Reviewerly/exHarmony huggingface dataset:
Semi-supervised medical image classification based on CamMix

Collecting a large amount of labeled data is crutial for training deep neural network, which is a limitation for medical image classification because it necessarily involves expert knowledge. To mitigate this problem of insufficient labeled medical data, in this work, we propose a novel semi-supervised framework for medical image classification. For unlabeled data, we apply the consistency-based strategy to produce high-quality pseudo label, which encourages model to output the same predictions under different perturbations. In addition, we present a novel mixed sample data augmentation CamMix to effectively exploit the relation between samples, mixing pairs of input data and labels according to the class activation map mask. We have evaluated our proposed method on two public medical image datasets, interstitial lung disease dataset and ISIC 2018 skin lesion analysis dataset. The results demonstrate superior performance of our method over other existing methods on the two datasets. Meanwhile, our proposed CamMix performs better than the current mixed sample data augmentation methods.
Making Multimedia Visible: Finding New Pathways of Discoverability and Access for Media Collections

Streaming video has increased the size of academic library media holdings, while leaving patrons on their own to find what they need. As a result, media collections are now exponentially larger and richer while simultaneously being less discoverable by students. That’s because the way libraries organize this information serves the most information-literate users—while potentially leaving others behind. This session explored the history of library media in an effort to understand the reasons why media tends to be one of the most mysterious parts of academic library collections. It also encourages librarians to assess the accessibility of their own media collections and find creative strategies for making these important resources accessible to all.
Improving the Accuracy of the Annotation Algorithm in Pattern-Based Tennis Game Video

Automatically annotating the game of tennis using video playback is a high potential but has many challenges. In this research, deep learning in annotating tennis games with the integration of computer vision and machine learning is discussed. The experiments of this research are performed using a set of video images and the implementation of the CNN algorithm. The proposed method was compared with NAIVE BAYES, SVM, HMM, and S-SVM methods. The results show that well-tuned channel neural networks have the best performance among the strategies. U sing deep neural network convolution in Comparisons and evaluations showed that annotation is performed with great accuracy. The accuracy obtained in this study is 0.92. CNN's proposed algorithm showed that with the necessary changes in network parameters, and this algorithm's techniques, the desired result achieved, and accuracy greatly increased.
Fake Website Prediction Using Random Forest

Fake websites are now producing billions of dollars in fraud at the cost of innocent Internet users. Users will have a difficult time manually identifying these websites as phoney due to their design and appearance. A large number of people buy things online and pay for them using various online payment platforms. Several websites require users to enter sensitive data in order to authenticate. However, some phishing websites make use of this information for nefarious purposes. Automated detection systems have arisen as a means of countering bogus websites, although the most of them are rather basic in terms of fraud and detecting methods. Here, in this project an website is been created for the users with the algorithm having an accuracy of 96% for detecting the website they are using in their daily routine.
Upwardly Abstracted Definition-Based Subontologies

In this paper, we present a method for extracting subontologies from $\mathcalELH $ ontologies for a set of symbols. The approach is focused on the generation of upwardly abstracted definitions of concepts, which is a technique for computing definitions expressed using closest primitive ancestors. The subontologies returned by the method are evaluated for quality and compared to extracts computed with locality-based modularisation and uniform interpolation. Our subontology generation method produces promising results in terms of size and relevance to the needs of domain experts.
Robustness of Quantum Systems Subject to Decoherence: Structured Singular Value Analysis?

We study the problem of robust performance of quantum systems under structured uncertainties. A specific feature of closed (Hamiltonian) quantum systems is that their poles lie on the imaginary axis and that neither a coherent controller nor physically relevant structured uncertainties can alter this situation. This changes for open systems where decoherence ensures asymptotic stability and creates a unique landscape of pure performance robustness, with the distinctive feature that closed-loop stability is secured by the underlying physics and needs not be enforced. This stability, however, is often detrimental to quantum-enhanced performance, and additive perturbations of the Hamiltonian give rise to dynamic generators that are nonlinear in the perturbed parameters, invalidating classical paradigms to assess robustness to structured perturbations such as singular value analysis. This problem is addressed using a fixed-point iteration approach to determine a maximum perturbation strength δ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> that ensures that the transfer function remains bounded, ║T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</inf> ║ < δ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> for δ < δ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> .
Adaptive Feature Norm for Unsupervised Subdomain Adaptation

In many real-world problems, obtaining labeled data for a specific machine learning task is expensive. Unsupervised Domain Adaptation (UDA) aims at learning a good predictive model for the target domain using labeled information from the source but only unlabeled samples from the target domain. Most of the previous methods tackle this issue with adversarial methods that contain several loss functions and converge slowly. Recently, subdomain adaptation, which focuses on nuances of the distribution of the relevant subdomains, is getting more and more attention in the UDA field. This paper proposes a technique that uses the adaptive feature norm with subdomain adaptation to boost the transfer gains. Subdomain adaptation can enhance the ability of deep adaptation networks by capturing the fine-grained features from each category. Additionally, we have incorporated an adaptive feature norm approach to increase transfer gains. Our method shows state-of-the-art results on the popular visual classification datasets, including Office-31, Office Home, and Image-CLEF datasets.
Automated anonymization of text documents in Polish

The anonymization of unstructured texts has become a very popular and widely researched topic. This is due not only to the latest GDPR regulation, but also due to the development of state-of-the-art models in the field of natural language processing. The texts required for building such models have to be anonymized before and very often have to be anonymized on the premises of data providers, not the machine learning teams. In this work, we present the use of machine learning models such as part-of-speech tagger or named entity recognizer and their integration with regular expressions for anonymization of unstructured texts in Polish. The goal is to create a system that recognizes many types of sensitive data and can remove, tag, and pseudo-anonymize (replace with words from the same category in an appropriate form) the detected tokens. To test the performance of this system, we prepared a manually annotated dataset containing different categories of sensitive data. The paper presents a detailed analysis of the proposed method’s performance. Moreover, a deployment architecture is discussed in the paper, that results in the creation of a scalable tool capable of processing a large amount of data that can be easily used.
Dense Video Captioning with Hierarchical Attention-Based Encoder-Decoder Networks

Dense video captioning is a challenging task with the goal of localizing and describing all events in an untrimmed video, taking into account both visual and text information. Although existing methods have made some achievements, most of them suffer from missing details and inferior captioning. Recent progress has been made in using object features to supplement more detailed information. However, due to the considerable number of objects in the video, the representation of learning objects is often noisy, which may interfere with the generation of correct captions. We also notice that realworld video-text data involve different granularity levels, such as objects/words and events/sentences. Therefore, we propose the hierarchical video-text attention-based encoder-decoder networks for dense video captioning. The proposed method successfully considers the hierarchy in the video and text and exploits the most relevant visual and text features when generating caption. Specially, we design a hierarchical attention encoder for learning complex visual information: an object attention module focusing on the most relevant objects and an event attention module modeling the long-range temporal context. A corresponding decoder has been built for translating multi-level features into the linguistic description, i.e., a word attention module to exploit the most correlated text features and a sentence attention module to leverage high-level semantic information. The proposed hierarchical attention mechanism achieves state-of-the-art performance on the ActivityNet Captions dataset.
Detection and Diagnosis of Breast Cancer Using Deep Learning

Breast Cancer (BC) is a cancerous growth that is a result of uncontrolled cell division in the mammary tissues, usually in the ducts and in the lobules. BC is the most dominant fast-growing cancer and one of the leading cause of cancer mortality in women. BC incidents are increasing swiftly every year around the world especially in developing countries due to grown life expectancy and assumption of western culture. The conventional process of detecting BC involves a clinical expert who observed the medical images of affected breast tissues and looks for structural changes, irregularities in cell forms, ordination of cells in the tissue and determining the stage of the cancer. As conventional interpretation is often time consuming, expensive and error prone; computer-aided detection (CAD) technique is used as an alternative to provide a more accurate, automatic, fast and reproducible procedure to detect BC. This research presents a fully automatic process of BC detection. Two well know filter such as Gaussian Blur (GB) and Detail Enhanced (DE) filter has been used here for the preprocessing purpose. Convolutional Neural Network (CNN) classifier has been used here for classification. The proposed model is performed on an openly accessible dataset named Breast Histopathology Image dataset and the outcome exhibits the sharpness of our proposed model. The obtained accuracy is 87.49%, 88.46% and 88.10% in Case-I, Case-II and Case-III, respectively.
Gradient Corrected Approximation for Binary Neural Networks

Binary neural networks (BNNs), where both activations and weights are radically quantized to be {-1, +1}, can massively accelerate the run-time performance of convolution neural networks (CNNs) for edge devices, by computation complexity reduction and memory footprint saving. However, the non-differentiable binarizing function used in BNNs, makes the binarized models hard to be optimized, and introduces significant performance degradation than the full-precision models. Many previous works managed to correct the backward gradient of binarizing function with various improved versions of straight-through estimation (STE), or in a gradual approximate approach, but the gradient suppression problem was not analyzed and handled. Thus, we propose a novel gradient corrected approximation (GCA) method to match the discrepancy between binarizing function and backward gradient in a gradual and stable way. Our work has two primary contributions: The first is to approximate the backward gradient of binarizing function using a simple leaky-steep function with variable window size. The second is to correct the gradient approximation by standardizing the backward gradient propagated through binarizing function. Experiment results show that the proposed method outperforms the baseline by 1.5% Top-1 accuracy on ImageNet dataset without introducing extra computation cost.
On Cognitive Searching Optimization in Semi-Markov Jump Decision Using Multistep Transition and Mental Rehearsal

Cognitive searching optimization is a subconscious mental phenomenon in decision making. Aroused by exploiting accessible human action, alleviating inefficient decision and shrinking searching space remain challenges for optimizing the solution space. Multiple decision estimation and the jumpy decision transition interval are two of the cross-impact factors resulting in variation of decision paths. To optimize the searching process of decision solution space, we propose a semi-Markov jump cognitive decision method in which a searching contraction index bridges correlation from the time dimension and depth dimension. With the change state and transition interval, the semi-Markov property can obtain the action by limiting the decision solution to the specified range. From the decision depth, bootstrap re-sampling utilizes mental rehearsal iteration to update the transition probability. In addition, dynamical decision boundary by the interaction process limits the admissible decisions. Through the flight simulation, we show that proposed index and reward vary with the transition decision steps and mental rehearsal frequencies. In conclusion, this decision-making method integrates the multistep transition and mental rehearsal on semi-Markov jump decision process, opening a route to the multiple dimension optimization of cognitive interaction.
Efficient GPU-Implementation for Integer Sorting Based on Histogram and Prefix-Sums

In this paper, we propose integer sorting algorithms based on histogram and prefix-sums and we show that their GPU-implementations are faster than the fastest sorting GPU-implementations in Thrust and/or CUB library for several input data. In particular, our algorithm is very useful in the cases that the maximum value of input data is smaller than the number of input data and/or the number of kinds of input data is smaller than the maximum value of input data.
Deep Semi-Supervised Learning via Dynamic Anchor Graph Embedding Learning

Recently, deep semi-supervised graph embedding learning has shown much promise on text and image recognition tasks when the number of labeled data is limited. By introducing an auxiliary unsupervised task of predicting the neighborhood context in the graph, these approaches effectively mine the structure information provided by abundant unlabeled data. However, existing methods usually adapt to datasets whose graph is explicitly given or predefined, which cannot handle large-scale datasets with unknown graphs. Besides, the edge connections and weights are fixed during the training process in these methods, which fails to use the current feature information extracted by the model. In this paper, we propose a novel deep semi-supervised dynamic anchor graph embedding learning algorithm. More specifically, we build a two-branch architecture to learn the single-sample local features and the global features in the graph simultaneously. The first branch constrains its outputs to be consistent with different perturbations of the same single sample. And the output features are dedicated to constructing the dynamic anchor graph. The second branch utilizes the graph and the model prediction to sample the context. Then the global graph embed dings are learned based on the context. Finally, the outputs of the two branches are aggregated to jointly predict the class labels. Extensive experimental results on several image and text datasets have shown that the proposed method is able to improve the performance of existing graph embedding learning methods and outperform many state-of-the-art methods on image classification.
Intuitionistic Fuzzy Graph Morphological Topology

In this paper, we define morphological topology (\(\mathcal {M}\)-topology) on intuitionistic fuzzy graph (IFG). We also define neighbourhood graph, continuity and isomorphism between \(\mathcal {M}\)-topologies.
An Efficient Parallelized Huffman Decoding PE

An efficient parallelized Huffman decoding PE is proposed in this paper. The classic Huffman decoding algorithm is optimized first in parallel processing and then four hardware modules are designed based on that. The proposed PE can decode different numbers of streams with various compression ratios. The hardware architecture is fully verified at RTL level and synthesized with GF 12nm technology lib. The simulation shows a better decoding performance especially with wide data width and high compression ratio settings.
PROLEPSIS: Binary Analysis and Instrumentation of IoT Software for Control-Flow Integrity

Nowadays, the growing pervasiveness of digital components and their interconnection in the so-called Internet of Things, raises serious questions regarding security and integrity not only of the data exchanged, but also of the devices themselves and the software they run. Code-Reuse Attacks (CRA) are one of the most powerful binary attack paradigms, aiming to exploit memory vulnerabilities such as buffer overflows to force the application to execute an unintended sequence of instructions present in memory. To counter such a kind of attacks, ensuring the program's control-flow integrity (CFI) appears to be the most promising solution presented so far. A plethora of CFI implementations have been offered in the literature and by vendors, based on control-flow monitors located at the software level or even into hardware extensions. However, many proposed solutions opt for coarse-grained checks, or insert enforcement before all flow transfers. For software running on IoT platforms, where resources are usually limited, protections can increase the footprint in an unsustainable way. This paper presents PROLEPSIS, an automated binary code analysis tool for IoT applications written for ARM platforms. With an optimised search, the tool is able to identify only those executable point (control-flow instructions) that are really at risk of control-flow hijacking. Each recognised insecure point is instrumented according to a custom technique of choice, either based on a software or a hardware monitor, depending on the specific application needs.
Multiple Instance Relational Learning for Video Anomaly Detection

Most existing video anomaly detection methods are dependent on strong supervision to achieve satisfactory performance, which could be laborious and impractical. Besides, methods using weakly supervised learning less consider the relations among event proposals. To this end, we propose an anomaly event detection method by the instance-based event proposal generation and the proposal relation learning. Specifically, the event proposals are generated by sampling temporal distributions from the multiple instance learning (MIL), while relations among the proposals are captured by graph convolutional network for anomaly localization and classification. The proposed method is free from strong frame-level supervision but only requires video-level annotations. We conduct experiments on four datasets, i.e., UCF crime, UCSD-Peds, UMN, and CityScene, and show state-of-the-art performance on anomaly recognition and detection tasks.
Assisting researchers in bibliographic tasks: A new usable, real‐time tool for analyzing bibliographies

Abstract The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform.
A Survey on Privacy Enhancements for Massively Scalable Storage Systems in Public Cloud Environments

Increasing network speeds and storage resource requirements have led the vast majority of industry players to intensively use cloud as a commodity service. Although the advantages obtained by following this trend are obvious, a multitude of privacy concerns arise regarding the confidentiality and integrity of data. In order to overcome privacy issues, some cloud providers and third-party software applications are now offering solutions which are capable of performing on-the-fly data encryption, in a simplistic manner for their clients. When it comes to encryption, there are a set of elements which should be known by the rightful owner, including the key, which should be kept secret, and the algorithm, which should be public in order to be reviewed. There are use cases when the secret key is managed by the client of the service and other use cases when the key is managed by the cloud provider. However, both generic use cases are subject to compromise due to viruses or human errors. Another security risk which arises for companies is caused by the fact that a single entity with access to the key could compromise trade secrets, thus causing irremediable loses.