Reviewerly/exHarmony huggingface dataset:
Diverse Reviewer Suggestion for Extending Conference Program Committees

Automated reviewer recommendation for scientific conferences currently relies on the assumption that the program committee has the necessary expertise to handle all submissions. However, topical discrepancies between received submissions and reviewer candidates might lead to unreliable reviews or overburdening of reviewers, and may result in the rejection of high-quality papers. In this work, we present DiveRS, an explainable flow-based reviewer assignment approach, which automatically generates reviewer assignments as well as suggestions for extending the current program committee with new reviewer candidates. Our algorithm focuses on the diversity of the set of reviewers assigned to papers, which has been mostly disregarded in prior work. Specifically, we consider diversity in terms of professional background, location and seniority. Using two real world conference datasets for evaluation, we show that DiveRS improves diversity compared to both real assignments and a state-of-the-art flow-based reviewer assignment approach. Further, based on human assessments by former PC chairs, we find that DiveRS can effectively trade off some of the topical suitability in order to construct more diverse reviewer assignments.
A cross-layer based optimized feature selection scheme for intrusion detection in wireless sensor network

The Wireless Sensor Networks (WSNs) contain a significant quantity of sensor nodes that computes and communicates for data transmission. The data packet sensed and transmitted contains various cross layer feature set that includes many important information. Many essential aspects, which include storage capability, consumption of energy, and, computational power should be taken into account while dealing with the data packets. On the other hand, many past researchers have carried out their work in order to detect intrusion utilizing cross-layer packets but fail in detecting them at the same time. Cross-layer and feature selection techniques play a key role in building an efficient Intrusion Detection System (IDS). An advantage of using the cross-layer technique is to achieve a higher correlation among different layers of the protocol so that one layer can use the parametric information of the other layer by breaking the traditional layer barriers. In this work, we propose a cross-layer based multi-feature selection model for intrusion detection in WSNs. Firstly, an optimized multi-feature selection algorithm is proposed for selecting efficient and useful features from the cross-layered architecture of the network. Secondly, a multi class intrusion detection model is proposed for the classification of different cross-layer based intrusion in the network. The proposed algorithm is developed for providing total security to cross-layer based networks by selecting prominent features and detecting intrusion at the same time. The simulation results are utilizing on real-time intrusive data from the network by analyzing the proposed model.
Human Action Recognition Method Based on Motion Excitation and Temporal Aggregation Module

Aiming at the problem of low modeling efficiency and feature loss of temporal modeling in human action recognition, we propose a human action recognition method based on Motion Excitation and Temporal Aggregation module (META). The method can capture multi-state and multi-scale temporal information to achieve effective motion excitation. Firstly, temporal relational sampling is performed on video frames; Secondly, META is proposed to capture multi-state and multi-scale temporal information. META is composed of Multi-scale Motion Excitation module (MME) and Squeeze and Excitation Temporal Aggregation module (SETA). MME captures the feature level temporal difference by transforming the features into the temporal channel, which directly establishes the relationship between features and temporal channel, and solves the problem of low modeling efficiency. SETA transforms the local convolution into a set of sub-convolutions. Multiple sub-convolutions form hierarchies to extract features together and share the results of the upper convolutional layer, which increases the final temporal receptive field and solves the problem of feature loss. Moreover, the optical flow features are extracted through Cross modality pre-training to improve the utilization of temporal information. Finally, the human action recognition result is carried out by combining spatiotemporal two stream features. Experimental results show that the accuracy of this method in UCF101 and HMDB-51 is 96.0% and 71.2% respectively, which is higher than other studies in the same period.
The Span-Based Transformer Classifier for Named Entity Recognition

Named entity recognition is a basic task in the field of natural language processing. The existing named entity recognition model has the problem of poor recognition effect on specific domain data sets. Aiming at the inconsistency of sequence labeling caused by the shortcomings of the traditional BIO (B-begin, I-inside, O-outside) annotation method, a named entity recognition model based on transformer is proposed, which uses the feature extraction of encoder in transformer and the labeling method of limited interval. Firstly, the BERT pre-training model is used to perform word-level dynamic vector representation of the input text. Then the vector representation of the text is intercepted by the span-based labeling method, and the feature of text and candidate named entity is extracted by self-attention mechanism. The results of experiment on the dataset in the industrial informatization field show the effectiveness of the model.
One-Stage Image Inpainting with Hybrid Attention

Recently, attention-related image inpainting methods have achieved remarkable performance. They reconstruct damaged regions based on contextual information. However, due to the time-consuming two-stage coarse-to-fine architecture and the single-layer attention manner, they often have limitations in generating reasonable and fine-detailed results for irregularly damaged images. In this paper, we propose a novel one-stage image inpainting method with a Hybrid Attention Module (HAM). Specifically, the proposed HAM contains two submodules, namely, the Pixel-Wise Spatial Attention Module (PWSAM) and the Multi-Scale Channel Attention Module (MSCAM). Benefit from these, the reconstructed image features in spatial dimension can be further optimized in channel dimension to make inpainting results more visually realistic. Qualitative and quantitative experiments on three public datasets show that our proposed method outperforms state-of-the-art methods.
Novel Optimized Framework for Video Processing in IoRT Driven Hospitals

Internet of Remote things (IoRT) has gained recent attention and is considered as one most prominent research topics being carried out by numerous researchers worldwide.IoRT is being used in various applications and this paper mainly concentrates on the healthcare industry wherein it could be used effectively for patient monitoring.IoRT plays a crucial role in monitoring the patients in any healthcare center remotely by allowing simultaneous video transmissions possible from the emergency areas like Intensive Care Unit (ICU).Considering general scenarios, the video transmissions are done by the main use of Gaussian distribution.With the help of the proposed IoRT based system, the video transmission could be effectively done by making use of short-term snapshots.This system enables a wide range of intermittent distributions.As the system basically works on multiple videos, denoising these videos is the primary challenge.Imagedenoising is one of the important pre-processing stages, used to improve the overall quality of the images used in video transmission.The use of a Median Filter is done to minimize the overall Signal-to-Noise Ratio (SNR).Even though numerous types of Median filters are available, in this work we have made use of a 3D (Dimension) filter to get more effective results as the edges of the video frames are preserved.The paper has made use of a Hybrid algorithm combining the Cuckoo Search Optimization technique along with a Genetic Algorithm (GA) to optimize the frames in the video.The use of this hybrid algorithm paves a way to obtain equal intensity-based frames.For analyzing collective performance Peak Signal Noise Ratio (PSNR), Universal Image Quality Index (UIQI) and Structural Similarity Index (SSI) are used as quality assessment metrics to deliver a high-performance noise-free optimized video.The simulation results show better performance when compared with other image Noise Filtering Methods and other optimization algorithms when used in IoRT driven hospitals.
The Role of Human Resource Management in Enhancing Organizational Information Systems Security

Emerging human resource management (HRM) practices are focusing on background checks, training and development, employer-employee relations, responsibility and accountability, and monitoring of information systems security resources. Information systems security ensures that appropriate resources and adequate skills exist in the organization to effectively manage information security projects. This chapter examined the role of HRM in enhancing organizational information systems security. Using importance-performance map analysis, the study found training, background checks, and monitoring as crucial HRM practices that could enhance organizational information systems security. Moreover, four indicators, consisting of training on mobile devices security; malware management; background checks; and monitoring of potential, current, and former employees recorded high importance but with rather low performance. Consequently, these indicators should be improved. On the contrary, the organizations placed excessive focus on responsibility, accountability, and employee relations.
Causal Feature Learning

Causal Feature Learning (CFL) is a domain-general framework that learns macro-level causes and effects from micro-level data. In contrast to traditional coarsening methods, CFL bases its aggregation on the relation between two micro-variables, providing macro-level representations of each variable with respect to itâ€™s relationship with the other. Here, we implement a CFL software package that aims to reduce the barriers to running CFL. With customizable models, thorough documentation, and helper methods to interpret results, CFL makes it possible for researchers to easily analyze and identify potentially relevant features in their data.
Random Assembly Task Evaluation Based on Human-Robot Collaboration

Based on human-robot collaboration, this paper discusses the human factor assessment in independent assembly and cooperative assembly in random assembly task, and provides a theoretical basis for the extensive application of human-robot collaboration. An experimental method was proposed to obtain real-time eye movement data of eight participants by using an eye tracker. Then relevant eye movement parameters are analyzed. And human factors are comprehensively evaluated in both subjective and objective aspects by combining NASA-TLX Subjective Rating Scale. Compared with independent operation, human-robot collaboration has more advantages in random task and lower consumption in all aspects of humans.
Integrated approach to cybersecurity of a small-sized business system

Computer systems are used everywhere, it is impossible to find a company or a home without computer devices where data of commercial and private type are stored. This data should be protected, but not enough attention is paid to cybersecurity problems in small-sized computer systems, because of the lack of professionals and lack of funds. This paper goes into analysis of the most common computer threats and ways to protect computer systems and proposes an integrated approach to security system engineering in the view of minimizing its costs.
Educational Leaders Training in the Context of Digitalization

The paper outline main problems of the training of educational leaders, heads of educational organizations in the context of innovative development of the Russian and world education systems, taking into account global and regional socio-cultural and economic processes. The analysis of the possibility of online education in the magistracy in the areas of “Leadership in Education”, “Management in Education”, “Strategic Leadership” were done. The international and domestic experience of training leaders of education, heads of educational organizations for professional activities in a changing environment has been studied. Global trends in the training leaders of education, heads of educational organizations were analyzed. The article presents training standards for educational organizations, school leaders in Russia and some countries of the world. The standards for training leaders of education were studied. The features of the implementation and functioning of management systems in educational institutions were considered. The assessment of the effectiveness of education management were analyzed.
Interopera: An Efficient Cross-Chain Trading Protocol

Abstract With the rapid development of blockchains, blockchain systems are moving on from a stand-alone manner to cross-chain interactions, and achieving interoperability is emerging as one of the essential features of blockchains. Unfortunately, existing mechanisms such as XCLAIM mostly focus on exchanging assets between two blockchains and it is slow and expensive to process each cross-chain trade among more than two blockchains as multiple transactions are required. In this paper, we present Interopera, a decentralized and efficient cross-chain trading protocol among two or more blockchains. Interopera atomically processes each cross-chain trade faster and more cheaply with fewer transactions by a two-phase lock/unlock process. Interopera also achieves efficient cross-chain communication by our presented Partitioned-FlyClient and Tx-FlyClient. Partitioned-FlyClient is based on FlyClient but more efficient with smaller proof size, reducing the storage and bandwidth overheads. Tx-FlyClient maintains efficiency even when cross-chain trades become frequent, instead of other mechanisms only being effective under low cross-chain trades volumes. We also develop a proof-of-concept implementation and the results demonstrate high efficiency of our protocol.
An Intrinsic Review on Securitization using Blockchain

This paper presents a blockchain-based Securitization Model, which simplifies the transactional methods. This paper elaborates upon integrating the components of Securitization with Distributed Ledger Technology (DTL) and provides a process flow of transactions occurring. The process which once led to the 2008 Financial Crisis has been improved in terms of security and reliability. Using this revolutionizing model, securities, which were once considered opaque & risky, will now be informative and gauged accordingly, thereby smoothening the process of transforming non-tradable assets into tradable securities. This paper highlights how blockchain features, i.e., smart contracts, decentralization, authentication & immutability, will increase efficiency. Furthermore, the paper explores boons of the amalgamation, i.e., reduced risk, enhanced transparency, lower costs, proficient processes and robust authentication without compromising information asymmetry, high transactional costs and nebulous portfolio rating. The goal of this paper is to portray how the integration will augment the cardinal Securitization process.
Stateless and Rule-Based Verification for Compliance Checking Applications

Context: Underlying computational model has an important role in any computation. The state and transition (such as in automata) and rule and value (such as in Lisp and logic programming) are two comparable and counterpart computational models. Both of deductive and model checking verification techniques are relying on a notion of state and as a result, their underlying computational models are state dependent. Some verification problems (such as compliance checking by which an under compliance system is verified against some regulations and rules) have not a strong notion of state nor transition. Behalf of it, these systems have a strong notion of value symbols and declarative rules defined on them. Objective: SARV (Stateless And Rule-Based Verification) is a verification framework that designed to simplify the overall process of verification for stateless and rule-based verification problems (e.g. compliance checking). Method: In this paper, a formal logic-based framework for creating intelligent compliance checking systems is presented. We define and introduce this framework, report a case study and present results of an experiment on it. Results: The case study is about protocol compliance checking for smart cities. Using this solution, a Rescue Scenario use case and its compliance checking are sketched and modeled. An automation engine for and a compliance solution with SARV are introduced. Based on 300 data experiments, the SARV-based compliance solution outperforms famous machine learning methods on a 3125-records software quality dataset. Conclusions: The proposed SARV method could be beneficial for stateless verification in compliance checking applications.
Integrating Multiple Techniques to Enhance Medical Data Classification

Improving classification performance is an essential task in medical data classification. In the current medical data classification technique, if data pre-processing is not performed, the approach is more time consuming and has less classification accuracy. Here, the authors proposed two pre-processing techniques for enhancing the classification performance on medical data. The first pre-processing technique is noise filtering to improve the data quality. The second pre-processing bag of words technique is used for better feature selection. Subsequently, the hybrid fuzzy neural network approach is used for classification to handle data imprecision during classification. This arrangement of data pre-processing and the fuzzy neural classifier method improve classification accuracy.
A Research Agenda for Constructing an Environment to Help Develop a Creative Software

Creative software and its development have been studied for a long while in description languages. However, the development of creative software has some defects so that there is little result appearing. For example, the properties of a creative software such as useful and surprising are too subjective to allow the requirement specifications and thus development in interactive languages. According to our experience, current SOA provides a practical background and rich set of executional services so that the research may be worked to construct an environment to help the development of creative software in interactive languages. This paper proposes a development environment used to help constructing a creative software in interactive languages, with SOA.
Hilpinen’s Theory of Inquiry

Risto Hilpinen’s name is associated with seminal contributions to many areas of analytic philosophy, including influential work on deontic logic and the theory of artifacts, as well as important early investigations into inductive logic and indefeasibility analyses of knowledge. A somewhat lesser known body of Hilpinen’s work concerns the theory of inquiry, whereby the central questions are how to conceptualize and understand human investigations from a general epistemological perspective. The present contribution traces the development of Hilpinen’s theory of inquiry through his main contributions to the topic in the 1980s and 1990s with special attention to how the problems in the initial formulation of the theory were eventually given more detailed and convincing solutions.
Space Time-Based Optofluidic Imaging Analyser for Intelligent Blood Coagulation Diagnosis

The coagulation composite index (CI) is a key parameter for comprehensive assessment of blood coagulation kinetics, which has significant meaning for guiding surgery, transfusion, and patient recovery treatment, et al. Herein, we innovatively propose a smart optofluidic medical device via artificial intelligent assisted space time-based optical clotting imaging analysing for accurate and convenient blood coagulation diagnosis. The developed space time-based imaging system enables continuous imaging capture in blood clotting, the smartphone based edge computing was designed for continuous and accurate optically reconstructed clotting biophysical properties (clotting area and gray value) acquisition and recording. The time-based recorded data with differential computation was then imported into a cloud-based neural network for coagulation diagnosis. In this work, the 119 clinical blood samples (73 health, CI value: -3-3; 28 hypocoagulation, CI value: <-3; and 18 hypercoagulation, CI value: >3) were set for space time-based neural network diagnostic model development. And the strong performance of this system has been validated via a double-blind 30 clinical samples test comparison between thromboelastography (TEG) instrument and this system, the results show that the system exhibits an excellent diagnostic accuracy of 100%. This work provides a promising method for low testing cost, portable and smart coagulation diagnosis with potential economic and social benefits.
Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query Tagger

Semantic Query Labeling is the task of locating the constituent parts of a query and assigning domain-specific semantic labels to each of them. It allows unfolding the relations between the query terms and the documents' structure while leaving unaltered the keyword-based query formulation. In this paper, we investigate the pre-training of a semantic query-tagger with synthetic data generated by leveraging the documents' structure. By simulating a dynamic environment, we also evaluate the consistency of performance improvements brought by pre-training as real-world training data becomes available. The results of our experiments suggest both the utility of pre-training with synthetic data and its improvements' consistency over time.
Machine Learning-Based Identification of Collusive Users in Twitter Stream

Twitter has become a go-to platform for most of us to share our ideas and opinions online, this has also become a place to influence people online by sharing facts and opinions. People with a higher follower count are considered to be more credible and often use this follower count as a way of determining the price of promotion for any company/individual. But buying fake followers online today is as easy as ordering a snack online therefore it is a major challenge to identify such users as they can spread fake news, incite violence, etc. We collected a dataset for a total of 1497 users consisting of 713 genuine users and 784 collusive users but many accounts were already disabled by Twitter or the user had made their account private for which cannot be accessed by Twitter API. Therefore, the final dataset was of 1066 users with 484 genuine and 598 collusive users. We propose 65 features for the analysis of users including two novel categories of features, Keyword analysis, and Timestamp Clustering analysis. In this project, we attempt to tackle this problem using machine learning models and giving users two confidence indices Personal Confidence Index (प) and Cumulative confidence index (क). We were able to achieve an accuracy of 87 percent and were able to develop an online platform Know-Your-Twitterati to analyze a user in real-time and show relevant user features on the dashboard.