Reviewerly/exHarmony huggingface dataset:
Private Blockchain-Cloud System (PBCS) for Healthcare Services

Blockchain in the recent times stands as a buzz word to deal with huge set of real time applications that function in a manual way rather than in an automated manner. There may be several intermediate phases involved to access legitimate records of significance in various sectors like financial industry, supply chain market, educational organizations, healthcare systems etc. But, when the amount of data is enormous, then manual steps to infer a conclusion after undergoing verification and validation phases, is a time-consuming approach. Hence, there is a necessity to automate the process flow using emerging tools and technologies such as blockchain. Towards this end, a novel Private Blockchain-Cloud System (PBCS) for Healthcare Services is proposed in this work. An model is presented to secure the patient records via e-block access platform where only the person holding the legitimate key can access the data. Security is further strengthened by deploying the blocks of a blockchain in cloud environment.
Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching

Neural text matching models have been widely used in community question answering, information retrieval, and dialogue. However, these models designed for short texts cannot well address the long-form text matching problem, because there are many contexts in long-form texts can not be directly aligned with each other, and it is difficult for existing models to capture the key matching signals from such noisy data. Besides, these models are computationally expensive for simply use all textual data indiscriminately. To tackle the effectiveness and efficiency problem, we propose a novel hierarchical noise filtering model, namely Match-Ignition. The main idea is to plug the well-known PageRank algorithm into the Transformer, to identify and filter both sentence and word level noisy information in the matching process. Noisy sentences are usually easy to detect because previous work has shown that their similarity can be explicitly evaluated by the word overlapping, so we directly use PageRank to filter such information based on a sentence similarity graph. Unlike sentences, words rely on their contexts to express concrete meanings, so we propose to jointly learn the filtering and matching process, to well capture the critical word-level matching signals. Specifically, a word graph is first built based on the attention scores in each self-attention block of Transformer, and key words are then selected by applying PageRank on this graph. In this way, noisy words will be filtered out layer by layer in the matching process. Experimental results show that Match-Ignition outperforms both SOTA short text matching models and recent long-form text matching models. We also conduct detailed analysis to show that Match-Ignition efficiently captures important sentences and words, to facilitate the long-form text matching process.
Cluster Analysis of Russian Universities Based on the Dynamics of Their Performance Indicators

The article presents the results of a study, the aim of which is to investigate the dynamics of the development of Russian higher education institutions based on an analysis of their performance indicators. The sample includes 535 universities from 80 constituent entities of the Russian Federation. It presents the results of the clustering of universities on the basis of changes in indicators of six variables characterizing the key areas of activity of universities for the period from 2013/2014 to 2017/2018 academic years. The characteristics of each cluster are described, an inter-cluster comparison of quantitative indicators of the activities of universities is presented. As a result of the calculations of the average annual growth rates of six key indicators characterizing the activities of universities, fve clusters were identifed that diﬀer in their development trajectories. Thus, the universities that belong to Cluster 1 over a fve-year period retained or improved their positions in the main areas of educational activity and managed to maintain income growth at an average level. However, their indicators in such areas as international and research activities, as well as infrastructure development (provision of educational and laboratory facilities) have decreased. In universities from clusters 2 and 3, all performance indicators changed in a fairly balanced way. At the same time, the indicator of an increase in the internationalization of the students’ body was signifcantly higher in Cluster 2, while educational organizations from Cluster 3 showed a signifcant increase in the publication activity of academic staﬀ. The results of the activities of universities in Cluster 4 were positive in all considered areas. This cluster had the highest average annual growth rates in the average Unifed State Exam (USE) score and publication activity of the academic staﬀ. Educational organizations from Cluster 5, on the one hand, achieved the best results in attracting foreign students, increasing the proftability of their activities and the provision of teaching and laboratory facilities but, on the other hand, their average annual growth rate of the average USE score has signifcantly decreased. The study represents one of the frst attempts to cluster Russian universities based on the analysis of changes in their performance indicators. Previous studies on the clustering of universities were mainly based on the analysis of one-time indicators. The approach proposed by the author makes it possible to compare indicators of the dynamics of development of higher educational institutions of diﬀerent size and scope. Further research in this area could be aimed at analyzing a larger number of performance indicators of universities and studying in detail their strategies for a deeper understanding of the reasons for the diﬀerences in their eﬀectiveness.
Lung Nodule Detection of CT and Image-Based GLCM and RLM CT Scan Using the Support Vector Machine (SVM) Method

Lung cancer is the most common cause of cancer death globally. Early detection of lung cancer will greatly beneficial to save the patient. This study focused on the detection of lung cancer using classification with the Support Vector Machine (SVM) method based on the features of Gray Level Co-occurrence Matrices (GLCM) and Run Length Matrix (RLM). The lung data used were obtained from the Cancer imaging archive Database, consisting of 500 CT images. CT images were grouped into 2 clusters, including normal and lung cancer. The research steps include: image processing, region of interest segmentation, and feature extraction. The results indicate that the system can detect the CT-image of SVM classification where the default parameter only provides an accuracy of 85.63%. It is expected that the results will be useful to help medical personnel and researchers to detect the status of lung cancer. These results provide information that detection of lung nodules based on GLCM and RLM features that can be detected is better. Furthermore, selecting parameters C and γ on SVM. Keywords: cancer, nodule, support vector machine (SVM).
RICNN: A ResNet&amp;Inception convolutional neural network for intrusion detection of abnormal traffic

To secure cloud computing and outsourced data while meeting the requirements of automation, many intrusion detection schemes based on deep learning are proposed. Though the detection rate of many network intrusion detection solutions can be quite high nowadays, their identification accuracy on imbalanced abnormal network traffic still remains low. Therefore, this paper proposes a ResNet &amp;Inception-based convolutional neural network (RICNN) model to abnormal traffic classification. RICNN can learn more traffic features through the Inception unit, and the degradation problem of the network is eliminated through the direct mapping unit of ResNet, thus the improvement of the model?s generalization ability can be achievable. In addition, to simplify the network, an improved version of RICNN, which makes it possible to reduce the number of parameters that need to be learnt without degrading identification accuracy, is also proposed in this paper. The experimental results on the dataset CICIDS2017 show that RICNN not only achieves an overall accuracy of 99.386% but also has a high detection rate across different categories, especially for small samples. The comparison experiments show that the recognition rate of RICNN outperforms a variety of CNN models and RNN models, and the best detection accuracy can be achieved.
Evaluasi Usability Dan Perbaikan Desain Antarmuka Aplikasi Info Pdam Tirta Satria Menggunakan Metode Use Questionnare

PERUMDA Tirta Satria is a regional public company engaged in the distribution of water in Banyumas Regency. PERUMDA Tirta Satria has a mobile application called Info PDAM Banyumas. The number of complaints experienced by users while using Info PDAM Banyumas application is the main reason to test the usability of this application. The usability testing method used in this study uses the USE Questionnaire. The results of the usability level obtained from the test are 70.85% which fall into the Eligible or Good category. From the results obtained after the evaluation, it shows that there are usability problems found in the application, such as unclear text, a stiff and unattractive interface, disproportionately large icon sizes, uninformative help menus, and problems when the user make customer complaints because of the unclear flow. To answer the problems that exist in this study, researcher made improvement recommendations for the interface by using Google Material Design as the basis.
Acceptance of Robotic Transportation in Small Workshops: A China-Iran Cross-Cultural Study

In this research, we developed a cost-effective automated guided vehicle for small clothing production workshops that ordinary workers in such workplaces can operate. We acquired workers’ opinions in multiple workshops about using the robots (with some social capabilities) and working alongside them. We performed the tests in China and Iran to investigate and compare different preferences and priorities in two countries with different cultural backgrounds. We used the UTAUT questionnaire and conducted two-way ANOVA tests considering two independent factors: Nationality and Gender. The results showed that workers in Iran and China have relatively similar expectations from a social AGV, with no contrast in the mean score of women and men. We also observed that Iranian participants have fewer concerns about the safety of working around the robot, which may be due to greater exposure of Chinese workers to automated robots and the potential dangers of working in the same environment with the robot. Moreover, we observed that female workers of both nationalities feel they need more help to work with the robots than male workers. We hope that the results presented in this cross-cultural study assists in developing an understanding of the attitude of workers in small workshops toward automated transportation.
Research Data: The Future of FAIR White paper

2021 marks five years since the publication of the FAIR data principles. With a wide range of opinions and commentary, this white paper looks at the real-world impact of FAIR, and considers what will be next for research data and open science.Find out about research data at Springer Nature: https://go.sn.pub/research-data
Learning from Demonstrations using Signal Temporal Logic

Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and also raises concerns of safety and interpretability in the learned control policies. To address these issues, we use Signal Temporal Logic to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards, and also define interesting causal dependencies between tasks such as sequential task specifications. We validate our approach through experiments on discrete-world and OpenAI Gym environments, and show that our approach outperforms the state-of-the-art Maximum Causal Entropy Inverse Reinforcement Learning.
Asbestos and Additive Manufacturing: Addressing Early Concerns Surrounding Manufacturing 3D-Printing Technology Using Asbestos Litigation as a Model

This paper tackles basic issues that will face latent-disease cases involving emissions from 3D-printing technology by comparing those issues to those faced throughout the jurisprudence surrounding asbestos-related, latent-disease cases.
Factors motivating governmental employees in the United Arab Emirates

To enhance the performance of organizations, the motivation of employees is a critical factor. The challenge is that motivation can be dependent on the culture and time of analysis. The timely studies are needed for different regions. The purpose of this study is to perform the motivation factors analysis of government employees to enhance their performance. Herzberg’s two-factor motivation theory was used, where the focus is on motivators, which were compared with financial rewards. Another classification of motivators and movers that contributes to ideas was extracted from the literature and included in the questionnaire survey. A survey was designed and 64 employees working in governmental service sectors in the United Arab Emirates (UAE) responded to the questionnaire. Statistical analysis methods such as two proportion z-test, Chi-square, and Fisher tests, were used. Results showed that recognition, which was selected by 44% of employees, is the only source of satisfaction that has a higher effect than financial rewards. In addition, a sense of achievement, the opportunity to take responsibility, work itself, as well as advancement prospects have the same or lower effect than financial rewards. Moreover, results showed that, generally, factors associated with motivators are more important for ideas’ contribution than movers, except for the desire to overcome frustration at work. The practical value of the results is obvious since motivation can significantly enhance the performance of organizations in the UAE.
Instructions New Technology of Color Image Encryption Based Two Improved Vigenere Laps Separated by a Genetic Mutation

This document traces the development of a new cryptosystem using two circuits ensured by a deep Vigenere classical technique improvement. This new technique employs several dynamic substitutions matrices attached to chaotic replacement functions; whose construction will be detailed. The first round will start by modifying the seed pixels based on the initial values calculated from the original image, and will be infected through the chaotic map used to overcome the uniform image problem, followed by the injection of Vigenere technology improvements. The output vector will be subdivided into three sized blocks for future application of deeply improved genetic mutations to better adapt to medicine and color image encryption. The second round will increase the complexity of the attack and improve the installed systems. Simulations performed on a large number of images of different sizes and formats ensure that our approach is not exposed to known attacks.
Past Forward: Electricity’s Perilous Narrative Arc

Summary form only given, as follows. In L. Frank Baum’s 1901 classic "The Master Key: An Electrical Fairy Tale", Rob Josyln, a young boy who is wild about all things electrical, accidentally summons the Demon of Electricity to his attic workshop. Similarly to the genie in Aladdin’s lamp, the demon offers Rob three gifts for each of the next three weeks to demonstrate the extraordinary potential of electricity. The fantastical inventions Rob receives foreshadow modern-day Tasers, streaming video, and augmentedreality glasses. Adventure and near disaster ensue. Eventually, Rob decides to avoid further mishap and politely refuses any more gifts. In his sci-fi morality tale, Baum seems to recognize that the world of 120 years ago was not prepared for this futuristic technology. Are we ready now?
Statistical ZAPs from Group-Based Assumptions

We put forth a template for constructing statistical ZAPs for NP. Our template compiles NIZKs for NP in the hidden-bit model (which exist unconditionally) into statistical ZAPs using a new notion of interactive hidden-bit generator (\(\mathsf {IHBG}\)), which adapts the notion of hidden-bit generator to the plain model by building upon the recent notion of statistically-hiding extractable commitments. We provide a construction of \(\mathsf {IHBG}\) from the explicit hardness of the decision Diffie-Hellman assumption (where explicit refers to requiring an explicit upper bound on the advantage of any polynomial-time adversary against the assumption) and the existence of statistical ZAPs for a specific simple language, building upon the recent construction of dual-mode hidden-bit generator from (Libert et al., EUROCRYPT 2020). We provide two instantiations of the underlying simple ZAP:- Using the recent statistical ZAP for the Diffie-Hellman language of (Couteau and Hartmann, CRYPTO 2020), we obtain statistical ZAPs for NP assuming (the explicit hardness of) DDH in \(\mathbb {G}_1\) and kernel-DH in \(\mathbb {G}_2\) (a search assumption which is weaker than DDH), where \((\mathbb {G}_1,\mathbb {G}_2)\) are groups equipped with an asymmetric pairing. This improves over the recent work of (Lombardi et al., EUROCRYPT 2020) which achieved a relaxed variant of statistical ZAP for NP, under a stronger assumption.- Using the recent work of (Couteau et al., EUROCRYPT 2020), we obtain statistical ZAPs for NP assuming the explicit hardness of DDH, together with the assumption that no efficient adversary can break the key-dependent message one-wayness of ElGamal with respect to efficient functions over groups of size \({2^\uplambda }\) with probability better than \({\mathsf {poly} (\uplambda )/2^{(c + o(1)) \cdot \uplambda }}\), denoted \({2^{-c\uplambda }}\)-\(\mathsf {OW\text {-}KDM}\), for a constant \(c = 1/2\), in pairing-free groups. Note that the latter is a search discrete-log-style falsifiable assumption, incomparable to DDH (in particular, it is not known to imply public-key encryption).
Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism

In the real scene, because pedestrians are occluded or the size of pedestrians is small, the convolutional neural network cannot fully extract their features, resulting in poor detection results.In two adjacent frames, the same pedestrian is prone to errors when doing data association, which makes the pedestrian tracking effect unsatisfactory.In order to solve this problem, the pedestrian tracking algorithm based on Anchor-free idea is improved.A fusion context information module is proposed to enhance the model's feature extraction ability for different receptive fields, and improve the model's detection and tracking performance when the pedestrian size is small.In addition, in order to let the model learn to pay attention to the effective information of the feature layer.A coordinated attention mechanism is introduced to guide the model to learn the weights of different channels and different regions of the feature layer, and to improve the tracking performance of the model when pedestrians are occluded.In the experiment, the tracking performance of the model was verified on the MOT16 dataset.Experimental results show that compared with other main popular person tracking algorithms, the improved algorithm has higher tracking accuracy and lower pedestrian ID switching times.Its tracking accuracy is 70.74.
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization

Anderson mixing (AM) is an acceleration method for fixed-point iterations. Despite its success and wide usage in scientific computing, the convergence theory of AM remains unclear, and its applications to machine learning problems are not well explored. In this paper, by introducing damped projection and adaptive regularization to classical AM, we propose a Stochastic Anderson Mixing (SAM) scheme to solve nonconvex stochastic optimization problems. Under mild assumptions, we establish the convergence theory of SAM, including the almost sure convergence to stationary points and the worst-case iteration complexity. Moreover, the complexity bound can be improved when randomly choosing an iterate as the output. To further accelerate the convergence, we incorporate a variance reduction technique into the proposed SAM. We also propose a preconditioned mixing strategy for SAM which can empirically achieve faster convergence or better generalization ability. Finally, we apply the SAM method to train various neural networks including the vanilla CNN, ResNets, WideResNet, ResNeXt, DenseNet and RNN. Experimental results on image classification and language model demonstrate the advantages of our method.
Robust fuzzy factorization machine with noise clustering-based membership function estimation

Factorization machine (FM) is a promising model-based algorithm for collaborative filtering (CF), but can bring inferior performances if datasets include users having low confidence. In this paper, a robust FM model is proposed by introducing the noise clustering-based noise rejection mechanism into Fuzzy FM, which utilizes fuzzy memberships of users for considering the responsibility of each user in FM modeling. By automatically updating fuzzy memberships with user-wise criteria of prediction errors, the FM model is better fitted to reliable users and is expected to improve the generalization ability for predicting the preference degrees of unknown items. The characteristics of the proposed method are demonstrated through numerical experiments with MovieLens movie evaluation data such that the prediction ability for not only the training ratings but also the test ratings of reliable users can be improved by carefully tuning the noise sensitivity weight.
Benchmarking feature selection methods with different prediction models on large-scale healthcare event data

With the development of the Electronic Health Record (EHR) technique, vast volumes of digital clinical data are generated. Based on the data, many methods are developed to improve the performance of clinical predictions. Among those methods, Deep Neural Networks (DNN) have been proven outstanding with respect to accuracy by employing many patient instances and events (features). However, each patient-specific event requires time and money. Collecting too many features before making a decision is insufferable, especially for time-critical tasks such as mortality prediction. So it is essential to predict with high accuracy using as minimal clinical events as possible, which makes feature selection a critical question. This paper presents detailed benchmarking results of various feature selection methods, applying different classification and regression algorithms for clinical prediction tasks, including mortality prediction, length of stay prediction, and ICD-9 code group prediction. We use the publicly available dataset, Medical Information Mart for Intensive Care III (MIMIC-III), in our experiments. Our results show that Genetic Algorithm (GA) based methods perform well with only a few features and outperform others. Besides, for the mortality prediction task, the feature subset selected by GA for one classifier can also be used to others while achieving good performance.