Reviewerly/exHarmony huggingface dataset:
Using FHIR to Construct a Corpus of Clinical Questions Annotated with Logical Forms and Answers.

This paper describes a novel technique for annotating logical forms and answers for clinical questions by utilizing Fast Healthcare Interoperability Resources (FHIR). Such annotations are widely used in building the semantic parsing models (which aim at understanding the precise meaning of natural language questions by converting them to machine-understandable logical forms). These systems focus on reducing the time it takes for a user to get to information present in electronic health records (EHRs). Directly annotating questions with logical forms is a challenging task and involves a time-consuming step of concept normalization annotation. We aim to automate this step using the normalized codes present in a FHIR resource. Using the proposed approach, two annotators curated an annotated dataset of 1000 questions in less than 1 week. To assess the quality of these annotations, we trained a semantic parsing model which achieved an accuracy of 94.2% on this corpus.
Group Recommender Systems Based on Members’ Preference for Trusted Social Networks

With the development of the Internet of Things (IoT), the group recommender system has also been extended to the field of IoT. The entities in the IoT are linked through social networks, which constitute massive amounts of data. In group activities such as group purchases and group tours, user groups often exhibit common interests and hobbies, and it is necessary to make recommendations for certain user groups. This idea constitutes the group recommender system. However, group members’ preferences are not fully considered in group recommendations, and how to use trusted social networks based on their preferences remains unclear. The focus of this paper is group recommendation based on an average strategy, where group members have preferential differences and use trusted social networks to correct for their preferences. Thus, the accuracy of the group recommender system in the IoT and big data environment is improved.
A Hybrid Technique for Server Consolidation in Cloud Computing Environment

Abstract The goal of data centers in the cloud computing environment is to provision the workloads and the computing resources as demanded by the users without the intervention of the providers. To achieve this, virtualization based server consolidation acts as a vital part in virtual machine placement process. Consolidating the Virtual Machines (VMs) on the Physical Machines (PMs) cuts down the unused physical servers, decreasing the energy consumption, while keeping the constraints for CPU and memory utilization. This technique also reduces the resource wastage and optimizes the available resources efficiently. Ant Colony Optimization (ACO) that is a well-known multi objective heuristic algorithm and Grey Wolf Algorithm (GWO) has been used to consolidate the servers used in the virtual machine placement problem. The proposed Fuzzy HAGA algorithm outperforms the other algorithms MMAS, ACS, FFD and Fuzzy ACS compared against it as the number of processors and memory utilization are lesser than these algorithms.
Purity speed limit of open quantum systems from magic subspaces

We introduce the concept of magic subspaces for the control of dissipative $N$- level quantum systems whose dynamics are governed by the Lindblad equation. For a given purity, these subspaces can be defined as the set of density matrices for which the rate of purity change is maximum or minimum. Adding fictitious control fields to the system so two density operators with the same purity can be connected in a very short time, we show that magic subspaces allow us to derive a purity speed limit, which only depends on the relaxation rates. We emphasize the superiority of this limit with respect to established bounds and its tightness in the case of a two-level dissipative quantum system. The link between the speed limit and the corresponding time-optimal solution is discussed in the framework of this study. Explicit examples are described for two- and three-level quantum systems.
Increase of the Number of International students in Russian Universities as a Component of the state Education Policy

The article discusses the urgent problem of attracting international students to Russian universities as well as their readiness to attract a new type of student. The work considers aspects of useful international experience of the issue: today when education has become one of the main priorities in countries with developing economies, many of them seek to establish partnerships with leading world universities. The authors made recommendations for adjusting the current situation in the Russian Federation, where the most intensive flow of international students comes from the former USSR republics studying at the expense of budgetary funds. At the same time, the choice for international students is limited to universities of the country’s largest cities — Moscow and St. Petersburg, and this requires a change. Also considered is the growth factor of the position of Russian universities in international rankings, high positions in which play a significant role in enhancing the flow of international students into the country.
SASOBUS: Semi-automatic Sentiment Domain Ontology Building Using Synsets

In this paper, a semi-automatic approach for building a sentiment domain ontology is proposed. Differently than other methods, this research makes use of synsets in term extraction, concept formation, and concept subsumption. Using several state-of-the-art hybrid aspect-based sentiment analysis methods like Ont + CABASC and Ont + LCR-Rot-hop on a standard dataset, the accuracies obtained using the semi-automatically built ontology as compared to the manually built one, are slightly lower (from approximately 87% to 84%). However, the user time needed for building the ontology is reduced by more than half (from 7 h to 3 h), thus showing the usefulness of this work. This is particularly useful for domains for which sentiment ontologies are not yet available.
Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

&lt;span&gt;In biometric systems, compression takes important place especially in order to reduce the size of the information stored or transmitted through the distributed biometric systems. It is also noted that the compression techniques induce loss of information in the compressed images that can affect the effectiveness of biometric systems. The main objective of our contribution is to examine the efficacy of the used method to offer an optimal compression quality in these kind of images without considerable distortion. In order to evaluate the efficacy of the compression process, we use two kinds of evaluation, full-reference image quality assessment and a new proposed textural quality analysis of the compressed images. In this paper, we use a second-generation wavelet transform to improve the compression study in biometric images. The basic idea of this algorithm is the quincunx wavelet transform coupled to a modified progressive encoder called SPIHT-Z encoding.&lt;/span&gt;
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution

As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our proposed approach, SARFA (Specific and Relevant Feature Attribution), generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweighs irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare SARFA with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches. For the code release and demo videos, see https://nikaashpuri.github.io/sarfa-saliency/.
Automatische Evaluation der Humanübersetzung: BLEU vs. METEOR

Abstract Human evaluation (HE) of translation is generally considered to be valid, but it requires a lot of effort. Automatic evaluation (AE) which assesses the quality of machine translations can be done easily, but it still requires validation. This study addresses the questions of whether and how AE can be used for human translations. For this purpose AE formulas and HE criteria were compared to each other in order to examine the validity of AE. In the empirical part of the study, 120 translations were evaluated by professional translators as well as by two representative AE-systems, BLEU/ METEOR, respectively. The correlations between AE and HE were relatively high at 0.849 ** (BLEU) and 0.862 ** (METEOR) in the overall analysis, but in the ratings of the individual texts, AE and ME exhibited a substantial difference. The AE-ME correlations were often below 0.3 or even in the negative range. Ultimately, the results indicate that neither METEOR nor BLEU can be used to assess human translation at this stage. But this paper suggests three possibilities to apply AE to compromise the weakness of HE.
LOAD BALANCING IN CLOUD COMPUTING

Cloud computing is a new Internet technology that focuses on commercial computing. Load balancing helps improve the performance of a centralized server. This paper analyzes various algorithms using the tool of analysis, namely, cloud-based Analytics. A comparison of load distribution algorithms is also made.
Simulation Research on the Binary Equilibrium Optimization Algorithm

In this paper, we describe the binary version of the newly raised equilibrium optimization algorithm (EOA) we called here. The binary equilibrium optimization (BEO) algorithm is transferred by ways of a series of transfer functions from the continuous domain. Experiments and logic analysis show that the Sigmoid function would not be expected to perform better due to the slow slope of changes in value. Henceforth V-shape functions are raised. Simulation experiments are carried out and verifications are made with benchmark functions to find the best V-shaped transfer function. Experimental results show that the erfFcn named in this paper as follows perform the best and it could represent the V-shaped transfer functions for future work.
Data Augmentation Using Generative Adversarial Networks for Electrical Insulator Anomaly Detection

Electrical insulators, which are widely used for electricity transmission, are prone to be damaged and need constant maintenance. Traditionally, the inspection job is time-consuming and dangerous as workers have to climb up the electricity tower. Deep learning, which has offered a safe and quick way to inspections, however, requires large amounts of data. Generative adversarial networks (GANs) are introduced as a novel approach to augmenting data. However, traditional state-of-art GANs are either incapable of generating high quality images or fail to generate minority class images when minority class examples are far less. In order to mitigate drawbacks of existing GANs, a novel GAN model, Balancing and Progressive GANs (BPGANs), was proposed for effectively making use of all classes information and generating high quality images simultaneously. Results showed that PGANs, StyleGANs, and BPGANs were able to generate high-resolution images and improve classification performance. PGANs achieved the better results than BPGANs. This might be because BPGANs only provides 2 additional latent codes since it is a binary classification, having little effect on generating desired images. BPGANs seemed to have difficulties generating class-specific images, which might be because that the classification loss is too little compared to the source loss and optimization was more focused to optimize the source loss. This indicated that learning representations of data progressively from low resolution to high resolution is an effective approach, however, embedding class label information in the fashion of AC-GANs and BGANs might not be appropriate for augmenting binary class datasets.
Vulnerabilities detection using attack recognition technique in multi-factor authentication

Authentication is one of the essentials components of information security. It has become one of the most basic security requirements for network communication. Today, there is a necessity for a strong level of authentication to guarantee a significant level of security is being conveyed to the application. As such, it expedites challenging issues on security and efficiency. Security issues such as privacy and data integrity emerge because of the absence of control and authority. In addition, the bigger issue for multi-factor authentication is on the high execution time that leads to overall performance degradation. Most of existing studies related to multi-factor authentication schemes does not detect weaknesses based on user behavior. Most recent research does not look at the efficiency of the system by focusing only on improving the security aspect of authentication. Hence, this research proposes a new multi-factor authentication scheme that can withstand attacks, based on user behavior and maintaining optimum efficiency. Experiments have been conducted to evaluate this scheme. The results of the experiment show that the processing time of the proposed scheme is lower than the processing time of other schemes. This is particularly important after additional security features have been added to the scheme.
Experience-Embedded Visual Foresight

Visual foresight gives an agent a window into the future, which it can use to anticipate events before they happen and plan strategic behavior. Although impressive results have been achieved on video prediction in constrained settings, these models fail to generalize when confronted with unfamiliar real-world objects. In this paper, we tackle the generalization problem via fast adaptation, where we train a prediction model to quickly adapt to the observed visual dynamics of a novel object. Our method, Experience-embedded Visual Foresight (EVF), jointly learns a fast adaptation module, which encodes observed trajectories of the new object into a vector embedding, and a visual prediction model, which conditions on this embedding to generate physically plausible predictions. For evaluation, we compare our method against baselines on video prediction and benchmark its utility on two real-world control tasks. We show that our method is able to quickly adapt to new visual dynamics and achieves lower error than the baselines when manipulating novel objects.
The university library as a proactive system

The preparedness and ability to accept, understand and create innovations is a factor of efficient development of modern society. The modern libraries have to be information centers to concentrate the world information resources within. The libraries’ main task is to generate, preserve and systematize resources and make them accessible to the users. The library is examined as a system being influenced by external and internal factors. The library as an independent element in its turn impacts the information and education environment of the universities. The e-libraries offer the wide scope of services to support universities’ education and research activities. Therefore, the university educational strategy should facilitate the transfer to an innovative model. The priority development of education system has to prepare individuals for competing in the society of dominating intellectual resources and innovations. The libraries have to be proactive in generating their own strategic programs and the university strategies, too.The authors examine the university library as an integrated system comprising the elements of diverse characteristics and complexity influence of external and internal factors. The e-library services and possibilities being offered to users are analyzed. The concept of “active university library” is defined as the system of user information services provided in any format and with any media based on traditional and networked services as well as on query-leading basis. The e-library promotion through information selective dissemination is discussed. The principle of active e-library’s impact on the library’s proactivity is promoted.
Real-Time Cloud-Based Load Balance Algorithms and an Analysis

Advancement in communication technologies has also made a positive impact by increase in the computation. Cloud computing is an internet-based computational utility which has reduced the cost of computation and cutting short of larger investments. Cloud is service-oriented architecture with decentralized computation. The SWOT analysis of the cloud computing can be used virtually in every industry to improve the service delivery and improvement; in return, it improves the business. There is a need of a cloud computing system which can use the cloud for the high-performance applications, increased scalability, ability to handle sudden request traffic increase, flexibility to change when applying new topologies, business continuity with complete flexibility, and overall improvement in the cloud system performance. Various load balancing techniques are available in cloud computing which are needed to study for the development of in advent of new emerging technologies like IoT, robotics, and AI.
A Key Selected S-Box Mechanism and Its Investigation in Modern Block Cipher Design

The block cipher is an important means to provide data confidentiality in reality, and the S-box is an essential part in most of modern block cipher designs. In 1973, Feistel used a key selected S-box mechanism in his early block cipher designs, whose idea is to let each S-box have two different states and use a key bit to select which of the two states is to be used in an encryption or decryption operation. However, this key selected S-box mechanism has not got much attention in modern block cipher design with the DES block cipher published in 1977. In this paper, we revisit Feistel’s key selected S-box mechanism, give a generalised version of Feistel’s key selected S-box mechanism, compare it with existing close notions, and design the LBC example cipher to demonstrate that the generalised key selected S-box mechanism can be advantageous over the ordinary S-box mechanism in modern block cipher design for improving security and/or performance without intensifying computational effort and space in some application environments.
Penentuan Ambang Curah Hujan untuk Memprediksi Kejadian Longsor

The mayority of landslide occur due to high rainfall in certain time in areas that have geological potential landslides. Therefore, it is necessary to conduct a research on rainfall characteristics that trigger landslides. So that, it can be used to establish the relationship between rainfall and predicted landslides. This research is aimed to analyze and determine the daily and 3 days rainfall thresholds used for landslides early warning system (LEWS). The case study is placed in both very high risks and high area landslide areas, based on the ground motion maps of the Geological Agency. The analysis was done based on landslide event data from BNPB, as well as TRMM (Tropical Rainfall Measuring Mission) and ECMWF (The European Center for Medium-Range Weather Forecasts) rainfall data. Identification of the rain trigger values from the TRMM rain data was done after collecting landslide incidents at various locations.. The results show that the rainfall threshold values are 61 mm per day and 91 mm per 3 days Keywords:Â Rainfall treshold,landslide prediction, TRMM, ECMWF
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models

Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data. This structured survey provides an overview of the evolving research area by categorising reported weaknesses in models and datasets and the methods proposed to reveal and alleviate those weaknesses for the English language. We summarise and discuss the findings and conclude with a set of recommendations for possible future research directions. We hope it will be a useful resource for researchers who propose new datasets, to have a set of tools to assess the suitability and quality of their data to evaluate various phenomena of interest, as well as those who develop novel architectures, to further understand the implications of their improvements with respect to their model's acquired capabilities.
Reconstruction of Sea Level Data around the Korean Coast Using Artificial Neural Network Methods

Lee, E.-J.; Chae, J.-Y., and Park, J.-H., 2020. Reconstruction of sea level data around the Korean coast using Artificial Neural Network methods. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1172–1176. Coconut Creek (Florida), ISSN 0749-0208.The coastal sea level is an important element for better understanding and clarifying of oceanic physical phenomena in the coastal regions. However, interruptions of sea level measurements happen quite often, and hence it is crucial to find out an optimum method to fill in data gaps to retrieve continuous sea level time series. Traditionally, single-variable methodologies such as spline interpolation and ARIMA model have been utilized to fill the data gap. Sea level time series can be divided into regular astronomical tides and its residual, meteorological tides. The former can be predicted precisely, while the latter needs a specific methodology to reconstruct. In this study, we examine the feasibility of artificial neural network methods for reconstruction of the sea level time series through Long-Short Term Memory (LSTM) networks, one of Recurrent Neural Networks suitable for application to sequential data. Input data is composed of oceanic, atmospheric, and processed variables in consideration of air-sea interactions. Optimization is applied to five coastal tide station data to evaluate the effects of sea level change-related variables. Our artificial neural network methods show a remarkable performance in reconstructing continuous sea level time series with an error less than 8 cm and the Pearson correlation coefficient over 0.85, implying that 85% of sea level can be explained by oceanic and atmospheric variables.
Facets of the PIE Environment for Proving, Interpolating and Eliminating on the Basis of First-Order Logic

PIE is a Prolog-embedded environment for automated reasoning on the basis of first-order logic. Its main focus is on formulas, as constituents of complex formalizations that are structured through formula macros, and as outputs of reasoning tasks such as second-order quantifier elimination and Craig interpolation. It supports a workflow based on documents that intersperse macro definitions, invocations of reasoners, and -formatted natural language text. Starting from various examples, the paper discusses features and application possibilities of PIE along with current limitations and issues for future research.