Reviewerly/exHarmony huggingface dataset:
IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.
Fast Reaching Finite Time synchronization Approach for Chaotic Systems With Application in Medical Image Encryption

This article proposes a fast reaching finite time synchronization approach for chaotic systems along with its application to medical image encryption. First, an adaptive terminal sliding mode tracking approach with fast reaching condition is designed to synchronize the chaotic systems at the transmitter and receiver ends in finite time. Then, a chaotic cryptosystem, using synchronized chaotic systems as secret keys generator, is proposed to enhance the security of medical image transmission and/or storage. The applicability and efficiency of the proposed synchronization approach is assessed using a simulation as well as an analytical study. The analysis encompassed security tools such as histogram analysis, correlation test, and information entropy change the rate of the number of pixels and unified average changing intensity. The obtained results confirmed the robustness and fast convergence rate of the proposed synchronization approach. The security analysis also shows that the proposed cryptosystem displays acceptable levels of resistance to various attacks.
A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems. A classical hyper-heuristic framework consists of two levels, including the high-level heuristic and a set of low-level heuristics. The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic. In this study, a Cooperative Multi-Stage Hyper-Heuristic (CMS-HH) algorithm is proposed to address certain combinatorial optimization problems. In the CMS-HH, a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution. In the search phase, an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution. In addition, a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time. The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems, including Boolean satisfiability problems, one-dimensional packing problems, permutation flow-shop scheduling problems, personnel scheduling problems, traveling salesman problems, and vehicle routing problems. The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.
Biased Random Forest For Dealing With the Class Imbalance Problem

The class imbalance issue has been a persistent problem in machine learning that hinders the accurate predictive analysis of data in many real-world applications. The class imbalance problem exists when the number of instances present in a class (or classes) is significantly fewer than the number of instances belonging to another class (or classes). Sufficiently recognizing the minority class during classification is a problem as most algorithms employed to learn from data input are biased toward the majority class. The underlying issue is made more complex with the presence of data difficult factors embedded in such data input. This paper presents a novel and effective ensemble-based method for dealing with the class imbalance problem. This paper is motivated by the idea of moving the oversampling from the data level to the algorithm level, instead of increasing the minority instances in the data sets, the algorithms in this paper aims to “oversample the classification ensemble” by increasing the number of classifiers that represent the minority class in the ensemble, i.e., random forest. The proposed biased random forest algorithm employs the nearest neighbor algorithm to identify the critical areas in a given data set. The standard random forest is then fed with more random trees generated based on the critical areas. The results show that the proposed algorithm is very effective in dealing with the class imbalance problem.
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces

Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces.We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant.We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) -a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don't appear to be isometric.In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.⇤
NTIRE 2019 Challenge on Real Image Denoising: Methods and Results

This paper reviews the NTIRE 2019 challenge on real image denoising with focus on the proposed methods and their results. The challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern raw-RGB and (2) the standard RGB (sRGB) color spaces. The tracks had 216 and 220 registered participants, respectively. A total of 15 teams, proposing 17 methods, competed in the final phase of the challenge. The proposed methods by the 15 teams represent the current state-of-the-art performance in image denoising targeting real noisy images.
A geometric view of optimal transportation and generative model

In this work, we give a geometric interpretation to the Generative Adversarial Networks (GANs). The geometric view is based on the intrinsic relation between Optimal Mass Transportation (OMT) theory and convex geometry, and leads to a variational approach to solve the Alexandrov problem: constructing a convex polytope with prescribed face normals and volumes. By using the optimal transportation view of GAN model, we show that the discriminator computes the Wasserstein distance via the Kantorovich potential, the generator calculates the transportation map. For a large class of transportation costs, the Kantorovich potential can give the optimal transportation map by a close-form formula. Therefore, it is sufficient to solely optimize the discriminator. This shows the adversarial competition can be avoided, and the computational architecture can be simplified. Preliminary experimental results show the geometric method outperforms the traditional Wasserstein GAN for approximating probability measures with multiple clusters in low dimensional space.
It’s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution

This paper treats gender bias latent in word embeddings.Previous mitigation attempts rely on the operationalisation of gender bias as a projection over a linear subspace.An alternative approach is Counterfactual Data Augmentation (CDA), in which a corpus is duplicated and augmented to remove bias, e.g. by swapping all inherently-gendered words in the copy.We perform an empirical comparison of these approaches on the English Gigaword and Wikipedia, and find that whilst both successfully reduce direct bias and perform well in tasks which quantify embedding quality, CDA variants outperform projection-based methods at the task of drawing non-biased gender analogies by an average of 19% across both corpora.We propose two improvements to CDA: Counterfactual Data Substitution (CDS), a variant of CDA in which potentially biased text is randomly substituted to avoid duplication, and the Names Intervention, a novel name-pairing technique that vastly increases the number of words being treated.CDA/S with the Names Intervention is the only approach which is able to mitigate indirect gender bias: following debiasing, previously biased words are significantly less clustered according to gender (cluster purity is reduced by 49%), thus improving on the state-of-the-art for bias mitigation.
Self-Sovereign Identity in a Globalized World: Credentials-Based Identity Systems as a Driver for Economic Inclusion

After introducing key concepts and definitions in the field of digital identity, this paper will investigate the benefits and drawbacks of existing identity systems on the road towards achieving self-sovereign identity. It will explore, in particular, the use of blockchain technology and biometrics as a means to ensure the “unicity” and “singularity” of identities, and the associated challenges pertaining to the security and confidentiality of personal information. The paper will then propose a model of blockchain-based self-sovereign identity based on attestations, claims, credentials and permissions, which is globally portable across the life of an individual. Such a system is not dependent on any particular government or organization for administration or legitimacy, although it might include government issued identification and biometrics as one of many indicia of identity. Such a solution based on a recorded and signed digital history of actions is a system that best approximates the fluidity and granularity of identity, enabling individuals to express only specific facets of their identity, depending on the parties with whom they wish to interact. This paper focuses on two case studies to explain how such a credentials system could work in specific contexts: (1) Kiva’s identity protocol for building credit history in Sierra Leone, and (2) World Food Programme’s Building Blocks program for delivering cash aid to refugees in Jordan. Finally, the paper will explore what the future might look like when blockchain-based cryptocurrencies and self-sovereign identity intersect. With digital transactions functioning as identity claims within an ecosystem based on self-sovereign identity, new business models might emerge, such as identity insurance schemes, along with the emergence of value-stable cryptocurrencies (“stablecoins”) functioning as local currencies.
How Blockchain Technology Can Benefit Marketing: Six Pending Research Areas

The proliferation of sophisticated e-commerce platforms coupled with mobile applications has ignited growth in business-to-consumer (B2C) commerce, reshaped organizational structures and revamped value creation processes. Simultaneously‚ new technologies have altered the dynamics of brand marketing‚ enabling a broader reach and more personalized targeting aimed at increasing brand trust and enhancing customer loyalty. Today‚ the Internet allows marketers to penetrate existing markets, create new online marketplaces and to generate new demand. This dynamic market engagement uses new technologies to target consumers more effectively. In this conceptual paper, we discuss how blockchain technology can potentially impact a firm’s marketing activities. More specifically, we illustrate how blockchain technology can empower the consumer-centric paradigm and foster disintermediation‚ combat click fraud, reinforce trust and transparency, enable privacy protection, empower security and enable creative loyalty programs. We present six propositions that will guide future blockchain-related research in the area of marketing.
SolarNet: A sky image-based deep convolutional neural network for intra-hour solar forecasting

The exponential growth of solar energy poses challenges to power systems, mostly due to its uncertain and variable characteristics. Hence, solar forecasting, such as very short-term solar forecasting (VSTSF), has been widely adopted to assist power system operations. The VSTSF takes inputs from various sources, among which sky image-based VSTSF is not yet well-studied compared to its counterparts. In this paper, a deep convolutional neural network (CNN) model, called the SolarNet, is developed to forecast the operational 1-h-ahead global horizontal irradiance (GHI) by only using sky images without numerical measurements and extra feature engineering. The SolarNet has a set of models that generate fixed-step GHI in parallel. Each model is composed of 20 convolutional, max-pooling, and fully-connected layers, which learns latent patterns between sky images and GHI in an end-to-end manner. Numerical results based on six years data show that the developed SolarNet outperforms the benchmarking persistence of cloudiness model and machine learning models with an 8.85% normalized root mean square error and a 25.14% forecasting skill score. The SolarNet shows superiority under various weather conditions.
Boosting the Transferability of Adversarial Samples via Attention

The widespread deployment of deep models necessitates the assessment of model vulnerability in practice, especially for safety- and security-sensitive domains such as autonomous driving and medical diagnosis. Transfer-based attacks against image classifiers thus elicit mounting interest, where attackers are required to craft adversarial images based on local proxy models without the feedback information from remote target ones. However, under such a challenging but practical setup, the synthesized adversarial samples often achieve limited success due to overfitting to the local model employed. In this work, we propose a novel mechanism to alleviate the overfitting issue. It computes model attention over extracted features to regularize the search of adversarial examples, which prioritizes the corruption of critical features that are likely to be adopted by diverse architectures. Consequently, it can promote the transferability of resultant adversarial instances. Extensive experiments on ImageNet classifiers confirm the effectiveness of our strategy and its superiority to state-of-the-art benchmarks in both white-box and black-box settings.
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models

We study knowledge-grounded dialogue generation with pre-trained language models.To leverage the redundant external knowledge under capacity constraint, we propose equipping response generation defined by a pretrained language model with a knowledge selection module, and an unsupervised approach to jointly optimizing knowledge selection and response generation with unlabeled dialogues.Empirical results on two benchmarks indicate that our model can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment.
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge

Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks.To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLARE, which introduces word-level linguistic knowledge including part-of-speech tag and sentiment polarity (inferred from SentiWordNet) into pretrained models.We first propose a contextaware sentiment attention mechanism to acquire the sentiment polarity of each word with its part-of-speech tag by querying SentiWord-Net.Then, we devise a new pre-training task called label-aware masked language model to construct knowledge-aware language representation.Experiments show that SentiLARE obtains new state-of-the-art performance on a variety of sentiment analysis tasks 1 .
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition

Named entity recognition (NER) is a wellstudied task in natural language processing.Traditional NER research only deals with flat entities and ignores nested entities.The span-based methods treat entity recognition as a span classification task.Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition.To tackle these issues, we propose a two-stage entity identifier.First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundaryadjusted span proposals with the corresponding categories.Our method effectively utilizes the boundary information of entities and partially matched spans during training.Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities.In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference.Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.
Bone Cancer Detection Using Feature Extraction Based Machine Learning Model

Bone cancer is considered a serious health problem, and, in many cases, it causes patient death. The X-ray, MRI, or CT-scan image is used by doctors to identify bone cancer. The manual process is time-consuming and required expertise in that field. Therefore, it is necessary to develop an automated system to classify and identify the cancerous bone and the healthy bone. The texture of a cancer bone is different compared to a healthy bone in the affected region. But in the dataset, several images of cancer and healthy bone are having similar morphological characteristics. This makes it difficult to categorize them. To tackle this problem, we first find the best suitable edge detection algorithm after that two feature sets one with hog and another without hog are prepared. To test the efficiency of these feature sets, two machine learning models, support vector machine (SVM) and the Random forest, are utilized. The features set with hog perform considerably better on these models. Also, the SVM model trained with hog feature set provides an <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> <mn>1</mn> </math> -score of 0.92 better than Random forest <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>F</mi> <mn>1</mn> </math> -score 0.77.
Group LSTM: Group Trajectory Prediction in Crowded Scenarios

The analysis of crowded scenes is one of the most challenging scenarios in visual surveillance, and a variety of factors need to be taken into account, such as the structure of the environments, and the presence of mutual occlusions and obstacles. Traditional prediction methods (such as RNN, LSTM, VAE, etc.) focus on anticipating individual’s future path based on the precise motion history of a pedestrian. However, since tracking algorithms are generally not reliable in highly dense scenes, these methods are not easily applicable in real environments. Nevertheless, it is very common that people (friends, couples, family members, etc.) tend to exhibit coherent motion patterns. Motivated by this phenomenon, we propose a novel approach to predict future trajectories in crowded scenes, at the group level. First, by exploiting the motion coherency, we cluster trajectories that have similar motion trends. In this way, pedestrians within the same group can be well segmented. Then, an improved social-LSTM is adopted for future path prediction. We evaluate our approach on standard crowd benchmarks (the UCY dataset and the ETH dataset), demonstrating its efficacy and applicability.
A Novel Robust Reversible Watermarking Scheme for Protecting Authenticity and Integrity of Medical Images

It is of great importance in telemedicine to protect authenticity and integrity of medical images. They are mainly addressed by two technologies, which are region of interest (ROI) lossless watermarking and reversible watermarking. However, the former causes biases on diagnosis by distorting region of none interest (RONI) and introduces security risks by segmenting image spatially for watermark embedding. The latter fails to provide reliable recovery function for the tampered areas when protecting image integrity. To address these issues, a novel robust reversible watermarking scheme is proposed in this paper. In our scheme, a reversible watermarking method is designed based on recursive dither modulation (RDM) to avoid biases on diagnosis. In addition, RDM is combined with Slantlet transform and singular value decomposition to provide a reliable solution for protecting image authenticity. Moreover, ROI and RONI are divided for watermark generation to design an effective recovery function under limited embedding capacity. Finally, watermarks are embedded into whole medical images to avoid the risks caused by segmenting image spatially. Experimental results demonstrate that our proposed lossless scheme not only has remarkable imperceptibility and sufficient robustness but also provides reliable authentication, tamper detection, localization, and recovery functions, which outperforms existing schemes for protecting medical images.