Reviewerly/exHarmony huggingface dataset:
Multicentric evaluation of analytical performances digital morphology with respect to the reference methods by manual optical microscopy

Optical microscopic (OM) evaluation of peripheral blood (PB) cells is still a crucial step of the laboratory haematological workflow. The morphological cell analysis is time-consuming and expensive and it requires skilled operator. To address these challenges, automated image-processing systems, as digital morphology (DM), were developed in the last few years. The aim of this multicentre study, performed according to international guidelines, is to verify the analytical performance of DM compared with manual OM, the reference method.Four hundred and ninety PB samples were evaluated. For each sample, two May Grunwald-stained and Giemsa-stained smears were performed and the morphological evaluation of cells was analysed with both DM and OM. In addition, the assessment times of both methods were recorded.Comparison of DM versus OM methods was assessed with Passing-Bablok and Deming fit regression analysis: slopes ranged between 0.17 for atypical, reactive lymphocytes and plasma cells (LY(AT)) and 1.24 for basophils, and the intercepts ranged between -0.09 for blasts and 0.40 for LY(AT). The Bland-Altman bias ranged between -6.5% for eosinophils and 21.8% for meta-myemielocytes. The diagnostic agreement between the two methods was 0.98. The mean of assessment times were 150 s and 250 s for DM and OM, respectively.DM shows excellent performance. Approximately only 1.6% of PB smears need the OM revision, giving advantages in terms of efficiency, standardisation and assessment time of morphological analysis of the cells. The findings of this study may provide useful information regarding the use of DM to improve the haematological workflow.
A Novel Parameter Initialization Technique Using RBM-NN for Human Action Recognition

Human action recognition is a trending topic in the field of computer vision and its allied fields. The goal of human action recognition is to identify any human action that takes place in an image or a video dataset. For instance, the actions include walking, running, jumping, throwing, and much more. Existing human action recognition techniques have their own set of limitations when it concerns model accuracy and flexibility. To overcome these limitations, deep learning technologies were implemented. In the deep learning approach, a model learns by itself to improve its recognition accuracy and avoids problems such as gradient eruption, overfitting, and underfitting. In this paper, we propose a novel parameter initialization technique using the Maxout activation function. Firstly, human action is detected and tracked from the video dataset to learn the spatial-temporal features. Secondly, the extracted feature descriptors are trained using the RBM-NN. Thirdly, the local features are encoded into global features using an integrated forward and backward propagation process via RBM-NN. Finally, an SVM classifier recognizes the human actions in the video dataset. The experimental analysis performed on various benchmark datasets showed an improved recognition rate when compared to other state-of-the-art learning models.
Quantifying multipartite quantum entanglement in a semi-device-independent manner

We propose two semi-device-independent approaches that are able to quantify unknown multipartite quantum entanglement experimentally, where the only information that has to be known beforehand is quantum dimension, and the concept that plays a key role is nondegenerate Bell inequalities. Specifically, using the nondegeneracy of multipartite Bell inequalities, we obtain useful information on the purity of target quantum state. Combined with an estimate of the maximal overlap between the target state and pure product states and a continuous property of the geometric measure of entanglement we shall prove, the information on purity allows us to give a lower bound for this entanglement measure. In addition, we show that a different combination of the above results also converts to a lower bound for the relative entropy of entanglement. As a demonstration, we apply our approach on 5-partite qubit systems with the MABK inequality, and show that useful lower bounds for the geometric measure of entanglement can be obtained if the Bell value is larger than 3.60, and those for the relative entropy of entanglement can be given if the Bell value is larger than 3.80, where the Tsirelson bound is 4.
Smart System for Recognizing Daily Human Activities Based on Wrist IMU Sensors

Recognizing daily human activity using machine learning techniques is of great interest to many researchers working in the field of human health monitoring. Recently, wearable sensors have been used extensively for human activity recognition (HAR) for their great ability for capturing human actions during his daily life. Wearable wrist sensors have the advantage of being easily and comfortably worn. Extracting multimodal data from such sensors could enhance recognition rates leading to a healthier life style. Machine learning (ML) techniques have exciting capabilities, and can be used to facilitate HAR process. In this paper, a new daily HAR system is proposed for accurately recognizing daily human activity based on multimodal data from a wearable IMU wrist sensor. Two publically available datasets are employed to examine its effectiveness. The results indicate that the proposed HAR system is competitive to other recent related HAR approaches. This proves that the proposed HAR system is robust and, can be used for health monitoring applications.
Machine Learning Programming with Tensorflow 2.0

So far, we have learned about artificial intelligence, under which we have machine learning and its sub-set, deep learning. We have also learned about the Python programming language, which is popularly used for machine learning coding. We even got familiar with the Jupyter Notebook interface, in which we can write, edit, and debug our programs. We then saw how we can combine Python with Jupyter Notebook as an efficacious way to write our code. After this, we were introduced to the TensorFlow library as an important package within Python, and once we understood how the library was useful, we proceeded to learn about its recent upgrade—TensorFlow 2.0—which has additional features and abilities that make our machine learning models easier to build.
Automatic foreground extraction from imperfect backgrounds using multi-agent consensus equilibrium

Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. Our proposed method is very robust and produces significantly better estimates than state-of-the-art background subtraction, video segmentation and alpha matting methods. The key innovation of our method is a novel information fusion technique. The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate. Such integration is particularly important when handling complex scenes with imperfect background. We show how the framework is developed, and how the individual components are built. Extensive experiments and ablation studies are conducted to evaluate the proposed method.
A Symbolic Approach for Counterfactual Explanations

In this paper titled A Symbolic Approach for Counterfactual Explanations we propose a novel symbolic approach to provide counterfactual explanations for a classifier predictions. Contrary to most explanation approaches where the goal is to understand which and to what extent parts of the data helped to give a prediction, counterfactual explanations indicate which features must be changed in the data in order to change this classifier prediction. Our approach is symbolic in the sense that it is based on encoding the decision function of a classifier in an equivalent CNF formula. In this approach, counterfactual explanations are seen as the Minimal Correction Subsets (MCS), a well-known concept in knowledge base reparation. Hence, this approach takes advantage of the strengths of already existing and proven solutions for the generation of MCS. Our preliminary experimental studies on Bayesian classifiers show the potential of this approach on several datasets.
Usage of Web Mining for Sales and Corporate Marketing

The usage of web has grown to a big extent recently. Web is not only a source of information but is also a great platform to run a business successfully. Some of the major usage of web in E-commerce is retail, sales, and marketing areas. One of the technologies which serve as a tool in these areas is web mining. The process of data mining is to extract the required patterns of information in big database. Web mining is one of the categories of data mining. It uses data mining techniques for extracting interesting information from web. The main goal of this paper is to study the web mining in detail and to learn its usage in corporate sales and marketing.
Tag's Depth-Based Expert Profiling Using a Topic Modeling Technique

Expert finding and expert profiling are two important tasks for organizations, researchers, and work seekers. This importance can also be seen in online communities especially with the explosion of social networks. Expert finding on one hand addresses the task of finding the right person with the appropriate knowledge or skills. Expert profiling on the other hand gives a concise and meaningful description of a candidate expert. This paper focuses on what social tagging can bring to improve expert finding and profiling. A novel expertise indicator that models and assesses an expert based on the expert's tagging activities is proposed. First, tags are used as interest indicator to build candidate's profiles; then, Latent Dirichlet Allocation algorithm (LDA) is used to construct the tags distribution over topics by exploiting the tag's semantic characteristics. Topics of interest are then filtered using tag's depth. The latter is finally used as the expertise indicator. Experiments performed on the stack overflow dataset show the accuracy of the proposed approach.
Inverse iteration quantum eigensolvers assisted with a continuous variable

Abstract The capacity for solving eigenstates with a quantum computer is key for ultimately simulating physical systems. Here we propose inverse iteration quantum eigensolvers, which exploit the power of quantum computing for the classical inverse power iteration method. A key ingredient is constructing an inverse Hamiltonian as a linear combination of coherent Hamiltonian evolution. We first consider a continuous-variable quantum mode (qumode) for realizing such a linear combination as an integral, with weights being encoded into a qumode resource state. We demonstrate the quantum algorithm with numerical simulations under finite squeezing for various physical systems, including molecules and quantum many-body models. We also discuss a hybrid quantum–classical algorithm that directly sums up Hamiltonian evolution with different durations for comparison. It is revealed that continuous-variable resources are valuable for reducing the coherent evolution time of Hamiltonians in quantum algorithms.
Direct, diffuse and total solar radiation data set in La Guajira, Magdalena and Cesar departments -Colombia

This article aims to show direct, diffuse, and total solar radiation in the departments of La Guajira, Magdalena, and Cesar, located on the Caribbean coast of Colombia. In addition, data on climatic variables such as temperature, pressure, and relative humidity measured through different sensors located in these meteorological stations are presented. The data obtained by these stations correspond to measurements from 1993 to 2013 allowed the estimation of the parameters of the total, direct and diffuse solar radiation for each department, by mean of the Bird and Hulstrom model and parameterizations of the Mächler and Iqbal model. In addition, five climatological scenarios that could occur using these data were calculated.
Attention Based Dual Branches Fingertip Detection Network and Virtual Key System

Gesture and fingertip are becoming more and more important mediums for human-computer interaction (HCI). Therefore, algorithms of gesture recognition and fingertip detection have been extensively investigated. However, problems mainly remain in how to achieve a win-win situation between speed and accuracy, and how to deal with complex interaction environment. To rectify these problems, this paper proposes an attention-based dual branches network that can efficiently fulfill both fingertip detection and gesture recognition tasks. In order to deal with complex interaction environment, we combine both channel-wise attention and spatial-wise attention into the fingertip detection model. The extensive experiments demonstrate that our novel model is both effective and efficient. In the experiment, our proposed model achieves the average fingertip detection error at around 2.8 pixels in 640×480 video frame, and the average recognition accuracy among eight gestures reaches $99%$. Moreover, the average forward time is about 8 ms. Due to the light-weight design, this model can also achieve high-efficiency performance on CPU. In addition, we design a virtual key system based on our proposed model, which can allow users to complete the "clicking" operation naturally in virtual environment. Our proposed system can perform well with a single normal RGB camera without any pre-processing (e.g., image segmentation or contour extraction), which can significantly reduce the complexity of the interaction system.
Products And Movie Recommendation System For Social Networking Sites

Recommendation systems are an integral part of information filtering system in data science, that are widely used in order to identify the pattern a user would likely choose on the basis of the previous choices of the user as well as from studying the pattern in which others have chosen. For a fact, the recommendation can never be a cent percent correct at providing recommendations to the user but can be close enough to please them to a certain extent. Thus, the same is widely used in the industries these days to get higher profit and have a good hold in the market. The data scientists of every company design some algorithm that studies the information from the social network and clusters the data. There can be a single algorithm for classification like k-Means clustering or Hidden Markov model or can be done by bagging and boosting techniques. With this technique of displaying the movies or products into the profile of a particular customer, they not only increase their business but also enhances the customer experiences but there are several issues related to the standard techniques like the cold start problem, shrill attack, etc. thereby increasing the scope of research in this field. This work deals with both Collaborative Filtering and Content-Based Filtering to form a product and movie recommendation system for the social networking sites that shows the effectiveness of collaborative filtering and portrays the challenges faced by content-based filtering.
On the Longest Common Subsequence of Conjugation Invariant Random Permutations

Bukh and Zhou conjectured that the expectation of the length of the longest common subsequence of two i.i.d random permutations of size $n$ is greater than $\sqrt{n}$. We prove in this paper that there exists a universal constant $n_1$ such that their conjecture is satisfied for any pair of i.i.d random permutations of size greater than $n_1$ with distribution invariant under conjugation. More generally, in the case where the laws of the two permutations are not necessarily the same, we give a lower bound for the expectation. In particular, we prove that if one of the permutations is invariant under conjugation and with a good control of the expectation of the number of its cycles, the limiting fluctuations of the length of the longest common subsequence are of Tracy-Widom type. This result holds independently of the law of the second permutation.
Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach

This paper develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable, to influence also the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the Asymmetric Laplace distribution. Maximum likelihood estimates are obtained through the EM algorithm without parametric assumptions on the random effects distribution. In addition, a penalized version of the EM algorithm is presented to tackle the problem of variable selection. The proposed statistical method is applied to the well-known RAND Health Insurance Experiment dataset which gives further insights on its empirical behavior.
PreKindergarten and kindergarten virtual school programs under <scp>COVID</scp>‐19: A two‐case comparative study

Abstract This case study aims to explore and compare two cases of virtual school programs, one asynchronized and one synchronized, regarding their pedagogical design and operationalization. The authors collected data from observations of learning activities and analyses of teaching materials. A comparison was drawn on the benefits and drawbacks of two teaching modes, and the strategies the instructors use to address the limitations.
Modeling Angle-Based Pointing Tasks in Augmented Reality Interfaces

The target in an augmented reality (AR) interface is often located in the distance and outside the initial AR viewport, requiring the user to rotate the AR device, instead of translating it, to complete the search and selection of the target.In order to quantitatively analyze rotating the AR device in the AR interface in the selection of a target located outside the initial visual window, we propose and experimentally verify a quantitative model based on angle measurement under two selection techniques: with and without cursor.The results show that the model proposed in this paper can accurately describe the interactive process when a rotating AR device completes the target selection task.With cursor angle measurement achieves a model fitting value of R 2 = 0.937, and without cursor angle measurement achieves a model fitting value of R 2 = 0.915; hence, good modeling of user performance in AR selection tasks is achieved.
Transmissive LED Touch Display for Engineered Marble

In this paper, we propose a thin display panel system for converting an existing kitchen or vanity tabletop with an engineered marble into a smart touch display. The display system, which consists of an LED matrix panel including IR-LEDs and photodiodes, will be placed on the rear side of a tabletop of a kitchen or washbasins. The normal LED light is used for a visual display transmitted with a thick opaque resin, and the infrared light is used for detecting touch areas and objects using the same resin panel. This display panel can be used for various applications and can be touched with wet and/or dirty hands. In this paper, we describe the system overview, the optical characteristics of blurred display via an engineered marble plate, and an touch-sensing experiment. We also provide some applications of the smart touch display.
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification

The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks. These tasks, which cover a large variety of domains, will be shown to the automated system in a random order. Each time when the tasks are switched, the information of the new task will be hinted with its corresponding training set. Thus, every submitted solution should contain an adaptation routine which adapts the system to the new task. Compared to the first edition, the 2020 edition includes advances of 1) more speech tasks, 2) noisier data in each task, 3) a modified evaluation metric. This paper outlines the challenge and describe the competition protocol, datasets, evaluation metric, starting kit, and baseline systems.
Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation

Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods.
Coherent randomized benchmarking

Randomized benchmarking is a powerful technique to efficiently estimate the performance and reliability of quantum gates, circuits and devices. Here we propose to perform randomized benchmarking in a coherent way, where superpositions of different random sequences rather than independent samples are used. We show that this leads to a uniform and simple protocol with significant advantages with respect to gates that can be benchmarked, and in terms of efficiency and scalability. We show that e.g. universal gate sets, the set of $n-$qudit Pauli operators or more general sets including arbitrary unitaries, as well as a particular $n-$qudit Clifford gate using only the Pauli set, can be efficiently benchmarked. The price to pay is an additional complexity to add control to the involved quantum operations. However we demonstrate that this can be done by using auxiliary degrees of freedom that are naturally available in basically any physical realization, and are independent of the gates to be tested.
Adoption Challenges of Code Randomization

Languages in the C family are distinguished by their efficiency, maturity, and their lack of guardrails compared to other mainstream language in use today. Their efficiency properties kept these languages relevant as new ones appeared. Their lack of memory safety and the resulting vulnerabilities is an ongoing challenge.