Reviewerly/exHarmony huggingface dataset:
Adoption of diagnostic digital pathology in Finland

Digital pathology (DP) means digitizing histological glass slides for the assessment on a computer screen. In clinical diagnostics, DP is expected to reduce costs due to improved pathologist productivity with the aid of image analysis, workload distribution, and more accurate subspecialty-based diagnoses. The digital workflow also provides many advantages to research and education. However, the adoption of DP into clinical diagnostics has been slow. In this study, the current state and attitudes of the adoption of DP were explored with a questionnaire sent to the persons in charge of digitalization in all public pathology laboratories in Finland.&#x0D; Most of the respondents (75.0%) considered that there is a sufficient amount of validation studies about the safety of DP. The benefits of DP were seen in e.g. tumor boards (64.3%), logistics (64.3%), diagnostic quality (50.0%), and consultations (64.3%). All but one (92.9%) expected the level of digitalization to reach 100% in the following 12 years. The price of digitalization, specifically the cost of storage, was considered to be the most important barrier to a large-scale adoption of DP.&#x0D; The results suggest that there is a need for a substantial governmental financing: the virtual slide images could be stored in a central national image archive (e.g. Kvarkki) after diagnostics, leading to a remarkably reduced cost of local storage and an accelerated large-scale adoption of DP in the Finnish pathology laboratories. This would lead to improved diagnostic efficacy and quality by enabling better workload management locally and nationally. A central DP repository could serve as an invaluable database for e.g. biobank research
Self-modifiable image processing library for model-based design on FPGAs

This paper describes highly configurable hardware modules, included in XIL XSGImgLib library, capable of speed up the hardware implementation of video and image processing systems using the model-based design flow provided by Xilinx System Generator. As part of this work, generic architectures were developed to exploit specific characteristics of some processing blocks, which can be self-modified using a novel software procedure developed for MATLAB®. This procedure, along with the generic architecture and the configuration options, allows the abstraction about the specific details of the hardware implementation, as well as the adjustment of the resources consumption of the high-speed image and video processing application for embedded systems with weight, volume and power consumption constrains like smart cameras, video surveillance and autonomous vehicles. The use of this video and image processing library is illustrated by the development of a segmentation application on a Spartan-6 LX45 FPGA although any Xilinx's FPGA is supported.
The threat of surveillance capitalism

Using Shoshana Zuboff’s 2019 book, The Age of Surveillance Capitalism, the essay explores this latest form of capitalism and Zuboff’s claims about its organization. Her arguments are compared and contrasted with David Eggers novel, and the movie that came out of it, called The Circle, as well as other perspectives on capitalism (Marx, Barry Unsworth’s Sacred Hunger) and the current dominance of social media companies (especially Alphabet/Google, Facebook, and Amazon) from Evgeny Morozov, Natasa Dow Schüll, Zeynep Tufekci, Steve Mann and Tim Wu. Zuboff’s description and critique of Surveillance Capitalism is a convincing and important addition to our understanding of the political economy of the early 21st Century and the role of giant monopolistic social media companies in shaping it.
ViViD Cuckoo Hash: Fast Cuckoo Table Building in SIMD

Hash Tables play a lead role in modern databases systems during the execution of joins, grouping, indexing, removal of duplicates, and accelerating point queries. In this paper, we focus on Cuckoo Hash, a technique to deal with collisions guaranteeing that data is retrieved with at most two memory access in the worst case. However, building the Cuckoo Table with the current scalar methods is inefficient when treating the eviction of the colliding keys. We propose a Vertically Vectorized data-dependent method to build Cuckoo Tables - ViViD Cuckoo Hash. Our method exploits data parallelism with AVX-512 SIMD instructions and transforms control dependencies into data dependencies to make the build process faster with an overall reduction in response time by 90% compared to the scalar Cuckoo Hash.
Determination of news biasedness using content sentiment analysis algorithm

&lt;span lang="EN-US"&gt;Nowadays, i&lt;/span&gt;&lt;span lang="EN-US"&gt;dentifying news biases in the social media is one of the most fundamental problems. News bias is a complex process that comprises several dimensions to be taken into account and it is interlinked with social, political and economic problems. In general, news bias has the ability to reflect opinion of people about a topic or government policies and actions. The proposed algorithm develops a system which can detect the biasedness of news topics from different news Websites.&lt;/span&gt;&lt;span&gt;This approach automatically collects the news contents from various online news media portals and then consolidates them for the determination of news biasedness. &lt;/span&gt;&lt;span lang="EN-US"&gt;In the experimental study, the news topics are gathered from various Websites of U.S., U.K., and India. For training dataset 3265 news sentences were collected under various news topics from 20 different news Websites. The effectiveness of classification of algorithm is proved by the extensive experimental study. The proposed algorithm provides a method improves the determination of news biasedness, which in turn may help in providing impartial, unbiased and reliable information.&lt;/span&gt;
Administrative Innovations as Necessary Condition of Competitiveness in Schools

In the present-day education environment, requiring managerial flexibility in assessing ongoing changes, innovation becomes a prerequisite for effective management, and professional and psychological readiness of managers to apply management innovation -an indicator of managerial potential.Management innovation is today the "bottleneck" of educational management theory and practice.There are no theoretical models that are directly related to managerial innovation; technological innovations take priority in the practice of educational management; incorrect interpretation of innovations blunts their effectiveness.The school's competitive advantage today is not determined primarily by capital accumulation, but by the ability of management to initiate, appreciate and use changes, to learn faster than the rest from the experience of the changes.New theories of growth emphasize that it is the development of innovations that is an engine of sustainable educational growth.These circumstances determine management innovations as the domain that is the most in-demand for the development of education under the conditions of today.
Optimization performance management with FCAPS and ITILv3: opportunities and obstacles

&lt;p&gt;With the need for rapidly growing network services each year; the factors of reliability, availability and quality become extremely critical for organizations, groups, institutions and governments to have in the network services implementation. In this case, several rural governments requires the improvement of its network performance management in establishing their internal and external operation to align with the grand design by the central government in improving citizen’s satisfaction through public service. Testing was done using scenarios for output analysis and packet loss as parameters. The results of the proposed topology obtained a productivity of 541.43 Kbps and a packet loss of 0.07%. While the current topology gets a rate of 421.28 Kbps and packet loss of 0.22%. It can be concluded that the proposed topology is still better than the current topology. The optimization of performance management is based on the FCAPS method which emphasize in the performance monitoring and data analysis with the support of ITILv3 that contains documentation and policy guidance to conduct quality performance management. In addition, performance management activities will occur with processes in the design of ITIL services and existing conditions.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
ANALISIS DAN PERANCANGAN E-BISNIS DALAM BUDIDAYA DAN PENJUALAN IKAN CUPANG MENGGUNAKAN METODELOGI OVERVIEW.

Technological, social, and cultural developments cause many companies to be established to achieve certain goals in human needs.Electronic Business or E-Business is an activity of transactions, buying and selling, business conducted through electronic devices or the internet so that companies can directly interact with customers, suppliers and business partners.Cupang fish is one of the easy-to-maintain ornamental fish, Cupang fish cultivation does not need a large area and large capital, it can be done as a home-based business.One of the features of Cupang fish is its durability, it is able to live in a place or water environment that is minimal and lack of oxygen, can be maintained in small jars without an aerator, this ability is obtained because cuppang fish have a labyrinth cavity as in the human lung, the labyrinth can make it survive in an oxygen-poor environment.Besided that we can make this cuppang fish business online (e-bisnis) so that the fish business will increase and reach the global market.
Nonconvex Stochastic Nested Optimization via Stochastic ADMM

We consider the stochastic nested composition optimization problem where the objective is a composition of two expected-value functions. We proposed the stochastic ADMM to solve this complicated objective. In order to find an $ε$ stationary point where the expected norm of the subgradient of corresponding augmented Lagrangian is smaller than $ε$, the total sample complexity of our method is $\mathcal{O}(ε^{-3})$ for the online case and $\mathcal{O} \Bigl((2N_1 + N_2) + (2N_1 + N_2)^{1/2}ε^{-2}\Bigr)$ for the finite sum case. The computational complexity is consistent with proximal version proposed in \cite{zhang2019multi}, but our algorithm can solve more general problem when the proximal mapping of the penalty is not easy to compute.
Grounding Human-to-Vehicle Advice for Self-driving Vehicles

Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human actions, but they lack semantic understanding of image contents. This makes them brittle and potentially unsafe in situations that do not match training data. Here, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Attention mechanisms tie controller behavior to salient objects in the advice. We evaluate our model on a novel advisable driving dataset with manually annotated human-to-vehicle advice called Honda Research Institute-Advice Dataset (HAD). We show that taking advice improves the performance of the end-to-end network, while the network cues on a variety of visual features that are provided by advice. The dataset is available at https://usa.honda-ri.com/HAD.
Incremental processing of noisy user utterances in the spoken language understanding task

The state-of-the-art neural network architectures make it possible to create spoken language understanding systems with high quality and fast processing time.One major challenge for real-world applications is the high latency of these systems caused by triggered actions with high executions times.If an action can be separated into subactions, the reaction time of the systems can be improved through incremental processing of the user utterance and starting subactions while the utterance is still being uttered.In this work, we present a model-agnostic method to achieve high quality in processing incrementally produced partial utterances.Based on clean and noisy versions of the ATIS dataset, we show how to create datasets with our method to create low-latency natural language understanding components.We get improvements of up to 47.91 absolute percentage points in the metric F 1 -score.
Next Generation Sequencing Data and its Compression

Abstract Over the past few years the amount of digital memory and network traffic used by sequenced biological data has increased dramatically. Genomic projects such as HapMap, 1000 Genomes, etc., have come to the collection and description of genomes of 2,504 individuals from 26 populations, and they contributed to exponential growth of databases of this type and to the development of increasingly efficient technologies. Thanks to the large-scale sequencing of samples of DNA, the interest and the new research in these areas by the scientific community are suddenly grown. In a very short time researchers have developed hardware tools, analysis software, algorithms, private databases and infrastructures to support genomics. In this paper we analyse different approaches for compressing digital files generated by Next-Generation Sequencing tools containing nucleotide sequences and we discuss and evaluate the compression performance of generic compression tools such as gzip and bzip2 by confronting them with a specific system that was designed specifically for genomic file compression: quip .
#politicallyincorrect: The Pejoration of Political Language

How is the term â€œpolitical correctnessâ€ understood in the context of modern American politics, particularly in the context of the 2016 election? More specifically, what triggers perceived offensiveness in political language? At the crux of the matter is the distinction between oneself or oneâ€™s social group, those perceived as â€œthe other,â€ and what one is and is not allowed to say in a social forum. This study aims to analyze common language usage and identify factors contributing to the offensification of political language in social media and the types of language in social media that trigger a sense of political outrage. We examine Facebook and Twitter memes, using API searches referring directly to the terms â€œPCâ€ or â€œpolitically correct.â€ Dedoose, a text content-analysis package is used to identify recurring themes in online interactions that are used to criticize perceived political enemies. Results show that themes primarily related to â€œfeminismâ€ and â€œredneckâ€ reflect cross-cutting cleavages in the political landscape primarily related to Hillary Clintonâ€™s candidacy. We also identify significant cleavages in racial identity and quantify these statistically. Our results compliment other recent studies which aim to gauge the impact of social media on political and social polarization.
Arguing Ecosystem Values with Paraconsistent Logics

The valuation of ecosystem services prompts dialogical settings where non-trivially inconsistent arguments are often invoked. Here, I propose an approach to the valuation of ecosystem services circumscribed to a logic-based argumentation framework that caters for valid inconsistencies. This framework accounts for preference formation processes underpinned by a paraconsistent model of logical entailment. The value of an ecosystem service is produced in the form of an ordering over competing land-use practices, as per the arguments surviving semantical probing.
RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search

Early neural network architectures were designed by so-called "grad student descent". Since then, the field of Neural Architecture Search (NAS) has developed with the goal of algorithmically designing architectures tailored for a dataset of interest. Recently, gradient-based NAS approaches have been created to rapidly perform the search. Gradient-based approaches impose more structure on the search, compared to alternative NAS methods, enabling faster search phase optimization. In the real-world, neural architecture performance is measured by more than just high accuracy. There is increasing need for efficient neural architectures, where resources such as model size or latency must also be considered. Gradient-based NAS is also suitable for such multi-objective optimization. In this work we extend a popular gradient-based NAS method to support one or more resource costs. We then perform in-depth analysis on the discovery of architectures satisfying single-resource constraints for classification of CIFAR-10.
Proposed agorithm for regression-based prediction with bulk noise

&lt;span&gt;The noise has incited an original data due to a network with an inferior SNR. In case of the bulk noise, the insightful content within the data is substantially squeezed. A cost-effective method will challenge to quarantine the insights, so that information can be utilized more resourcefully. To achieve this aim, it is essential to iron the bulk noise content out, and then calculate the analytics of the clean data. As noise is bulk so some statistical methodologies such as averaging or randomizing are employed. A prediction using the regression-based model with bulk noise for the experiment in practice is introduced. The decomposition approach to separate the insights is exploited. The proposed algorithm achieves a (local) solution at each computing step and selects the best solution in view of global impacts. The correlation coefficient, average error, absolute error and mean squared error are used to constitute the prediction. Results from MOA simulation will be compared to actual data in the succeeding time. The prediction with bulk noise using the proposed algorithm outperforms.&lt;/span&gt;
Contextual Grounding of Natural Language Entities in Images

In this paper, we introduce a contextual grounding approach that captures the context in corresponding text entities and image regions to improve the grounding accuracy. Specifically, the proposed architecture accepts pre-trained text token embeddings and image object features from an off-the-shelf object detector as input. Additional encoding to capture the positional and spatial information can be added to enhance the feature quality. There are separate text and image branches facilitating respective architectural refinements for different modalities. The text branch is pre-trained on a large-scale masked language modeling task while the image branch is trained from scratch. Next, the model learns the contextual representations of the text tokens and image objects through layers of high-order interaction respectively. The final grounding head ranks the correspondence between the textual and visual representations through cross-modal interaction. In the evaluation, we show that our model achieves the state-of-the-art grounding accuracy of 71.36% over the Flickr30K Entities dataset. No additional pre-training is necessary to deliver competitive results compared with related work that often requires task-agnostic and task-specific pre-training on cross-modal dadasets. The implementation is publicly available at https://gitlab.com/necla-ml/grounding.
Latent Multi-view Semi-Supervised Classification

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.
PRIVATE INITIATIVE AS A FACTOR PROMOTING THE OPENING OF NARYSHKIN READING ROOM IN TAMBOV

The article examines the issues concerning the opening of Naryshkin reading room in Tambov. It is shown that the opening of a new library promoted education of different groups of citizens. Relevance of the study is conditioned by the important role of Naryshkin reading room in the cultural development of Tambov residents, in the development of new forms of reading culture. Scientific originality of the paper involves comprehensive study on the place of private charity in the regional library system, evaluation of qualitative and quantitative contribution of certain donators to replenishing the funds of the people’s reading room.
Implementation of Fuzzy Mamdani Logic Method for Student Drop Out Status Analytics

One of The Contribution of Information Technology in the World of Education at University is the System of Executive. the Information that needed by the Executive Department is an Information Student Data that supposed to be drop out. all this time the Process to determine the Student Faculty of Science and Technology Status as Drop out at Sunan Gunung Djati (SGD) States Islamic University Bandung, had not to apply the Automatic Sysytem, based on this case, therefore we need a Solution to handle it, by Develop the Executive Information System. The Method that we need for Developing this System is by using the Fuzzy Mamdani Logic Method, there are 4 Steps to bring the Output of this Method 1.) Creating a Fuzzy set, 2.) Application of Function Implication 3.) Composition Law, 4.) Defuzzification. Based on this research, we will make a comparison between Manual Calculation and System Calculation, with the Accuracy 100%. The final result of this Method can be referenced for System Executive to Analyze the Student that should be given a Warning or Drop Out.
Scene Graph based Image Retrieval -- A case study on the CLEVR Dataset

With the prolification of multimodal interaction in various domains, recently there has been much interest in text based image retrieval in the computer vision community. However most of the state of the art techniques model this problem in a purely neural way, which makes it difficult to incorporate pragmatic strategies in searching a large scale catalog especially when the search requirements are insufficient and the model needs to resort to an interactive retrieval process through multiple iterations of question-answering. Motivated by this, we propose a neural-symbolic approach for a one-shot retrieval of images from a large scale catalog, given the caption description. To facilitate this, we represent the catalog and caption as scene-graphs and model the retrieval task as a learnable graph matching problem, trained end-to-end with a REINFORCE algorithm. Further, we briefly describe an extension of this pipeline to an iterative retrieval framework, based on interactive questioning and answering.