Reviewerly/exHarmony huggingface dataset:
A Blockchain Based Framework for Smart Greenhouse Data Management

The safe exchanging and storing of Internet of Things (IoT) data in greenhouse scenarios has intrigued researchers from both management and information science. The authentic and secure data is the key foundation of data analysis and decision making. On the other hand, the traditional centralized cloud computing system has a fatal failure that once the central server being successfully attacked, all the data will get lost. The backups will be difficult to retrieve and the system recovering will be time consuming. This paper introduces a double-chain based on IoT data storage system supporting knowledge management abilities of greenhouse scenario. The system collects data from Greenhouse sensors and store the data in Inter Planetary File System (IPFS) network, after the IPFS hash have been generated, the network again rehashes the values and upload onto the Ethereum chain for public queries. The distributed data storage improves the security of data, also supports the safe exchanging of information as well as the sharing of data between entities, improving the efficiency of knowledge management.
PalCom Middleware-Based Blockchain Challenges on Healthcare System

Blockchain is an emerging technology with decentralized (Nakamoto, S., Bitcoin, A. (2008). Peer-to-peer electronic cash system. BN Publishing, La Vergne, TN, USA) and distributed technology utilized in third trusted party (TTP) for securing and solving cyberattacks. Healthcare system is a potential application having a strong impact on trustless and transparent system. The scope of PalCom middleware is to enable the GUI development and maintains the huge data values using blockchain. The PalCom directives use the commands/parameters in a limited version to access online patients. It aims to deliver a compiled working of blockchain to keep secure and safe digital relationships using private key cryptography and authentication. The challenges include fragmented patient data, medical record integrity, smart contract security, and privacy. This work includes using the Hyperledger and authentication algorithm. To solve the issues of provided data-intensive domain, i.e., healthcare system, the research shows the benefit and performance based on the cryptography algorithm and provided with the adherence to data regulations and standards.
Frequent Itemset Mining Algorithms—A Literature Survey

A prominent subfield of data mining is Frequent Itemset Mining which explores mysterious and hidden patterns in the transaction database. However, as the volume of data increases, the mining of hidden patterns of the frequent itemset is more time-consuming. Moreover, dominant memory consumption is required in mining where the hidden pattern of the frequent itemset computation is complicated through the algorithm. Therefore, a powerful algorithm is needed to mine the hidden patterns of the frequent itemset within a more precise execution time and with lower consumption of memory while the size of data increases over the period. This study article focuses on the pros and cons of FP-growth, LP-growth, FIU-tree, IFP-growth algorithm for frequent pattern discovery, and more efficient frequent pattern mining algorithms can be further carried out.
Site selection for solar photovoltaic system installation using analytical hierarchy process model in Azerbaijan

The most suitable sites for solar photovoltaic power installations are determined through a comprehensive assessment of the meteorological, economic and environmental criteria of the energy potential areas. The basic criteria for location selection are evaluated using an analytical hierarchy process method based on multi-criteria decision-making technique for large-scale solar photovoltaic projects. The analytical hierarchy process model is also applied to evaluate areas of high solar potential and factors that are primary criteria for determinate the site suitability index modelling. This method considers various conditions, such as production and technological considerations, which aim to maximise the short-term profit from the project and the efficiency of power generation. In the study, a consistency ratio of suitable localities was determined and proper alternatives for the construction of photovoltaic installations were evaluated. In addition to local meteorology and related satellite measurement data, the country’s radiation values also were compared by converting a digital elevation model data using the tool «Area solar radiation» in GIS. As a result of calculating the site suitability index with the ArcGIS weighted overlay tool, it was concluded that 1.17 % (1016.8 km2) of the country are the most suitable sites for the installation of solar PV systems. These areas mainly include Khizi, Gobustan, Hajigabul, Beylagan, Sharur, Babek and Jeyranchol zones. The total number of locations identified accross the country, classified into 3 categories according to their level of suitability, includes 40 sites. Eight of these high suitability sites, all in Nakhchivan Autonomous Republic, contain 11 % (109.2 km2) of the total potential area. The remaining 32 sites, corresponding to areas with medium and low energy potential, cover 28 % (284.6 km2) and 61 % (623 km2), respectively. When these areas are completely covered with PV panels, it will be possible to fully supply the energy demand of the country with solar energy.
Document Ranking for Curated Document Databases Using BERT and Knowledge Graph Embeddings: Introducing GRAB-Rank

Curated Document Databases (CDD) play an important role in helping researchers find relevant articles in scientific literature. Considerable recent attention has been given to the use of various document ranking algorithms to support the maintenance of CDDs. The typical approach is to represent the update document collection using a form of word embedding and to input this into a ranking model; the resulting document rankings can then be used to decide which documents should be added to the CDD and which should be rejected. The hypothesis considered in this paper is that a better ranking model can be produced if a hybrid embedding is used. To this end the Knowledge Graph And BERT Ranking (GRAB-Rank) approach is presented. The Online Resource for Recruitment research in Clinical trials (ORRCA) CDD was used as a focus for the work and as a means of evaluating the proposed technique. The GRAB-Rank approach is fully described and evaluated in the context of learning to rank for the purpose of maintaining CDDs. The evaluation indicates that the hypothesis is correct, hybrid embedding outperforms individual embeddings used in isolation. The evaluation also indicates that GRAB-Rank outperforms a traditional approach based on BM25 and a ngram-based SVR document ranking approach.
Research Blended Teaching Based on “Intemet plus”-Taking the course of computer network as an Example

The appearance of internet plus accelerates the change of traditional classroom teaching mode. In order to better integrate online resources with traditional classroom and achieve the purpose of improving teaching effect. Taking the computer network course as an example, this paper analyzes the nature and objectives of the course and the characteristics of the students, and reconstructs the traditional teaching contents, teaching methods and teaching evaluation methods by using the online massive open online course, micro-courses and other teaching resources and rain classroom teaching platform. The practice results show that, this hybrid teaching mode combining online and offline not only improves students' learning autonomy, but also improves the teaching effect.
Multilabel Classification Based on Graph Neural Networks

Typical Laplacian embedding focuses on building Laplacian matrices prior to minimizing weights of connected graph components. However, for multilabel problems, it is difficult to determine such Laplacian graphs owing to multiple relations between vertices. Unlike typical approaches that require precomputed Laplacian matrices, this chapter presents a new method for automatically constructing Laplacian graphs during Laplacian embedding. By using trace minimization techniques, the topology of the Laplacian graph can be learned from input data, subsequently creating robust Laplacian embedding and influencing graph convolutional networks. Experiments on different open datasets with clean data and Gaussian noise were carried out. The noise level ranged from 6% to 12% of the maximum value of each dataset. Eleven different multilabel classification algorithms were used as the baselines for comparison. To verify the performance, three evaluation metrics specific to multilabel learning are proposed because multilabel learning is much more complicated than traditional single-label settings; each sample can be associated with multiple labels. The experimental results show that the proposed method performed better than the baselines, even when the data were contaminated by noise. The findings indicate that the proposed method is reliably robust against noise.
Privacy-Preserving Auction for Heterogeneous Task Assignment in Mobile Device Clouds

In the past several decades, despite the continuous improvement of mobile devices, mobile applications requiring resources are still beyond its capacity to be completed on a single device. Due to high fairness and distribution efficiency, cloud auctions of mobile device allocation has recently attracted a lot of attention. However, most of them focus on efficiency but ignore the privacy-preserving issues in cloud auction process, which will have a significant impact on the authenticity of auctions. In this paper, we propose PAHTA, a privacy-preserving auction for heterogeneous task assignment in mobile device clouds by using Yao’s garbled circuits and homomorphic encryption. Meanwhile, we design a set of sub-protocols to support secure arithmetic operations, including secure division protocol and secure selection protocol. We further analyze the security and effectiveness of the system theoretically and prove that it can ensure sufficient and strong privacy protection under the semi-honest adversary model. Based on a large number of experiments, these results are consistent with the theoretical analysis.
Attention-Based Neural Machine Translation Approach for Low-Resourced Indic Languages—A Case of Sanskrit to Hindi Translation

Deep learning techniques are very powerful and they have achieved remarkable performance in complex learning tasks. Deep learning inspired neural machine translation (NMT) is a proficient technique that has outperforms traditional models of the machine translation task. Performing machine-aided translation on Indic languages has always been a challenging task, considering their rich and diverse grammar. The neural machine translation has shown quality results compared to the traditional machine translation approaches. The task becomes outdare when it comes to low-resourced language like Sanskrit. We developed our Sanskrit-Hindi parallel corpus through different sources and refined it with the help of human language experts. Our parallel corpus currently contains nearly 10 K language pairs. The paper presents the use of attention mechanisms in encoder-decoder based sequences to sequence learning on Sanskrit-to-Hindi bilingual corpus. The use of two separate networks for encoding and decoding of the source and the target vector, respectively, increases the joint conditional probability. The attention mechanism shows promising results by focusing on the particular parts of the given Sanskrit sentences. We used Tesla T4 GPU for training the NMT model. The experimental results show that our model performs well in inference mode.
Biased differential distinguisher – Cryptanalysis of reduced-round SKINNY

SKINNY is a lightweight tweakable block cipher which received a great deal of cryptanalytic attention due to its elegant structure and efficiency. Despite the cryptanalytic efforts the security margins are remaining high. This has led to SKINNY being used as a component of multiple submissions in the NIST Lightweight Competition, an effort to standardize a lightweight AEAD scheme. Inspired by the SKINNY competitions, multiple attacks on it were reported in different settings (e.g. single vs. related-tweakey) using different techniques (impossible differentials, zero-correlation, meet-in-the-middle, etc.). In this paper we revisit some of these attacks, identify issues with several of them, and offer a series of improved attacks which were experimentally verified. Our best attack can attack up to 18 rounds of SKINNY-64 using 260 chosen plaintexts data, 2116 time, and 2112 memory.
Coded Gradient Aggregation: A Tradeoff Between Communication Costs at Edge Nodes and at Helper Nodes

The increasing amount of data generated at the edge/client nodes and the privacy concerns have resulted in learning at the edge, in which the computations are performed at edge devices and are communicated to a central node for updating the model. The edge nodes have low bandwidth and may be available only intermittently. There are helper nodes present in the network that aid the edge nodes in the communication to the server. The edge nodes communicate the local gradient to helper nodes which relay these messages to the central node after possible aggregation. Recently, schemes using repetition codes and maximum-distance-separable (MDS) codes were proposed. It was observed that in MDS scheme the communication between edge nodes and helper nodes is optimal but with an increased cost of communication between helper and master. An upper bound on the communication cost between helpers and master was obtained. In this paper, a tradeoff between communication costs at edge nodes and helper nodes is established with the help of pyramid codes, a well-known class of locally repairable codes. The communication costs at both the helper nodes and edge nodes are exactly characterized. Using the developed technique, the exact communication cost at helper nodes can be computed for the scheme using MDS codes.
Research on Intelligent Financial Framework of Colleges and Universities in Information Age

This paper expounds the connotation and characteristics of intelligent finance, analyzes the key technology of intelligent finance, and introduces an intelligent financial framework model for universities and colleges, which at least includes infrastructure service layer, data service layer, software service layer, interaction service layer, as well as corresponding information standard system and security technical guarantee system. At last, an application context for intelligent finance is provided.
Program Specialization as a Tool for Solving Word Equations

The paper focuses on the automatic generating of the witnesses for the word equation satisfiability problem by means of specializing an interpreter which tests whether a composition of variable substitutions of a given word equation system produces its solution. We specialize such an interpreter w.r.t. the equation system, while the substitutions are unknown. We show that several variants of such interpreters, when specialized using the basic unfold/fold specialization methods, are able to construct the whole solution sets for some classes of the word equations whose left- and right-hand sides share variables. We prove that the specialization process wrt the constructed interpreters gives a simple syntactic criterion of the satisfiability of the equations considered, and show that the suggested approach can solve some equations not solvable by Z3str3 and CVC4, the widely-used SMT-solvers.
Reliable Perceptual Loss Computation for GAN-Based Super-Resolution With Edge Texture Metric

Super-resolution (SR) is an ill-posed problem. Generating high-resolution (HR) images from low-resolution (LR) images remains a major challenge. Recently, SR methods based on deep convolutional neural networks (DCN) have been developed with impressive performance improvement. DCN-based SR techniques can be largely divided into peak signal-to-noise ratio (PSNR)-oriented SR networks and generative adversarial networks (GAN)-based SR networks. In most current GAN-based SR networks, the perceptual loss is computed from the feature maps of a single layer or several fixed layers using a differentiable feature extractor such as VGG. This limited layer utilization may produce overly textured artifacts. In this paper, a new edge texture metric (ETM) is proposed to quantify the characteristics of images and then it is utilized only in the training phase to select an appropriate layer when calculating the perceptual loss. We present experimental results showing that the GAN-based SR network trained with the proposed method achieves qualitative and quantitative perceptual quality improvements compared to many of the existing methods.
Accuracy Evaluation of Horizon Shading Estimation Based on Fisheye Sky Imaging

Measuring horizon shading and modeling available irradiance at a prospective site is necessary for accurate estimation of the energy yield of photovoltaic (PV) systems, as well as the expected operation and availability of PV powered products. Fisheye sky imaging is a relatively simple approach to characterize the surrounding horizon, however it's accuracy in estimating the shading loss on short timescales is not well quantified in the literature. In this work we evaluate the shade irradiance loss estimation accuracy of a horizon shading model based on fisheye sky images compared to actual local irradiance measurements from the site of interest. The results show that small errors in he horizon line estimation can lead to high irradiance estimation errors, for short timescales. However, these are mostly averaged out if the sampling period is 15 minutes or higher. The horizon shading maps obtained both with a commercial shading analysis tool, as well as self-calculated from raw fisheye images, tend to overestimating the direct beam shading and small relative errors are observed. Furthermore, ground reflected irradiance has a great influence on vertical surfaces, which was not accounted for in this work.
MFS: A Brain-Inspired Memory Formation System for GAN

Generative adversarial networks (GANs) are subject to catastrophic forgetting when learning stream of data. Inspired by the knowledge of neuroscience, this article develops a memory formation system (MFS) to establish memory for GANs. MFS is composed of three modules, including the identifier, weights upgrade (WU), and weights reactivate (WR). These modules simulate the function of encoding, consolidating, and retrieving in memory formation of human. Identifier creates indexes to label continuous tasks and these indexes are used as a cue when the corresponding tasks are recalled. WU and WR work as synaptic consolidation and system consolidation, respectively. In WU, a novel method, weight saliency measure (WSM) is proposed to measure the saliency of weight. Valuable weights are protected and invaluable weights are released for update when GAN is trained for the new task. In WR, traditional data-replay methods are improved by selecting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> representatives in each class and their feature vectors are restored. When pseudo data of this class are regenerated, only the samples with distance less than a threshold can be used in future training. Experimental results based on the testing of continual image generation and continual 3-D reconstruction show that MFS can establish a memory system to handle the catastrophic forgetting problem effectively.
Semantic Representation of a Geo-Social User Profile for a Personalised Information Retrieval

The user-centred information retrieval needs to introduce semantics into the user modelling for a meaningful representation of user interests. The semantic representation of the user interests helps to improve the identification of the user’s future cognitive needs. In this paper, we present a semantic-based approach for a personalised information retrieval. This approach is based on the design and the exploitation of a user profile to represent the user and his interests. In this user profile, we combine an ontological semantics issued from WordNet ontology, and a personal semantics issued from the different user interactions with the search system and with his social and situational contexts of his previous searches. The personal semantics considers the co-occurrence relations between relevant components of the user profile as semantic links. The user profile is used to improve two important phases of the information search process: (i) expansion of the initial user query and (ii) adaptation of the search results to the user interests.
A Comparative Analysis of Feature Extraction Algorithms for Augmented Reality Applications

The algorithms based on image feature detection and matching are critical in the field of computer vision. Feature extraction and matching algorithms are used in many computer vision problems, including object recognition and structure from motion. Each feature detector and descriptor algorithm’s computational efficiency and robust performance have a major impact on image matching precision and time utilization. The performance of image matching algorithms that use expensive descriptors for detection and matching is addressed using existing approaches. The algorithm’s efficiency is measured by the number of matches found and the number of faults discovered when evaluated against a given pair of images. It also depends on the algorithm that detects the features and matches them in less amount of time. This paper examines and compares the different algorithm (SURF, ORB, BRISK, FAST, KAZE, MINEIGEN, MSER) performances using distinct parameters such as affine transformation, blur, scale, illumination, and rotation. The Oxford dataset is used to assess their robustness and efficiency against the parameters of interest. The time taken to detect features, the time taken to match images, the number of identified feature points, and the total running time is recorded in this study. The quantitative results show that the ORB and SURF algorithms detect and match more features than other algorithms. Furthermore, they are computationally less expensive and robust compared to other algorithms. In addition, the robustness of ORB and SURF is quite high in terms of outliers, and the amount of time taken to match with the reference is also significantly less. However, the efficiency of SURF reduces against blur transformation. FAST is good in detecting corners but lacks efficiency under different transformations. Experiments show that when each algorithm is subjected to numerous alterations, it has its own set of advantages and limitations.
Distantly Supervised Named Entity Recognition with Spy-PU Algorithm

Named entity recognition is the basis of natural language processing tasks. In the field of Chinese named entity recognition, tag data sparseness is the core reason that limits the performance of named entity recognition models. To solve the problem, we propose a general approach, which can improve the effect of Chinese named entity recognition with a little samples. A key feature of the proposed method is that it can automatically label the unlabeled text through distant supervision hypothesis and use the Spy-PU algorithm to reduce the negative impact of unlabeled entity problem. Experimental results show that the method has better performance on four types of public data sets: MSRA, OntoNotes4.0, Resume and Weibo, and can effectively alleviate the impact of label data sparseness.