Reviewerly/exHarmony huggingface dataset:
Deep Convolutional Neural Network AlexNet and Squeezenet for Maize Leaf Diseases Image Classification

Maize productivity growth is expected to increase by the year. However, there are obstacles to achieving it. One of the causes is diseases attack. Generally, maize plant diseases are easily detected through the leaves. This article discusses maize leaf disease classification using computer vision with a convolutional neural network (CNN). It aims to compare the deep convolutional neural network (CNN) AlexNet and Squeezenet. The network also used optimization, stochastic gradient descent with momentum (SGDM). The dataset for this experiment was taken from PlantVillage with 3852 images with 4 classes i.e healthy, blight, spot, and rust. The data is divided into 3 parts: training, validation, and testing. Training and validation are 80%, the rest for testing. The results of training with cross-validation produce the best accuracy of 100% for AlexNet and Squeezenet. Furthermore, the best weights and biases are stored in the model for testing data classification. The recognition results using AlexNet showed 97.69% accuracy. While the results of Squeezenet 44.49% accuracy. From this experiment environment, it can be concluded that AlexNet is better than Squeezenet for maize leaf diseases classification.
CALVAL EVALUATION OF DESIS PRODUCTS IN AMIAZ PLAIN AND MAKHTESH RAMON TEST SITES, SOUTHERN ISRAEL

Abstract. The new era of hyperspectral remote (HSR) sensors in orbit is approaching. Missions such as CHIME of the European Space Agency (ESA), EMIT/SBG of NASA, EnMAP of the German Aerospace Center (DLR), and SHALOM of the Israel Space Agency (ISA) will launch in the near future, while other HSR sensors are already in orbit, such as DESIS of DLR, PRISMA of the Italian Space Agency (ASI), and HISUI of the Japan Aerospace Exploration Agency (JAXA). Vicarious calibration (VC) of satellite sensors is vital to tracking a sensor's performance during its lifetime and is a routine procedure in any satellite mission. Accordingly, searching for ideal sites for CAL/VAL operation is an important task that should be part of the mission planning. This study demonstrates two areas in southern Israel that can be acquired in one overpass as VC sites: Amiaz Plain (AP) and Makhtesh Ramon (MR), which were evaluated for their fulfillment of all VC requirements for HSR sensors. AP (5 km2 of homogeneous bright target) was found suitable for radiometric calibration, and MR (200 km2) for spectral and thematic validations. We checked the applicability of these sites using a high-end airborne HRS sensor (AisaFENIX 1K sensor with 420 bands, 375–2500 nm spectral range, and 1.5-m spatial resolution) along with comprehensive field studies and ground measurements. Accordingly, we developed an operational VC protocol to use these sites for both radiometric and spectral quality inspection of HRS satellites. We demonstrated this capability on recent PRISMA and DESIS reflectance products. Here we provide these analyses and recommend how to use these areas to further examine DESIS data's performance. We call for collaborations with individuals and space agencies in using these VC sites, where we will provide ground-truth information and fulfill any other requirements for VC.
A Lightweight Top-Down Multi-Person Pose Estimation Method Based on Symmetric Transformation and Global Matching

The top-down human pose estimation method usually faces the following problems: (i) The target detection result is not well applied in the pose estimation network. (ii) Difficulty of human detection in the crowded state. (iii) The complicated model leads to a long training time. Aiming at the issues above, a lightweight multi-person pose estimation method based on symmetric transformation and global matching is proposed. Symmetric transformation module adds spatial transformation network(STN) and spatial de-transformer network(SDTN) before and after the single-person pose estimation(SPPE) to extract high-quality single-person pose regions from inaccurate human candidate frames. Global matching method is used to transform the key point prediction problem into the optimal matching problem of the human body-key point graph, and solve the questions of misjudgment and error detection of pose estimation in the crowded state. Finally, depth wise separable convolution and inverted residual model are used to reduce the complexity of model, so as to improve the running speed while balancing the accuracy of the algorithm. Experiments show that the algorithm proposed in this paper not only enhance the overall performance of the multi-person posture estimation network in the crowded state, but also improves the running speed significantly, which further confirms the effectiveness and competitiveness of this algorithm.
Instant Rephotography

When traveling, a very common issue is to ask for the help of a stranger to take a photo on a touristic spot. Indeed, often the results of this short collaboration are not as previously imagined. In this paper, we introduce the concept of instant rephotography, a technique based on a simple and intuitive user interface, and in an efficient and low cost algorithm that finds the matching between two images. The user interface guides the photographer to position the camera in the right place that was once imagined by the camera owner, automatically taking the new photo. To correctly choose the new photo, we propose an algorithm based on ORB (Oriented FAST and Rotated BRIEF) feature points acquired from the reference and current photos, and on inertial sensors embedded in the photo device, a smartphone. To validate the concepts introduced, we implemented an Android App called Smart-Tourist Camera, conceived to help tourists to take good pictures during travels. Three different user studies involving 124 volunteers were conducted and results have shown that the photographs taken with the application are perceived as better than otherwise, and that users would use the App during their travels.
Image Encryption Based on Hopfield Neural Network and Bidirectional Flipping

Many encryption systems face two problems: the key has nothing to do with the plaintext; only a single chaotic sequence is adopted during the encryption. To solve the problems, this paper proposes an image encryption method based on Hopfield neural network and bidirectional flipping. Firstly, the plaintext image was segmented into blocks, the resulting image matrix was block scrambled, and each block was bidirectionally flipped to complete the scrambling process. After that, the plaintext image was processed by the hash algorithm to obtain the initial values and control parameters of the chaotic system, producing a pseudo-random sequence. Then, a diffusion matrix was generated through the optimization by Hopfield neural network and used to derive a ciphertext image through diffusion transformation. Experimental results show that our algorithm is highly sensitive to plaintext, strongly resistant to common attacks, and very efficient in encryption.
Tracking by dynamic template: Dual update mechanism

Recently, Siamese trackers have received widespread attention for visual object tracking owing to their good balance between speed and performance. Those Siamese trackers heavily depend on target template while conventional practice fixes the template to initial frame. This strategy makes it unable to cope with variation of target appearance, which often leads to tracking failures and causes the gap in performance from other tracking methods. Despite the performance gain achieved by few template update methods with target templates generated by the tracked results, these tracked templates are easy to accumulate errors and cause tracking drift. In this paper, we propose two template update mechanisms to effectively adapt the target template during the tracking process which is dubbed as DTDU (Dynamic Template with Dual Update). Unlike predecessors that directly use the tracked template, we use initial template to perform similar transformation to the tracked template. Then the similar transformed template and the tracked template are combined linearly to capture the variation of target appearance. These updated templates are stored in a memory bank and retrieved to generate the final target template. In order to enhance quick update of memory bank to accommodate the target appearance, we use the retrieved template to further update the templates in memory bank for subsequent tracking. Extensive experiments on OTB-2015, VOT2016, VOT2018 and GOT-10k datasets have proved the effectiveness of these two update mechanisms and the proposed tracker achieves a real-time speed of 44 fps.
Research on operation status and fault deduction system design of transformer in large sports venues

The problem of low transformer load rate (less than 60%) in the actual operation of large stadiums and gymnasiums has affected the waste of power energy and the improvement of power cost in the industry. The transformer failure of large stadiums and gymnasiums may further cause serious power accidents in the future. In order to solve the above problems, firstly, this paper analyzes the transformer load rate data of the two large sports venues(Nanchang International Sports and Tennis Stadium in Jiangxi Province)and probes into the design data and calculation coefficient of the transformer of the large sports venues; Secondly, this paper puts forward some suggestions on how to improve the operation load rate of transformers in stadiums from the aspects of transformer design and selection of demand coefficient; Thirdly, the paper establishes the system hardware environment by using firewall, special switch and information database, and then realizes the design of software deduction program through correlation analysis, fault diagnosis and cluster deduction, and completes the design of transformer fault deduction system in large sports venues. Finally, the results clearly show that the transformer load rate is relatively low in large sports stadiums(the highest value is only about 55% and well below the 60% standard). On other hand, compared with the traditional system and based on big data association, the average relative errors of fault points 1, 2 and 3 decreased by 67.8%, 56.4% and 51.6% respectively and the diagnosis time has also been greatly shorten by the transformer fault deduction system. In short, this paper uses association rules to mine and obtain transformer fault characteristics, combined with the advantages of virtual reality technology, designs an effective and reliable transformer fault deduction visualization system for large sports venues.
Teaching and Learning Sciences within the Covid-19 Pandemic Era in a Greek University Department

Around the globe Covid-19 pandemic has influenced not only the education, but also our everyday life, among other aspects. In Greece, distance learning started to get in use widely in tertiary education, since the first national lockdown was announced and reshaped education in many ways. In the University of Thessaly, in the Department of Mathemat­ics, undergraduate students opt for a lot of different courses to attend and due to the Covid-19 crisis all of them are taught via web platforms. Some of the most significant theoreti­cal subjects are «Calculus» (with applications in Science and Mechanics), «Physics» (Classi­cal Mechanics) and «Philosophy of Science». In addition, some other applied subjects are «Programming Languages» and «Digital Technologies in Mathematics Education» and the impact the above have, generally in edu­cation and society itself. In this paper, we describe the different ways students have re­acted regarding learning Sciences in a distance learning environment. We split our case study in two parts. The first one is about the way students experience e-learning and the second one is about their suggestions for the next day. Integrating e-questionnaires and interviews and taking into account parameters like economic factors and the permanent residence issue, we asked the students about their preferences among face-to-face learning, distance learning and a blended model. Remarks about the academic life and the possible ways of taking the extra step, after the Covid-19 crisis ceases to exist, are made.
Domain-aware multi-modality fusion network for generalized zero-shot learning

Generalized zero-shot learning (GZSL) is a challenging problem which aims to recognize images from both seen and unseen classes. Existing research suffers from the bias problem, which means that the model tends to misclassify an unseen sample to seen classes. Moreover, recent methods mainly focus on using a single semantic representation for knowledge transfer (e.g., attributes). Although some try to utilize multiple information, they only use simple concatenation or transformations and the performance is limited. To solve GZSL problem, we propose a two-step method aimed at overcoming these two challenges progressively. Firstly, a local neighborhood based gating model is designed to leverage both the distribution of original data space and a learned latent space for domain detection. The model is used to separate seen and unseen samples, and then decompose GZSL into a conventional zero-shot learning (ZSL) problem and a supervised classification problem. Then, we design a graph convolutional network (GCN) based model for fusing multiple semantic modalities to promote the solution of the decomposed ZSL problem. By using one primary modality as input and another for construction of node relationships, our model is able to fuse multiple information effectively and helps to learn more discriminative visual classifiers. We test our method, local neighborhood based domain aware and GCN based multi-modality fusion network (LND-GMF) on five benchmark datasets. The results show that our method out-performs state-of-the-art methods with a large margin.
ABM-SpConv-SIMD: Accelerating Convolutional Neural Network Inference for Industrial IoT Applications on Edge Devices

Convolutional Neural Networks (CNNs) have been widely deployed, while traditional cloud data-centers based applications suffer from the bandwidth and latency network demand when applying to Industrial-Internet-of-Things (IIoT) fields. It is critical to migrate the CNNs inference to edge devices for efficiency and security concerns. However, it is challenging to deploy complex CNNs on resource-constraint IIoT edge devices due to a large number of parameters and intensive floating-point computations. In this paper, we propose ABM-SpConv-SIMD, an on-device inference optimization framework, aiming at accelerating the network inference by fully utilizing the low-cost and common CPU resource. ABM-SpConv-SIMD first adopts a model optimizer with pruning and quantization, which produces <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>Sp</u></i> arse <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>Conv</u></i> olutional models. And then, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>A</u></i> ccumulation- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>B</u></i> efore- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>M</u></i> ultiplication mechanism is proposed to reduce multiplication operations. Additionally, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>SIMD</u></i> instructions, which are commonly available on cost-effective edge devices, are employed to improve the performance of convolutions. We have implemented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ABM-SpConv-SIMD</i> base on the ARM Compute Library software framework and evaluated on Hikey970 and Raspberry Pi devices with two representative models AlexNet and ResNet50. The results show that the ABM-SpConv-SIMD can significantly improve the performance, and achieve on average of 1.96x and 1.73x speedup respectively over the baseline implementation with negligible loss of accuracy.
Grayscale medical image segmentation method based on 2D&amp;3D object detection with deep learning

Abstract Background Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data. Methods Combining thresholding method and deep learning, this paper presents a novel method by using 2D&amp;3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes’ bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object. Results Three groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance. Conclusions The prominent segmentation results demonstrate that the built method based on 2D&amp;3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images.
A Sentiment Analysis Method for Teaching Evaluation Texts Using Attention Mechanism Combined with CNN-BLSTM Model

In view of the problems that most existing emotion analysis models ignore the relationship between emotions and are not suitable for students, an emotion analysis model of teaching evaluation text based on deep learning is proposed. Firstly, combining the advantages of CNN extracting phrase features and BLSTM extracting sequence features, the CNN-BLSTM model is constructed to effectively enhance the extraction ability of text information. Then, the attention mechanism is used to adaptively perceive the context information, extract the text features that affect students’ emotion, and construct the CNN-BLSTM-AT model. Finally, the CNN-BLSTM-AT model is used to analyze the students’ emotion types in the dataset, and the mini-batch gradient descent method is used for model training. The experiment uses the weibo_senti_100k dataset to demonstrate the performance of the proposed model. The results show that adding the attention mechanism can improve the accuracy of the model by about 0.23. Also, its recall rate is not less than 0.57 and the minimum value of F1 is 0.748, which is better than other comparison models, thus demonstrating the effectiveness of the proposed model.
Identification and Analysis of Neurodegenerative Diseases with Twin Layered CNN Using Gait Dynamics

This study identifies the gait dynamics of Neurodegenerative disease (NDD) patients using a deep convolution neural network (CNN) approach.Human gait examination perceives individuals from the manner in which they walk, which gives clues in identifying specific disorders.Precise identification of the specific NDD assists the doctors to begin early treatment strategies.The physionet public database containing the gait dynamics of Parkinson's, Amyotrophic Lateral Sclerosis, Huntington's Diseases, and the healthy subjects is used in this work.The gait parameters such as left stride intervals, right stride intervals, left swing intervals, right swing intervals, left stance intervals and right stance intervals of each disease group are utilized.Deep Twin layered CNN (TLCNN) is applied as a feature extractor, which automatically extracts deep features from the input gait signals.Further statistical features are also measured for these inputs by implementing Fast Walsh Hadamard Transform method (FWHT).The tuned neighbourhood component analysis (TNCA) is utilized as a feature selection algorithm to select prime features from the extracted deep feature vector.The classification of each disease group is made with the selected features by the Random forest and Multi SVM machine learning approach, which gives the accuracy of 99.89%, and 97.06% in classification of NDD and healthy person.Thus, the proposed TLCNN_FWHT_TNCA_RF gait classification approach based on CNN and machine learning is accurate and reliable to classify the particular neurodegenerative disorder.
Reversible Data Hiding Based on Multiple Pairwise PEE and Two-Layer Embedding

Recent reversible data hiding (RDH) work tends to realize adaptive embedding by discriminately modifying pixels according to image content. However, further optimization and computational complexity remain great challenges. By presenting a better incorporation of pixel value ordering (PVO) prediction and pairwise prediction-error expansion (PEE) technologies, this paper proposes a new RDH scheme. The largest/smallest three pixels of each block are utilized to generate error-pairs. To achieve optimization of the distribution of error pairs, two-layer embedding is introduced such that full-enclosed pixels of each block can be used to determine how to optimally define the spatial location of pixels within block. Then, to modify error pairs with less distortion introduced, the shifted pairing error is involved in the separable utilization of the other one; i.e., it serves as the context for recalculating the other one. Since the recalculation is equivalent to expansion bins selection, various extensions of original pairwise PEE are designed, parameterized, and combined into the so-called multiple pairwise PEE, with which the 2D histogram can be divided into a set of sub-ones for more accurate modification. The experimental results verify the superiority of the proposed scheme over several PVO-based schemes. On the Kodak image database, the average PSNR gains over original PVO-based pairwise PEE are 0.83 and 0.99 dB for capacities of 10,000 and 20,000 bits, respectively.
Cuproof: Range Proof with Constant Size

Zero-Knowledge Proof is widely used in blockchains. For example, zk-SNARK is used in Zcash as its core technology to identifying transactions without the exposure of the actual transaction values. Up to now, various range proofs have been proposed, and their efficiency and range-flexibility have also been improved. Bootle et al. used the inner product method and recursion to construct an efficient Zero-Knowledge Proof in 2016. Later, Benediky Bünz et al. proposed an efficient range proof scheme called Bulletproofs, which can convince the verifier that a secret number lies in [0,2κ-1] with κ being a positive integer. By combining the inner-product and Lagrange's four-square theorem, we propose a range proof scheme called Cuproof. Our Cuproof can make a range proof to show that a secret number v lies in an interval [a,b] with no exposure of the real value v or other extra information leakage about v. It is a good and practical method to protect privacy and information security. In Bulletproofs, the communication cost is 6+2logκ, while in our Cuproof, all the communication cost, the proving time and the verification time are of constant sizes.