Reviewerly/exHarmony huggingface dataset:
Overcoming Forgetting in Local Adaptation of Federated Learning Model

Federated learning allows multiple clients to train a global model without data exchanging. But in real world, the global model is not suitable for all clients because they may hold heterogenous data and have personalized and individual demands, which will directly weaken the motivation of them to participate in federated learning. To make each client benefits from federated learning, researchers propose to train personalized models from global model using local data. However, the lack of raw data in the model retraining process will lead to the challenge of forgetting, which can deprive the personalized model of the benefits gained from federated learning. In extreme cases (e.g., the client lacks certain classes of data), the ability to recognize the lacked data may even be completely forgotten. To this end, we propose a local adaptation method to overcome forgetting, which add the generator synthetic data to local adaptation to realize model updating incrementally. We test our method on real-world datasets, and the results show that when adopting the proposed method on local adaptation, the clients can get flexible adaption ability to new data as well as keep the original recognition capability of the global model even in extreme cases.
Efficient Approaches for Data Augmentation by Using Generative Adversarial Networks

In the present and future, data is the most valuable thing in the world. Therefore, it is now a challenge for everyone in every sector to work with data. Collecting data to predict or collecting data to analyze is a very valuable task. Moreover every new research, new machine learning method, and algorithms testing depends on a massive amount of different data. Furthermore, it is also a security issue for many fields to share actual data. It is always hard to find the perfect data set. It is not just about figuring out huge amounts of data. Many other data analysis processes need to be performed on that dataset to make it worthwhile. To overcome this problem, data augmentation is one of the suitable solutions. The idea behind data augmentation is to create a new dataset that depends on some existing dataset features. Generative Adversarial Networks (GANs) are a class of machine learning frameworks introduced by Ian Goodfellow in 2014. They are a game-like way to learn and generate new datasets. GANs have two parts, one is the generator, and the second is the discriminator. They play against each other to win the game. We will use our data set on the GAN model using some specific hyperparameter value and optimizer, which we will find out through our experiment. Finally, we will produce a CSV file with model-generated synthesis data and visualize the performance statistic of our model in the graph. This article will explain the different facts related to Data Augmentation, and GANs.
Divide and Imitate: Multi-cluster Identification and Mitigation of Selection Bias

Machine Learning can help overcome human biases in decision making by focussing on purely logical conclusions based on the training data. If the training data is biased, however, that bias will be transferred to the model and remains undetected as the performance is validated on a test set drawn from the same biased distribution. Existing strategies for selection bias identification and mitigation generally rely on some sort of knowledge of the bias or the ground-truth. An exception is the Imitate algorithm that assumes no knowledge but comes with a strong limitation: It can only model datasets with one normally distributed cluster per class. In this paper, we introduce a novel algorithm, Mimic, which uses Imitate as a building block but relaxes this limitation. By allowing mixtures of multivariate Gaussians, our technique is able to model multi-cluster datasets and provide solutions for a substantially wider set of problems. Experiments confirm that Mimic not only identifies potential biases in multi-cluster datasets which can be corrected early on but also improves classifier performance.
Secure Interoperation of Blockchain and IPFS Through Client Application Enabled by CP-ABE

In recent years, personal health data can be collected via wearable devices and sensors and used for healthcare services improvement through data sharing. To share sensitive personal health data securely, many frameworks and approaches using blockchain-based systems have been proposed. However, the issue of letting individuals control and manage their data with privacy-preserving is still to be solved. In this paper, we propose a new model of Individual-Initiated Auditable Access Control (IIAAC) enabled with blockchain, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) and IPFS (Inter-Planetary File System) for privacy-preserved data sharing. We describe the system architecture, its main components for our proposed model, and the protocols to make blockchain and IPFS interoperate with each other via a client application, including key generation, data publication, and data retrieval. We further build an experiment environment to evaluate the performance of our proposed model and architecture. Experiment results demonstrate the feasibility of our proposed model and system architecture for privacy-preserved data sharing.
Adaptive Feature Generation for Online Continual Learning from Imbalanced Data

Online continual learning (OCL) is the setting where deep neural network (DNN) incrementally learns new tasks with online data streams. The major problem in OCL is catastrophic forgetting, that DNN forgets the acquired knowledge on previous tasks quickly. Recently emerged studies tackle a more realistic problem that the data follows an imbalanced class distribution in OCL by storing particular exemplars. However, preserving exemplars causes memory burden and privacy issues. In this paper, we propose a non-exemplar based method—Adaptive Feature Generation (AdaFG) for OCL from imbalanced data, which tackles the class imbalance and catastrophic forgetting problems simultaneously. Specifically, we argue that one common reason for these problems is the decision boundaries of minority or old classes with few or no samples are affected by majority classes. Therefore, we first maintain a representative prototype for each class in the feature space, which dynamically changes with the streaming data to approximate the class mean feature. Then, we generate new features adaptively for old and minority classes based on their prototypes and train the DNN’s classifier to adjust the decision boundaries. Experiments on three popular datasets demonstrate AdaFG’s effectiveness in consolidating previous knowledge and addressing the class imbalance problem without preserving exemplars.
Named Entity Linking on Handwritten Document Images

Named Entity Linking (NEL) is an information extraction task that semantically enriches documents by recognizing mentions of entities in a text and matching them against an entry in a Knowledge Base (KB). This semantic information is fundamentally important for realizing a semantic search. Furthermore, it serves as a feature for subsequent tasks (i.e. Question Answering) as well as for improving the user experience. Current NEL approaches and datasets from the Document Image Analysis community are mainly focusing on machine-printed documents and do not consider handwriting. This is mainly due to the lack of annotated NEL handwriting datasets. To fill this gap, we manually annotated the well known IAM and George Washington datasets with NEL labels and created a synthetic handwritten version of the AIDA-CoNLL dataset. Furthermore, we present an evaluation protocol as well as a baseline approach.
DCT-DWT-FFT Based Method for Text Detection in Underwater Images

Text detection in underwater images is an open challenge because of the distortions caused by refraction, absorption of light, particles, and variations depending on depth, color, and nature of water. Unlike existing methods aimed at text detection in natural scene images, in this paper, we have proposed a novel method for text detection in underwater images through a new enhancement model. Based on observations that fine details of text in image share with high energy, spatial resolution, and brightness, we consider Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Fast Fourier Transform (FFT) for image enhancement to highlight the text features. The enhanced image is fed to a modified Character Region Awareness for Text Detection (CRAFT) model to detect text in underwater images. To explore enhancement methods, we evaluate six combinations of image enhancement techniques, namely, DCT-DWT-FFT, DCT-FFT-DWT, DWT-DCT-FFT, DWT-FFT-DCT, FFT-DCT-DWT, FFT-DWT-DCT. Experimental results on our dataset of underwater images and benchmark datasets of natural scene text detection, namely, MSRA-TD500, ICDAR 2019 MLT, ICDAR 2019 ArT, Total-Text, CTW1500, and COCO Text show that the proposed method performs well for both underwater and natural scene images and outperforms the existing methods on all the datasets.
Recognition of Visual Arabic Scripting News Ticker From Broadcast Stream

News ticker recognition is a vital area of research due to its applications such as information analysis, opinion mining and language translation for media regulatory authorities.Without automated systems, manual anatomizing is difficult.In this paper, we focus on the automatic Arabic and Urdu news ticker recognition system.It mainly consists of ticker segmentation and text recognition to generate textual data for various online services.Our work investigates character-wise explicit segmentation and syntactical models with Kufi and Nastaleeq fonts.Various network models anticipate learning of deep representations by homogenizing the classes regardless of inter-symbol correlations and linguistic taxonomy.The proposed learning model incorporates fairness by maximizing the balance among sensitive features of characters in a unified manner.Furthermore, we demonstrate the efficiency of the proposed model by carrying out experiments using customized news tickers datasets with accurate character-level and component-level labeling.Moreover, our method is evaluated on a challenging Urdu Printed Text Images (UPTI) dataset that only provides ligature based annotations.The proposed method attains 98.36%, outperforms the current state of the art method.Ablation investigations show that our technique enhances the performance of character classes with low symbol frequencies.
Thermal Infrared Object Tracking Based on Adaptive Feature Fusion

SiamRPN++ has achieved excellent performance on thermal infrared object tracking. However, it directly fuses multi-layer features using weighted summation, which has the problem of insufficient feature fusion. In this paper, we propose an adaptive feature fusion module. It can fuse the features of different layers by adaptively allocating channel weights. Meanwhile, CIoU loss is used to make the regression of the bounding box more accurate. Experimental results show that the proposed method improves the baseline algorithm effectively and achieves excellent tracking accuracy and efficiency. The proposed method has strong robustness, effectively dealing with some challenges such as interference and occlusion. Therefore, the proposed method is valuable in practical application.
Improved Image Classification Accuracy by Convolutional Neural Networks

Along with the development of mobile Internet, images are increasingly used as a more effective information carrier, and deep learning methods have a great efficiency advantage over traditional algorithms in dealing with image classification problems. This paper presents a simple and efficient deep learning method to train a convolutional neural network (CNN) for image classification on a large-scale supervised fashion NBIST image dataset. We first preprocessed the dataset, after which it was fed into the neural network for a minimum of five epochs of training. In the end, the accuracy rate can reach more than 83.6%. This CNN model has a greater efficiency advantage over traditional feature description and detection algorithms and is more suitable for today's image classification tasks.
Framework for improving usage of library services and resources in the private higher education in South Africa

Purpose This study aims to investigate a framework for improving library services and resource usage in a private higher education institution in South Africa. Marketing of library services and resources has a great impact on their usage. If marketing is not properly applied, libraries can turn into the unused institution, render useless or unused services, no matter how good their available services and resources are. Design/methodology/approach This multi-method study used questionnaires to collect data from both staff and students at the private higher education institution concerned. The questionnaire was augmented with data collected from staff using group interviews. It is unfortunate that the target institution approved the conducting of the study only on the condition that their name could not be disclosed or used in any study reports. On this basis, the institution is referred to as Campus A for the purposes of this paper. Findings The study discovered that Campus A lacked key marketing tools, such as a marketing policy for the library, and that it also failed to take advantage of new platforms such as social media and other available computer software and technologies. This has resulted in its users being unaware of certain important services and resources rendered by the library. Originality/value The study proposed a framework for library marketing practices to improve services and resources usage as a benchmark for libraries. The study also recommends that the management of Campus A library come together to gather information for the development of an appropriate library marketing policy and also ensure that their marketing strategies take advantage of the latest technology, including social media, by means of which their clients may be easily reached.
Empirical Wavelet Transform in Speech Signal Compression Problems

In this scientific research it is proposed to apply a modern method of empirical wavelet transform based on the construction of a family of adaptive wavelet functions to increase the efficiency of spectral analysis of speech signals, and their subsequent compression or filtering. If we take as a basis the features of the frequency spectrum of the Fourier speech signal, then the task is equivalent to the construction of a set of band wavelet filters. One way to achieve adaptability is to keep in mind that compact wavelet filter media is directly dependent on, where is the information we need in the spectrum of the speech signal, that is, the larger amplitudes of the Fourier spectrum carry more important information to restore function, and hence for speech intelligibility, and small amplitudes are less important. Indeed, the properties of the function of the internal empirical mode are equivalent to the statement, that the spectrum of this function has a compact carrier and is concentrated around a certain frequency depending on the signal under study, which makes this approach adaptive and increases the efficiency of compression and filtering of speech signals.
Hateful Speech and Hostile Environments

This paper examines Mary Kate McGowan’s account of oppressive speech. McGowan argues that ordinary hateful speech can oppress by enacting discriminatory norms, and further, that this enactment sometimes renders the speech regulable under current United States law. In response, the paper raises two sets of questions. First, it asks about the contents of the norms enacted by a given hateful utterance, and specifically, about what determines those contents. Second, the paper also questions McGowan’s emphasis on the distinction between causing harm and enacting (or constituting) harm. It argues that, contra that emphasis, this distinction doesn’t matter for the regulability of speech in McGowan’s central cases.
Historical Computer Science today: "ambiguous understanding"? (contemporary discussions)

The article contains a critical analysis of two theses presented in a recently published monograph on historical information systems. The book contains a lot of useful information, including a description of the author's experience in developing such systems. Both theses discussed are related to the authors' assessment of the evolution of an interdisciplinary field focused on the use of information/digital technologies and data analysis methods in historical research. We are talking about historical computer science, which has passed a 30-year path in our country. The first controversial thesis of the authors of the book boils down to the statement that "historical informatics has not found sufficient and deserved recognition among traditional historians." В The first controversial thesis of the authors of the book boils down to the statement that "historical informatics has not found sufficient and deserved recognition among traditional historians." In the second thesis, we are talking about the fact that in many ways the field of research and development, previously related to historical computer science, is now being considered in line with digital history. Both theses are disputed in the article, the corresponding arguments are given. The specificity of the Russian model of historical informatics, which distinguishes it from the relatively recent versions of digital history, is shown. This specificity is determined by the balance of the structure of historical informatics, combining its "resource" component with an analytical one aimed at obtaining meaningful meaningful results (and with an emphasis on the second component).
Comparing Grover’s Quantum Search Algorithm with Classical Algorithm on Solving Satisfiability Problem

The emergence of quantum computing provides us the possibility of solving tasks that might take years classically in just a few minutes. For certain problems, quantum computing exhibits quantum supremacy, meaning that the quantum solution runs exponentially faster than classical algorithms and is able to completely take over classical computers. This high efficiency of quantum computing comes not only from the hardware but also the software, quantum algorithms. The algorithms utilize the qubits to make calculations in order to fulfill specific tasks with the lowest time complexity possible. One such algorithm is named the Grover’s algorithm, which is able to perform database search in $\mathcal{O}(\sqrt{N})$, and it runs much faster than the traditional algorithm that takes $\mathcal{O}(N)$ time to solve the same task. For example, when the task is to find the even integers from N integers, traditional computation will need to run through all of the N integers one by one, making at least N steps of calculation, while by using Grover’s algorithm only around $\sqrt{N}$ calculations are needed. This exponential speed-up makes Grover’s algorithm one of the most important quantum algorithms. Grover’s algorithm has a wide application in many fields and is able to improve the time complexity exponentially. One task that can be solved using Grover’s algorithm is the satisfiability problem. This type of problem asks the computer to find a set of values (commonly true or false) for several variables such that they satisfy certain constraints. We use k-SAT problems to refer to satisfiability problems with k boolean variables to be determined. Grover’s algorithm can effectively solve the k-SAT problem by performing the database search on $2 ^{N}$ possible states of the variables. The algorithm’s square root optimization on searching helps to improve the efficiency of this solution significantly. Furthermore, this optimization of Grover’s algorithm may play a more important role when k grows larger, and consequently the efficiency of the quantum solution could improve faster relative to the traditional solution. Yet this hypothesis is never tested due to the lack of a general k-SAT quantum algorithm. No quantum algorithms solving k-SAT problems where k is greater than 3 have been proposed, thus no test has been performed to compare the quantum solution and the classical solution on more general k-SAT problems. In this research, we formulate a general quantum solution for k-SAT problem and compare such solution with the best classical algorithm to determine whether and when the quantum algorithm performs better on satisfiability problems. The comparison will be done through both theoretical deduction as well as real-world implementation. At the end of this research, we will determine whether the proposed quantum algorithm outperforms the classical algorithm on solving k-satisfiability problems.
Institutional Repositories for Open Access in Open Doar Used Dspace Software: A Global Perspective

Repositories and helps to extend the liberal traditions of information access. There are various open-source software available on the web with a diverse range of features and capabilities and are used by different Institutional, Disciplinary, Aggregating, and Governmental Repositories. D-Space and E-Prints were widely used open-source software for institutional repositories, and prospective users sometimes wonder which one to adopt. The aims of these are very different, although their domains of application do overlap. The paper also describes and summarises the comparative study of prominently used open-source software and its usage in Open DOAR (Directory of Open Access Repositories).
Revisiting Consistency Regularization for Semi-supervised Learning

Consistency regularization is one of the most widely-used techniques for semi-supervised learning (SSL). Generally, the aim is to train a model that is invariant to various data augmentations. In this paper, we revisit this idea and find that enforcing invariance by decreasing distances between features from differently augmented images leads to improved performance. However, encouraging equivariance instead, by increasing the feature distance, further improves performance. To this end, we propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss, that imposes consistency and equivariance on the classifier and the feature level, respectively. Experimental results show that our model defines a new state of the art for various datasets and settings and outperforms previous work by a significant margin, particularly in low data regimes. Extensive experiments are conducted to analyze the method, and the code will be published.
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction

We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them.