Reviewerly/exHarmony huggingface dataset:
Multi cloud-based secure privacy preservation of hospital data in cloud computing

The growth of cloud computing has led to privacy concerns abundantly. Any organisation/user sends all the information to the cloud service provider and so the organisation's/user's data security is a concern. Data privacy and security issues can be solved by establishing clear policies that enable authorised data access and security. User authentication is the primary basis for access control and so using cryptographic encryption mechanism like key policy attribute-based encryption we can provide strong authentication to ensure that data can be viewed by only who have to access it. Followed by which a never compromised integrity mechanism like SHA-256 hash mechanism is used to ensure that data is not modified in transit. These hashes are concatenated in a way to form top hash by structuring in a Merkle hash tree. They are used by the erasure code to find lost data during any of the crashes. To make it efficient and to find the data loss, third party auditors are installed to check and report any changes in any of the cloud storage. Data recovery is by means of retrieving the data from another cloud that has the replica of these data.
The different behaviors of thermal noise in collective quantum steering and genuinely tripartite steering induced by atomic coherence

Abstract Multipartite quantum steering has important application in quantum information processing. Here we study the influence of thermal noise on the collective quantum steering and genuine tripartite steering in a system of four-level inverted Y-type atoms, in which the atomic coherence is initially prepared and the cavity is coupled to a thermal reservoir. It is found that for the cases of the balanced and unbalanced cavity losses, the thermal noise plays a positive role in realizing the collective steering. However the case is different for the genuine tripartite steering effect. For the case of either balanced or unbalanced cavity losses, the thermal noise would destroy genuine tripartite steering effect and reduce the parameter region of genuine tripartite steering. The thermal noise plays a negative role in realizing genuine tripartite steering. Specially, it is verified that the genuine tripartite steering does not reject the bipartite steering, which is very different from the collective quantum steering requiring no bipartite steering. The present scheme may provide some help for potential applications such as quantum communication.
CCA: Cost-Capacity-Aware Caching for In-Memory Data Analytics Frameworks

To process data from IoTs and wearable devices, analysis tasks are often offloaded to the cloud. As the amount of sensing data ever increases, optimizing the data analytics frameworks is critical to the performance of processing sensed data. A key approach to speed up the performance of data analytics frameworks in the cloud is caching intermediate data, which is used repeatedly in iterative computations. Existing analytics engines implement caching with various approaches. Some use run-time mechanisms with dynamic profiling and others rely on programmers to decide data to cache. Even though caching discipline has been investigated long enough in computer system research, recent data analytics frameworks still leave a room to optimize. As sophisticated caching should consider complex execution contexts such as cache capacity, size of data to cache, victims to evict, etc., no general solution often exists for data analytics frameworks. In this paper, we propose an application-specific cost-capacity-aware caching scheme for in-memory data analytics frameworks. We use a cost model, built from multiple representative inputs, and an execution flow analysis, extracted from DAG schedule, to select primary candidates to cache among intermediate data. After the caching candidate is determined, the optimal caching is automatically selected during execution even if the programmers no longer manually determine the caching for the intermediate data. We implemented our scheme in Apache Spark and experimentally evaluated our scheme on HiBench benchmarks. Compared to the caching decisions in the original benchmarks, our scheme increases the performance by 27% on sufficient cache memory and by 11% on insufficient cache memory, respectively.
Mapping Potentially Acid Generating Material on Abandoned Mine Lands Using Remotely Piloted Aerial Systems

Weathering and transport of potentially acid generating material (PAGM) at abandoned mines can degrade downstream environments and contaminate water resources. Monitoring the thousands of abandoned mine lands (AMLs) for exposed PAGM using field surveys is time intensive. Here, we explore the use of Remotely Piloted Aerial Systems (RPASs) as a complementary remote sensing platform to map the spatial and temporal changes of PAGM across a mine waste rock pile on an AML. We focus on testing the ability of established supervised and unsupervised classification algorithms to map PAGM on imagery with very high spatial resolution, but low spectral sampling. At the Perry Canyon, NV, USA AML, we carried out six flights over a 29-month period, using a RPAS equipped with a 5-band multispectral sensor measuring in the visible to near infrared (400–1000 nm). We built six different 3 cm resolution orthorectified reflectance maps, and our tests using supervised and unsupervised classifications revealed benefits to each approach. Supervised classification schemes allowed accurate mapping of classes that lacked published spectral libraries, such as acid mine drainage (AMD) and efflorescent mineral salts (EMS). The unsupervised method produced similar maps of PAGM, as compared to supervised schemes, but with little user input. Our classified multi-temporal maps, validated with multiple field and lab-based methods, revealed persistent and slowly growing ‘hotspots’ of jarosite on the mine waste rock pile, whereas EMS exhibit more rapid fluctuations in extent. The mapping methods we detail for a RPAS carrying a broadband multispectral sensor can be applied extensively to AMLs. Our methods show promise to increase the spatial and temporal coverage of accurate maps critical for environmental monitoring and reclamation efforts over AMLs.
Homeland security video surveillance system utilising the internet of video things for smart cities

In the contemporary video surveillance system, there have been many efforts made to maintain high security and extend the coverage areas in most of the countries around the world. The deployment of many surveillance cameras and sensors capable of detecting abnormal and meaningful events on the territories' streets and airports is an aspect of internal security. There are two main problems that affect the homeland security system, and the security cameras and sensors are not enough to cover all areas in the country. This is because of the high cost of the video surveillance cameras and the sensor installations, and the non-standardisation of security cameras and sensors manufacturing, which is due to the differentiated infrastructure of companies or organizations that provide home security. The authors introduce a design and hardware implementation of a motion estimation (ME) co-processor that can be used for video surveillance cameras in homeland security. The proposed ME co-processor, if adopted in video surveillance cameras, can be connected utilising an internet of video things infrastructure (IoVT). The proposed co-processor is suited for high-efficiency encoding video surveillance systems (H.265/HEVC). Furthermore, to reduce the memory I/O, data reuse Level A and Level B have been used in the proposed architecture while taking full advantage of the hardware resources. Moreover, an effective local memory has been used to reuse the data during the process of loading both the search area and the current block into the processing element array (PE array). The performance of the proposed architecture has been calculated using subjective and quantitative measures techniques and compared to the full search block-based motion estimation (FSBB-ME) algorithm. Moreover, the proposed architecture achieves a very high video resolution accuracy that is similar to the accuracy of the FSBB-ME algorithm. Modelism-version10.4a has been used for simulation and time verification testing proposes. The proposed ME co-processor can be embedded in the compressing decompressing and high definition broadcast for video surveillance systems.
Development of Program Model for Improving Intrapersonal Intelligence Based on FLIPPEDLEARNING

Purpose: Intrapersonal Intelligence is a fundamentally required competence for a human to do its part in a social environment and lead a life without regret. This Intrapersonal Intelligence is essential intelligence for all human beings, regardless of age. However, the recent fandom situation has weakened many people s mental strength due to increased emotional anxiety and depression. The variable pandemic had a great influence not only on one individual but also on society as a whole. The prolonged COVID-19 situation around the world has left modern people increasingly impoverished. Therefore, the time has come for Intrapersonal Intelligence more than ever. Therefore, this study developed a program model for the purpose of improving Intrapersonal intelli-gence. Method: The educational model developed for the purpose of enhancing Intrapersonal intelligence was ana-lyzed by literature based on previous studies to derive program components. In addition, for the convenience of learners in the digital age, it is designed based on Flipped Learning, which enables continuous learning and has high timeliness. In order to improve the completeness of the program, a procedure for verifying reliability and validity was carried out by review of seven doctors in educational engineering and instructional design experts. Results: The main factors that constitute the contents of the Intrapersonal intelligence improvement pro-gram consisted of self-understanding, self-regulation, and self-design . The detailed elements for each factor are understanding self-emotional and comprehension of ability , future design and achievement motivation , and mutual regulation and impulse control . In addition, the Intrapersonal intelligence improvement program based on Flipped Learning was designed to fully understand the prerequisite learning materials through the pre-learning materials and to conduct discussion and coaching at the same time in the in-class. Conclusion: This study follows the same context as prior research that demonstrates that existing Intraper-sonal intelligence can be improved through learning. However, this study utilizes open educational resources as a medium for prior learning in a methodical approach to teaching and learning, and in-class mainly uses coach-ing and discussion, which are non-directive teaching methods. As a result, I hope that through the Intrapersonal intelligence improvement program, modern people can recognize themselves right away, control their emo-tions, design a positive future, and lead a happy life.
Fighting African Capital Flight: Trajectories, Dynamics and Tendencies

An April 2015 World Bank report on attainment of the Millennium Development Goal (MDG) extreme poverty target has revealed that extreme poverty has been decreasing in all regions of the world with the exception of sub-Saharan Africa (SSA), in spite of the sub-region enjoying more than two decades of growth resurgence. This study builds on a critique of Piketty’s ‘capital in the 21st century’ and recent methodological innovations on reverse Solow-Swan to review empirics on the adoption of common policy initiatives against a cause of extreme poverty in SSA: capital flight. The richness of the dataset enables the derivation of 14 fundamental characteristics of African capital flight based on income-levels, legal origins, natural resources, political stability, regional proximity and religious domination. The main finding reveals that regardless of fundamental characteristic, from a projection date of 2010, a genuine timeframe for harmonizing policies is between 2016 and 2023. In other words, the beginning of the post-2015 agenda on sustainable development goals coincides with the timeframe for common capital flight policies.
Analysis of transportation access and geological knowledge based on GIS technologies for exploration planning in Altai-Sayany fold area

The paper assesses transportation access for various regions of the southwestern Altai-Sayany fold area (ASFA) and analyzes geological knowledge about gold based on spatial data analysis using GIS technologies. This work is based on our technique. This technique allowed to convert qualitative concepts (access, knowledge and development) into quantitative indicators useful to compare various clusters within one region as well as various regions. The main constituents of transportation access (distance from roads, topography slope and terrain trafficability) are characterized in detail. The main computation methods and design characteristic visualization techniques are presented. Quantification of gold knowledge density allowed validation of its diversity and recognition of underexplored areas. Based on complexing the obtained digital data, the paper shows that underexplored gold districts and clusters are identified in the southern, less accessible part. Correlation of trafficability and geological knowledge is revealed; these indicators are found to affect metallogenic zoning concepts. The conclusion is validated that the current ideas about ASFA gold potential and metallogenic zoning are determined by both geology and trafficability, and the current geological know-ledge level.
Optical multiple-image encryption in discrete multiple-parameter fractional Fourier transform scheme using complex encoding, theta modulation and spectral fusion

We present a novel encryption method for multiple images in a discrete multiple-parameter fractional Fourier transform scheme, using complex encoding, theta modulation and spectral fusion. All pairs of original images are encoded separately into a complex signal. The spectrum of each complex signal can then be scattered into various positions in the spectral plane and multiplexed into one spectral image with a combination of theta modulation and spectral fusion. After Fourier transforming back to the spatial domain, the multiplexed signal is encrypted in the discrete multiple-parameter fractional Fourier transform domain. Information about the original images can only be successfully decrypted given the possession of all correct keys. The parameters of chaotic pixel scrambling for the proposed method enlarge the key space. Moreover, the proposed method solves the crosstalk problem of multiple images and improves the multiplexing capacity. Numerical simulations demonstrate the effectiveness of the proposed method.
Gauravarora@HASOC-Dravidian-CodeMix- FIRE2020: Pre-training ULMFiT on Synthetically Generated Code-Mixed Data for Hate Speech Detection.

This paper describes the system submitted to Dravidian-Codemix-HASOC2020: Hate Speech and Offensive Content Identification in Dravidian languages (Tamil-English and Malayalam-English). The task aims to identify offensive language in code-mixed dataset of comments/posts in Dravidian languages collected from social media. We participated in both Sub-task A, which aims to identify offensive content in mixed-script (mixture of Native and Roman script) and Sub-task B, which aims to identify offensive content in Roman script, for Dravidian languages. In order to address these tasks, we proposed pre-training ULMFiT on synthetically generated code-mixed data, generated by modelling code-mixed data generation as a Markov process using Markov chains. Our model achieved 0.88 weighted F1-score for code-mixed Tamil-English language in Sub-task B and got 2nd rank on the leader-board. Additionally, our model achieved 0.91 weighted F1-score (4th Rank) for mixed-script Malayalam-English in Sub-task A and 0.74 weighted F1-score (5th Rank) for code-mixed Malayalam-English language in Sub-task B.
A recommendation-based reading list system prototype for learning and resource management

A reading list is a list of reading items recommended by an academic to assist students’ acquisition of knowledge for a specific subject. Subsequently, the libraries of higher education institutions collect and assemble reading lists according to specific courses and offer the students the reading list service. However, the reading list is created based on localised intelligence, restricted to the academic’s knowledge of their field, semantics, experience and awareness of developments. This investigation aims to present the views and comments of academics, and library staff, on an envisaged aggregated reading list service, which aggregates recommended reading items from various higher education institutions. This being the aim, we build a prototype, which aggregates reading lists from different universities and showcase it to 19 academics and library staff in various higher education institutions to capture their views, comments and any recommendations. In the process, we also showcase the feasibility of collecting and aggregating reading lists, in addition to understanding the process of reading lists creation at their respective higher education institutions. The prototype successfully showcases the creation of ranked lists of reading items, authors, topics, modules and courses. Academics and library staff indicated that aggregated lists would collectively benefit the academic community. Consequently, recommendations in the form of process implementations and technological applications are made to overcome and successfully implement the proposed aggregated reading list service. This proof-of-concept demonstrates potential benefits for the academic community and identifies further challenges to overcome in order to scale it up to the implementation stage.
On-demand Key Distribution for Cloud Networks

Emerging fine-grained cloud resource billing creates incentives to review the software execution footprint in virtual environments. Operators can use novel virtual execution environments with ever lower overhead: from virtual machines to containers, to unikernels and serverless functions. However, the execution footprint of security mechanisms in virtualized deployments has either remained the same or even increased. In this demo, we present a novel key provisioning mechanism for cloud networks that unlocks scalable use of symmetric keys and significantly reduces the related computational load on network endpoints.
Air Navigation: An Integrated Test Method for Airborne Objects’ Identification Systems

The article analyses the methods of increasing the efficiency of the processes of airborne objects’ classification along with the possible solutions to the problems of airspace operating procedure. The methods of formalizing and assessing the information quality are developed to classify airborne objects. Formulated is the verification method of the fuzzy logical system’s software used to classify airborne objects. Particular attention is paid to the analysis of the completeness and reliability of the input information, the properties of the mechanism of logical conclusions having deterministic decision-making branches on airborne objects and decision-making branches based on fuzzy logical inference with the formation of the «confidence» vector towards decision options. The article is just the first part of a group of methods proposed to the scientific community for the purpose of simplifying the processes of identifying airborne objects illegally located in controlled areas and, in particular, verifying the software used for this purpose. It also continues a series of articles dealing with the problems of air navigation.
The laboratory construction effectiveness evaluation of high-education institutions via the FAHP method and its application

The laboratory is an important platform and practice place for colleges and universities to carry out teaching and research activities. From the characteristics of laboratory construction in high-education institutions and the fuzzy hierarchy analysis method, this paper takes high-education institutions as the research object, and establishes an efficient laboratory construction effectiveness evaluation system and model based on basic conditions, practicality, progressiveness, construction results. Furthermore, the construction effectiveness of quantum communication technology laboratory is evaluated by using of this presented method. The evaluation result $\tilde{P}(A)$=[0.53 0.23 0.20 0.04 0.01] is “excellent 53%, good 23%, medium 20%, qualified 4%, unqualified 1% The results show that the method proposed in this paper is reasonable and feasible.
Face Image Inpainting Network based on Dense Multi-Scale Fusion of Local Binary Patterns

With the development of convolutional neural networks, Deep Learning (DL) has also made great breakthroughs in the field of image inpainting. Deep Learning-based models can repair large-area missing defaced images, but usually produce various unpleasant artifacts in borders and highly textured areas. To solve this problem, this paper proposed a new end-to-end two-stage repair network. In the first stage, a dense multi-scale fusion of LBP is used to generate rich structural information of the missing regions to guide the inpainting network in the second stage to repair. In the second stage, the Bi-directional Skip Connections mechanism is used to flexibly aggregate high-level semantic features and low-level visual features. Experiments on CelebA-HQ public face data and the results show that this paper’s model solves this problem.
Exploring the Effect of NULL Usage in Source Code

In this paper we propose to use causal inference (CI) to reason about code smells, or anti-patterns. CI provides methods to estimate the magnitude of effect of certain interventions on the studied system of variables based on observational data only. We would like to estimate the average effect of using a certain pattern on some code quality characteristic. If code quality characteristic systematically deteriorates when a certain pattern is used, it can serve as confirmation that it is in fact an anti-pattern. In the present study, we narrow down the scope and focus on one notorious case of code smells, the usage of NULL. We investigate the effect of using a selection of NULL-based patterns on code complexity metrics. The experiments on open source Java code show preliminary confirmation that NULL is in fact an anti-pattern under certain conditions. We come to a conclusion that in order to fully answer the research question, a better underlying model of the code development process is needed. Moreover, in order to confirm the detrimental effect of patterns, one should investigate their effect on a more diverse set of code characteristics, in addition to code complexity metrics.
Does She Wink or Does She Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models

Recent progress in pretraining language models on large corpora has resulted in large performance gains on many NLP tasks.These large models acquire linguistic knowledge during pretraining, which helps to improve performance on downstream tasks via fine-tuning.To assess what kind of knowledge is acquired, language models are commonly probed by querying them with 'fill in the blank' style cloze questions.Existing probing datasets mainly focus on knowledge about relations between words and entities.We introduce WDLMPro (Word Definition Language Model Probing) to evaluate word understanding directly using dictionary definitions of words.In our experiments, three popular pretrained language models struggle to match words and their definitions.This indicates that they understand many words poorly and that our new probing task is a difficult challenge that could help guide research on LMs in the future.