Reviewerly/exHarmony huggingface dataset:
Label propagation via local geometry preserving for deep semi-supervised image recognition

In this paper, we propose a novel transductive pseudo-labeling based method for deep semi-supervised image recognition. Inspired from the superiority of pseudo labels inferred by label propagation compared with those inferred from network, we argue that information flow from labeled data to unlabeled data should be kept noiseless and with minimum loss. Previous research works use scarce labeled data for feature learning and solely consider the relationship between two feature vectors to construct the similarity graph in feature space, which causes two problems that ultimately lead to noisy and incomplete information flow from labeled data to unlabeled data. The first problem is that the learned feature mapping is highly likely to be biased and can easily over-fit noise. The second problem is the loss of local geometry information in feature space during label propagation. Accordingly, we firstly propose to incorporate self-supervised learning into feature learning for cleaner information flow in feature space during subsequent label propagation. Secondly, we propose to use reconstruction concept to measure pairwise similarity in feature space, such that local geometry information can be preserved. Ablation study confirms synergistic effects from features learned with self-supervision and similarity graph with local geometry preserving. Extensive experiments conducted on benchmark datasets have verified the effectiveness of our proposed method.
Introducing Application Awareness Into a Unified Power Management Stack

Effective power management in a data center is critical to ensure that power delivery constraints are met while maximizing the performance of users' workloads. Power limiting is needed in order to respond to greater-than-expected power demand. HPC sites have generally tackled this by adopting one of two approaches: (1) a system-level power management approach that is aware of the facility or site-level power requirements, but is agnostic to the application demands; OR (2) a job-level power management solution that is aware of the application design patterns and requirements, but is agnostic to the site-level power constraints. Simultaneously incorporating solutions from both domains often leads to conflicts in power management mechanisms. This, in turn, affects system stability and leads to irreproducibility of performance. To avoid this irreproducibility, HPC sites have to choose between one of the two approaches, thereby leading to missed opportunities for efficiency gains.This paper demonstrates the need for the HPC community to collaborate towards seamless integration of system-aware and application-aware power management approaches. This is achieved by proposing a new dynamic policy that inherits the benefits of both approaches from tight integration of a resource manager and a performance-aware job runtime environment. An empirical comparison of this integrated management approach against state-of-the-art solutions exposes the benefits of investing in end-to-end solutions to optimize for system-wide performance or efficiency objectives. With our proposed system-application integrated policy, we observed up to 7% reduction in system time dedicated to jobs and up to 11% savings in compute energy, compared to a baseline that is agnostic to system power and application design constraints.
Face Detection using Support Vector Mechine with PCA

Face recognition is a popular subject in biometrics research which has distinct advantages because of its non-contact process. This technology has gained popularity because of its large application value and market value, like video surveillance system for real time tracking of suspicious object. In this paper we focus on the image face which has to be correctly recognized using support vector Machine (SVM) techniques with Principle Components Analysis (PCA) which extract the features and reduce dimensionality. Also we have used KNN classifier. The SVM with PCA produces more accurate result compare to other methods. This paper achieved 92% successful recognition rate for detecting different face databases.
Research on Image Super-Resolution Reconstruction Based on Transformer

The existing super-resolution methods have the problems of blurring contour and losing details, which cannot make full use of the internal and external information of the image. On the basis of convolutional neural network, a super-resolution model embedded in Transformer module is proposed. Through the fusion of CNN and Transformer, image feature extraction and corrupted information recovery are performed. The training process is divided into an unsupervised pre-training process on the ImageNet subset and a supervised fine-tuning training process on the DIV2K data set. Contrastive loss is introduced into the loss function to achieve the consistency of super-resolution image category and texture. It is compared with the current mainstream image super-resolution methods on the standard 5 benchmark data sets. Experiments show that compared with other algorithms, this algorithm can better recover the high-frequency information of the image, and generate super-resolution results with clear details and fine structure.
Translate the Facial Regions You Like Using Self-Adaptive Region Translation

With the progression of Generative Adversarial Networks (GANs), image translation methods has achieved increasingly remarkable performance. However, most available methods can only achieve image level translation, which is unable to precisely control the regions to be translated. In this paper, we propose a novel self-adaptive region translation network (SART) for region-level translation, which uses region-adaptive instance normalization (RIN) and a region matching loss (RML) for this task. We first encode the style and content image for each region with style and content encoder. To translate both shape and texture of the target region, we inject region-adaptive style features into the decoder by RIN. To ensure independent translation among different regions, RML is proposed to measure the similarity between the non-translated/translated regions of content and translated images. Extensive experiments on three publicly available datasets, i.e. Morph, RaFD and CelebAMask-HQ, suggest that our approach demonstrate obvious improvement over state-of-the-art methods like StarGAN, SEAN and FUNIT. Our approach has further advantages in precise control of the regions to be translated. As a result, region level expression changes and step-by-step make-up can be achieved. The video demo is available at (https://youtu.be/DvIdmcR2LEc).
Video Shot Boundary Detection Using Principal Component Analysis (PCA) and Deep Learning

After COVID-19 pandemic, with the tremendously increasing reliance on digital media platform all over the world, advanced video surveillance, video content analysis, and video retrieval tasks have become an essential field which makes traditional method for shot boundary detection (SBD) less efficient, especially for gradual transition type shot boundary. SBD is an important preliminary step in video processing which helps to separate video sequences into shots. Large videos (news, national ceremony, sports, movies, etc.) contain several types of shot transitions (gradual, cut), and to detect them with higher accuracy an efficient SBD method is needed. Previous researchers have proposed methods for SBD by using Supervised machine learning approaches, however, data training is a limitation for them. Some previous papers have proposed unsupervised approaches, however detection for gradual transition shot boundaries has not achieved good accuracy. This paper proposes a distance calculating algorithms for SBD by using principal component analysis (PCA) features and then deep learning method to improve the detection precision of gradual transition shot boundary frames and normal frames. Firstly, PCA is used for feature extraction of the video frames and the most significant Eigenvectors and Eigenvalues are analyzed. Secondly, a distance calculating algorithm is used on the adjacent video frames depending on their Eigenvectors and Eigenvalues to determine the shot boundaries. Finally, to improve detection accuracy, false detection boundaries are reviewed and classified using CNN. Experimental results show the efficiency of the proposed method.
Deep Learning Approach for Breast Ultrasound Image Segmentation

Recent advances in computer-aided diagnosis (CAD) technology have brought about more possibilities to segment and classify breast tumors. In the past decades, breast related diseases have significantly grown among women and have become a leading cause of death worldwide. An effective way to diminish breast cancer is to offer a proper diagnosis in the early stages of the disease by using ultrasound images. Our work proposes a modified U-Net architecture equipped with pre-trained inception residual blocks as an encoder for breast ultrasound (BUS) image segmentation. To enhance the performance, we increased the depth of the network by adapting the inception blocks. Our proposed solution consists of a preprocessing stage, feature extraction based on inception layers and a basic U-Net decoder. We utilized two public datasets named BUSI and UDIAT. This model predicts a mask for regions of interest (ROI) in BUS images by utilizing residual connections to ensure a minimum error rate and to preserve dimensionality. Our results show improved performance over the existing U-Net architecture, as well as the more recent deep adversarial learning and Selective K-U-Net models.
Enhancing Neural Machine Translation With Dual-Side Multimodal Awareness

Multimodal machine translation (MMT) aims to translate a sentence in the source language into the target language with the context of an associated image. According to where the visual information is employed, previous approaches can be categorized into two types: directly injecting the visual information at the input side or exploiting it as a visual constraint at the objective side. In this work, we propose an IO-MMT model which exploits the visual assistance in dual sides to fully exploit the visual information for multimodal machine translation. It contains a relation-aware multimodal transformer to simultaneously exploit the objects and their spatial relationships in the image with a graph at the input-side and a novel visual assistance structure to further improve visual consistency of the translation at the objective-side. Experimental results under both normal setting and input degradation settings on the Multi30k benchmark dataset show that combining the visual assistance in dual sides consistently outperforms single-side MMT models and achieves the state-of-the-art results on EN-DE and EN-FR translation tasks. We will release the codes and models at https://github.com/syuqings/MMT.
RGNAM: recurrent grid network with an attention mechanism for single-image dehazing

Single-image dehazing is a critical problem since haze existence degrades the quality of images and hinders most advanced computer vision tasks. Early methods solve this problem via the atmospheric scattering model, which estimate the intermediate parameters and then recover a clear image by low-level priors or learning on synthetic datasets. However, these model-based methods do not hold in various scenes. Recently, many learning-based methods have directly recovered dehazed images from inputs, but these methods fail to deal with dense haze and always lead to color distortion. To solve this problem, we build a recurrent grid network with an attention mechanism, named RGNAM. Specifically, we propose a recurrent feature extraction block, which repeats a local residual structure to enhance feature representation and adopts a spatial attention module to focus on dense haze. To alleviate color distortion, we extract local features (e.g., structures and edges) and global features (e.g., colors and textures) from a grid network and propose a feature fusion module combining trainable weights and channel attention mechanisms to merge these complementary features effectively. We train our model with smooth L1 loss and structural similarity loss. The experimental results demonstrate that our proposed RGNAM surpasses previous state-of-the-art single-image dehazing methods on both synthetic and real haze datasets.
Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations.
Towards Continuous Automatic Audits of Social Media Adaptive Behavior and its Role in Misinformation Spreading

In this paper, we argue for continuous and automatic auditing of social media adaptive behavior and outline its key characteristics and challenges. We are motivated by the spread of online misinformation, which has recently been fueled by opaque recommendations on social media platforms. Although many platforms have declared to take steps against the spread of misinformation, the effectiveness of such measures must be assessed independently. To this end, independent organizations and researchers carry out audits to quantitatively assess platform recommendation behavior and its effects (e.g., filter bubble creation tendencies). The audits are typically based on agents simulating the user behavior and collecting platform reactions (e.g., recommended items). The downside of such auditing is the cost related to the interpretation of collected data (here, some auditors are advancing automatic annotation). Furthermore, social media platforms are dynamic and ever-changing (algorithms change, concepts drift, new content appears). Therefore, audits need to be performed continuously. This further increases the need for automated data annotation. Regarding the data annotation, we argue for the application of weak supervision, semi-supervised learning, and human-in-the-loop techniques.
An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.
Is Blockchain Technology Applicable in Small and Medium-Sized Enterprises?

The purpose of this research is to investigate the applicability of blockchain technology in Small and Medium Enterprises (SMEs). We have examined the advantages and the disadvantages of the use of this emerging technology in SMEs, based on the existing literature on this research topic. Applying blockchain in SMEs has many advantages: promoting transparency in the organization, facilitating market analysis for better decision-making, eliminating reconciliation costs, and making auditing easier. However, it has some disadvantages, such as; the reshuffling of job positions and the absence of clarity related to the industrial standards. Therefore, we proposed some recommendations in order to adopt this technology successfully. Firstly, the SMEs should design the distributed ledger technology (DLT) in a way to have flexible governance and regulations so that it will be easily upgradeable; additionally, it is essential that the adopted DLT design by an SME can be migrated to a different blockchain whenever the need arises. Also, DLT designers should ensure that measures are taken to limit the collection of personal information. The SMEs should also establish trust and interoperability to realize the full benefits of DLT. Finally, SMEs should invest in research that will ensure more knowledge about the perfection of blockchain technology.
A Global Appearance and Local Coding Distortion Based Fusion Framework for CNN Based Filtering in Video Coding

In-loop filtering is used in video coding to process the reconstructed frame in order to remove blocking artifacts. With the development of convolutional neural networks (CNNs), CNNs have been explored for in-loop filtering considering it can be treated as an image de-noising task. However, in addition to being a distorted image, the reconstructed frame is also obtained by a fixed line of block based encoding operations in video coding. It carries coding-unit based coding distortion of some similar characteristics. Therefore, in this paper, we address the filtering problem from two aspects, (i) global appearance restoration for disrupted texture and (ii) local coding distortion restoration caused by fixed pipeline of coding. Accordingly, a three-stream global appearance and local coding distortion based fusion network is developed with a high-level global feature stream, a high-level local feature stream and a low-level local feature stream. Ablation study is conducted to validate the necessity of different features, demonstrating that the global features and local features can complement each other in filtering and achieve better performance when combined. To the best of our knowledge, we are the first one that clearly characterizes the video filtering process from the above global appearance and local coding distortion restoration aspects with experimental verification, providing a clear pathway to developing filter techniques. Experimental results demonstrate that the proposed method significantly outperforms the existing single-frame based methods and achieves 13.5%, 11.3%, 11.7% BD-Rate saving on average for AI, LDP and RA configurations, respectively, compared with the HEVC reference software.
Filtering-Based Instance Selection Method for Overlapping Problem in Imbalanced Datasets

The overlapping problem occurs when a region of the dimensional data space is shared in a similar proportion by different classes. It has an impact on a classifier’s performance due to the difficulty in correctly separating the classes. Further, an imbalanced dataset consists of a situation in which one class has more instances than another, and this is another aspect that impacts a classifier’s performance. In general, these two problems are treated separately. On the other hand, Prototype Selection (PS) approaches are employed as strategies for selecting appropriate instances from a dataset by filtering redundant and noise data, which can cause misclassification performance. In this paper, we introduce Filtering-based Instance Selection (FIS), using as a base the Self-Organizing Maps Neural Network (SOM) and information entropy. In this sense, SOM is trained with a dataset, and, then, the instances of the training set are mapped to the nearest prototype (SOM neurons). An analysis with entropy is conducted in each prototype region. From a threshold, we propose three decision methods: filtering the majority class (H-FIS (High Filter IS)), the minority class (L-FIS (Low Filter IS)), and both classes (B-FIS). The experiments using artificial and real dataset showed that the methods proposed in combination with 1NN improved the accuracy, F-Score, and G-mean values when compared with the 1NN classifier without the filter methods. The FIS approach is also compatible with the approaches mentioned in the relevant literature.
Data Hiding Using PVD and Improving Security Using RSA

Steganography deals with hiding information, which offers ultimate security in defense, profitable usages, thus sending the imperceptible information, will not be bare or distinguished by others. In this paper, a multidirectional PVD hiding scheme with RSA algorithm is proposed. Secret bits are embedded in three directions of a color image by dividing the non-overlapping blocks into R, G, b channels by selecting the minimum pixels of each block regrouping. In order to ensure the security of an stego-image, the work proposed by scheme is used, and in addition to that RSA algorithm is proposed.
Vertex-Face/Zeta correspondence

We present the characteristic polynomial for the transition matrix of a vertex-face walk on a graph, and obtain its spectra. Furthermore, we express the characteristic polynomial for the transition matrix of a vertex-face walk on the 2-dimensional torus by using its adjacency matrix, and obtain its spectra. As an application, we define a new walk-type zeta function with respect to the transition matrix of a vertex-face walk on the two-dimensional torus, and present its explicit formula.