Reviewerly/exHarmony huggingface dataset:
Blockchain for Internet of Things

Internet of Things (IoT) technology is digitizing the physical world by connecting enormous and heterogeneous devices and unleashing great economic benefit. However, data privacy, security and trust issues in current solutions are seriously limiting the adoption of IoT applications. Blockchain, a decentralized and tamperresistant ledger, maintains consistent and immutable blocks of data at different servers and has the potential to tackle the security concerns in IoT applications. Inherent features in IoT, such as the massive IoT devices, heterogeneous IoT networks, limited battery, low computing power and communication bandwidth, make it hard to directly adopt blockchain technology in IoT application. This chapter presents a comprehensive survey on existing blockchain and IoT technologies and emphasizes on the challenges and limitation. Current studies, projects and designs on Blockchain-IoT systems are introduced and compared to illustrate the feasibility of the integration of blockchain and IoT. Blockchain technologies that can potentially address the critical challenges in IoT applications and suit the features of the same are identified with potential adaptations and enhancements elaborated on blockchain data structures, key blockchain technologies and consensus protocols. Future research directions of blockchain are collated for effective adoption in IoT applications.
Entity Linking for Historical Documents: Challenges and Solutions

Named entities (NEs) are among the most relevant type of information that can be used to efficiently index and retrieve digital documents. Furthermore, the use of Entity Linking (EL) to disambiguate and relate NEs to knowledge bases, provides supplementary information which can be useful to differentiate ambiguous elements such as geographical locations and peoples’ names. In historical documents, the detection and disambiguation of NEs is a challenge. Most historical documents are converted into plain text using an optical character recognition (OCR) system at the expense of some noise. Documents in digital libraries will, therefore, be indexed with errors that may hinder their accessibility. OCR errors affect not only document indexing but the detection, disambiguation, and linking of NEs. This paper aims at analysing the performance of different EL approaches on two multilingual historical corpora, CLEF HIPE 2020 (English, French, German) and NewsEye (Finnish, French, German, Swedish), while proposes several techniques for alleviating the impact of historical data problems on the EL task. Our findings indicate that the proposed approaches not only outperform the baseline in both corpora but additionally they considerably reduce the impact of historical document issues on different subjects and languages.
Developing a Reassembling Algorithm for Broken Objects

The research on reassembling broken objects has many important applications, such as cultural relics restoration, medical surgery and solving puzzle. Because of the complicated surfaces of the fractured object pieces, it is not easy to extract salient features from them. It becomes even more difficult and very time-consuming to reassemble broken objects when the fragments are severely corroded or some of them are lost. In order to improve the accuracy and speed of 3D fragment reassembling, an effective and efficient fragment reassembling algorithm based on point clouds is proposed this article. This method first extracts keypoints and their concavity and convexity according to the symbolic projection distance of the point cloud, and then uses the local neighborhood information of the keypoints to construct a multi-scale covariance matrix descriptor. Furthermore, by calculating the similarity of the covariance matrix descriptors, the initial pairs of match points are obtained. Finally, the geometric constraints are gradually added to optimize the sampling so as to find good hypotheses as quickly as possible. By doing so, the search space is narrowed continuously in each iteration of the process to speed up the hypothesis test. We have conducted extensive experiments. The results show that the proposed method can fuse multiple features of the fragments effectively and achieve an outstanding matching effect on the defected fragments, and that the proposed method is faster than the existing methods in literature.
An Effective Semi-Supervised Multi-Label Least Squares Twin Support Vector Machine

Multi-label twin support vector machine (MLTSVM), being an effective multi-label classifier based on twin support vector machine (TSVM), has been widely studied and applied due to its excellent classification performance. However, there are some disadvantages in classical MLTSVM: (a) MLTSVM needs to solve a series of quadratic programming problems (QPPs), which makes its learning speed lower. (b) For multi-label learning problems, it is very difficult to obtain all labels of all samples. In fact, the datasets that we can obtain only contain a small amount of labeled samples and a large amount of partially labeled and unlabeled samples. However, MLTSVM can only use expensive labeled samples and ignore cheap unlabeled and partially labeled samples. For the drawbacks, we propose a novel semi-supervised multi-label least squares twin support vector machine, called SS-MLLSTSVM. Firstly, to speed up solving, SS-MLLSTSVM introduces the least squares idea into each sub-classifier of MLTSVM, which makes each sub-classifier only need to solve a system of linear equations, instead of one QPP. Secondly, SS-MLLSTSVM can make full use of the geometric information in unlabeled and partially labeled samples by introducing manifold regularization term into each sub-classifier. The experimental results on the benchmark datasets show that, compared with the existing multi-label classification algorithms, our SS-MLLSTSVM has better classification performance.
Security based Approach of SHA 384 and SHA 512 Algorithms in Cloud Environment

Cloud computing is going to be the next big thing in the era of internet world.As the world moves ahead towards enhancement of the cloud features we also have to take a serious consideration of the enhancing cloud securities to protect the end-user's data.The major usage of this cloud feature are widely applied in the cloud based IT sector for operation advancement, Data storage; on-demand software delivery and many more operations which also requires the scrutiny of the data that is being shared.This research work is carried out to enhance the security of the end-users using secured hashing algorithm.In this, the principles of hash utility are applied that makes the intruders difficult to decode the encrypted password of the user.Even if the intruder or even the administrator of the server tries to decode the encryption for multiple attempts to decode the password the hash codes of the server keeps on changing for multiple attempts.This research work gives a new solution for the concern that is getting raised on the data privacy.The usage of the secured hashing algorithm helps to develop platform that ensure data security for the end-user in the cloud environment.For that the Secure Hashing Algorithm (SHA) 384& SHA 512 is taken and implemented by a practical approach in cloud.For the implementation of these two algorithms, the major attacks faced by end user namely Brute force attack, Man in Middle attack and Rainbow attack are experimented under cloud platform.From the experimental results, it is identified that the best algorithm to protect the data.
A One-step Approach to Covariate Shift Adaptation.

A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution. However, such an assumption is often violated in the real world due to non-stationarity of the environment or bias in sample selection. In this work, we consider a prevalent setting called covariate shift, where the input distribution differs between the training and test stages while the conditional distribution of the output given the input remains unchanged. Most of the existing methods for covariate shift adaptation are two-step approaches, which first calculate the importance weights and then conduct importance-weighted empirical risk minimization. In this paper, we propose a novel one-step approach that jointly learns the predictive model and the associated weights in one optimization by minimizing an upper bound of the test risk. We theoretically analyze the proposed method and provide a generalization error bound. We also empirically demonstrate the effectiveness of the proposed method.
Study of geological struсture of Naftalan field and adjacent areas by use of seismic attribute analysis of Mesozoic deposits

The paper is devoted to the application of attribute analysis of 3D seismic survey data in order to clarify the geological structure of Naftalan -Northern Naftalan area of Ganja oil and gas province.It gives information about Naftalan field, which is considered to be an ancient brachyanticline type deposit in Azerbaijan.A brief history of field exploration by geological and geophysical methods is considered.It is especially noted that there are two types of oil produced in the field: heavy oil with curative properties and light fraction of oil of commercial (industrial) meaning.Our goal was to study in more detail the geological structure of the field to detect intervals and zones of light oil accumulation.Therefore, in 2012 3D seismic survey works were carried out at the Naftalan field.With the help of geological evaluation of the data cube obtained as a result of 3D seismic data processing, the interval of deposits was selected, in which the V seismic horizon was traced, reflecting the surface of Upper Cretaceous deposits, which were confidently traced throughout the study area.In this interval, several attributes were calculated and their results were analyzed.Attributes that are similar or weakly informative in the results of the analysis were not used in subsequent studies to avoid repetition.In order to identify and trace disjunctive dislocations of different amplitudes, the most effective attributes, such as Chaos, Variance, Ant tracking, were identified and used to clarify the geological structure of the study area.All results of 3D seismic data processing were given in the form of cubes of seismic attributes.By analyzing the attributes cubes, the geological structure of the field was studied across the surface of Upper Cretaceous deposits and the most effective attributes were selected.As a result, a structural map for V-SH (top of Upper Cretaceous) was drawn.
Improved LSTM-based Prediction Method for Highly Variable Workload and Resources in Clouds

A large number of services provided by cloud/edge computing systems have become the most important part of Internet services. In spite of their numerous benefits, cloud/edge providers face some challenging issues, e.g., inaccurate prediction of large-scale workload and resource usage traces. However, due to the complexity of cloud computing environments, workload and resource usage traces are highly-variable, thus making it difficult for traditional models to predict them accurately. Traditional models fail to deal with nonlinear characteristics and long-term memory dependencies. To solve this problem, this work proposes an integrated prediction method that combines Bi-directional and Grid Long Short-Term Memory network (BG-LSTM) models to predict workload and resource usage traces. In this method, workload and resource usage traces are first smoothed by a Savitzky-Golay filter to eliminate their extreme points and noise interference. Then, an integrated prediction model is established to achieve accurate prediction for highly-variable traces. Using real-world workload and resource usage traces from Google cloud data centers, we have conducted extensive experiments to show the effectiveness and adaptability of BG-LSTM for different traces. The performance results well demonstrate that BG-LSTM achieves better prediction results than some typical prediction methods for highly-variable real-world cloud systems.
PoP-Net: Pose over Parts Network for Multi-Person 3D Pose Estimation from a Depth Image

In this paper, a real-time method called PoP-Net is proposed to predict multi-person 3D poses from a depth image. PoP-Net learns to predict bottom-up part representations and top-down global poses in a single shot. Specifically, a new part-level representation, called Truncated Part Displacement Field (TPDF), is introduced which enables an explicit fusion process to unify the advantages of bottom-up part detection and global pose detection. Meanwhile, an effective mode selection scheme is introduced to automatically resolve the conflicting cases between global pose and part detections. Finally, due to the lack of high-quality depth datasets for developing multi-person 3D pose estimation, we introduce Multi-Person 3D Human Pose Dataset (MP-3DHP) as a new benchmark. MP-3DHP is designed to enable effective multi-person and background data augmentation in model training, and to evaluate 3D human pose estimators under uncontrolled multi-person scenarios. We show that PoP-Net achieves the state-of-the-art results both on MP-3DHP and on the widely used ITOP dataset, and has significant advantages in efficiency for multi-person processing. MP-3DHP Dataset and the evaluation code have been made available at: https://github.com/oppo-us-research/PoP-Net.
Once Upon a Time, in AmeriFlux

Abstract In October 2020, under COVID‐19 quarantine, AmeriFlux held its largest and one of its most successful annual meetings. Historically, ∼100 scientists attend; this meeting had over 400 registrants and participants. Participants expressed that this was among the best virtual meetings that they had ever attended, and 100% of post‐meeting survey respondents stated that they would attend again. Feedback revealed the meeting fostered a strong sense of connection to the AmeriFlux community, especially among early career and international scientists. How did a feeling of strong connection to the community arise from the seemingly cold and isolated structures of virtual meetings? The meeting emphasized Diversity, Equity, and Inclusion (DEI), which resulted in an unexpected enhancement of science communication and community connections. Additionally, the meeting experimented with an online virtual gaming‐like world (Gather.town), where users controlled video/audio‐enabled avatars in a conference center and poster hall environment to create spontaneous conversations and discussions. In lieu of a social‐bonding field trip, participants showed videos of their field sites accompanied by informal banter, which were watched in group settings in Gather. Social mixers were structured over Zoom breakout rooms that were limited in size to promote participation with accessible games. Science talks were selected based on appeal to a demographically diverse organizing committee, which enhanced appeal to a broad meeting audience. Finally, breakout reports were given not in the format of bullet point slides, but instead as creatively improvized fairytales, which dramatically enhanced engagement. Here, we describe some of the process that went into the AmeriFlux 2020 meeting. Keeping with the theme of experimenting and fairytales, we present this narrative in the form of a fairytale; and, without further ado…
The geometry of signal space: a case study of direct mapping between seismic signals and event distribution

SUMMARY Under favourable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal-space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection and scaling, with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning and possible application to studies of repeating events in induced, volcanic and glacial seismicity. Our results place limits on the widely held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.
Anytime Inference with Distilled Hierarchical Neural Ensembles

Inference in deep neural networks can be computationally expensive, and networks capable of anytime inference are important in scenarios where the amount of compute or input data varies over time. In such networks the inference process can interrupted to provide a result faster, or continued to obtain a more accurate result. We propose Hierarchical Neural Ensembles (HNE), a novel framework to embed an ensemble of multiple networks in a hierarchical tree structure, sharing intermediate layers. In HNE we control the complexity of inference on-the-fly by evaluating more or less models in the ensemble. Our second contribution is a novel hierarchical distillation method to boost the predictions of small ensembles. This approach leverages the nested structure of our ensembles, to optimally allocate accuracy and diversity across the individual models. Our experiments show that, compared to previous anytime inference models, HNE provides state-of-the-art accuracy-computation trade-offs on the CIFAR-10/100 and ImageNet datasets.
ADADRIFT: An Adaptive Learning Technique for Long-history Stream-based Recommender Systems

Adaptive recommender systems are increasingly showing their importance as profiling is a dynamic problem. Their goal is to update recommendation models as new interactions take place, thus swiftly adapting to drifts in the user's behavior and desires, and item's audience. However, existing recommendation algorithms usually do not perform well during drifts, as they take long to adapt to changes, or these updates are suboptimal since they account for all profiles' preferences equally, which is often untrue as each individual and its changes are unique. In this paper, we propose the ADADRIFT algorithm to deal with user and item-based drifts in adaptive recommender systems using personalized learning rates based on profile statistics. The experiments using stream-based recommender systems (ISGD and BRISMF) across four different datasets show that ADADRIFT surpasses ADADELTA with significant improvements in recommendation rates. The best results appear when the data streams have a long history of the users' or items' interactions and drifts become noticeable. The experimentation in this work highlight the importance of handling drifts in recommender systems.
Modern and Innovative Approaches to the Organization of Students’ Self-Education in Higher Educational Institutions

Self-education is gaining momentum. Today, the main challenge for universities is to launch open online courses that anyone can access. Such an initiative is a signal to educational organizations that they must rebuild their educational process. It is necessary not only to broadcast information, but also to engage in tutoring support of students, immerse them in real projects and tasks. This article highlights new viewpoints of methodology of organization of students' self - education in higher educational institution.
Comparison of Classification Performances of Mathematics Achievement at PISA 2012 with the Artificial Neural Network, Decision Trees and Discriminant Analysis

This study aims to compare the performances of the artificial neural network, decision trees and discriminant analysis methods to classify student achievement. The study uses multilayer perceptron model to form the artificial neural network model, chi-square automatic interaction detection (CHAID) algorithm to apply the decision trees method and linear discriminant analysis. The performance of each method has been investigated in different sample sizes when classifying into different numbered subgroups. The study has revealed that the artificial neural network has the best performance in large, medium and small sample sizes when classifying into six, three and two subgroups. In the very small sample size, which has homogeneous variance-covariance matrices, the discriminant analysis performs the best, while in the very small sample size, which does not have homogeneous variance-covariance matrices, it is the discriminant analysis which performs the best when classifying into six subgroups and the artificial neural network performs the best when classifying into two and three subgroups. Considering the performances of the methods with respect to sample size, it can be concluded that as the sample size gets smaller, the performance of the decision trees method gets worse, whereas the performance of the discriminant analysis method improves. No correlation of this kind has been found with regard to the artificial network method.
Systems Engineering Architecture Framework for Physical Protection Systems

A physical protection system (PPS) integrates people, procedures, and equipment for the protection of assets or facilities against theft, sabotage, or other malevolent intruder attacks. In this paper we focus on the architecture modeling of PPS to support the communication among stakeholders, analysis and guiding the systems development activities. A common practice for modeling architecture is by using an architecture framework that defines a coherent set of viewpoints. Existing systems engineering modeling approaches appear to be too general and fail to address the domain-specific aspects of PPSs. On the other hand, no dedicated architecture framework approach has been provided yet to address the specific concerns of PPS. In this paper, we present an architecture framework for PPS (PPSAF) that has been developed in a real industrial context focusing on the development of multiple PPSs. The architecture framework consists of six coherent set of viewpoints including facility viewpoint, threats and vulnerabilities viewpoint, deterrence viewpoint, detection viewpoint, delay viewpoint, and response viewpoint. We illustrate the application of the architecture framework for the design of a PPS architecture of a building.
Implementation of AES Algorithm using Verilog

Cryptography is very important now-a-days for data security and integrity as the ecommerce and internet applications has increased. But, it has least importance in many cases because of extra memory and other requirements needed for the implementation. The main aim of this work is to implement Advanced Encryption Standard (AES) Encryption using Verilog. To protect data like electronics, cryptographic algorithms are used. The digital information can be encrypted and decrypted by the block cipher of AES algorithm. It can be implemented with the key length 128, 192, 256 bits. Each round of encryption associated with delay can be reduced by AES parallel design.