Reviewerly/exHarmony huggingface dataset:
A deep fusion framework for unlabeled data-driven tumor recognition

Traditional pattern recognition problems are usually accomplished through two successive stages of representation and classification, the generalization ability and stability are difficult to guarantee for small samples and category imbalance. For tackling these problems, an unlabeled data-driven representation learning classification (RLC) fused model is constructed by integrating representation learning and classification into one model, rather than simple putting the two stages together. The RLC fused model mainly focuses on interactive iteratively optimizing representation learning and classification in a model, guiding and reinforcing each other. Under the framework of RLC, a deep nonnegative matrix factorization (NMF) is adopted for representation learning by complementing the advantages of NMF and deep learning, and avoiding complex network structure and parameter modulation. The framework is called deep NMF-RLC fusion model, which can achieve good performance for binary classification even the simplest linear regression classifier is used. The model explores useful information embedded in unlabeled data, and is suitable for small training samples and unbalanced classification. The performance of the proposed framework is verified on genetic-based tumor recognition, which contains all three stages of early diagnosis, tumor type recognition and postoperative metastasis. Experiments show that, compared with the published state-of-the-art methods and results, there are significant improvements in classification accuracy, specificity and sensitivity.
Differentially Private Federated Learning: Algorithm, Analysis and Optimization

Federated learning (FL), a type of collaborative machine learning framework, is capable of helping protect users’ private data while training the data into useful models. Nevertheless, privacy leakage may still happen by analyzing the exchanged parameters, e.g., weights and biases in deep neural networks, between the central server and clients. In this chapter, to effectively prevent information leakage, we investigate a differential privacy mechanism in which, at the clients’ side, artificial noises are added to parameters before uploading. Moreover, we propose a K-client random scheduling policy, in which K clients are randomly selected from a total of N clients to participate in each communication round. Furthermore, a theoretical convergence bound is derived from the loss function of the trained FL model. In detail, considering a fixed privacy level, the theoretical bound reveals that there exists an optimal number of clients K that can achieve the best convergence performance due to the tradeoff between the volume of user data and the variances of aggregated artificial noises. To optimize this tradeoff, we further provide a differentially private FL based client selection (DP-FedCS) algorithm, which can dynamically select the number of training clients. Our experimental results validate our theoretical conclusions and also show that the proposed algorithm can effectively improve both the FL training efficiency and FL model quality for a given privacy protection level.
Multi-Agent based Capital Market Management System: A Distributed Framework for Trading and Regulation

Stock Market plays a vital role in the economy of every nation. Having a transparent market may boost the confidence of not only stock brokers but also that of investors. One of the major problems that make investors to shy away from the market is lack of transparency. Another Problem which affect the market regulators is the lack of a system that enable them to check for compliance easily. In this work, an agent based distributed framework is presented. The idea behind the proposed system is that having one system that will serve all the market stake holders will guaranty strict compliance to the market rules, easier to manage and difficult manipulate by the market operators. The implementation of the proposed system followed Multi-Agent Software Engineering (MaSE) Methodology. The evaluation of the system show that, the distributed system developed using Java Agent Development Framework (JADE) is capable of addressing problems of reliability, compliance and transparency.
Library and Information Support Services during COVID-19: A Case Study of Dr. B. R. Ambedkar Central Library, Jawaharlal Nehru University, Delhi

Covid-19 pandemic has posed challenges to all walks of life and libraries are not exception to that. The paper traces the status and trends in extending library and information services during Covid-19 period. The approaches adopted by LIS professionals to overcome restrictions in libraries and information access such as Online chat, texting, Instant Messaging System (IMS) and other social network platforms have been explored. The paper discusses the policy decisions taken by Jawaharlal Nehru University; Delhi, precautionary measures, and revised plans of the Dr B. R. Ambedkar Central Library taken with regard to physical and digital spaces and library and information services to avoid/minimize challenges posed by Covid-19 pandemic. The virtual services provided during the COVID 19 period in real time through video kiosks, live chat, email or phone are enumerated. The paper explains with the example of JNU Central Library how it continues to assist staff, faculty, researchers and students with finding, using and referencing information relevant to their research or study as well. A detailed account has been given about ‘Digital Tracking System ’, Patron Attendance System, Seating Plan of the JNU Library. The study concludes that increased research and publication activity during the prevailing time has led to intensifying the services relating to online plagiarism check and research support services including reference management. Regular webinars were conducted to orient the users of the University. Dr B R Ambedkar Central library, JNU also initiated the online lecture and workshop on Research Methodology to help the ongoing research of its M. Phil and PhD scholars.
Inharmonious Region Localization

The advance of image editing techniques allows users to create artistic works, but the manipulated regions may be incompatible with the background. Localizing the inharmonious region is an appealing yet challenging task. Realizing that this task requires effective aggregation of multi-scale contextual information and suppression of redundant information, we design novel Bi-directional Feature Integration (BFI) block and Global-context Guided Decoder (GGD) block to fuse multi-scale features in the encoder and decoder respectively. We also employ Mask-guided Dual Attention (MDA) block between the encoder and decoder to suppress the redundant information. Experiments on the image harmonization dataset demonstrate that our method achieves competitive performance for inharmonious region localization. The source code is available at https://github.com/bcmi/DIRL.
Multi-Scale Attention Constraint Network for Fine-Grained Visual Classification

Capturing subtle yet discriminative features constitutes a great challenge in fine-grained visual classification due to the large intra-class and small inter-class variances. Main-stream works for this problem localize at attention mechanism and feature relationship learning. However, existing methods treat the features in isolation while neglecting the effect of attention-enhanced features on relationships between different network layers. In this paper, we propose a novel attention-based method by Multi-Scale Attention Constraint network composed of two important components: (1) a feature extractor with lightweight group-wise enhanced attention blocks that guides the generation of high representation features; and (2) a multi-scale regularizer that explores the relationships between different features. Extensive experiments show that our approach achieves state-of-the-art performance on standard benchmark datasets. Moreover, we introduce a new dataset, consisting of comprehensive surgical instrument categories based on three common surgeries, to support the classification and inventory work of surgical instruments.
Improved accuracy of recognizing of low-quality face images using two directional matrix in 2D-PCA algorithms and euclidean distance

Abstract Face recognition is a technique that can be used to distinguish the characteristic facial patterns of a person. A very influencing factor in the facial recognition process is image quality, so it can affect the level of accuracy. Improving the accuracy of low-quality facial recognition can be done by processing the image for its feature extraction using Two Directional Matrix on 2D-PCA. From the extraction process, the Eigenfaces value is then generated to be classified. Image classification is done by using Euclidean Distance. Furthermore, the accuracy results between image recognition with or without Two Directional Matrix will be compared. The data used were the AT&amp;T face of database and database of Essex. Of the 18 tests carried out on the Two Directional Matrix method, it was proven to improve facial recognition accuracy by as many as 12 trials. In 5 other experiments the accuracy decreased and 1 experiment was unable to increase or decrease facial recognition accuracy. The highest accuracy result in the experiment using AT&amp;T was 98.25%, while in Essex it was 97.27%. Suggestions for further research by conducting experiments with more diverse dataset ratios in order to get better low-quality image accuracy results.
MapReduce Model Using FPGA Acceleration for Chromosome Y Sequence Mapping

Genome assemblies sequenced by a Whole Genome Shotgun (WGS) project predict an organism's function and history. Sequence alignment is the foundation of bioinformatics by a computational search through large genome sequence databases, which generally requires enormous amounts of memory and takes a long execution time. In this paper, an Optimized Smith-Waterman algorithm based on the Gotoh algorithm with an affine gap for accuracy alignment, the divide and conquer technique, and the MapReduce framework implemented to establish a parallel process. This model was implemented on Virtex 7 field-programmable gate arrays (FPGAs). These techniques provide a better performance, reduce the hardware requirements, improve the accuracy, increase the computational throughput, and accelerate the alignment process for big data available in a complete Y chromosome. The hardware proposed system can achieve high performance, low time consumption 1.699 ns, and decrease FPGA utilization for big data alignments Y chromosome is used as an example.
Algorithms for a Topology-aware Massively Parallel Computation Model

Most of the prior work in massively parallel data processing assumes homogeneity, i.e., every computing unit has the same computational capability and can communicate with every other unit with the same latency and bandwidth. However, this strong assumption of a uniform topology rarely holds in practical settings, where computing units are connected through complex networks. To address this issue, Blanas et al. \citeblanas2020topology recently proposed a topology-aware massively parallel computation model that integrates the network structure and heterogeneity in the modeling cost. The network is modeled as a directed graph, where each edge is associated with a cost function that depends on the data transferred between the two endpoints. The computation proceeds in synchronous rounds and the cost of each round is measured as the maximum cost over all the edges in the network. In this work, we take the first step into investigating three fundamental data processing tasks in this topology-aware parallel model: set intersection, cartesian product, and sorting. We focus on network topologies that are tree topologies, and present both lower bounds as well as (asymptotically) matching upper bounds. Instead of assuming a worst-case distribution as in previous results, the optimality of our algorithms is with respect to the initial data distribution among the network nodes. Apart from the theoretical optimality of our results, our protocols are simple, use a constant number of rounds, and we believe can be implemented in practical settings as well.
Binary de Bruijn Sequences via Zech’s Logarithms

The focus of this work is to show how to combine Zech’s logarithms and each of the cycle joining and cross-join pairing methods to construct binary de Bruijn sequences. Basic implementations are supplied as proofs of concept. The cycles, in the cycle joining method, are typically generated by a linear feedback shift register. We prove a crucial characterization that determining Zech’s logarithms is equivalent to identifying conjugate pairs shared by any two distinct cycles. This speeds up the task of building a connected adjacency subgraph that contains all vertices of the complete adjacency graph. Distinct spanning trees in either graph correspond to cyclically inequivalent de Bruijn sequences. As the cycles are being joined, guided by the conjugate pairs, we track the changes in the feedback function. We show how to produce certificates of existence for spanning trees of certain types to conveniently handle large order cases. The characterization of conjugate pairs via Zech’s logarithms, as positional markings, is then adapted to identify cross-join pairs. A modified m-sequence is initially used, for ease of generation. The process can be repeated on each of the resulting de Bruijn sequences. Most prior constructions in the literature measure the complexity of the corresponding bit-by-bit algorithms. Our approach is different. We aim first to build a connected adjacency subgraph that is certified to contain all of the cycles as vertices. The ingredients are computed just once and concisely stored. Simple strategies are offered to keep the complexities low as the order grows.
Auto-Segmentation on Liver with U-Net and Pixel De-Convolutional Network

Purpose: To improve the liver auto-segmentation performance of threedimensional (3D) U-net by replacing the conventional up-sampling convolution layers with the Pixel De-convolutional Network (PDN) that considers spatial features.Methods: The U-net was originally developed to segment neuronal structure with outstanding performance but suffered serious artifacts from indirectly unrelated adjacent pixels in its up-sampling layers.The hypothesis of this study was that the segmentation quality of the liver could be improved with PDN in which the up-sampling layer was replaced by a pixel de-convolution layer (PDL).Seventy-eight plans of abdominal cancer patients were anonymized and exported.Sixty-two were chosen for training two networks: 1) 3D U-Net, and 2) 3D PDN, by minimizing the Dice loss function.The other sixteen plans were used to test the performance.The similarity Dice and Average Hausdorff Distance (AHD) were calculated and compared between these two networks.Results: The computation time for 62 training cases and 200 training epochs was about 30 minutes for both networks.The segmentation performance was evaluated using the remaining 16 cases.For the Dice score, the mean ± standard deviation were 0.857 ± 0.011 and 0.858 ± 0.015 for the PDN and U-Net, respectively.For the AHD, the mean ± standard deviation were 1.575 ± 0.373 and 1.675 ± 0.769, respectively, corresponding to an improvement of 6.0% and 51.5% of mean and standard deviation for the PDN.Conclusion: The PDN has outperformed the U-Net on liver auto-segmentation.The predicted contours of PDN are more conformal and smoother when compared with the U-Net.
Your Honor, on Social Media: The Judicial Ethics of Bots and Bubbles

Many judges sitting in county, state, and federal courts in the United States have joined the ranks of social media users. Judges post on social media. They engage followers on Twitter and friends on Facebook. Like other humans using social media, judges may unwittingly encounter bots simulating human behavior or “bubbles” created by algorithms. This article is the first to examine at length the potential threat to judicial impartiality arising from judges interacting with bots and bubbles—both present, known phenomena on social media. While much scholarly literature has delved into ethical issues implicated by judges’ social media contacts, very little attention has been focused on the impact of automated bots and algorithmic bubbles on judges’ impartiality.Judges are expected to be impartial. Impartiality has long been a cornerstone of the judicial role. With social media bots and bubbles that put social media users at greater risk of believing falsehoods and of seeing only what confirms their own beliefs, judicial impartiality, a central feature of the judicial system, may be under threat at a level not encountered in the past. In this article, I argue for increased precautions and awareness to help maintain judicial impartiality in light of the potential unfortunate impact of social media bots and bubbles.
Predicting the Age of Scientific Papers

In this paper we show how the age of scientific papers can be predicted given a diachronic corpus of papers from a particular domain published over a certain time period. We first train ordinal regression models for the task of predicting the age of individual sentences by fine-tuning series of BERT models for binary classification. We then aggregate the prediction results on individual sentences into a final result for entire papers. Using two corpora of publications from the International World Wide Web Conference and the Journal of Artificial Societies and Social Simulation, we compare various result aggregation methods, and show that the sentence-based approach produces better results than the direct document-level method.
CI-GAN : Co-Clustering By Information Maximizing Generative Adversarial Networks

Simultaneously clustering rows and columns of a matrix, co-clustering can exploit the complex relationships between two different domains and identify groups of distinct nature. In this work, we introduce CI-GAN, a novel GAN-based approach for co-clustering. The model exploits two distinct GAN that cluster each domain independently and combines them intelligently by maximizing the mutual information between the input data and the generated co-clusters. From the experiments constructed with image, audio, and text datasets, it is found that such a systematic way of sharing information between the networks can improve co-clustering performance substantially; when CI-GAN is compared against five standard algorithms, it consistently reports the highest accuracy on both synthetic and real datasets.
Data Architecture of Monitoring Platform for Marketing Operation Under Smart Grid

In the context of modern big data, power grid information technology is a particularly important work for power grid staff. In the process of the in-depth construction of smart power grid, the demand for the integration of monitoring data of marketing operation is also increasing, and the data exchange is developing towards the direction of complexity, which requires the continuous improvement of the real-time performance of data and the risks in performance, security and management risks generated by data integration should be reduced. It is particularly important to create a unified management tool for data interaction. Through the professional R&D team in big data technology, the marketing operation monitoring data sharing can be highlighted, to create an integrated marketing data center. At present, the construction of marketing operation monitoring information is the focus of the work, which can effectively promote the development of big data. Therefore, this paper analyzes the data architecture of the marketing operation monitoring platform under the smart grid for the reference of relevant research.
Multi-evaluation System of English Teaching Guided by Constructivism

The emergence of constructivism theory provides a strong theoretical support for college English teaching reform. With the in-depth implementation of classroom reforms, especially multimedia teaching, the traditional method of making summative evaluations of students' learning based on exams has been difficult to meet the requirements of modern teaching, and establishing a new teaching mechanism that can adapt to the dynamics of students' overall development a diversified, continuous, more objective and reasonable evaluation system has become our inevitable choice. This article first analyzes the necessity of establishing a college English teaching evaluation system. Then it analyzed its existing problems. Then, combined with the characteristics of current college English teaching, it analyzes the college English teaching evaluation strategy guided by the constructivist theory, hoping to provide a useful reference for the current college English teaching.
A new approach: semisupervised ordinal classification

Semisupervised learning is a type of machine learning technique that constructs a classifier by learning from a small collection of labeled samples and a large collection of unlabeled ones. Although some progress has been made in this research area, the existing semisupervised methods provide a nominal classification task. However, semisupervised learning for ordinal classification is yet to be explored. To bridge the gap, this study combines two concepts ?semisupervised learning? and "ordinal classification" for the categorical class labels for the first time and introduces a new concept of "semisupervised ordinal classification". This paper proposes a new algorithm for semisupervised learning that takes into account the relationships between the class labels, especially class orderings such as low, medium, and high. We also performed an extensive empirical study that involves 10 benchmark ordinal datasets with different quantities of labeled samples varying from 15% to 50% with an increment of 5%, aiming to evaluate the performance of our method by combining different base learners. The experimental results were also validated with a nonparametric statistical test. The experiments show that the proposed method improves the classification accuracy of the model compared to the existing semisupervised method on ordinal data.
Compressed Indexes for Fast Search of Semantic Data (Extended Abstract)

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. We propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60% less space and speeding up query execution by a factor of 2-81 times.
A note on an alternative Gentzenization of RW+∘

Abstract In our paper [3], a sequent system for the contraction‐less positive relevant logic with co‐tenability, , is considered. Its sequent system with the rule of cut, which does not involve the truth constant t , is presented, and the proof that it admits the elimination of cut is given. However, in the proof of the cut‐elimination theorem some forms of proofs, which may cause difficulties for the cut‐elimination argument, are not considered. The purpose of this paper is to present them and to re‐prove that admits the elimination of cut.