Reviewerly/exHarmony huggingface dataset:
Particle size effect on geochemical composition of experimental soil mixtures relevant for unmixing modelling

Sediment fingerprinting experiments have been used to demonstrate the sensitivity of numerical mixing model outputs to different particle size distributions in source materials and experimental sediment mixtures. The study aims to examine further grain size effects in the distribution of geochemical elements by soils through a laboratory experiment simulating mixing and sorting processes, to investigate if different size fractions are influencing fingerprinting analyses and unmixing model results. Multiple particle size fractions are analysed to understand the relationship between particle size and source signal through elemental signatures. FingerPro model was applied to unmix six experimental mixtures with known percentages contribution from three experimental sources. The experimental design comprised four different setups with a specific size fraction for sources (S) and mixtures (M). Setups A (S <63 and M <63 μm) and B (S <20 and M <20 μm) relies upon a comparable particle size fraction for sources and mixtures, while C (S <63 and M <20 with PSC) and D (S <63 and M <20) address particle size impacts simulating fine enrichment, with and without a single particle size correction factor, respectively. Tracers were extracted after applying two statistical tests, the range test (RT) and a combination of RT, Kruskal-Wallis (KW) and DFA tests thus obtaining the set of optimum tracers for each mixture. Our findings indicate that source apportionment results are sensitive to tracer selection and particle size. The most accurate source apportionment results were achieved when comparing sources and mixtures with the <63 μm grain-size fraction (setup A) by using the set of tracers extracted after RT, KW and DFA tests, (mean RMSE: 2%, AE: 2%). Larger errors were obtained progressively for setups B, C and D with better results when using more number of tracers from RT (mean RMSE: 7, 10, 13%, AE: 8, 11, 15%, respectively). The main strength of using experimental mixtures with a known contribution of the sources relies on reducing the uncertainty of the unmixing model outputs, one of the main limitations in fingerprint studies. The impact of SSA on the elemental concentration is difficult to predict because the positive linearity between them does not apply equally to all elements and this assumption needs to be constantly examined and considered for fingerprinting studies. Otherwise, the use of a single particle size correction factor could negatively affect unmixing results. The outcomes of this research will help to develop appropriate strategies for sediment fingerprinting, contributing to our knowledge of processes affecting sediment geochemistry and sediment transport across different particle sizes.
Cloth-Changing Person Re-identification with Self-Attention

The basic assumption in the standard person reidentification (ReID) problem is that the clothing of the target person IDs would remain constant over long periods. This assumption creates errors during real-world implementations. In addition, most of the methods that handle ReID use CNN-based networks and have found limited success because CNNs can exploit only local dependencies and suffer the loss of information due to the use of downsampling operations. In this paper, we focus on a more challenging, realistic scenario of long-term cloth-changing ReID (CC-ReID). We aim to learn robust and unique feature representations that are invariant to clothing changes to address the CC-ReID problem. To overcome the limitations faced by CNNs, we propose a Vision-transformer-based framework. We also propose to intuitively exploit the unique soft-biometric-based discriminative information such as gait features and pair them with ViT feature representation for allowing the model to generate long-range structural and contextual relationships that are crucial for re-identification task in the long-term scenario. To evaluate the proposed approach, we perform experiments on two recent CC-ReID datasets, PRCC and LTCC. The experimental results show that the proposed approach achieves state-of-the-art results on the CC-ReID task.
SEAR: Scaling Experiences in Multi-user Augmented Reality

In this paper, we present the design, implementation, and evaluation of SEAR, a collaborative framework for Scaling Experiences in multi-user Augmented Reality (AR). Most AR systems benefit from computer vision (CV) algorithms to detect, classify, or recognize physical objects for augmentation. A widely used acceleration method for mobile AR is to offload the compute-intensive tasks (e.g., CV algorithms) to the network edge. However, we show that the end-to-end latency, an important metric of mobile AR, may dramatically increase when offloading AR tasks from a large number of concurrent users to the edge. SEAR tackles this scalability issue through the innovation of a lightweight collaborative local caching scheme. Our key observation is that nearby AR users may share some common interests, and may even have overlapped views to augment (e.g., when playing a multi-user AR game). Thus, SEAR opportunistically exchanges the results of offloaded AR tasks among users when feasible and leverages compute resources on mobile devices to relieve, if necessary, the edge workload by intelligently reusing these results. We build a prototype of SEAR to demonstrate its efficacy in scaling AR experiences. We conduct extensive evaluations through both real-world experiments and trace-driven simulations. We observe that SEAR not only reduces the end-to-end latency, by up to 130×, compared to the state-of-the-art adaptive edge offloading scheme, but also achieves high object-recognition accuracy for mobile AR.
Personalized Motion Planning and Tracking Control for Autonomous Vehicles Obstacle Avoidance

A personalized motion planning and tracking control framework is proposed to prevent collisions between autonomous vehicles and obstacles ahead. The presented framework consists of a motion planning generator and a cooperative tracking controller. For motion planning, a multi-constrained numerical optimization method is used to generate the obstacle avoidance motion planning for autonomous vehicles according to the individual needs of the passengers. For tracking control, a cooperative longitudinal and lateral motion controller is designed to track the planning results. The longitudinal controller is composed of a model predictive controller, and the lateral controller is composed of a nonlinear steering controller. The planning results show that the adopted motion planning algorithm can achieve personalized motion planning according to the needs of the passengers. Simulink and CarSim co-simulation results show the excellent tracking performance of the proposed cooperative controller. The performance analysis results show that the proposed framework can improve the corresponding driving performance according to the individual needs of the passengers.
The design of smart classroom for modern college English teaching under Internet of Things

This study aims to improve the efficiency of modern college English teaching. With interactive teaching as the core element, smart classrooms as technical support, and informationization, automation, and interaction as the main body, a smart system for college English teaching is established based on cloud computing and Internet of Things (IoT). The system is built using the B/S architecture and verified by specific example data, to prove the effectiveness of the proposed smart system for college English teaching based on the IoT. It is found that the smart platform for English teaching based on the IoT not only effectively improves the stability of the system, but also enhances the personal experience of students. The coordinated operation of the various modules reduces the response time of the system. When the number of users reaches 500, the average response time of the system is 3.65 seconds, and the memory and occupancy rate of the system are reduced. Students who receive smart classrooms for teaching have a greater improvement in the test results of various aspects of English without teacher intervention. The proposed model can significantly improve the performance of poor students and reduce the gap in learning performance in the class, which provides reliable research ideas for smart teaching in modern colleges and universities.
Exploring Alzheimer's Disease Prediction with XAI in various Neural Network Models

Using a number of Neural Network Models, we attempt to explore and explain the prediction of Alzheimer's in patients in various stages of the disease, using MRI imaging data. Alzheimer's disease(AD) often described as dementia is one of the major neurological dysfunctionalities among humans and does not yet have a proven detection system; unless the final stage symptoms of AD starts to be seen. It is observed that multimodal biological, imaging and other available neuropsychological data can ensure a high percentage of separation among (AD) patients from cognitively normal elders. However, they cannot surely predict or detect early enough that patients with mild cognitive impairment (MCI) can get converted into Alzheimer's disease dementia in the future. But the research done till date shows a high probable detection rate in which they used the pattern classifier built on various longitudinal data. So in this paper we experimented with the existing Neural Network models to detect Alzheimer's disease in its early stage by classification techniques; and will be using a recent hybrid dataset in the process to have four separate classification in total. And also explored the exact region for which that specific classification occurs for the patients, looking at the T1 weighted MRI scans from a hybrid dataset from Kaggle [1] using the LIME based XAI(Explainable Artificial Intelligence) framework. For the Convolution Neural Network Models we are using Resnet50, VGG16 and Inception v3 and received 82.56%, 86.82%, 82.04% of categorical accuracy respectively.
Influenza, dengue and common cold detection using LSTM with fully connected neural network and keywords selection

Symptom-based machine learning models for disease detection are a way to reduce the workload of doctors when they have too many patients. Currently, there are many research studies on machine learning or deep learning for disease detection or clinical departments classification, using text of patient's symptoms and vital signs. In this study, we used the Long Short-term Memory (LSTM) with a fully connected neural network model for classification, where the LSTM model was used to receive the patient's symptoms text as input data. The fully connected neural network was used to receive other input data from the patients, including body temperature, age, gender, and the month the patients received care in. In this research, a data preprocessing algorithm was improved by using keyword selection to reduce the complexity of input data for overfitting problem prevention. The results showed that the LSTM with fully connected neural network model performed better than the LSTM model. The keyword selection method also increases model performance.
Improved object detection algorithm for drone-captured dataset based on yolov5

Object detection is a basic task on computer vision, recently drone-captured scenarios had a wide range of applications in the industry. In this paper, firstly we will introduce some characteristics of yolov5s structure and some defects of yolov5s on tiny-size object detection. Secondly, to solve the defects mentioned above we will focus on the improvement based on the yolov5s structure to VisDrone dataset. Due to some characteristics of UAV dataset, the target scale changes greatly at different flight altitude, some target areas captured by drone show high density, which may bring some blurring or occlusion and some images may cover a large area, so we try to improve the structure of yolov5 for better performance. An additional prediction head is added to detect tiny-scale targets, which is also used to deal with large size variance of objects, and an EPSA net module is added in the middle of the backbone to explore the self-attention potential of feature representation. Moreover, extra up sampling is added in the neck part to optimize the detection of tiny-size target samples. Finally, it is proved by experiments that new yolov5s structure improve the value of mAP@.5 about 7% and improve the value of mAP@.5:.95 about 5% on VisDrone-2019-DET dataset.
Fuzzy modeling of desired chaotic behavior in secure communication systems

Communication technologies play a key role in various fields. Hybrid Soft Computing approaches have significant potential for design and investigation of complex systems. In this paper, we use combination of fuzzy logic and chaos theory to model uncertainty and complexity of a secure communication system. Fuzzy rule base is used to describe dependence of behavior of a chaotic system on its parameters and initial conditions. The rule base is constructed by applying fuzzy clustering to a large data set. The approach is characterized by its relatively low computational complexity due to the use of fuzzy rule base instead of intensive simulations of a fuzzy chaotic system. Complexity of this hybrid fuzzy-chaotic approach assures an increased level of security. Trade-off between complexity and security may be achieved by generation of transmitted information using fuzzy modeling of chaotic behavior. Computer simulations are used to verify feasibility and effectiveness of the proposed approach.
On the privacy protection of indoor location dataset using anonymization

Indoor positioning is becoming more popular with increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS). Location fingerprinting is widely deployed in indoor localization, and extensive daily use of different kinds of mobile devices in indoor environments results in generating a huge amount of location data of people. However, publishing this location based dataset by Location Service Provider (LSP) to any third party, such as researchers or commercial organizations, potentially threatens users' privacy. Several studies have been done to anonymize the user location data, but protecting the users' information against various attacks in an indoor environment is still a challenging issue. This paper proposes a novel framework for publishing privacy preserving location data employing five anonymization techniques: k-anonymity, ℓ-diversity, t-closeness, (α,k)-anonymity, and δ-presence. In the proposed framework, although the LSP cannot find the exact location of the users, it can provide them online location services at the same time and publish the anonymized privacy protected dataset afterward. The practical feasibility of applying the proposed framework is verified on both simulated and two real-world datasets. The results indicate that the published location dataset can protect the identity and location information of users while providing location information for third parties for further purposes.
Knowledge management effects and performance in health care: a systematic literature review

Knowledge management is intended to achieve organisational goals through effective management of knowledge resources and performance. This paper presents a review of knowledge management effects and performance in health care. Six databases were searched, the searches employing Boolean operators and combinations of key words. In total, 16 articles fulfilled the criteria set for inclusion. Data were analysed using inductive content analysis. The review shows that in health care knowledge management effects and performance have been viewed from various perspectives: it has been linked to health care functions such as management, finance, patient care, quality and safety, IT, continued improvement of clinical operations, and organisational culture. However, the effects of knowledge management extend also to employees' work, job satisfaction, learning, knowledge distribution, and productivity. This review aims to summarise the recent research on knowledge management effects and performance in health care. Some of the effects are universal and some are context-specific.
Co-Segmentation Aided Two-Stream Architecture for Video Captioning

The goal of video captioning is to generate captions for a video by understanding visual and temporal cues. A general video captioning model consists of an Encoder-Decoder framework where Encoder generally captures the visual and temporal information while the decoder generates captions. Recent works have incorporated object-level information into the Encoder by a pretrained off-the-shelf object detector, significantly improving performance. However, using an object detector comes with the following downsides: 1) object detectors may not exhaustively capture all the object categories. 2) In a realistic setting, the performance may be influenced by the domain gap between the object-detector and the visual-captioning dataset. To remedy this, we argue that using an external object detector could be eliminated if the model is equipped with the capability of automatically finding salient regions. To achieve this, we propose a novel architecture that learns to attend to salient regions such as objects, persons automatically using a co-segmentation inspired attention module. Then, we utilize a novel salient region interaction module to promote information propagation between salient regions of adjacent frames. Further, we incorporate this salient region-level information into the model using knowledge distillation. We evaluate our model on two benchmark datasets MSR-VTT and MSVD, and show that our model achieves competitive performance without using any object detector.
Single-Trace Side-Channel Attacks on ω-Small Polynomial Sampling: With Applications to NTRU, NTRU Prime, and CRYSTALS-DILITHIUM

This paper proposes a new single-trace side-channel attack on lattice-based post-quantum protocols. We target the ω-small polynomial sampling of NTRU, NTRU Prime, and CRYSTALS-DILITHIUM algorithm implementations (which are NIST Round-3 finalist and alternative candidates), and we demonstrate the vulnerabilities of their sub-routines to a power-based side-channel attack. Specifically, we reveal that the sorting implementation in NTRU/NTRU Prime and the shuffling in CRYSTALS-DILITHIUM's ω-small polynomial sampling process leaks information about the ‘-1’’0’, or ’+1' assignments made to the coefficients. We further demonstrate that these assignments can be found within a single power measurement and that revealing them allows secret and session key recovery for NTRU/NTRU Prime, while reducing the challenge polynomial's entropy for CRYSTALS-DILITHIUM. We execute our proposed attacks on an ARM Cortex-M4 microcontroller running the reference software submissions from NIST Round-3 software packages. The results show that our attacks can extract coefficients with a success rate of 99.78% for NTRU and NTRU Prime, reducing the search space to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">41</sup> or below. For CRYSTALS-DILITHIUM, our attack recovers the coefficients’ signs with over 99.99% success, reducing rejected challenge polynomials’ entropy between 39 to 60 bits. Our work informs the proposers about the single-trace vulnerabilities of their software and urges them to develop single-trace resilient software for low-cost microcontrollers.
ASM-VoFDehaze: a real-time defogging method of zinc froth image

When the ambient temperature is low, a large amount of water mist and dust will inevitably appear around the zinc flotation cell, forming haze, which seriously affects the extraction of flotation froth image features. General defogging methods of natural image are difficult to obtain satisfactory results for such industrial haze image. Therefore, we propose a real-time defogging method based on ASM-VoFD (Atmospheric Scattering Model and the Variable-order Fractional Differential). First, the dark pixel ratio is used to detect fog in froth image, which solves the redundant calculation caused by unnecessary defogging operations. Second, the linear transformation of the atmospheric scattering model is used to calculate the initial transmission map, and the gaussian filter is used to optimize the initial transmittance, and the haze-free image is restored with atmospheric light estimation. Finally, a variable order fractional differential operator is used to enhance the edges and texture details of the restored froth image, which solves the problems of blurred edges and low contrast. The experiments show that the algorithm has a good defogging effect on the industrial images, enhance the edges of the image, and can be effectively implemented in O(N) time to meet the application requirements of real-time flotation monitoring.
Design of Intelligent Recognition English Translation Model Based on Deep Learning

Nowadays, the intercommunication and translation of global languages has become an indispensable condition for friendly communication among human beings around the world. The advancement of computer technology developed the machine translation from academic research to industrial applications. Additionally, a new and popular branch of machine learning is deep learning which has achieved excellent results in research fields such as natural language processing. This paper improved the performance of machine translation based on deep learning network and studied the intelligent recognition of English-Chinese machine translation models. This research mainly focused on solving out-of-vocabulary (OOV) problem of machine translation on unregistered words and rare words. Moreover, it combined stemming technology and data compression algorithm Byte Pair Encoding (BPE) and proposed a different subword-based word sequence segmentation method. Using this method, the English text is segmented into word sequences composed of subword units, and, at the same time, the Chinese text is segmented into character sequences composed of Chinese characters using unigram. Secondly, the current research also prevented the decoder from experiencing incomplete translation. Furthermore, it adopted a deep-attention mechanism that can improve the decoder's ability to obtain context information. Inspired by the traditional attention calculation process, this work uses a two-layer calculation structure in the improved attention to focus on the connection between the context vectors at different moments of the decoder. Based on the neural machine translation model Google Neural Machine Translation (GNMT), this paper conducted experimental analysis on the above improved methods on three different scale datasets. Experimental results verified that the improved method can solve OOV problem and improve accuracy of model translation.