Reviewerly/exHarmony huggingface dataset:
Research on Cloud Storage Technology in Digital Campus Big Data Scenario

Realizing campus intelligent management and service mode by combining cloud storage technology is the focus of current research in digital campus construction work. By analyzing the IT development trend of digital campus, the defective problems of traditional storage system, from the architecture design and platform construction of cloud storage system for digital campus, we propose a cloud storage construction scheme for campus big data scenario, and discuss the application of virtualization, streamlined configuration, storage optimization and other technologies in cloud storage. The cloud storage technology is applied in the construction of digital campus, which strongly accelerates the process of digital campus informatization.
Application of Thematic Modeling Methods in Text Topic Recognition Tasks to Detect Telephone Fraud

The Internet has emerged as a powerful infrastructure for worldwide communication and human interaction. Some unethical use of this technology spam, phishing, trolls, cyberbullying, viruses caused problems in the development of mechanisms that guarantee affordable and safe opportunities for its use. Currently, many studies are being conducted to detect spam and phishing. The detection of telephone fraud has become critically important, as it entails huge losses. Machine learning and natural language processing algorithms are used to analyze a huge amount of text data. Fraudsters are identified using text mining and can be implemented by analyzing the terms of a word or phrase. One of the difficult tasks is to divide this huge unstructured data into clusters. There are several thematic modeling models for these purposes. This article presents the application of these models, in particular LDA, LSI and NMF. A data set has been formed. A preliminary analysis of the data was carried out and signs were constructed for models in the task of recognizing the subject of the text. The approaches of keyword extraction in the tasks of text topic recognition are considered. The key concepts of these approaches are given. The disadvantages of these models are shown, and directions for improving text processing algorithms are proposed. The evaluation of the quality of the models was carried out. Improved models thanks to the selection of hyperparameters and changing the data preprocessing function.
Oxytocin Massage and Breastfeeding on Demand in the Uterine Involution Process

Introduction: Involution begins as soon as the placenta is delivered. During uterine involution, the uterus will return to its pre-pregnancy state and (estimated) size. Aim: The aim of this case study is to carry out comprehensive midwifery care to support maternity women the normal process of uterine involution includes IMD, oxytocin massage, and on demand breastfeeding. Method: This research method is a case report of midwifery care. The subjects in this study were mothers who gave birth at the Independent Midwife Practice as many as 3 people. Results: Correct breastfeeding techniques on demand because on-demand breastfeeding helps produce contractions by releasing the hormone oxytocin Conclusion: Uterine involution progressed well until the 7th day postpartum, this was supported by a good process starting from the IMD process after the baby was born, followed by early mobilization, direct breastfeeding, and oxytocin massage.
Blockchain Based Secured Vehicle Insurance Framework using Hyperledger Composer

Abstract The manual settlement of traditional vehicle insurance policies is atroublesome operation. The settlement procedure is difficult due to anumber of factors, including concealed requirements from the insurer orcharges of fraud by the insured. Additionally, this method takes a longperiod, which makes it incredibly ineffective. The use of blockchain technologyand smart contracts in the vehicle insurance industry has thepotential to completely alter this situation. Any transaction or modificationto a block is made following the decentralized validator entity,not a specific individual. The smart contract is a special feature that isrecorded on the blockchain and is activated when certain requirementsare met. This study proposes a framework for using blockchain-basedsmart contracts for vehicle insurance contracts. A private hyperledgernetwork is used to host this blockchain and intelligent contract-basedinfrastructure. The JavaScript programming language is used to createsmart contracts. To verify the transactions, the system employsthe Proof of Authority consensus mechanism. This framework canaddress all the safety and confidence problems associated with traditionalinsurance policies through the deployment of blockchain technology.
Interactive visual analytics for medical data: application to COVID-19 clinical information during the first wave

Biomedical data recorded as a result of clinical practice are often multi-domain -involving lab measurements, medication, patient attributes, logistic information-, and also highly unstructured, with high rates of missing data and asynchronously sampled measurements.In this scenario, we need tools capable of providing a broad picture prior to more detailed analyses.We present here a visual analytics approach that uses the morphing projections technique to combine the visualization of a t-SNE projection of clinical time series, with views of other clinical or patient's information.The proposed approach is demonstrated on an application case study of COVID-19 clinical information taken during the first wave.
An Effective Security Comparison Protocol in Cloud Computing

Abstract Secure comparison protocol is an important branch of secure multi-party computation(SMPC), which compares the size of input data without disclosing any information between participants. The development of cloud computing provides an application platform for SMPC, but it also brings new challenges. In cloud computing with SMPC, clients need to process their own data and submit the processed data to a cloud server, which then performs the computation. In this process, not only the clients need to maintain an honest state at all times, but sensitive data on the cloud server side may also be exposed. In this paper, zero-knowledge proof and homomorphic encryption techniques are used to improve Damgård-Geisler-KrØigaard(DGK) comparison protocol. The improved secure comparison protocol can not only safely calculate private data, but also be applicable to malicious participant model. Finally, the security analysis shows that the proposed scheme not only ensures the privacy security of participants, but also ensures the data fairness of comparison protocols.
PCA improves the adversarial robustness of neural networks

Deep neural networks perform well in many visual recognition tasks, but they are sensitive to adversarial input perturbation.More robust models can be learned when attacks are applied to the training data or preprocessing is used.However, the effect of preprocessing is frequently underestimated and it has not received sufficient attention as it usually does not affect the network's clean accuracy.Here, we seek to demonstrate that preprocessing can play a role in improving adversarial robustness.Our empirical results show that principal component analysis, a simple yet effective preprocessing method, can significantly improve neural networks' robustness for both regular and adversarial training.
Towards Better Transition Modeling in Recurrent Neural Networks: the Case of Sign Language Tokenization

Recurrent neural networks can be used to segment sequences such as videos, where transitions can be challenging to detect.This paper benchmarks strategies to better model the transition between states.The specific task of SL video tokenization is chosen for the evaluation, as it remains challenging.Tokenizers are the cornerstone of natural language processing pipelines.There exist powerful tokenizers for text data, but sign language (SL) video tokenizers are still under development.Benchmarked strategies prove to be useful to improve SL videos tokenization, but there is still room for improvement to better model state transitions.
Modular Representations for Weak Disentanglement

The recently introduced weakly disentangled representations proposed to relax some constraints of the previous definitions of disentanglement, in exchange for more flexibility.However, at the moment, weak disentanglement can only be achieved by increasing the amount of supervision as the number of factors of variations of the data increase.In this paper, we introduce modular representations for weak disentanglement, a novel method that allows to keep the amount of supervised information constant with respect the number of generative factors.The experiments shows that models using modular representations can increase their performance with respect to previous work without the need of additional supervision.
Reinforcement learning for constructing low density sign representations of Boolean functions

Boolean functions (BFs) can be uniquely represented with polynomial functions by representing True and False with ±1.With the 'sign-representation' framework, i.e., when the sign of the polynomials is used instead of the exact ±1, the representation is not unique anymore, and several measures of sign-representation become the target of research.One such measure is the polynomial threshold function density (PTF density), i.e., the minimum number of monomials that suffices to sign-represent a given BF.Several algorithms can find sign-representations with a low number of monomials; however, to find a representation with the minimum number of monomials possible is a combinatorial search problem.The recent success of reinforcement learning (RL) algorithms in solving combinatorial search problems poses the question of whether RL can perform well in finding sign-representations with a low number of monomials.To address this question, we focused on Deep Q-Networks (DQN) and explored its applicability to the sign-representation problem.To be concrete, we present our work on modeling RL agents for solving the signrepresentation problem and give our results on the application of DQN to BFs with a low number of variables (n = 4).Our results indicate that the trained DQN agent generalizes well and exploits intrinsic structure of BFs, such as their equivalence in terms of certain equivalence relations.
Deep Semantic Segmentation in Skin Detection

Deep semantic segmentation is a task that identifies objects and their boundaries in images, to do that a classification task is performed at the pixel level to tag whether a pixel belongs to an object.In skin detection, areas of images are classified as skin or non-skin regions.In this work, we report a short survey of the recent literature covering the task to help researchers in selecting the most suitable method for their application and to expand the knowledge about the available datasets for this topic.A compact empirical evaluation comparing recent models and a new ensemble model is reported.
Neural Architecture Search for Sentence Classification with BERT

Pre training of language models on large text corpora is common practice in Natural Language Processing.Following, fine tuning of these models is performed to achieve the best results on a variety of tasks.In this paper we question the common practice of only adding a single output layer as a classification head on top of the network.We perform an AutoML search to find architectures that outperform the current single layer at only a small compute cost.We validate our classification architecture on a variety of NLP benchmarks from the GLUE dataset.
Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification

Data and information are nowadays frequently available in multiple modalities like different sensor signals, textual descriptions, graph structures, and other formats.The maximum information from these heterogeneous representations can be obtained by fusing the various modalities by specific embeddings or proximity measures.Current approaches are widely limited in the fusion model and the applied measures, especially when the given data is non-vectorial.We propose a model to learn the spectral properties of the different inner product representations in a joined optimization problem.The approach is evaluated on various multimodal data and compared to modern multiple-kernel learning and baseline techniques.* MM and MR are supported by the Bavarian HighTech agenda and the Würzburg Center for Artificial Intelligence and Robotics (CAIRO).Additionally, we thank Dr. Benjamin
Input Routed Echo State Networks

We introduce a novel Reservoir Computing (RC) approach for multi-dimensional temporal signals.Our proposal is based on routing the different dimensions of the driving input towards different dynamical sub-modules in a multi-reservoir architecture.At the same time, controllable interconnections among the sub-modules allow modeling the interplay between the different dynamics that might be required by the task.Experiments on synthetic and real-world time-series classification problems clearly show the advantages of the proposed approach in dealing with multi-dimensional signals in comparison to standard RC neural networks.
Appearance-Context aware Axial Attention for Fashion Landmark Detection

Fashion landmark detection is a fundamental task in several fashion image analysis problems.The associated challenges involving non-rigid structures and variations in style and orientation makes it extremely hard to accurately detect the landmarks.In this paper, we propose Appearance-Context network (ACNet), which encapsulates both global and local contextual information extending the axial attention mechanism.We design axial attention augmented local appearance network and introduce a novel Global-Context aware axial attention module which aggregates the global features attending discriminatory cues across height, width and channel axes.The proposed ACNet architecture outperforms existing methods on two large-scale fashion landmark datasets.
Graph Neural Networks for Propositional Model Counting

Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks.Nevertheless, counting problems such as propositional model counting (#SAT) are still mostly approached with traditional solvers.Here we tackle this gap by presenting an architecture based on the GNN framework for belief propagation (BP) of [1], extended with self-attentive GNN and trained to approximately solve the #SAT problem.We experimentally show that our model, trained on a small set of random Boolean formulae, is able to scale effectively to much larger problem sizes, outperforming state of the art approximate solvers.Moreover, we show that it can be efficiently fine-tuned to provide good generalization results on different formulae distributions, such as those coming from SATencoded combinatorial problems.Related Work.In [5] a graph neural network model is proposed to solve the weighted disjunctive normal form counting problem (weighted #DNF).Moreover, a significant
Continual Incremental Language Learning for Neural Machine Translation

The paper provides an experimental investigation of the phenomena of catastrophic forgetting for Neural Machine Translation systems.We introduce and describe the continual incremental language learning setting and its analogy with the classical continual learning scenario.The experiments measure the performance loss of a naive incremental training strategy against a jointly trained baseline, and we show the mitigating effect of the replay strategy.To this end, we also introduce a prioritized replay buffer strategy informed by the specific application domain.
The Method of Probabilistic Nodes Combination in Handwritten Character Reconstruction

Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D curve modeling and handwriting identification by using the set of key points. Nodes are treated as characteristic points of signature or handwriting for modeling and writer recognition. Identification of handwritten letters or symbols need modeling and the model of each individual symbol or character is built by a choice of probability distribution function and nodes combination. PNC modeling via nodes combination and parameter γ as probability distribution function enables curve parameterization and interpolation for each specific letter or symbol. Two-dimensional curve is modeled and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.
Automatic Learning of Subword Dependent Model Scales

To improve the performance of state-of-the-art automatic speech recognition systems it is common practice to include external knowledge sources such as language models or prior corrections. This is usually done via log-linear model combination using separate scaling parameters for each model. Typically these parameters are manually optimized on some held-out data. In this work we propose to optimize these scaling parameters via automatic differentiation and stochastic gradient decent similar to the neural network model parameters. We show on the LibriSpeech (LBS) and Switchboard (SWB) corpora that the model scales for a combination of attentionbased encoder-decoder acoustic model and language model can be learned as effectively as with manual tuning. We further extend this approach to subword dependent model scales which could not be tuned manually which leads to 7% improvement on LBS and 3% on SWB. We also show that joint training of scales and model parameters is possible and gives additional 6% improvement on LBS.
Metrics identification in the ISO/IEC 29110 standard implementation for the improvement of its work products: A case study

Currently, the ISO/IEC 29110 standard has been released due to the importance it has taken to improve the quality of software products developed by small software development entities. However, even in many of the small software development entities there is a lack of knowledge of the correct application of the different processes and their respective quantitative evaluation. Therefore, the objective of this article is to analyze the documentation of a project presented by a company, to evaluate the implementation of the ISO/IEC 29110 standard, to identify metrics that can be applied to evaluate the status of the project and indicate recommendations.
Blast Vibration Prediction

Summary For mining operators it is essential to predict peak ground velocity (PGV) to minimize the impact of vibrations to sensitive areas nearby. There are numerous empirical formulas for this purpose and one of these is the USBM Scaled Distance (SD) approach. We investigate this approach and explored other strategies to predict the PGV. The Data for that study was derived from a array of 81 acceleration sensors deployed over several months inside and next to the mine. The large array made it possible to calibrate the decay exponent b in common blast gathers. We also present a method where we sequentially invert for the charge exponent c and then for the decay exponent. Finally we introduce a joint inversion where we invert the whole dataset to constrain the global b and c values. With that measurements we were able to compare the approaches and make prediction tests. Based on the key performance indicators like the root mean square residual and coefficient of determination we came to the conclusion that the currently used USBM SD approach performed worst. We also gained insights in parameter distributions and data uncertainty of the measured PGV.
Design Guidelines for Inclusive Speaker Verification Evaluation Datasets

Recently, Basu and Holstlag (2021) proposed a unified f ramework f or describing outer length scales (OLS).By utilizing this framework, we document various characteristics of OLS in nocturnal boundary layers over the US Great Plains.
From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation

Speech-to-speech translation (S2ST) converts input speech to speech in another language.A challenge of delivering S2ST in real time is the accumulated delay between the translation and speech synthesis modules.While recently incremental text-tospeech (iTTS) models have shown large quality improvements, they typically require additional future text inputs to reach optimal performance.In this work, we minimize the initial waiting time of iTTS by adapting the upstream speech translator to generate high-quality pseudo lookahead for the speech synthesizer.After mitigating the initial delay, we demonstrate that the duration of synthesized speech also plays a crucial role on latency.We formalize this as a latency metric and then present a simple yet effective duration-scaling approach for latency reduction.Our approaches consistently reduce latency by 0.2-0.5 second without sacrificing speech translation quality.
Persistent Rule-based Interactive Reinforcement Learning

Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-time. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Additionally, the information provided by each interaction is not retained and instead discarded by the agent after a single-use. In this work, we propose a persistent rule-based interactive reinforcement learning approach, i.e., a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Our experimental results show persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer. Moreover, rule-based advice shows similar performance impact as state-based advice, but with a substantially reduced interaction count.
Analysis of the skills that journalists should possess in the era of short video

In the era of short video, Tik Tok, Kuaishou and other short video apps have become an important way for news media to spread information.This paper will analyze the skills journalists should have in the era of short video by interpreting the basic features of short video and the positive and negative effects of news reports.
An analysis of College Students' English learning motivation under the network environment

the network environment not only brings convenience to people's life, but also brings a new direction to education.Under the background of network, information technology can make students' learning break through the classroom, but has diversified ways.Therefore, students should pay attention to the application of network technology in their daily learning.Teachers should also change their teaching ideas in time, improve college students' English learning motivation in the network environment, promote the development of English learning in the network environment, and enable students to meet the needs of today's society.The author makes an in-depth study on learning motivation and learning under the network mode, and puts forward relevant measures to enhance students' learning motivation, in order to promote the improvement of College Students' English level.This requires teachers and students to work together to promote English learning and shape global talents, so as to promote the development and progress of society.