Reviewerly/exHarmony huggingface dataset:
Multi-label Learning By exploiting Correlations of Label Subsets

Multi-label learning studies exist in a wide range of diverse scenes such as text processing, image mining, emotion analysis, etc. Feature selection technologies are proposed to be a vital factor in the scene of multi-label learning, which can relieve the influence of curse of dimensionality, enhance the classification accuracy, and reduce the time of consumption of learning process. At present, a great number of multi-label feature selection algorithms are proposed by the researchers who are focused on the machine learning or other research fields. It is demonstrated that considering the label correlation when the method choices the importance feature subset will improve the multi-label learning methods performance. On this account, a method that based on correlations of label is proposed in this work.
Efficient Bitcoin Mining Using Genetic Algorithm-Based Proof of Work

Blockchain requires to validate the block with confirmed transactions from the unconfirmed pool of transactions through Miners. Miners pick up the transactions from the pool of unconfirmed transactions approximately more than 2000 and solve the algorithmic puzzle i.e. also known as proof of work within the limited period of time. To maximize the throughput per second requires optimization of the time period to solve the algorithm puzzle for validating the block. Conventionally, for unconfirmed transactions, miners solve the proof of work using brute force algorithms which consume a lot of electrical energy due to the huge number of computations. To optimize the time for block chain mining, this paper proposes a Genetic algorithm based block mining (GAMB) approach to fetch the transactions from the unconfirmed pool of transactions in order to validate the block within a limited period of time. It is a population based algorithm which attempts to solve the proof of work for multiple transactions in parallel. The performance of GAMB is evaluated for transactions from 1000 to 5000.
Semantic Preserving Siamese Autoencoder for Binary Quantization of Word Embeddings

Word embeddings are used as building blocks for a wide range of natural language processing and information retrieval tasks. These embeddings are usually represented as continuous vectors, requiring significant memory capacity and computationally expensive similarity measures. In this study, we introduce a novel method for semantic hashing continuous vector representations into lower-dimensional Hamming space while explicitly preserving semantic information between words. This is achieved by introducing a Siamese autoencoder combined with a novel semantic preserving loss function. We show that our quantization model induces only a 4% loss of semantic information over continuous representations and outperforms the baseline models on several word similarity and sentence classification tasks. Finally, we show through cluster analysis that our method learns binary representations where individual bits hold interpretable semantic information. In conclusion, binary quantization of word embeddings significantly decreases time and space requirements while offering new possibilities through exploiting semantic information of individual bits in downstream information retrieval tasks.
Enabling Electronic Bills of Lading by Using a Private Blockchain

The bill of lading (B/L) is one of the most important documents in international trade. Current advances started to adopt public blockchain technology for digitization but lacking privacy. We raise the research question, if a private blockchain is suitable to solve this shortcoming. To answer this research question, we followed the design science research methodology. We designed an architecture and evaluated it via implementation, using Hyperledger Fabric. First results show general feasibility and privacy enhancement but reveal a high complexity and difficult extendibility of Hyperledger Fabric. Preliminary results of a focus group shows that the approach peaked interested by practitioners.
Application of Machine Learning Algorithm in Art Field – Taking Oil Painting as an Example

Oil painting production is a very time-consuming task. This article uses the current generation confrontation network popular in machine learning to transfer the style of images, and directly convert real-world images into high-quality oil paintings. In view of the current popular AnimeGAN and CartoonGAN generative confrontation networks, there are problems such as serious loss of details and color distortion in image migration. In this paper, by introducing SE-Residual Block (squeeze excitation residual block), comic face detection mechanism and optimizing the loss function, a new BicycleGAN is proposed to solve the problem of serious loss of details in the AnimeGAN migration image. By adding DSConv (distributed offset convolution), SceneryGAN is proposed to speed up the training speed and eliminate the ambiguous pixel blocks in the CartoonGAN migration image. The experimental results show that compared with AnimeGAN and CartoonGAN, the method in this paper has a significant improvement in training speed, comic image generation quality, and image local realism.
Information management system of student status archives based on SSM framework

In order to improve the retrieval efficiency and accuracy of the student status file management system, this paper designs a student status file information retrieval system based on SSM framework. Based on the spring structure, the overall structure and frame of the management system are designed. The hardware system mainly includes information input module, information management module and information retrieval module. The software system uses the ontology semantic similarity method to calculate the feature vector and classification extraction threshold of the information of the student status file, realizes the classification extraction of the information of the student status file, and improves the efficiency and precision of the information retrieval of the student status file. The experimental results show that the designed system has high retrieval efficiency and precision, and can keep stable running state. The highest retrieval accuracy of the system can reach 96% .
Deep Regression Ensembles

We introduce a methodology for designing and training deep neural networks (DNN) that we call 'Deep Regression Ensembles' (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE has two components, randomly drawn input weights and output weights trained myopically (as if the final output layer) using linear ridge regression. Within a layer, each neuron uses a different subset of inputs and a different ridge penalty, constituting an ensemble of random feature ridge regressions. Our experiments show that a single DRE architecture is at par with or exceeds state-of-the-art DNN in many data sets. Yet, because DRE neural weights are either known in closed-form or randomly drawn, its computational cost is orders of magnitude smaller than DNN.
Some Insight into Designing a Visual Graph-Shaped Frontend for Keras and AutoKeras, to Foster Deep Learning Mass Adoption

The aim of this paper is to provide some insight into designing a visual graph-shaped frontend for Keras and AutoKeras, two flagship deep learning software platforms. We also placed this particular endeavor in the larger context of deep learning mass adoption, which is just happening in many application fields, pointing out what challenges it is facing. There are several underlying conditions for going on quickly: automating the end-to-end machine learning pipeline, continuing the advances in GPU technology for supporting computing speed-up and parallelization, using tensor-enabled and GPU compatible mathematical libraries, and designing versatile GUIs capable of visually representing the network configuration in shape of a directed acyclic graph. All these blocks must be hierarchically stacked. Designing a Graph-Shaped frontend for deep learning platforms is the last, but equally essential brick to this construction and finally was the motivation behind this paper. Tensor-enabled libraries such as TensorFlow and the recent Nvidia GPU technology are the foundation layer. Keras successfully attempted to simplify the access to TensorFlow, while AutoKeras adds the automation support on top of Keras. Our proposed frontend, called Visual Keras&Autokeras, is attempting to visually emulate all the APIs related to Keras Functional model and AutoKeras AutoModel, in a codeless environment.
HIV/AIDS Community Information Outreach Program (ACIOP): A Landmark NIH Conference and an Enduring NLM Role in Meeting the Affected Community’s Need for Information Access

In June 1993, the National Library of Medicine (NLM) joined with the National Institutes of Health's (NIH) Office of AIDS Research (OAR), and the National Institute of Allergy and Infectious Diseases (NIAID) to host a conference at a pivotal time in the HIV/AIDS epidemic to understand better the information needs of five major constituency groups: clinical researchers; clinical providers; news media and the public; patients; and the affected community. NLM's director, Donald A.B. Lindberg M.D., and staff sought to identify new program possibilities benefitting from the input of current and potential users of the Library's information services. Conference recommendations led to a key NLM policy change providing cost-free access to all AIDS data, and the establishment of the HIV/AIDS Community Information Outreach Program (ACIOP), which enabled new partnerships with local community-based organizations serving the affected community. Uniquely funded and long running, more than 300 ACIOP projects have been supported to-date. These projects have improved awareness and use of national HIV/AIDS information resources; enhanced information seeking skills; developed locally generated information resources; and enhanced the capacity of community-based organizations to use new information and computer technologies providing access to essential information resources and services.
Calculation of Bit Error Rates for Superdense and ALOHA based Quantum Communication

Quantum communication allows us to share information by using the quantum states of qubits. Superdense coding is a very popular protocol or scheme for quantum communication, which uses entangled qubits. Entangled qubits can also be used to share information using an ALOHA based protocol. Performance evaluation of these protocols is essential for their implementation in quantum communication. In this paper, we calculate the bit error rates (BERs) of these two entanglement-based quantum communication schemes. It has been noticed that the BER performance is improved in an ALOHA based scheme compared to the Superdense coding scheme. However, without using any error correction schemes, the BERs are still very high in both the cases compared to the classical communication methods. The BER performance can be further improved by using the appropriate error correction schemes, which are under investigation.
An Automatic Text Region Selection Method on Optical Inspection for Airborne Instrument

For text region selection on airborne instruments scene, the character-like elements such as pointers and grids have a negative effect on text detection, and the existing text detection methods are difficult to handle it. This paper proposes a modified text detection model based on Fully Connected Neural Network and U-net, which achieved better prediction performance of fewer noise pixels, fewer wrongly predicted areas and have relatively higher spatial consistency. To further address the problem of FCN lacking spatial consistency, a method of filtering False Positives by seed anchor was proposed in post processing. The simulation result shows that the improved FCN text detection model performs better than the original Fully Connected Neural Network in both precision and recall. Furthermore, the proposed post processing method further improved precision index.
RetinaMHSA: Improving in single-stage detector with self-attention

In recent years, object detection with two-stage methods is one of the highest accuracies, like faster R-CNN. One-stage methods which use a typical dense sampling of likely item situations may be speedier and more straightforward. However, it has not exceeded the two-stage detectors&#x0027; accuracy. This study utilizes a Retina network with a backbone ResNet50 block with multi-head self-attention (MHSA) to enhance one-stage method issues, especially small objects. RetinaNet is an efficient and accurate network and uses a new loss function. We swapped c5 in the ResNet50 block with MHSA, while we also used the features of the Retina network. Furthermore, compared to the ResNet50 block, it contains fewer parameters. The results of our study on the Pascal VOC 2007 dataset revealed that the number 81.86 &#x0025; mAP was obtained, indicating that our technique may achieve promising performance compared to several current two-stage approaches.
Kalman Filtering With Linear State Equality Constraints: A Method Based on Separating Variables

This article considers the minimum variance state estimation of linear dynamic systems with linear state equality constraints.The proposed method uses singular value decomposition to divide the constrained system states into deterministic and stochastic parts.The deterministic part can be independently determined through the constraint equations.The stochastic part consists of random variables which have to be determined through a filtering process.The measurement and dynamic equations of the system are also divided into stochastic and deterministic parts.In order to update the mean and covariance of the state vector's stochastic part, the measurement updating phase of the unconstrained Kalman Filter is used.Then, the deterministic part of the dynamic equation is used as a noisy measurement for the proposed method.Finally, the stochastic part of the dynamic equation is used to predict the mean and covariance of state vector's stochastic part.Simulations show that the proposed method provides superior performance compared to other methods in the literature especially for estimating the constrained unobservable states.
Analytic-Synthetic Method and Axiomatic Method

From antiquity, a number of methods have been put forward as an alternative to the analytic method. The chapter examines Aristotle’s analytic-synthetic method and its relation to the analytic method. Then, it examines Pappus’s analytic-synthetic method and its relation to reductio ad absurdum. Finally, it examines the material axiomatic method, a byproduct of Aristotle’s analytic-synthetic method, and the formal axiomatic method, the form of the axiomatic method that has prevailed for the past century.
Malaria Detection Using Machine Learning

In spite of great improvements in healthcare around the globe, malaria remains a huge burden, with millions of infections occurring yearly. The first step in fighting malaria is to have an accurate, fast, and trusted diagnosing method, which is a problem facing lab technicians worldwide. In Sudan, the burden of malaria is especially heavy because the healthcare system is weakly structured, the number of qualified personnel to conduct an accurate diagnosis is low, and the number of samples that can be tested per day is limited. Machine learning and deep learning algorithms have the potential for addressing these problems by partially automating the process. This chapter describes the design, implementation, and testing of a machine learning model that detects one of the most widespread malaria parasites (Plasmodium falciparum) using samples of thin blood smears on standard microscope slides that were taken by local laboratories in Khartoum, Sudan. A watershed segmentation technique is used to acquire the erythrocytes from microscopic blood sample images, followed by a deep learning classifier. The classifier is based on a convolutional neural network (CNN) obtained using transfer learning: first, a base CNN is trained using a large publicly available dataset of stained infected and uninfected cells; then, several layers are appended to the CNN, which is retrained using locally obtained cell images, with image augmentation. Results showed that although good performance is obtained on the public dataset with the base CNN, the retrained CNN performs poorly on local images. Two reasons for this are the small number of local images available for training, as well as the poor quality of local images. We conclude that these factors pose significant obstacles for the use of machine learning in biomedical applications in developing countries.
Spaced Repetition Based Adaptive E-Learning Framework

With global education moving towards online education, we see a lot of emerging techniques to help facilitate paradigm shift. Memorization being a fundamental part of the process of learning, we focus on making memorizing an efficient task for students. We plan to make use of 2 emerging methodologies that help us memorize and retain information better in the long term. These are: Spaced Repetition: this technique helps in retaining information efficiently. Adaptive Learning: This will help us make the process of learning personalized, so that the user is always challenged just enough to enable growth. To help users stay consistent, we plan to add game-like elements to the system to help facilitate regular use which will lead to better results.
Introduction to Multi-agent Systems

Multi-agent systems (MASs) are rapidly increasing with the advancement of technologies, especially in the domains of e-commerce, health care, planning, telecommunication, supply chain, etc. Scholars across different disciplines have also become interested in it, such as computer science, electronics, engineering, and civil engineering. The arrangement in some environment that is equipped for an independent activity to complete its target is known as the agent. Decisions are made by the agents on a particular action to solve the task and complete its objective using various inputs like the past actions and interactions with it are surrounding agents. Human–computer interaction (HCI) implementation employing a multi-agent system is growing daily. Some contemporary applications in this sector covered by several technologies include telehealth, air traffic management, mixed reality, learning from demonstration, and psychological aspects of agents using HCI.
A Blockchain-based Electric Data Sharing System with Privacy Protection

We put forward an energy data sharing system with privacy protection based on the blockchain. Pointing at the characteristic that individual energy data is sensitive whereas the statistics are usually publicly available due to their huge research/industrial value, our system keeps individual information secret while makes statistics selectively public. Blockchain and smart edge computing service are combinedly introduced to collect data, encrypt data and upload data in an automatic and public way. Smart contracts are used to gather encrypted data and do homomorphic operation to get encrypted statistics, where only a data center can decrypt the results. Moreover, our system includes a decentralized feedback system to guarantee the correctness of the results through Pedersen commitment, such that if a data center deviates from the protocol, it will automatically get punished.
Research on Creepage Distance Measurement Method of Rod Insulator Based on Image Stitching

Insulator creepage distance is an important index to characterize its insulation performance parameters. Aiming at the problems of large measurement errors and low measurement efficiency of existing rod-shaped insulator creepage distance measurement methods, the image measurement method is introduced and the shortcomings of conventional image measurement algorithms when directly applied to insulator creepage distance measurement is analyzed. A method for measuring the creepage distance of rod insulators based on image stitching is proposed. The stitched image results are analyzed and the measurement results of a variety of methods are compared. The data show that image stitching method’s standard deviation of repeated measurement of creepage distance is less than 1/6 of the traditional method, and the measurement efficiency is more than 4 times higher than that of the traditional method. The image stitching method provides an accurate, stable and efficient new method for the creepage distance measurement of rod insulators.
Fast Retrieval of Large Entries With Incomplete Measurement Data

In network-wide monitoring, finding the large monitoring data entries is a fundamental network management function. However, the retrieval of large entries is extremely difficult and challenging as a result of incompleteness of network measurement data. Enlightened by tensor model’s strong capability of information representation and extraction, we model the network-wide monitoring data as a 3-way tensor. With tensor completion, the retrieval can be performed after recovering all missing entries. However, this not only incurs an extremely high cost when the tensor is large, but is also unnecessary. Instead, to quickly retrieve large entries at low cost, we transform the large entry retrieving problem to a cosine similarity searching problem, and propose two algorithms: 1) Quickly reordering the factor vectors based on Locality Sensitive Hashing (LSH) hash table so that vectors with small cosine distances are placed in the same hash bucket; 2) Quickly finding the similar vector of a queried one that the two together determine a large entry without incurring the high cost of recovering all entries through the dot products. In the process of LSH table building and similarity query, several novel techniques are proposed, including LSH table representation with the LSH forest, good hash table building to support the flexible search of cosine similarity, and bit-shifting-based quick similarity query. Our experimental studies on 4 real world datasets indicate that our technique is at least up to 60 times faster than the approach based on direct tensor completion.
OntoReqC: An Ontology Focused Integrative Approach for Classification of Software Requirements

Software products start to develop based on the client requirements that the software demands and classified requirements can result in well-developed software. This paper proposes the OntoReqC framework, a requirements classifier that has an accuracy of 95.49%. OntoReqC amalgamates ontologies, domain markers and is optimized with the ant colony optimization algorithm. This model obtains functional and nonfunctional ontologies from the unified modeling language models and software requirement artifact using OntoCollab and Protégé. Domain markers are derived from software requirements specification document using linked open data cloud for domain enrichment making this model knowledge-based. Ontologies and domain markers are combined to get content development network which is then semantically aligned with requirement terms obtained from preprocessed requirements dataset. Ant colony optimization has been applied to the results, producing the classified requirements.
The Search Module

The search module offers data structures and algorithms for efficiently finding exact and approximate matches between the so-called query sequences and a text (also called subject sequence(s) or the reference). Query and subject may each be a single sequence or a collection of sequences, and typically the total subject size is much larger than the total query size. Bioinformatical examples of the model are DNA reads searched in a genome or translated transcriptome reads searched in a protein database.