Reviewerly/exHarmony huggingface dataset:
A machine learning recommender system based on collaborative filtering using Gaussian mixture model clustering

With the shift toward online shopping, it has become necessary to customize customers' needs and give them more choices. Before making a purchase, buyers research the products' features. The recommender systems facilitate the search task for customers by narrowing down the search space within specific products that align with the customer's needs. A recommender system uses clustering to filter information, calculating the similarity between members of a cluster to determine the factors that will lead to more accurate predictions. We propose a new method for predicting scores in machine learning recommender systems using the Gaussian mixture model clustering and the Pearson correlation coefficient. The proposed method is applied to MovieLens data. The results are then compared to three commonly used methods: Pearson correlation coefficients, K‐means, and fuzzy C‐means algorithms. As a result of increasing the number of neighbors, our method shows a lower error than others. Additionally, the results depict that accuracy will increase as the number of users increases. Our model, for instance, is 5% more accurate than existing methods when the neighbor size is 30. Gaussian mixture clustering chooses similar users and takes into account the scores distance when choosing nearby users that are similar to the active user.
How the adoption of feature toggles correlates with branch merges and defects in open‐source projects?

Context: Branching has been widely adopted in version control to enable collaborative software development. However, the isolation caused by branches may impose challenges on the upcoming merging process. Recently, companies like Google, Microsoft, Facebook, and Spotify, among others, have adopted trunk-based development together with feature toggles. This strategy enables collaboration without the need for isolation through branches, potentially reducing the merging challenges. However, the literature lacks evidence about the benefits and limitations of feature toggles to collaborative software development. Objective/Method: In this paper, we study the effects of applying feature toggles on 949 open-source projects written in 6 different programming languages. We first identified the moment in which each project adopted a feature toggles framework. Then, we observed whether the adoption implied significant changes in the frequency or complexity of branch merges and the number of defects, and the average time to fix them. Finally, we compared the obtained results with results obtained from a set of control projects that do not use feature toggles frameworks. Results/Conclusion: We could observe a reduction in the average merge effort and an increase in the average total time needed to fix defects after adopting feature toggles frameworks. However, we could not confirm that this increase was influenced by the use of feature toggles.
The Survey on Handwritten Mathematical Expressions Recognition

Recognition of handwritten mathematical expressions is one of the challenging problems that has been studied and researched for a long. Many techniques have been researched to build a system that produces high-performance mathematical expression recognition systems. Plentiful research has been done on handwritten mathematical text though it is symbols and or two-dimensional mathematical expressions. The amount of research witness the origins and trends of several techniques and displays the shift of trendline of recognition techniques from grammar-driven or parsing based methods to machine learning and deep learning models. Remarkable research has been done in the last few years in this domain of handwritten mathematical scripts. In this paper, a survey of various research efforts has been compiled to develop and enhance handwritten mathematical expressions. Recognition systems of handwritten math text are presented. Comparative analysis in terms of classification technique used, datasets, and their accuracies have been performed.
Classification and Generation of Microscopy Images with Plasmodium Falciparum via Artificial Neural Networks Using Low Cost Settings

Recent studies use machine-learning techniques to automatically detect parasites in microscopy images. However, these tools are trained and tested in specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc.) and different blood film slides preparation. Generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size. Taking into consideration all those aspects, in this paper, we describe a more complete view including both detection and generating synthetic images: i) an automated detection used to detect malaria parasites on stained blood smear images using machine learning techniques testing several datasets. ii) a generative approach to create synthetic images which can deceive experts, and provide new data for data transfer or augmentation. The tested architecture achieved 0.98 and 0.95 area under the ROC curve in classifying images with respectively thin and thick smear, in leave-one-out cross-validation settings. The generated images proved to be very similar to the original and difficult to be distinguished by an expert microscopist, which identified correctly the real data for one dataset but had 50% misclassification for another dataset of images. Moveover, the trained model was also tested on a low cost device as RaspberryPI 4 with display.
Optimal placement of sampling locations for identification of a two-dimensional space

In recent years, substantial attention has been given to the optimal placement and planning of sampling or monitoring based on the Value of Information (VoI). The drawback of the VoI approach is its typically high computation cost. The method proposed here can significantly reduce the computation cost of VoI by applying the theory of Gaussian Process Regression. Due to the low computation cost, global optimisation algorithms such as particle swarm optimisation can be used to optimise a set of sampling locations. The proposed method was applied to a soil contamination problem. For comparison purposes, twenty-one subjects (persons) were asked to follow a three-step procedure. In the first step, contamination levels at five fixed sampling locations are given. Based on this information, the contaminated area is estimated by the subject, who is then asked to place five additional sampling sites in order to gather more information. The procedure is repeated three times. The subject is ultimately asked to produce a final estimate of the contaminated area. It was found that the proposed method generally produced better agreement with the true contaminated area than did the participants’ empirical human judgment.
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets

Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important challenges when applied to small domains. Existing methods use transfer learning for I2I translation, but they still require the learning of millions of parameters from scratch. This drawback severely limits its application on small domains. In this paper, we propose a new transfer learning for I2I translation (TransferI2I). We decouple our learning process into the image generation step and the I2I translation step. In the first step we propose two novel techniques: source-target initialization and self-initialization of the adaptor layer. The former finetunes the pretrained generative model (e.g., StyleGAN) on source and target data. The latter allows to initialize all non-pretrained network parameters without the need of any data. These techniques provide a better initialization for the I2I translation step. In addition, we introduce an auxiliary GAN that further facilitates the training of deep I2I systems even from small datasets. In extensive experiments on three datasets, (Animal faces, Birds, and Foods), we show that we outperform existing methods and that mFID improves on several datasets with over 25 points. Our code is available at: https://github.com/yaxingwang/TransferI2I.
News text classification based on Bidirectional Encoder Representation from Transformers

In order to accurately and efficiently obtain information useful to us, people are paying more and more attention to the problem of data redundancy caused by excessive data information. In recent years, domestic and foreign researchers have proposed various frameworks for different natural language processing tasks, and different frameworks have different advantages and disadvantages. One of the classic problems in the field of natural language processing is text classification. News text classification is an important task that is easy to attract everyone's attention in our daily lives. This experiment is based on the BERT model under the Transformer framework to classify the news text data set. The same news text data set is compared with the RNN's long and short-term memory network. The evaluation index uses the general accuracy and loss value of the model classification. Experimental results show that the classification accuracy of the BERT model is significantly higher than that of the long and short-term memory network.
Philosophy, politics, and economics of cryptocurrency I: Money without state

Abstract In this article, we describe what cryptocurrency is, how it works, and how it relates to familiar conceptions of and questions about money. We then show how normative questions about monetary policy find new expression in Bitcoin and other cryptocurrencies. These questions can play a role in addressing not just what money is, but what it should be. A guiding theme in our discussion is that progress here requires a mixed approach that integrates philosophical tools with the purely technical results of disciplines like computer science and economics.
Analysis of Machine Learning and Deep Learning Classifiers to Detect and Classify Breast Cancer

In the presented study, the authors have compared the performances of various machine learning and deep learning algorithms on two separate datasets: the Wisconsin Diagnostic Breast Cancer dataset and the Wisconsin Breast Cancer Dataset. We evaluated the extent of their ability to correctly classify the samples as "malignant" or "benign". The respective performances of these algorithms were judged on the grounds of various evaluation metrics such as accuracy, precision, recall, specificity, and the AUC-ROC curve for both datasets. From the experimental results, we concluded that the deep learning approaches have provided better results on evaluation grounds than the machine learning ones. In quantitative terms, ANN performed most consistently among all the considered approaches for either dataset with an accuracy of 100% and 98% respectively.
Mitigating Port Starvation for Shallow-buffered Switches in Datacenter Networks

Explicit Congestion Notification (ECN) is widely utilized in modern data centers to achieve low latency and high throughput for various applications. In recent years, however, even with the sustainable growth of link bandwidth in data centers, the switch buffer size does not increase remarkably. Consequently, the standard per-port ECN scheme suffers from excessive packet loss. Though the shared-buffer ECN scheme alleviates the packet loss, we observe that it leads to severe unfairness, which we term as the Port Starvation problem. When flows destined for some ports have aggressively occupied the shared buffer, the later-arrival flows destined for other ports will be ECN-marked unfairly and obtain significantly lower throughput. To address the port starvation problem, we design a buffer-aware fair ECN-marking (BFEM) scheme for shallow-buffered switch. BFEM leverages the shared buffer to reduce packet loss and meanwhile punishes aggressive flows by ECN marking. We evaluate BFEM with both 40Gbps P4 testbed implementation and large-scale NS2 simulation. The test results show that, by improving fairness between egress ports, BFEM increases total link utilization and reduces the average flow completion time by up to 40% compared with the state-of-the-art per-port and shared-buffer ECN marking schemes.
On the foreign direct investment–economic growth relationship in Africa: does economic freedom mediate this relationship?

Purpose In this study, the authors examine how economic freedom mediates the impact of foreign direct investment (FDI) on economic growth in Africa. Design/methodology/approach By using data from 41 countries over the period 2000–2017, the authors invoke Seo and Shin's (2016) sample splitting approach while relying on the recently developed Seo et al. 's (2019) computationally robust bootstrap algorithm to achieve the purpose of this study. Findings The authors find evidence of economic freedom threshold that bifurcates the link between FDI and economic growth in Africa. More precisely, FDI does not improve overall economic growth for African countries whose economic freedom index is below the estimated threshold while significantly spurring growth for African countries with economic freedom above this threshold. Practical implications African countries need to strive towards improving their level of economic freedom through the strengthening of rule of law, reducing government size, promoting regulatory efficiency and further opening of the goods and capital markets. Originality/value The association between FDI and economic growth has been well documented. While the positive theoretical postulations are almost conclusive, empirical literature on the precise effect of FDI remains contentious and far from being settled. What is missing in the existing literature in Africa is whether countries' level of economic freedom mediates how FDI explains the variations in economic growth across African countries. The authors fill this research gap.
$$\mathsf {EMA}$$: Auditing Data Removal from Trained Models

Data auditing is a process to verify whether certain data have been removed from a trained model. A recently proposed method [] uses Kolmogorov-Smirnov (KS) distance for such data auditing. However, it fails under certain practical conditions. In this paper, we propose a new method called Ensembled Membership Auditing (\(\mathsf {EMA}\)) for auditing data removal to overcome these limitations. We compare both methods using benchmark datasets (MNIST and SVHN) and Chest X-ray datasets with multi-layer perceptrons (MLP) and convolutional neural networks (CNN). Our experiments show that \(\mathsf {EMA}\) is robust under various conditions, including the failure cases of the previously proposed method. Our code is available at: https://github.com/Hazelsuko07/EMA.
Deep K-TSVM: A Novel Profiled Power Side-Channel Attack on AES-128

The appearance of deep neural networks for Side-Channel leads to strong power analysis techniques for detecting secret information of physical cryptography implementations. Generally, deep learning techniques do not suffer the difficulties of template attacks such as trace misalignment. However, the generalization of a trained deep neural network that can accurately predict Side-Channel leakages largely depends on its adjustable variables (parameters of a neural network). Although pre-training is no longer mandatory, it is needed for parameter selection of a deep neural network to improve the success rate and provide a better insight into the network’s inner functionality. In this paper, we propose a novel model via Twin support vector with a deep kernel approach when targeting a hardware implementation of AES-128. The proposed model is pre-trained with the Restricted Boltzmann Machine method in a layer-wise manner and then fine-tuned via gradient descent. Further, we used the grid search technique for the selection of each hyperparameter which is used to compute class probabilities of every test trace in our deep model based side-channel attack. Based on our analysis and experiments, this model empirically shows its effectiveness by outperforming some of its competitors in profiling attack methods such as convolutional neural networks and multilayer perceptron models. We also evaluate our model on both masked and unmasked AES implementation. The results indicate that the proposed approach has achieved a success rate of greater than 99% even with a single trace using Keras library with Tensorflow. We investigate the correct “key rank" according to the number of traces; our model reaches the key rank ≤ 10 when attacking the third AES SBox.
Design and Implementation of Virtual Museum Learning Environment from the Perspective of Multimedia Learning Theory

With the advancement of technology, virtual museums have attracted much attention as a new learning resource type. Under the guidance of multimedia learning theory, this study designed and implemented a virtual Tujia instrumental museum (Vtujia) based on seven multimedia design principles—coherence, signaling, redundancy, spatial contiguity, temporal contiguity, multimedia, and personalization principle. A sample of 56 students was participated in our study to experience the Vtujia. Their attitudes on the learning tool were collected. The results indicated that the students hold a positive attitude towards the virtual museum and believed that the virtual museum could effectively promote meaningful learning.
Overview of Blockchain Data Privacy Protection

Blockchain has the advantages of decentralization and distributed sharing of global ledger, but at the same time, it also has the risk of data privacy leakage. In order to prevent the privacy of blockchain users from being stolen by malicious attackers, there is still a large space for development of various protection mechanisms. At first, this paper analyzes the architecture of chain block, the Blockchain data privacy threat mainly summarized data for the application layer, network layer, data privacy threats to privacy, consensus layer data and transaction layer data privacy threats to privacy, and respectively summarized this paper introduces the principle of all kinds of privacy protection mechanism, characteristics, and different ways of implementation, Finally, it points out the future research direction of blockchain data privacy protection.
Secure Fusion of Crypto-Stegano Based Scheme for Satellite Image Application

The transmission of satellite images in the Internet has elicited significant attention over the past few years, due to the foreword of cloud and web based satellite information. Today satellite images are easily transferable through the network for research related work. So, it became important to transfer the data securely and with adequate quality to the users. To maintain the quality of data, the combined security techniques of Steganography and Cryptography method is used. Steganography method is used to hide data into another image, audio or video and Cryptography method is used for sending information insensible to the Cyber attackers. This fusion method provides a unique way to protect the information. The paper proposed the fusion of Steganography algorithm that uses Least Significant Bit (LSB), Most Significant Bit (MSB) and Cryptography algorithm that uses Rivest Shamir Adleman (RSA).
FastqCLS: a FASTQ compressor for long-read sequencing via read reordering using a novel scoring model

Over the past decades, vast amounts of genome sequencing data have been produced, requiring an enormous level of storage capacity. The time and resources needed to store and transfer such data cause bottlenecks in genome sequencing analysis. To resolve this issue, various compression techniques have been proposed to reduce the size of original FASTQ raw sequencing data, but these remain suboptimal. Long-read sequencing has become dominant in genomics, whereas most existing compression methods focus on short-read sequencing only.We designed a compression algorithm based on read reordering using a novel scoring model for reducing FASTQ file size with no information loss. We integrated all data processing steps into a software package called FastqCLS and provided it as a Docker image for ease of installation and execution to help users easily install and run. We compared our method with existing major FASTQ compression tools using benchmark datasets. We also included new long-read sequencing data in this validation. As a result, FastqCLS outperformed in terms of compression ratios for storing long-read sequencing data.FastqCLS can be downloaded from https://github.com/krlucete/FastqCLS.Supplementary data are available at Bioinformatics online.
Stability for the training of deep neural networks and other classifiers

We examine the stability of loss-minimizing training processes that are used for deep neural networks (DNN) and other classifiers. While a classifier is optimized during training through a so-called loss function, the performance of classifiers is usually evaluated by some measure of accuracy, such as the overall accuracy which quantifies the proportion of objects that are well classified. This leads to the guiding question of stability: does decreasing loss through training always result in increased accuracy? We formalize the notion of stability, and provide examples of instability. Our main result consists of two novel conditions on the classifier which, if either is satisfied, ensure stability of training, that is we derive tight bounds on accuracy as loss decreases. We also derive a sufficient condition for stability on the training set alone, identifying flat portions of the data manifold as potential sources of instability. The latter condition is explicitly verifiable on the training dataset. Our results do not depend on the algorithm used for training, as long as loss decreases with training.
Remote Physical Therapy: Requirements for a Single RGB Camera Motion Sensing

The aim of this work is to assess the minimal technical requirements for using a simple RGB camera for motion sensing in a standard home environment. We experimentally verify the recording requirements for subsequent motion analysis. Our work contributes to the development of physical telerehabilitation without the need to use special HW and thus enable telerehabilitation for the general public, which is especially important during the COVID-19 lockdown. We have found out that such a system can work surprisingly well even with a low-cost camera in poor recording conditions and slow 3G internet connection.
Cartoon Explanations of Image Classifiers

We present CartoonX (Cartoon Explanation), a novel model-agnostic explanation method tailored towards image classifiers and based on the rate-distortion explanation (RDE) framework. Natural images are roughly piece-wise smooth signals—also called cartoon-like images—and tend to be sparse in the wavelet domain. CartoonX is the first explanation method to exploit this by requiring its explanations to be sparse in the wavelet domain, thus extracting the relevant piece-wise smooth part of an image instead of relevant pixel-sparse regions. We demonstrate that CartoonX can reveal novel valuable explanatory information, particularly for misclassifications. Moreover, we show that CartoonX achieves a lower distortion with fewer coefficients than state-of-the-art methods.