Reviewerly/exHarmony huggingface dataset:
PYNQ FPGA Hardware implementation of LeNet-5-Based Traffic Sign Recognition Application

Computer Vision is one of the most flagship fields in the last decade. Meanwhile, embedded boards (FPGAs) represent the technological advancement trends by including software processor and programmable logic units at the same FPGA core. However, the fusion of the embedded system and computer vision provides the nascent embedded vision field. This domain occupied all areas, even future Industry 4.0, by exploiting them in control and management tasks. Otherwise, embedded vision with artificial intelligence plays an important role in many vital applications such as advanced driver-assistance systems Otherwise, embedded vision with artificial intelligence plays an important role in many vital applications such as advanced driver-assistance systems in smart transportation context. This paper falls within this range, by implementing LeNet-5 Model-based Traffic Sign Recognition application on Xilinx embedded FPGA system. Implementation results prove that our Co-design prototype, including Processing System (PS) and Programmable Logic (PL) parts, achieves best performance in terms of hardware cost and run-time execution.
A Self-Supervised Deep Learning Framework for Unsupervised Few-Shot Learning and Clustering

The need to learn a good representation is a core problem central to AI. We present a self-supervised representation learning framework and demonstrate its use for few-shot classification and clustering. Our framework can be interpreted as repeatedly discovering new categories from learned embeddings and training a new embedding function with self-supervised signals to differentiate the discovered categories. In our framework, we first discover categories from unlabeled data. Next we post-process the previous partition results to remove outliers and derive prototypes of each category. We then construct few-shot learning tasks with previously selected data and augmented virtual data. Lastly, we iterative train the network through previous steps to learn the final representation. Our framework can considerably outperform previous baselines in unsupervised few-shot classification tasks on miniImageNet and Omniglot data sets. We also validate our learned representation on clustering tasks and demonstrate that our framework further improves upon the recent deep clustering methods.
Sentiment Lossless Summarization

The aim of automatic text summarization (ATS) is to extract representative texts from documents and keep major points of the extracted texts consistent with the original documents. However, most existing studies ignore sentimental information loss in the summarization process, which leads to sentiment loss summarization. To address the sentiment loss issue during summarization, we introduce a sentiment compensation mechanism into document summarization and propose a graph-based extractive summarization approach named Sentiment Lossless Summarization (SLS). SLS first creates a graph representation for a document to obtain the importance score (i.e., literal indicator) of each sentence. Second, sentiment dictionaries are leveraged to analyze the sentence sentiments. Third, during each summarization iteration, the sentences with the lowest scores are iteratively removed, and the sentiment compensation weights of the remaining sentences are updated. With the help of sentiment compensation during the summarization process, sentiment consistencies between candidate summaries and the original documents are maintained. Intrinsic evaluations conducted on the DUC2001, DUC2002, DUC2004, and Multi-News datasets demonstrate that our approach outperforms baselines and state-of-the-art summarization methods in terms of Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores. Additionally, to further evaluate SLS performance in sentiment retention, extrinsic evaluations are introduced, and summary quality in terms of sentiment loss is evaluated by measuring the prediction accuracy for sentiment polarities of either movie (IMDb dataset) or product (Amazon dataset) review summaries. The experimental results demonstrate that our approach can improve prediction accuracy by at most 6% compared to the baseline.
Hashing person re-ID with self-distilling smooth relaxation

Person re-identification (re-ID) has made substantial progress in recent years; however, it is still challenging to search for the target person in a short time. Re-ID with deep hashing is a shortcut for that but, limited by the expression of binary code, the performance of the hashing method is not satisfactory. Besides, to further speed up retrieval, researchers tend to reduce the number of feature bits, which will cause more performance degradation. In this paper, we design the attribute-based fast retrieval (AFR), which leverages the attribute prediction of the model trained in a binary classification manner tailor-made for hashing. The attribute information is also used to refine the global feature representation by an attribute-guided attention block (AAB). Then, to fully exploit deep feature to generate the hash codes, we propose a binary code learning method, named self-distilling smooth relaxation (SSR). In this method, a simple yet effective regularization is presented to distill the quantized knowledge in the model itself, thus mitigating the lack of semantic guidance in the traditional non-linear relaxations. We manually label attributes for each person in dataset CUHK03 and evaluate our method on four authoritative public benchmarks (Market-1501, Market-1501+500K, CUHK03, and DukeMTMC-reID). The experimental results indicate that with the SSR and AAB, we surpass all the state-of-the-art hashing methods. And compared with reducing the feature bits, the AFR strategy is more effective to save search time.
Genetic programming for stacked generalization

In machine learning, ensemble techniques are widely used to improve the performance of both classification and regression systems. They combine the models generated by different learning algorithms, typically trained on different data subsets or with different parameters, to obtain more accurate models. Ensemble strategies range from simple voting rules to more complex and effective stacked approaches. They are based on adopting a meta-learner, i.e. a further learning algorithm, and are trained on the predictions provided by the single algorithms making up the ensemble. The paper aims at exploiting some of the most recent genetic programming advances in the context of stacked generalization. In particular, we investigate how the evolutionary demes despeciation initialization technique, ϵ-lexicase selection, geometric-semantic operators, and semantic stopping criterion, can be effectively used to improve GP-based systems’ performance for stacked generalization (a.k.a. stacking). The experiments, performed on a broad set of synthetic and real-world regression problems, confirm the effectiveness of the proposed approach.
Incomplete Multi-view Clustering with Joint Partition and Graph Learning

Incomplete multi-view clustering (IMC) aims to integrate the complementary information from incomplete views to improve clustering performance. Most existing IMC methods try to fill the incomplete views or directly learn a common representation based on matrix factorization or subspace learning. The former may introduce useless even noisy information especially for data with a large missing ratio. The latter relies on the initialization and ignores the geometric structure of data. To address these issues, we propose a novel Joint Partition and Graph (JPG) learning method for IMC. Specifically, JPG jointly constructs local incomplete graph matrices, generates incomplete base partition matrices, stretches them to produce a unified partition matrix, and employs it to learn a consensus graph matrix. By this means, we transform incomplete multi-view data into a unified partition space and obtain the consensus graph in a mutual reinforcement manner. Moreover, a partition fusion strategy can allocate a large weight to the stretched base partition that is close to the unified matrix. The objective function is optimized in an alternating optimization fashion. Experimental results on several benchmark datasets demonstrate the effectiveness and superiority of JPG than the state-of-the-art baselines
Self-Supervised Learning For Detection Of Breast Cancer In Surgical Margins With Limited Data

Breast conserving surgery is a standard cancer treatment to resect breast tumors while preserving healthy tissue. The reoperation rate can be as high as 35% due to the difficulties associated with detection of remaining cancer in surgical margins. REIMS is a mass spectrometry method that can address this challenge through real-time measurement of molecular signature of tissue. However, the collection of breast spectra to train a cancer detection model is time consuming and large samples sizes are not practical. We propose an application of self-supervised learning to improve the performance of cancer detection at surgical margins using a limited number of labelled data samples. A deep model is trained for the intermediate task of capturing latent features of REIMS data without the use of cancer labels. The model compensates for the small data size by dividing the spectra into smaller patches and shuffling their order, generating new instances. By interrogating the shuffled data and learning the order of its patches, the model captures the characteristics of the data. The learnt weights from the model are then transferred to a subsequent network and fine-tuned for cancer detection. The proposed method achieved the accuracy, sensitivity and specificity to 97%, 91% and 100%, respectively, in data from 144 cancer and normal REIMS samples.
Unsupervised multi-sense language models for natural language processing tasks

Existing language models (LMs) represent each word with only a single representation, which is unsuitable for processing words with multiple meanings. This issue has often been compounded by the lack of availability of large-scale data annotated with word meanings. In this paper, we propose a sense-aware framework that can process multi-sense word information without relying on annotated data. In contrast to the existing multi-sense representation models, which handle information in a restricted context, our framework provides context representations encoded without ignoring word order information or long-term dependency. The proposed framework consists of a context representation stage to encode the variable-size context, a sense-labeling stage that involves unsupervised clustering to infer a probable sense for a word in each context, and a multi-sense LM (MSLM) learning stage to learn the multi-sense representations. Particularly for the evaluation of MSLMs with different vocabulary sizes, we propose a new metric, i.e., unigram-normalized perplexity (PPLu), which is also understood as the negated mutual information between a word and its context information. Additionally, there is a theoretical verification of PPLu on the change of vocabulary size. Also, we adopt a method of estimating the number of senses, which does not require further hyperparameter search for an LM performance. For the LMs in our framework, both unidirectional and bidirectional architectures based on long short-term memory (LSTM) and Transformers are adopted. We conduct comprehensive experiments on three language modeling datasets to perform quantitative and qualitative comparisons of various LMs. Our MSLM outperforms single-sense LMs (SSLMs) with the same network architecture and parameters. It also shows better performance on several downstream natural language processing tasks in the General Language Understanding Evaluation (GLUE) and SuperGLUE benchmarks.
Application of combined transfer learning and convolutional neural networks to optimize plasma spraying

Deep transfer learning can make full use of pre-trained neural networks and has been used in many cases with limited sample data. In this work, parameter-transfer learning was implemented to model the relationship between process control parameters and in-flight particle behavior. Six different parameter-transfer learning models were designed to fine-tune the variables of the convolutional neural network (CNN) model pre-trained with a dataset of yttria-stabilized zirconia (YSZ) particles. Then transfer learning models were trained with the new dataset obtained from simulation results of NiCrAlY particles and the losses of different models in the training set and test set were compared. Results indicate that the method in which the entire pre-trained CNN model was fine-tuned, combined with a decreasing learning rate, exhibited the lowest loss in the training dataset and the highest testing accuracy. Particle status distributions obtained from the control parameters predicted by the transfer learning model were found to be in good agreement with the corresponding designed target values.
LFI-CAM: Learning Feature Importance for Better Visual Explanation

Class Activation Mapping (CAM) is a powerful technique used to understand the decision making of Convolutional Neural Network (CNN) in computer vision. Recently, there have been attempts not only to generate better visual explanations, but also to improve classification performance using visual explanations. However, previous works still have their own drawbacks. In this paper, we propose a novel architecture, LFI-CAM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">***</sup> (Learning Feature Importance Class Activation Mapping), which is trainable for image classification and visual explanation in an end-to-end manner. LFI-CAM generates attention map for visual explanation during forward propagation, and simultaneously uses attention map to improve classification performance through the attention mechanism. Feature Importance Network (FIN) focuses on learning the feature importance instead of directly learning the attention map to obtain a more reliable and consistent attention map. We confirmed that LFI-CAM is optimized not only by learning the feature importance but also by enhancing the backbone feature representation to focus more on important features of the input image. Experiments show that LFI-CAM outperforms baseline models’ accuracy on classification tasks as well as significantly improves on previous works in terms of attention map quality and stability over different hyper-parameters.
WITHDRAWN: A survey on cloud computing approaches

Finally, “infrastructure as a software” has taken form as a cloud computing, which has long been thought about. The greatest feeling in the field of computation today is this change of perspective. This should be viewed more than innovation as an advancement of current technology. The cloud's fundamental criteria include scalability, adaptability, extendability and manageability, to satisfy the diverse needs of corporate organizations. As the cloud infrastructure improvements in the code situation becomes more apparent, the data center is transferred offsite to a third party and sells software instead of running on-site programs directly. By utilizing it businesses will save money on running expenses, so instead of thinking about specific IT challenges, they will concentrate on the core market. In comparison, the true portability is offered by the Web via the Internet via generic hardware only to access software and documentation everywhere in the world. We also defined in this paper the core principles of cloud storage in brief so that readers may understand an important feature of the newly established technology. .
Textile Chipless Tag for Gesture Recognition

In this paper a novel fully–textile chipless tag for gesture recognition applications is presented. The proposed tag consists of a microstrip line with transmission zeros controlled by the position of the fingers. This is obtained by associating to each finger an open ended stub that is electrically connected or not to the microstrip line depending on the finger position. A three–finger prototype fabricated by using a non woven conductive fabric and suitable to be integrated into a glove is presented and discussed. The reported results demonstrate the suitability of using the proposed tag for gesture recognition applications.
Fast, Self Supervised, Fully Convolutional Color Normalization Of H&amp;E Stained Images

Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different. Due to variations in staining protocols, reagent brands, and habits of technicians, color variation in digital histopathology images is quite common. Color variation causes problems for the deployment of deep learning-based solutions for automatic diagnosis system in histopathology. Previously proposed color normalization methods consider a small patch as a reference for normalization, which creates artifacts on out-of-distribution source images. These methods are also slow as most of the computation is performed on CPUs instead of the GPUs. We propose a color normalization technique, which is fast during its self-supervised training as well as inference. Our method is based on a lightweight fully-convolutional neural network and can be easily attached to a deep learning-based pipeline as a pre-processing block. For classification and segmentation tasks on CAMELYON17 and MoNuSeg datasets respectively, the proposed method is faster and gives a greater increase in accuracy than the state of the art methods.
Reversible Data Hiding in Encrypted Images Using Median Edge Detector and Two’s Complement

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.
Coordinate Descent Method for k-means

k-means method using Lloyd heuristic is a traditional clustering method which has played a key role in multiple downstream tasks of machine learning because of its simplicity. However, Lloyd heuristic always finds a bad local minimum, i.e., the bad local minimum makes objective function value not small enough, which limits the performance of k-means. In this paper, we use coordinate descent (CD) method to solve the problem. First, we show that the k-means minimization problem can be reformulated as a trace maximization problem, then a simple and efficient coordinate descent scheme is proposed to solve the maximization problem. Two interesting findings through theory are that Lloyd cannot decrease the objective function value of k-means produced by our CD further, and our proposed method CD to solve k-means problem can avoid produce empty clusters. In addition, according to the computational complexity analysis, it is verified CD has the same time complexity with original k-means method. Extensive experiments including statistical hypothesis testing, on several real-world datasets with varying number of clusters, varying number of samples and varying number of dimensions show that CD performs better compared to Lloyd, i.e., lower objective value, better local minimum and fewer iterations. And CD is more robust to initialization than Lloyd whether the initialization strategy is random or initialization of k-means++.
Super-resolution infrared imaging via multi-receptive field information distillation network

We propose a super-resolution (SR) infrared imaging method with a multi-receptive field information distillation network. We develop a parallel progressive feature purification model to optimize the feature extraction progress and retain feature in each dimension. We use the dilation convolution to enlarge the network's receptive field and keep the number of parameters steady. We reconstruct a SR infrared image by a sub-pixel method. A series of experiments are implemented. The imaging performance of the proposed method is validated by comparing with the results from classical interpolate Bicubic, and deep learning methods VDSR, SRResNet and IMDN. Experimental results suggest that the proposed method performs favorably against the four state-of-the-art SR algorithms in visual quality. The proposed system can realize high quality image reconstruction and 2-scale SR, and requires much less images numbers for training.
A Framework for Indonesian Grammar Error Correction

Grammatical Error Correction (GEC) is a challenge in Natural Language Processing research. Although many researchers have been focusing on GEC in universal languages such as English or Chinese, few studies focus on Indonesian, which is a low-resource language. In this article, we proposed a GEC framework that has the potential to be a baseline method for Indonesian GEC tasks. This framework treats GEC as a multi-classification task. It integrates different language embedding models and deep learning models to correct 10 types of Part of Speech (POS) error in Indonesian text. In addition, we constructed an Indonesian corpus that can be utilized as an evaluation dataset for Indonesian GEC research. Our framework was evaluated on this dataset. Results showed that the Long Short-Term Memory model based on word-embedding achieved the best performance. Its overall macro-average F 0.5 in correcting 10 POS error types reached 0.551. Results also showed that the framework can be trained on a low-resource dataset.
Learning a Model-Driven Variational Network for Deformable Image Registration

Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this issue and meanwhile retain the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using a variable splitting optimization scheme, we first convert the image registration problem, established in a generic variational framework, into two sub-problems, one with a point-wise, closed-form solution and the other one being a denoising problem. We then propose two neural layers (i.e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net (termed generalized denoising layer) to formulate the denoising problem. Finally, we cascade the three neural layers multiple times to form our VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac magnetic resonance imaging datasets show that VR-Net outperforms state-of-the-art deep learning methods on registration accuracy, whilst maintaining the fast inference speed of deep learning and the data-efficiency of variational models.