Reviewerly/exHarmony huggingface dataset:
SSLM: Self-Supervised Learning for Medical Diagnosis from MR Video.

In medical image analysis, the cost of acquiring high-quality data and their annotation by experts is a barrier in many medical applications. Most of the techniques used are based on supervised learning framework and need a large amount of annotated data to achieve satisfactory performance. As an alternative, in this paper, we propose a self-supervised learning approach to learn the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful spatial context-invariant representations. The downstream task in our paper is a class imbalanced multi-label classification. Different experiments show that the features learnt by the pretext model provide explainable performance in the downstream task. Moreover, the efficiency and reliability of the proposed pretext model in learning representations of minority classes without applying any strategy towards imbalance in the dataset can be seen from the results. To the best of our knowledge, this work is the first work of its kind in showing the effectiveness and reliability of self-supervised learning algorithms in class imbalanced multi-label classification tasks on MR video. The code for evaluation of the proposed work is available at this https URL
ANALYSIS OF THE ERROR OF COMPUTATION FAST TRANSFORMS OF FOURIER CLASS BASED ON CYCLIC CONVOLUTIONS

The features of the computational model of discrete transforms of Fourier class based on cyclic convolutions to determine the algorithmic calculation error are analyzed. Based on the approach of efficient computation of discrete transforms of Fourier class of arbitrary size N, using of a hashing array to transform a discrete basis matrix into a set of block-cyclic submatrices, the components of computational costs are considered. These components of computational costs depend on the type of transform, the size and the block-cycle structure of the transformation core. Examples of computational model and block-cyclic structure of matrices of simplified arguments of basis functions for mutually inverse discrete cosine transforms of types II, III are given. The computational model characterizes the accumulation of rounding errors at the stages of adding input data, computing cyclic convolutions, combining the results of convolutions. Discrete cyclic convolutions can be implemented using fast algorithms or a type of system that corresponds to digital filters with finite pulse characteristics. The possibility of parallel computation of the reduced number of cyclic convolutions makes the analysis of errors insensitive to rearrangement of their computations. The multiplication operations performed when computing the cyclic convolution uses a smaller number of basis coefficients equal to N/4 or N/2 depending on the size of transform. The formats of representation of real numbers in computer systems are considered, which also determine the magnitude of the computational error of transforms. The results of direct and fast computation of discrete cosine transform of type II based on cyclic convolutions with size N=58 in the format wit floating point of double precision and computation error between them are presented. The apriori process of studying the transform errors of the corresponding type and size by the method of mathematical modeling and computational experiment is approximate, which allows to predict the statistical averages of the accuracy of computing the discrete Fourier transform of arbitrary size based on cyclic convolutions.
On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies

We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream performance gains. Recent theories have suggested that pretrained language models acquire useful inductive biases through masks that implicitly act as cloze reductions for downstream tasks. While appealing, we show that the success of the random masking strategy used in practice cannot be explained by such cloze-like masks alone. We construct cloze-like masks using task-specific lexicons for three different classification datasets and show that the majority of pretrained performance gains come from generic masks that are not associated with the lexicon. To explain the empirical success of these generic masks, we demonstrate a correspondence between the Masked Language Model (MLM) objective and existing methods for learning statistical dependencies in graphical models. Using this, we derive a method for extracting these learned statistical dependencies in MLMs and show that these dependencies encode useful inductive biases in the form of syntactic structures. In an unsupervised parsing evaluation, simply forming a minimum spanning tree on the implied statistical dependence structure outperforms a classic method for unsupervised parsing (58.74 vs. 55.91 UUAS).
Study of Bagging Application in the Safe-Level Smote Method in Handling Unbalanced Classification

The problems of imbalanced class classification have been found in many real applications. It has potential to make the minority class instances tend to be classified into the majority class. This study examined the performance of bagging method’s application in safe-level SMOTE based on Support Vector Machine classifier. The data used consisted of three types based on the proportion of observations in the majority and minority classes. Each type of data has three variables, two independent variables and one variable dependent. The observations of independent variables were generated based on multivariate normal distribution, while dependent variables are binary. The results showed that the classifier has a high accuracy and sensitivity for all types of data for both in the imbalanced class and the balanced class (obtained by safe-level SMOTE and safe-level SMOTEBagging). Nevertheless, specificity was the main measure in assessing the performance of the classifier because it provides accuracy in classifying the minority class observations. The specificity increased when the number of observations between the two classes were approximately balance due to the implementation of safe-level SMOTE. The best performance of the Support Vector Machine in predicting minority class observations was achieved when bagging were applied in safe-level SMOTE. The specificity rate for all types of data were 77.93 percent, 78.46 percent, and 85.69 percent, respectively.
Analyzing the impact of vibration on YOLO algorithm performance

In this paper we will analyze the effect of vibration on the performance of the YOLO (You Only Look Once) algorithm on object classification. The YOLO algorithm will initially be trained using static imagery of common objects. Image processing techniques will then be used to create video clips with varying levels of blur due to vibration. These videos will then be used to evaluate the performance of the algorithm on object classification. Results of the classification will be presented. An initial summary of the classification results will be discussed in relation to the amount of vibration added to the imagery.
The Development of Web-Based Paperless Office System Using CodeIgniter Framework Case Study of Lembaga Pengembangan Cabang Ranting Muhammadiyah

Lembaga Pengembangan Cabang Ranting (LPCR) Muhammadiyah Yogyakarta is an institution that often conducts correspondence between institutions. The activity of recording incoming and outgoing mail data at LPCR Yogyakarta still uses the manual method by recording in a ledger. In addition, the length of time required in the letter of submission is also still found in the LPCR. One method that can be used to overcome this is paperless office, where the work environment that uses paper is eliminated or greatly reduced. The use of paperless office in addition to saving paper can also improve the efficiency of data processing and search because the form is already digital. This system was built using the Unified Software Development Process (USDP) development method, with the Unified Modeling Language (UML) modeling. In the initial process, the initial phase is an analysis of requirements for software requirements. Furthermore, at the elaboration stage, the maturation of concepts that have been analyzed in the previous phase is carried out. Then at the development stage, system development is done with the PHP programming language and MySQL database using the CodeIgniter framework. The final phase is the transition carried out alpha testing and beta testing. The alpha test results of the system built went well, while for the beta test results at the Administrator level, the results were 86.8% Strongly Agree and 13.2 Strongly Disagree, 86.4% Strongly Agree and 13.6% Strongly Disagree of Headoffice users, 91.3% Strongly Agree and 8.7% Strongly Disagree from the results of the operators' user answers and 96% Strongly Agree and 4% Strongly Disagree from the results of the Guest user. Based on the test results, the system built is feasible and can be used by LPCR Yogyakarta.
Improving coordination between teams of autonomous agents using TReVe

Distributed battle management of a group of autonomous agents e.g., unmanned air systems (UAS) facing a highly capable adversary, requires controlling a group of semi-isolated small teams, operating under extensive uncertainty about the enemy situation and also about the status and plans of some of its own agents. Barnstorm Research developed Trust, Refrain and Veto (TReVe), which allows agents to maintain higher situational awareness of teammates when in a denied communications environment. Barnstorm Research has shown that TReVe increases mission success via simulation when onboard RAIDER/FACE-enabled UAS in a denied communications environment.
CondenseNet V2: Sparse Feature Reactivation for Deep Networks

Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet has shown that this mechanism can be further improved if redundant features are removed. In this paper, we propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. In the proposed network, named CondenseNetV2, each layer can simultaneously learn to 1) selectively reuse a set of most important features from preceding layers; and 2) actively update a set of preceding features to increase their utility for later layers. Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.
Improving Zero-Shot Multi-Lingual Entity Linking

Entity linking -- the task of identifying references in free text to relevant knowledge base representations -- often focuses on single languages. We consider multilingual entity linking, where a single model is trained to link references to same-language knowledge bases in several languages. We propose a neural ranker architecture, which leverages multilingual transformer representations of text to be easily applied to a multilingual setting. We then explore how a neural ranker trained in one language (e.g. English) transfers to an unseen language (e.g. Chinese), and find that while there is a consistent but not large drop in performance. How can this drop in performance be alleviated? We explore adding an adversarial objective to force our model to learn language-invariant representations. We find that using this approach improves recall in several datasets, often matching the in-language performance, thus alleviating some of the performance loss occurring from zero-shot transfer.
A Novel Hadoop-Based Virtualization Automated Framework

With the large growth in relocation to distributed computing for customary exercise, the percent in Hadoop VIRTUALIZATION is the new necessity for antecedently scrutiny and advancement. The rearward percent for Hadoop VIRTUALIZATION in distributed computing is been depart to with the freshness correction and concerning virtual instrument. Significantly, the instrument division, the executives of virtual instrument is tellingly instate the select of research with the ongoing progression by discontinuous company and open quarry enucleate like AlphaVM, mickle-V, probity Virtual device, JPC (Virtual Machine), PowerVM, Sun xVM, VMware Workstation, and z LPARs. Different extreme mandatory section of Hadoop VIRTUALIZATION and virtual instrumentation illustration collection is been the bottleneck for the afore advancement. The interruption is being crouch to by bunching and competency regions planning, someway these present orders are no specimen for the recent assessment percent for the VM painting concentricity requirement. Afterward, in this thing, we approach a mass-based virtual instrument collection frame purporting for large volume organization overfill disjunctive requirements for efficiency highlights like reproduction and calculation of perform augmentation. This performance almost deeds the headway in performance for Hadoop VIRTUALIZATION the district.
The Analysis on the Countermeasures of Cultivating Teachers' Professional Quality in Colleges and Universities in the New Era

At the present stage, quality education is further promoted. The implementation of quality education puts forward higher requirements for the majority of teachers. As teachers in colleges and universities, it is more necessary for them to cultivate their own professional quality, which is the key to ensuring the quality of higher education to continuously increase. It is of great significance to the development of college education. This paper mainly introduces the importance of cultivating teachers’professional quality in colleges and universities in the new era and analyzes the effective measures of cultivating college teachers’professional quality. earning, and also provides important teaching enlightenment for college English teaching.
PyArmadillo: a streamlined linear algebra library for Python

While the Python language has extension frameworks such as NumPy and SciPy for providing functionality involving numerical arrays and scientific computing, these frameworks can be cumbersome to use in contrast to the well-established Matlab language. The issues include: a focus on operations involving multi-dimensional arrays rather than matrices, nested organisation of functions which increases code verbosity (leading to reduced user productivity), and syntax that significantly differs from Matlab. To address these shortcomings, we propose PyArmadillo, a streamlined linear algebra library for Python, with an emphasis on ease of use. PyArmadillo aims to provide a high-level syntax and functionality deliberately similar to Matlab/Octave, allowing mathematical operations to be expressed in a familiar and natural manner. Objects for matrices and cubes are provided, as well as over 200 associated functions for manipulating data stored in the objects. Integer, floating point and complex numbers are supported. Various matrix factorisations are provided through integration with LAPACK, or one of its high performance drop-in replacements such as Intel MKL or OpenBLAS. PyArmadillo is open-source software, distributed under the Apache 2.0 license; it can be obtained at this https URL or via the Python Package Index in precompiled form.
Postmodernism in Education and System Inversions

This paper is a detailed review of the article by P.A. Orekhovsky and V.I. Razumov ‘The Carnival Time: Russian Higher School and Science in the Postmodern Era’. The author considers the main problems shown in this article. In order to study these problems the author uses a method of analysis of inverse relations in hierarchical systems as a theoretical basis. System inversion is a form of relations in hierarchical systems, in which the lowest element receives the dominant value in the system, formally remaining in the same subordinate position. This situation can occur both in the social hierarchy and, for example, in the hierarchy of values. As a result of the developed inversion, contradictions accumulate in the system, which can lead to the collapse of this system or to a radical transformation. Such processes can be observed in modern education. This is why there is a priority of the visible over the existent, as it happens in the situation of ‘carnival’. The article by P.A. Orekhovsky and V.I. Razumov examines the postmodern cultural context in which modern education functions. In this regard, the author presents an interpretation of the postmodern situation from the point of view of analyzing system inversions. The current state of the educational sector is determined by the resolution of inversion in the system of human activity. This inversion covers the instrumental and symbolic aspects of human activity. Due to this, there are features of educational activities related to the introduction of digital technologies, which P.A. Orekhovsky and V.I. Razumov pay special attention to. The authors of the article describe the status of the modern teacher’s activity as ‘spiritual prostitution’. Indeed, the activities of some teachers can be described by this term. However, this happens when both the teacher and the student experience a value inversion (as is the case with ordinary prostitution). Instead of this model of behavior, the author suggests another one, more worthy, – a ‘soldier of culture’. ‘Soldiers of culture’ do not ‘provide educational services’, they have a mission to broadcast and enrich culture, which is the highest, terminal value.
Synchronization of Catch Fish Data in Fisheries e-Logbook with a Vessel Monitoring System

Indonesia as an archipelago country has many residents whose livelihoods is fishermen. Traditional fishermen who are scattered in various provinces need a tool that can help to record fish catch data after they sail. This study aims to propose a method for synchronizing fish catch data between e-logbook and a fishing vessel monitoring system. The Unix timestamp reference was used to help synchronize fish catch data with the position in the monitoring system, and it was expected to run well. The results of this study are fisheries e-logbooks for Android devices equipped with data synchronization features. In the future, it would be better if a tool was made to help transmit data from the middle of the sea for traditional fishermen.
Privacy and Integrity Preserving Computations with {CRISP}

In the digital era, users share their personal data with service providers to obtain some utility, e.g., access to high-quality services. Yet, the induced information flows raise privacy and integrity concerns. Consequently, cautious users may want to protect their privacy by minimizing the amount of information they disclose to curious service providers. Service providers are interested in verifying the integrity of the users' data to improve their services and obtain useful knowledge for their business. In this work, we present a generic solution to the trade-off between privacy, integrity, and utility, by achieving authenticity verification of data that has been encrypted for offloading to service providers. Based on lattice-based homomorphic encryption and commitments, as well as zero-knowledge proofs, our construction enables a service provider to process and reuse third-party signed data in a privacy-friendly manner with integrity guarantees. We evaluate our solution on different use cases such as smart-metering, disease susceptibility, and location-based activity tracking, thus showing its promising applications. Our solution achieves broad generality, quantum-resistance, and relaxes some assumptions of state-of-the-art solutions without affecting performance.
Molecular dominance investigation for large-sized parents&amp;nbsp;of Chinese Mitten Crab ( Eriocheir sinensis ) based on&amp;nbsp;ovarian transcriptome

Abstract Background: Germplasm degradation is one of the important issues for the healthy development of Chinese Mitten Crab ( Eriocheir sinensis ) industry. Nowadays, nursery units and crab farmers have realized the seedling superiority of large-sized parents (≥200g male, ≥150g female), but the underlying molecular mechanisms remain unclear. Here we investigated the advantage of breeding with large-sized parents based on ovarian transcriptome. Results: RNA isolation and transcriptome sequencing were performed using ovarian tissue obtained from three large-sized female E. sinensis and three medium-sized female E. sinensis . The differentially expressed genes (DEGs) were investigated between two groups, followed by the exploration of common DEGs (co-DEGs) among the DEGs in the current study and published genes for growth, reproduction, and immune response pathways. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate all co-DEGs. Finally, a pathway enrichment analysis was performed to investigate the potential pathways associated with the validated co-DEGs . A total of 5,307 up-regulated genes and 3,465 down-regulated genes were investigated between the two groups, of which 43 co-DEGs were explored when all the DEGs in the current study were integrated with 338 genes in 15 reproductive, immune, and growth pathways. A total of nine co-DEGs, including TRINITY_DN13931_c0_g1 , TRINITY_DN1908_c0_g2 , and TRINITY_DN6686_c0_g1 , were enrolled for further study after qRT-PCR validation. These validated co-DEGs were mainly enriched in pathways such as apoptosis, insulin signaling, and mTOR signaling. Conclusion: Our original work might lay an important molecular foundation for further exploring the advantages of using large-sized parents for E. sinensis breeding in the future. Based on the comparative analysis of gene expression, the advantages of breeding large-sized female E. sinensis parents at the molecular level, such as growth, reproduction, and immunity, were analyzed for the first time. The apoptosis pathways, insulin signaling pathways, and mTOR signaling pathway might be involved in the advantage of breeding large-sized female E. sinensis parents.
HYPER^2: Hyperbolic Poincare Embedding for Hyper-Relational Link Prediction

Link Prediction, addressing the issue of completing KGs with missing facts, has been broadly studied. However, less light is shed on the ubiquitous hyper-relational KGs. Most existing hyper-relational KG embedding models still tear an n-ary fact into smaller tuples, neglecting the indecomposability of some n-ary facts. While other frameworks work for certain arity facts only or ignore the significance of primary triple. In this paper, we represent an n-ary fact as a whole, simultaneously keeping the integrity of n-ary fact and maintaining the vital role that the primary triple plays. In addition, we generalize hyperbolic Poincaré embedding from binary to arbitrary arity data, which has not been studied yet. To tackle the weak expressiveness and high complexity issue, we propose HYPER^2 which is qualified for capturing the interaction between entities within and beyond triple through information aggregation on the tangent space. Extensive experiments demonstrate HYPER^2 achieves superior performance to its translational and deep analogues, improving SOTA by up to 34.5\% with relatively few dimensions. Moreover, we study the side effect of literals and we theoretically and experimentally compare the computational complexity of HYPER^2 against several best performing baselines, HYPER^2 is 49-61 times quicker than its counterparts.