Reviewerly/exHarmony huggingface dataset:
A DAPP Business Data Storage Model Based on Blockchain and IPFS

Blockchain technology has been applied in various fields, providing strong support for the medical field, supply chain and other industries, followed by the emergence of DApp based on blockchain smart contract technology. DApp as a distributed application model, implements part of the background business functions by the smart contract of blockchain to ensure the transparency, openness and traceability of key businesses, and can effectively resist DDoS attacks. However, blockchain data storage is expensive and not suitable for storing large amounts of data. This is a key barrier to the large amount of user data generated by DApp, and the risk of data loss, tampering and so on if the data is stored in a centralized service organization. In addition, smart contracts cannot be modified once deployed, and DApp often need frequent iterative updates in the later maintenance process, which will be faced with the need to modify the business logic of smart contracts. To solve the above problems, this paper proposes a user’s data storage model of DApp based on IPFS to reduce the storage cost of data, and designs a set of hot-swapping smart contract architecture based on contract address to manage DApp user data, so as to meet the functional business replacement in the later stage of smart contract. In addition, for the privacy security of data, this paper introduces the Diffie-Hellman key exchange technology as the encryption scheme to protect user data.
A Survey of Sentiment Analysis and Opinion Mining

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics, and their attributes. In this chapter, we first define the opinion mining problem. From the definition, we will see the key technical issues that need to be addressed. We then describe various key mining tasks that have been studied in the research literature and their representative techniques. After that, we discuss the issue of detecting opinion spam or fake reviews.
HCS-BBD

Thresholding is one of the most common techniques for image segmentation where an image is partitioned into several parts based on its histogram of pixel intensities. Conventional algorithms work efficiently for bi-level thresholding where an image is divided into fore- and background, but their efficiency drastically declines for the more complex case of multi-level thresholding due to the exhaustive search that is employed. To address this problem, in this paper we consider multi-level thresholding as an optimisation problem and propose a novel population-based algorithm, HCS-BBD, which is based on cuckoo search (CS) and biogeography-based optimisation (BBO). To this end, HCS-BBD integrates a heterogeneous cuckoo search strategy with a biogeography-based discovery operator. Our findings in comparison to state-of-the-art and recent population-based algorithms on different images convincingly demonstrate HCS-BBD's excellent capability in finding optimal threshold values.
Decentralized Aggregation Design and Study of Federated Learning

The advent of machine learning techniques has given rise to modern devices with built-in models for decision making and providing rich content to users. This typically involves processing huge volumes of data in central servers and sending updated models to end-user devices. There are two main concerns on this server architecture, one is the privacy of data that is being transferred to a central server and the other is volumes of data sent over the network for the model update. Federated Learning helps solve these problems by training models on local data within the device and aggregating the model with other devices. Federated Learning involves a central server for the aggregation and the resulting updates to the clients, here only model parameters are shared with the central server not the data itself thereby preserving privacy. But all the applications are not being compatible with Federated Learning and also there is a privacy concern of models being shared to the central server which can be susceptible to malicious attacks. In this paper, central server free Federated Learning, which is decentralized Federated Learning is used, where the parameters will be exchanged between the clients one to one and get their models updated removing the need for a central server for aggregation. Peer-to-peer techniques are used for communicating between clients and different node architectures to achieve better accuracy. This happens when the clients meet another client in a connected social network environment. The results show that the communication happens between clients in a decentralized fashion and thereby achieving privacy in a more trusted manner.
A Study of the Relationship between Volunteer Fire Brigade Management and the Efficiency of the Fire Administrations of Metropolitan Governments

This study sought to analyze the relationship between the operation of volunteer fire brigades and the efficiency of fire-fighting administrations. To this end, the efficiency of the fire-fighting administrations of 16 metropolitan and provincial governments in South Korea were measured using data envelopment analysis (DEA) from 2011 to 2018. In addition, the relationship between the operation efficiency of the volunteer fire brigade and the fire administration was analyzed using a Tobit regression model. DEA found that the technical efficiency based on constant returns to scale was 82.78 ± 18.12%, the technical efficiency based on variable returns to scale was 89.31 ± 12.46%, and the efficiency of the scale was 91.72 ± 10.56%. Tobit regression analysis found the ratio of volunteer fire brigade personnel, the budget ratio of the volunteer fire brigade, and the enforcement of the Act on the Establishment and Operation of Volunteer Fire Brigades to have significant positive effects on the pure technical efficiency of local governments. In addition, when the ratio of volunteer fire brigade personnel or ratio of budget had a positive effect on the efficiency of fire administration, the enforcement of the volunteer fire brigade statute exerted a positive adjustment effect. This study thus shows that volunteer fire brigades have a positive impact on the efficiency of the fire-fighting administrations of metropolitan and provincial governments.
From Producer Success to Retention: A New Role of Search and Recommendation Systems on Marketplaces

In online marketplaces, an increasing number of producers depend on search and recommendation systems to connect them with consumers to make a living. In this talk, we discuss how these systems will need to evolve from the traditional formulations by incorporating the producer value into their objectives. Jointly optimizing the ranking functions behind these systems on both consumer and producer values is a new direction and raises many technical challenges. To overcome these, we lay out an end-to-end solution and present the results of applying this solution on Facebook Marketplace.
Comparative Analysis of Anonymization Techniques

In recent years, the storage and management of personal data for individuals have increased rapidly, and the method of protecting personal information has grown rapidly. There are multiple purposes in which personal data can be misused. Privacy has become a major issue for high-level databases. To address these issues, several anonymization techniques have recently been suggested to safeguard data privacy. In this particular paper, we present a provisional analysis of K-anonymity, L-diversity, and T-Clogens anonymization methods based on privacy and security criteria for the higher dimensional database.
Genetic Improvement of Data for Maths Functions

We use continuous optimisation and manual code changes to evolve up to 1024 Newton-Raphson numerical values embedded in an open source GNU C library glibc square root sqrt to implement a double precision cube root routine cbrt, binary logarithm log2 and reciprocal square root function for C in seconds. The GI inverted square root x -1/2 is far more accurate than Quake’s InvSqrt, Quare root. GI shows potential for automatically creating mobile or low resource mote smart dust bespoke custom mathematical libraries with new functionality.
Regolith LANDSAT-8/OLI and Hyperion/EO-1 images classification in midwest of Brazil

The orbital LANDSAT-8/OLI sensor and SRTM altimetric images combined with Boolean algebra and complemented by fieldwork and XRD, chemical and spectroradiometric analysis, allowed to develop a mathematical model for mapping the tropical regolith in the Midwest of Brazil. It was used four mapping classes following the arithmetic band operations of 4/2 and (4+6)/5 to identify lateritic duricrusts, loosed iron concretions, nodules and pisoliths, (6/7)/(5/4) to parent rock, saprolite, mottled horizon and soil, and (5–4)/(5+4) to vegetation. Hyperspectral Hyperion/EO-1 image endorse the mathematical model identifying the pure pixels of kaolinite, goethite, and hematite in the lateritic duricrusts, loosed iron concretions, nodules and pisoliths occurrence pixels. Narrow and low reflectance spectroscopy concavity between 400 and 600 nm and a broad medium concavity between 800 and 1100 nm are the typical spectral features of the study lateritic duricrusts, highlighting the strong influence of the goethite and hematite contents. The development of these LANDSAT-8/OLI and Hyperion/EO-1 classification models and the identification of the specific reflectance spectroscopy features for the tropical lateritic terrains, provide a more precise regolith-geological architecture. It is very helpful for mineral exploration, for developed strategies for understanding the regolith landscape better and how surficial geochemical anomalies are formed, and for geomorphology interpretation in deeply weathered regions.
Exploring the construct of the new way of thinking in the digital environment

The aim of this study is to provide insights concerning the effects of the ubiquitous digital environment on the way people think and the subsequent need to equip young individuals with the necessary skills. Several studies focus on defining the so-called digital skills, also providing indications that higher cognitive skills are required. However, they do not examine how young individuals could perform better to adapt to the continuously evolving digital environment. To address this gap, the study introduces and analyses the construct of digital intelligence, representing the new way of thinking and behaviour in the digital environment. For the purposes of the study, a set of tests was given to students at Greek high schools, targeting 15–16 years old students and employing original tests to assess digital intelligence. According to the findings, digital intelligence is composed of: (1) logical reasoning, algorithms, and evaluation, (2) abstraction, decomposition, and patterns and generalisation, (3) digital emotional intelligence and communication, (4) digital safety and security, and (5) digital identity, use, literacy, and rights. Assessing digital intelligence, as proposed in this study, could be used to assist vocational guidance, employee selection and evaluation, and development of revised school curricula.
Making legacy Fortran code type safe through automated program transformation

Abstract Fortran is still widely used in scientific computing, and a very large corpus of legacy as well as new code is written in FORTRAN 77. In general this code is not type safe, so that incorrect programs can compile without errors. In this paper, we present a formal approach to ensure type safety of legacy Fortran code through automated program transformation. The objective of this work is to reduce programming errors by guaranteeing type safety. We present the first rigorous analysis of the type safety of FORTRAN 77 and the novel program transformation and type checking algorithms required to convert FORTRAN 77 subroutines and functions into pure, side-effect free subroutines and functions in Fortran 90. We have implemented these algorithms in a source-to-source compiler which type checks and automatically transforms the legacy code. We show that the resulting code is type safe and that the pure, side-effect free and referentially transparent subroutines can readily be offloaded to accelerators.
Robust Optimization for Probabilistic Protection with Primary and Backup Allocations under Uncertain Demands

This paper proposes a robust optimization model for probabilistic protection under uncertain capacity demands to minimize the total required backup capacity against multiple simultaneous failures of physical machines. Probabilistic protection restricts the probability that the workload caused by failures exceeds the backup capacity by a given survivability parameter. The proposed model determines both primary and backup virtual machine allocations simultaneously under the probabilistic protection guarantee. To express the uncertainty of capacity demands, we introduce an uncertainty set which considers the upper bound of the total demand and the upper and lower bounds of each demand. The robust optimization technique is applied to the optimization model to deal with two uncertainties, failure event and capacity demand. With this technique, the model is formulated as a mixed integer linear programming problem. Our proposed model reduces the total required backup capacity compared with the conventional model by determining both primary and backup VM allocations simultaneously.
Neurogenetic programming framework for explainable reinforcement learning

Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of programs, or via neural language models. We propose a novel method that combines both approaches using a concept of a virtual neuro-genetic programmer, or scrum team. We demonstrate its ability to provide performant and explainable solutions for various OpenAI Gym tasks, as well as inject expert knowledge into the otherwise data-driven search for solutions.
Privacy-Preserving eID Derivation to Self-Sovereign Identity Systems with Offline Revocation

Digital identities play a vital role in an increasingly digital world. These identities often rely on central authorities to issue and manage them. Central authorities have the drawback of being a central trusted party, representing a bottleneck and single point of failure with exclusive control of identity-related data. Self-sovereign identity (SSI) tackles those problems by utilizing distributed ledger technology and making users the sovereign owners of their identity data. Nevertheless, SSI, as recent technology, still lacks qualified identity data. This is especially a problem since sensitive services like eGovernment or banking services require identity data issued by a qualified identity provider; thus, SSI - based identities cannot be used for these services. In this paper, we propose a concept for deriving identity data from an existing identity system into an SSI in a fully privacy-preserving way by additionally supporting offline verification. This way, we enable a chain of trust from the existing identity system to the SSI system by introducing a novel trust model. Our concept utilizes novel cryptographic primitives to support efficient and privacy-preserving identity showing as well as revo-cation. To underline the feasibility of our concept, we implement a proof system and benchmark the related use cases.
RGB Image Watermarking Using DCT

In current era of technology, work over internet is increasing day by day due to which security becomes very critical issue. Therefore, to address this issue this paper presents an algorithm proposed for watermarking using DCT. Watermarking and de-watermarking using DCT are easy and very effective as compared to other existing transforms. This algorithm splits the color input image into three components, namely 'red,' 'green' and 'blue.' This digital image watermarking technique works effectively and efficiently for security purpose as compared to other techniques in spatial and DCT domains. In de-watermarking process, the received image is of good visual quality.
Marginal frequent itemset mining for fault prevention of railway overhead contact system

The overhead contact system (OCS), as the power source of electrified railway, has a complex composition and various types of faults, so it places high requirements on its fault prevention. In recent years, with the establishment of railway OCS fault database, association analysis has been used to implement fault prevention from system-wise perspective and provide guidance for operation and maintenance. However, due to the hierarchical structure of fault database, the existing frequent itemset mining has a lot of redundancy in the results, and cannot locate the most precise faults, which affects the decision-making and makes troubleshooting lack of pertinence. To address this issue, this paper proposed a new concept, called marginal frequent itemset, which is an itemset composed of as precise items as possible in hierarchical database that meets the threshold, and an alternative mining task: mining marginal frequent itemsets instead of all the frequent itemsets. Two methods, path transform and descending depth of itemset, are proposed for achieving mining a set of marginal frequent itemsets. Two novel measures, margin degree and marginal information quantity, are proposed to evaluate the content of the mining results. An efficient algorithm, named MFIMCL, is developed for mining cross-level marginal frequent itemsets from railway OCS fault database. Our performance study shows that MFIMCL has high performance and can obtain more key information and reduce the number of results. Furthermore, marginal frequent itemset mining can simplify the fault relation network constructed by association rules and optimize the decision-making process for fault prevention of railway OCS.
Geology and trace element geochemistry of the albitite hosted iron ore mineralization around Khetri copper deposit, India: Implications for an IOA type deposit

Numerous occurrences of medium to high grade (44.1 to 65 wt%) pocket type iron ore bodies within the Meso to Neo-Proterozoic albitites and meta-sedimentary rocks are exposed around the south and south-eastern part of the Khetri copper deposit, India. The magnetite-hematite rich iron ore bodies occur as massive, and vein fillings within the albitites/albitized phyllite host. Apatite, sphene, monazite, and REE phases are also associated with iron ore minerals. Sporadic albitite, pegmatite, and quartz-carbonate-fluorite veins are also intruded into the iron ore bodies. The V concentration with <1000 ppm (average 644 ppm) and lower value of Ti (average 88.79 ppm), Cr (average 196.58 ppm), Co (average 84.28 ppm) content measured by Laser Ablation ICP-MS and EPMA, suggest a significant role of the hydrothermal/metasomatic activity for the precipitation of magnetite in the study area. Further, relatively higher Ni/Cr (>1) ratio of magnetite from the study area signifies that the rate of solubility and mobility of Ni is more than Cr in a high-temperature hydrothermal fluid. Fe vs V/Ti plot refers towards the hydrothermal origin of the magnetite and Sn/Ga vs. Al/Co diagram suggests the magnetites have the signature of IOCG deposits. The multi-trace element variation diagram of magnetite is also analogous to the high-temperature hydrothermal origin like IOCG-IOA type deposits. Based on the chemistry, mineral association, and spatial occurrence of the magnetite with albitites (product of Na-metasomatism), the magnetite are considered to have deposited during the regional soda-metasomatism and classified under Iron Oxide Apatite (IOA) clan.
RLD Fisher information bound for multiparameter estimation of quantum channels

One of the fundamental tasks in quantum metrology is to estimate multiple parameters embedded in a noisy process, i.e., a quantum channel. In this paper, we study fundamental limits to quantum channel estimation via the concept of amortization and the right logarithmic derivative (RLD) Fisher information value. Our key technical result is the proof of a chain-rule inequality for the RLD Fisher information value, which implies that amortization, i.e., access to a catalyst state family, does not increase the RLD Fisher information value of quantum channels. This technical result leads to a fundamental and efficiently computable limitation for multiparameter channel estimation in the sequential setting, in terms of the RLD Fisher information value. As a consequence, we conclude that if the RLD Fisher information value is finite, then Heisenberg scaling is unattainable in the multiparameter setting.
Generalized multiple sparse information fusion for vehicle re-identification

Vehicle re-identification (reID) aims to search the target vehicle in a non-overlapping multi-camera network, which is important for the intelligent analysis in large scale of surveillance videos. Many existing methods have employed various techniques to achieve discriminative information. However, those methods always focus on the description of one view for the same vehicle images. Hence, a generated multiple sparse information fusion method is proposed for exploiting latent features from multi-views, which employs three different deep networks to extract multiple features from coarse to fine. And these features are regarded as multi-view features. Besides, to fuse these features reasonably, the paper transfers various features into a common space for better seeking distinctive features. Especially, besides ResNet, two feature learning networks are proposed to learn different features, respectively. One is designed to learn robust feature by dropping some features randomly when training the reID model. Another is to combine various salient features from different layers, which forms strong features for the reID task. Moreover, comprehensive experimental results have demonstrated that our proposed method can achieve competitive performances on benchmark datasets VehicleID and VeRi-776.
Varying Abstractions: a conceptual vs. distributional view on prepositional polysemy

The term ‘meaning’, as it is presently employed in Linguistics, is a polysemous concept, covering a broad range of operational definitions. Focussing on two of these definitions, meaning as ‘concept’ and meaning as ‘context’ (also known as ‘distributional semantics’), this paper explores to what extent these operational definitions lead to converging conclusions regarding the number and nature of distinct senses a polysemous form covers. More specifically, it investigates whether the sense network that emerges from the principled polysemy model of over as proposed by Tyler &amp;amp; Evans (2003; 2001) can be reconstructed by the neural language model BERT. The study assesses whether the contextual information encoded in BERT embeddings can be employed to succesfully (i) recognize the abstract sense categories and (ii) replicate the relative distances between the senses of over proposed in the principled polysemy model. The results suggest that, while there is partial convergence, the two models ultimately lead to different global abstractions because the imagistic information that plays a key role in conceptual approaches to prepositional meaning may not be encoded in contextualized word embeddings.