Reviewerly/exHarmony huggingface dataset:
Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images

Bone marrow biopsies play a central role in hematopathology for diagnosing a variety of diseases, staging lymphomas or performing follow-up progression. Tasks performed while examining biopsies include counting cells and estimating the ratio of various hematopoietic lineages. Inter- and intra-observer variability between hematopathologists in the outcome of these tasks has been shown to be significant, which could result in multiple pathologists diagnosing some patients differently. To that end, this paper presents a fully-convolutional neural network (FCNN) architecture to segment six classes in bone marrow trephine biopsies, which could assist hematopathologists in identifying and delineating cells, thus reducing inter- and intra-observer variability. Additionally, to show an application of the neural network to a clinically relevant task, the output of the network is used to train a classifier capable of distinguishing between normocellular and aplastic bone marrow. Results indicate the network is successfully capable of segmenting cells with an average detection rate of 83%. The classifier for distinguishing normocellular/aplastic bone marrow reaches an AUC of 0.990, showing that is capable of automatically identifying aplasia.
Development of Application for Simulation of Video Quality Degradation Artifacts

In this paper, we present an application that enables fast and controlled quality degradation of any given video sequence. Considering, that it also displays a preview of both degraded and reference video frames along with the corresponding quality degradation value, it can be a useful tool for education and research. Further, we describe the logical process of creating degradation artifacts, namely blurring, several types of electronic noise, blocking effect, color bleeding and mosquito noise. This paper also analyses the results of an experiment, conducted on video sequences affected by simulated artifacts and evaluated by two objective quality metrics. As expected, we certified that video quality is decreasing with rising intensity of each applied effect. Excluding sequences affected by any type of noise, video quality seems to be content dependent. Furthermore, we also discovered that noises impair video quality most critically, according to objective metrics.
Access services statistics: showing our story through LibInsight

Purpose The purpose of this paper is to evaluate the Springshare tool LibInsight, and describe the ways it was implemented at the R. Barbara Gitenstein Library at The College of New Jersey. Design/methodology/approach Going beyond traditional data collection, LibInsight allows the library to blend and analyze library data in a single dashboard. It enables decision-makers to see analytics presented visually, identify new patterns and quickly address problems or opportunities. LibInsight can visualize, evaluate and assess library services and collections and compare them both internally and against industry standards. Findings Historical data for data sets ranging from gate counts and circulation were uploaded into LibInsight. In addition, custom data sets were created and new data, such as patron activity at the circulation desk, was collected. Originality/value This is a review of the product, LibInsight, and data sets kept in access services.
Environmental Prediction Using Thermal Index Due to Statistical Physics of Q-Ising Model

We investigate environmental prediction using thermal index via the temperature-humidity index (THI) due to statistical mechanics of Q-state Ising model with small spins. We find that the environmental prediction realizes optimal THI, if we have perfect knowledges on the assumed true prior and the assumed transition probability from each original state to the observed one. Then, we find that the THI increases/decreases in comparison with the optimum with a decrease/increase in parameters expressing the temperature difference without using the model prior stabilizing the optimal thermal index. Also, we clarify that we can expand a range where the THI approximates its optimum by introducing the model prior.
Face 3D measurement by phase matching with infrared grating projection

In the current face 3D measurement technology, binocular stereo vision has been widely used. For the passive binocular 3D measurement system that does not need to project auxiliary light, it has the characteristics of simple system structure, but the result is not accurate enough and the algorithm is complex. Therefore, this paper proposes a fast measurement method for binocular stereo vision combined with infrared grating structure light. Because Digital-Light-Processing (DLP) projector has slow projection speed, dynamic images acquisition cannot be performed well, and when applied to the face 3D measurement, the eyes of the measured person will be stimulated by strong light, so a Micro-Electro-Mechanical System (MEMS) infrared projector is used in this paper. It has the advantages of high projection speed, high precision and no stimulation to the human eyes, so the MEMS projector can be well applied to 3D measurement of human face. The sinusoidal fringes are projected onto the face by the MEMS projector, and the phase is wrapped and unwrapped by phase measurement profilometry. In this paper, the four-step phase-shift method is used to calculate the wrapped phase, and the phase order is obtained according to the multi-frequency heterodyne principle. Fast matching of corresponding points of two image planes by combining epipolar and phase order constrained algorithms. The experiment verified that the highspeed, stable and low-cost face 3D measurement system was realized.
HASIL BELAJAR PESERTA DIKLAT TEKNIS SUBSTANTIF MEDIA PEMBELAJARAN BERBASIS MULTIMEDIA (STUDI DI KANTOR KEMENTERIAN AGAMA KOTA BEKASI)

This study aims to find out the training participants 'initial knowledge about Googlebased application programs and training participants' learning outcomes after following the learning process in the Substantive Technical Training on Multimedia-based Learning held at the Bekasi City Ministry of Religion. The research method used was descriptive, where the data collection techniques in this study used technical tests, namely by pre and post test to determine aspects of participant knowledge and product assessment sheets in the form of rating scale, to assess how far the training participants' ability to operate and make products based on Google based application. The results of this study indicate that the initial knowledge of training participants is on the criteria of "less", the percentage of 13.48%, after participating in the learning process the learning outcomes in the aspects of knowledge are in the criteria of "good", percentage of 81.67% an average score of 3.07 with the criteria of "good". Keywords: Google-based Multimedia; Learning Outcomes; Training
Fast head detection in arbitrary poses using depth information

Purpose This study aims to develop a real-time algorithm, which can detect people even in arbitrary poses. To cover poor and changing light conditions, it does not rely on color information. The developed method is expected to run on computers with low computational resources so that it can be deployed on autonomous mobile robots. Design/methodology/approach The method is designed to have a people detection pipeline with a series of operations. Efficient point cloud processing steps with a novel head extraction operation provide possible head clusters in the scene. Classification of these clusters using support vector machines results in high speed and robust people detector. Findings The method is implemented on an autonomous mobile robot and results show that it can detect people with a frame rate of 28 Hz and equal error rate of 92 per cent. Also, in various non-standard poses, the detector is still able to classify people effectively. Research limitations/implications The main limitation would be for point clouds similar to head shape causing false positives and disruptive accessories (like large hats) causing false negatives. Still, these can be overcome with sufficient training samples. Practical implications The method can be used in industrial and social mobile applications because of its robustness, low resource needs and low power consumption. Originality/value The paper introduces a novel and efficient technique to detect people in arbitrary poses, with poor light conditions and low computational resources. Solving all these problems in a single and lightweight method makes the study fulfill an important need for collaborative and autonomous mobile robots.
Improved BDD Binary Logic Algorithm: Hidden BDD Algorithm

This paper first introduces the principle of BDD algorithm, then gives the definition of 1-structure function and 0-structural function, and derives the 1-structure and 0-structure function of each logic gate based on the principle of disjoint of BDD. Next, the concept of hidden BDD is proposed. Then the algorithm flow is proposed, The algorithm does not need to sort the bottom event, nor does it need to construct and optimize BDD, that is, it can directly obtain the top event structure function and use the structure function to perform quantitative calculation. The algorithm can simplify the encoding process, reduce the related steps around the BDD in order to obtain the structure function, and improve the computational efficiency. Finally, a case is given to illustrate the correctness and effectiveness of the algorithm.
K-Mean Clustering

In this paper, I apply K-mean clustering method to test the validity of Bank notes by separating notes in the real or fake group automatically with Python 3 in the Jupyter Lab.
A Digital Mammogram Auto Classification Method Based on Fibroglandular Breast Tissue Density Evaluation by Image Similarity

It is crucial to assess the fibroglandular breast tissue density to define the degree of the risk that the healthy breast tissue will obscure the lesions. Subjective assessment criteria, proceed by the reading physicians by using the mammary gland concentrations on mammograms, are defined as the breast classification method. However, due to the existence of between observer’s variability, a computer-based quantitative classification method is required. The conventional method classifies according to the ratio of the Dmg region (mammary gland region) to the Dc region (fibroglandular breast tissue region). However, this does not include subjective evaluation elements. The purpose of this study is to improve the concordance rate with the subjective assessment by performing an automated classification based on image similarity. First, 130 cases of right MLO (Medio-Lateral Oblique) images, subjectively classified as fatty tissue, mammary gland diffuseness, non-uniform high density, and high density, were reclassified to two groups; fatty tissue and mammary gland diffuseness as Non-Dense breast, and non-uniform high density and high density as Dense breast. Next, as for evaluation images, 33 cases of both sides MLO images taken by different mammography devices were used. Finally, the image similarity analysis result using Normalized Cross-Correlation between the search image and the evaluation image was derived, and the degree of coincidence of subjective breast classification was calculated. As a result, the concordance rate between the conventional method and the subjective evaluation results of this method improved from 73 % to 91 %, and the kappa coefficient improved from 0.49 to 0.81. This result indicates that our approach is more useful for the automated classification of mammograms based on fibroglandular breast tissue density.
Rapid Consortium Blockchain for Digital Right Management

With the rapid achievement of internet technologies and digital multimedia, the strengthening of intellectual property rights remains an enormous challenge in digital right management (DRM). Toward the objective, the traditional DRM system relied on digital encryption and digital watermarking technology emphasizes much more on the encryption and decryption of digital property right than tracking digital property rights transaction and authorization, which is as important as encryption and decryption. To improve the situation, with the widely used blockchain and distributed ledgers technology (DLT) in a broad range of applications and different domains, this paper proposes a new DRM system named RCB (Rapid Consortium Blockchain)-DRM which has excellent ability to effectively trace the transaction of intellectual property matters and manage digital copyright with decentralization, tamper-proof, transparent digital date stored in master-slave rapid consortium blockchain. In the proposed master-slave rapid consortium blockchain, we adopt a new intelligent consensus mechanism (ICM) named PBS (Proof By System Nodes), improving the consensus efficiency, shortening the transaction confirmation time and improving the fault tolerance of the system, which can ensure the final consistency of each block and guarantee block generation stability without blockchain bifurcating.
Classification of Encrypted Text Based on Artificial Intelligence

Abstract With the development of internet, information security is essential. The traditional decryption method only works for single encryption method. New models based on Artificial Intelligence to decrypt different kinds of encryption methods. Data features were extracted by tf-idf algorithm and applied into different models. The primary models to decrypt message without knowing whether encryption methods are logistic regression, lightGBM, and ensemble algorithm to decrypt encryption data. Comparing the performance in the models based on logistic regression and lightGBM algorithm respectively, we concluded that both the accuracy and F1 score of logistic algorithm are better than those of lightGBM algorithm. We also made improvement on the model by using ensemble algorithm. A more preferable performance including the accuracy and F1 score has achieved with multiple algorithm. Therefore, ensemble algorithm is more suitable for building general model to decrypting different kinds of encryption methods.
A Fast Approach for Video Deblurring using Multi-Scale Deep Neural Network

In many computer vision tasks, the non-uniform blind deblurring is a serious issue. Camera shake, multiple object motions and scene depth variation are some of the major causes of motion blur which need to be tackled carefully. Video deblurring is an algorithmic approach which takes multiple corrupt, blurred and mis-focused images and estimates the clean images. In the literature, video based methodologies leverage the information available across neighbouring frames to deblur the images, which is in contrast to single image deblurring techniques. This is the reason that the performance of the video deblurring techniques depends on alignment of neighbouring frames. However, aligning neighbouring frames is a expensive task in terms of computation and it requires high level scene understanding so that one can clearly distinguish between regions that have been precisely aligned from those that have been not. Also, it is an important that any video deblurring methods should have minimum latency in real time. In this work, we gradually restore sharp video frame from a blurred one in a ‘course-to-fine' manner. We use a multi-scale deep learning based model for deblurring an image in an end-to-end manner. The experimental evaluation shows that the proposed technique performs faster when compared to other video deblurring technique with substantial improvement in qualitative and quantitative performance.
Construction and Empirical Analysis of Intelligent Prediction Model for Electric Grid Project Cost

Abstract China’s economic situation has entered a new normal development period, the growth rate of national economic development has fallen, and the state has comprehensively implemented various measures such as price reduction and fee reduction, which makes the operation of electric power enterprises face greater pressure. In this paper, Shandong power transmission and transformation project is taken as the research object, using big data analysis technology and current popular intelligent algorithm to build the intelligent prediction model of power grid project cost, to assist in investment decision-making, reduce the balance rate, strengthen the fine control of investment, improve the precise control level of investment, and enhance the competitiveness of enterprises.
College Students' Sports Education Activities Space Comfort Model Based on the Fusion of System Intelligent Optimization Algorithm

Aiming at the problem that the intelligent optimization algorithm has relatively low precision, large error and other problems in the analysis of the growth in the sports Education activity space of college students, a kind of college Students' Sports Education activities space comfort model based on the fusion of the system intelligent optimization algorithm is put forward in this paper. In the analysis of the design for the sports Education activities space of the college students, on the basis of the characteristics of comfort thus involved, the external form feature elements of the college Students' Sports Education Activities space and the comfort model for the sports activities space of college students are established. The degree of comfort is adopted as the standard, and the feature matching is performed to carry out detailed analysis on the degree of comfort of college Students' Sports Education Activities space from two perspectives, that is, the similarity and the degree of comfort. The system intelligent optimization degree is used to determine the structure and the adjacency matrix of the system to accurately obtain the comfort index of the college Students' Sports Education Activities space, so as to provide further guidance for the design of the sports activities space of the college students. Finally, the practicality and effectiveness of the model are verified through case analysis.
An Efficient Residual-Convolutional Neural Model for Handwritten Text Recognition

Offline handwritten text recognition from images is a long-standing research topic for researchers attempting to digitize large volumes of handmarked scanned documents. A novel neural network architecture, which integrates feature extraction, sequence learning and transcription into a unified framework, is proposed. It uses a combination of residual blocks and convolution to efficiently extract image features. The experimental results were benchmarked against publicly available IAM dataset.
Data Encryption Method of Ssl Digital Authentication Signature System Based on Privacy Protection

The data encryption design of SSL digital authentication signature system is designed to improve the confidentiality of SSL digital authentication signature system. In order to solve the problem of low cyclic shift key coding and decoding rate and strong sensitivity, a data encryption technology of SSL digital authentication signature system based on privacy protection is proposed. Firstly, the floating-point data of SSL digital authentication signature system are arithmetic encoded, and the random linear encryption key of SSL digital authentication signature system is designed by using Logistics chaotic linear mapping. Then the piecewise sample regression test method is used to design the data coding and decoding of SSL digital authentication signature system, and the data encryption and privacy protection technology of SSL digital authentication signature system is optimized. Finally, the encryption performance is tested by data simulation analysis. The results show that the encryption reliability of SSL digital authentication signature system is better, the risk of deciphering is reduced, and the secure transmission performance of data is improved.
Deep Matrix Tri-Factorization: Mining Vertex-wise Interactions in Multi-Space Attributed Graphs

Mining vertex-wise interactions in graphs helps reveal useful information in real-world applications, such as bioinformatics networks BioGRID and DrugBank and academic networks DBLP and Arxiv. A main challenge in developing a general learning method for this setting is that each vertex may be associated with features from heterogeneous feature spaces, representing very disparate information. Moreover, features could be raw and low-level, leading to sparse representations. Some solutions in this area treat all feature spaces as equally important and concatenate features from heterogeneous feature spaces into a single feature vector. Others harmonize different feature spaces by respecting their relative significance in mining vertex-wise interactions but requiring construct specialized harmonizing function and/or handcrafting expressive features, both of which entail expert knowledge. Motivated by this observation, we propose a new learning paradigm named Deep Matrix Tri-Factorization (DM3F), which draws insights from deep models: (i) DM3F replaces the linear combination with a neural architecture that can learn an arbitrary harmonizing function from data; and (ii) DM3F allows raw feature inputs and automatically extracts high-level feature representations via a layer-by-layer learning mechanism. These two characteristics of DM3F make it accessible for users without expert knowledge. DM3F includes two orthogonal and complementary models, allowing an ensemble mechanism to optimize its performance during both training and predicting. A theoretical analysis of DM3F reveals that it possesses several desirable properties, including that it strictly generalizes matrix factorization models. We demonstrate the performance of DM3F on two real-world datasets.MSC codesAttributed graphHeterogenous feature spaceMatrix tri-factorizationDrug-drug interactionCo-author network
Unary and Two-Variable Interval Logics

This paper shows that over finite word models, the interval logic of Halpern and Shoham (J Assoc Comput Mach 38(4):935–962, 1991) is expressively complete for two-variable logic with betweenness relations, introduced in Krebs et al. (Proceedings of the 31st LICS, 2016). Satisfiability of formulae can be checked in polynomial space.
Effective Face Verification Systems Based on the Histogram of Oriented Gradients and Deep Learning Techniques

In this paper, we proposed a face verification method. We experiment with a histogram of oriented gradients description combined with the linear support vector machine (HOG+SVM) as for the face detection. Subsequently, we applied a deep learning method called ResNet-50 architecture in face verification. We evaluate the performance of the face verification system on three well-known face datasets (BioID, FERET, and ColorFERET). The experimental results are divided into two parts; face detection and face verification. First, the result shows that the HOG+SVM performs very well on the face detection part and without errors being detected. Second, The ResNet-50 and FaceNet architectures perform best and obtain 100% accuracy on the BioID and FERET dataset. They also, achieved very high accuracy on ColorFERET dataset.