggles has opened up new research perspectives as far as studying cultural heritage is concerned, but has also made it necessary to reinvestigate the validity of more basic eye-tracking research done using flat stimuli. Our intention was to investigate the extent to which the flattening of stimuli in the 2D experiment affects the obtained results. Therefore an experiment was conducted using an eye tracker connected to virtual reality glasses and 3D stimuli, which were a spherical extension of the 2D stimuli used in the 2018 research done using a stationary eye tracker accompanied by a computer screen. The subject of the research was the so-called tunnel church effect, which stems from the belief that medieval builders deliberately lengthened the naves of their cathedrals to enhance the role of the altar. The study compares eye tracking data obtained from viewing three 3D and three 2D models of the same interior with changed proportions: the number of observers, the number of fixations and their average duration, time of looking at individual zones. Although the participants were allowed to look around freely in the VR, most of them still performed about 70–75% fixation in the area that was presented in the flat stimuli in the previous study. We deemed it particularly important to compare the perception of the areas that had been presented in 2D and that had evoked very much or very little interest: the presbytery, vaults, and floors. The results indicate that, although using VR allows for a more realistic and credible research situation, architects, art historians, archaeologists and conservators can, under certain conditions, continue to apply under-screen eye trackers in their research. The paper points out the consequences of simplifying the research scenario, e.g. a significant change in fixation duration. The analysis of the results shows that the data obtained by means of VR are more regular and homogeneous. Graphical Abstract
The Design of the Lightweight Smart Home System and Interaction Experience of Products for Middle-Aged and Elderly Users in Smart Cities

The research aims to improve the comfort and safety of the smart home by adding a motion recognition algorithm to the smart home system. First, the research status of motion recognition is introduced. Second, based on the requirements of the smart home system, a smart home system is designed for middle-aged and elderly users. The software system in this system includes intelligent control subsystems, intelligent monitoring subsystems, and intelligent protection subsystems. Finally, to increase the security of the smart home, the intelligent monitoring subsystem is improved, and an intelligent security subsystem is proposed based on a small-scale motion detection algorithm. The system uses three three-dimensional (3D) convolutional neural networks (CNNs) to extract three image features, so that the data information in the video can be fully extracted. The performance of the proposed intelligent security subsystem based on a small-scale motion detection algorithm is compared and analyzed. The research results show that the accuracy of the system on the University of Central Florida (UCF101) dataset is 94.64%, and the accuracy on the HMDB51 dataset is 90.11%, which is similar to other advanced algorithms. Observing whether there are dangers such as falling inside and outside the family through motion recognition technology has very important application significance for protecting people's personal safety, life, and health.
Bridging Sapling: Private Cross-Chain Transfers

Interoperability is one of the main challenges of blockchain technologies, which are generally designed as self-contained systems. Interoperability schemes for privacy-focused blockchains are particularly hard to design: they must integrate with the unique privacy features of the underlying blockchain so as to prove statements about specific transactions in protocols designed to obfuscate them. This has led to users being forced to weaken their privacy, e.g. by using centralised exchanges, to move assets from one chain to another. We present Zclaim, a framework for trustless cross-chain asset migration based on the Zcash privacy-protecting protocol. Zclaim integrates with an implementation of the Sapling version of Zcash on a smart-contract capable issuing chain in order to attain private cross-chain transfers. We show that a tokenised representation can be created via a set of collateralised intermediaries without relying on or revealing the total amount to any third party.
PAME

In emerging DNN serving systems, queries are usually batched to fully leverage hardware resources, and all the queries in a batch run through the complete model and return at the same time. According to our findings, some queries only need to pass through a portion of the DNN model to attain sufficient precision in a DNN service. These queries can have shorter latencies if they can return early in the middle of a model. Therefore, we propose precision-aware multi-exit inference serving, PAME, to achieve the above purpose. PAME provides a holistic scheme to build a multi-exit DNN model and a corresponding system-level design of the inference engine. We use representative CV and NLP benchmarks to evaluate PAME. PAME is adaptive to various DNN tasks and service loads. Experimental results show that PAME reduces 39.9% average latency without increasing the tail latency, while maintaining 99.68% precision of the original single-exit DNN models on average.
Silicon Photonics for Neuromorphic Computing and Artificial Intelligence: Applications and Roadmap

Artificial intelligence and neuromorphic computing driven by neural networks has enabled many applications. Software implementations of neural networks on electronic platforms are limited in speed and energy efficiency. Neuromorphic photonics aims to build processors in which optical hardware mimic neural networks in the brain. These processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. However, integrated optical neural networks are much smaller (hundreds of neurons) than electronic implementations (tens of millions of neurons). This raises a question: what are the applications where sub-nanosecond latencies and energy efficiency trump the sheer size of processor? We provide an overview of neuromorphic photonic systems and their real-world applications to machine learning and neuromorphic computing.
Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Abstract Recent studies report doubling numbers of deaths due to dementia. With such an escalating mortality rate related to cognitive decline diseases, like dementia, timely information on contributing factors and knowledge discovery from evidence-based repositories is warranted. A large amount of scholarly knowledge extracted from research findings on dementia can be understood only using human intelligence for arriving at quality inferences. Due to the unstructured data presented in such a massive dataset of scientific articles available online, gaining insights from the knowledge hidden in the literature is complex and time-consuming. Hence, there is a need for developing a knowledge management model to create, query and maintain a knowledge repository of key elements and their relationships extracted from scholarly articles in a structured manner. In this paper, an innovative knowledge discovery computing model to process key findings from unstructured data from scholarly articles by using the design science research (DSR) methodology is proposed. The solution caters to a novel composition of the cognitive script of crucial knowledge related to dementia and its subsequent transformation from unstructured into a structured format using graph-based next-generation infrastructures. The computing model contains three phases to assist the research community to have a better understanding of the related knowledge in the existing unstructured research articles: (i) article collection and construction of cognitive script, (ii) generation of Cypher statements (a knowledge graph query language) and (iii) creation of graph-based repository and visualization. The performance of the computing model is demonstrated by visualizing the outcome of various search criteria in the form of nodes and their relationships. Our results also demonstrate the effectiveness of visual query and navigation highlighting its usability.