Reviewerly/exHarmony huggingface dataset:
CNN-Based Broad Learning System

The Broad Learning System (BLS) has recently been proposed as an effective and useful method for pattern recognition. It overcomes the shortcomings of deep neural networks that are both time consuming and hardware dependent. BLS is a lightweight network structure. Although its advantages in speed and flexibility are obvious, there is a still a gap between its accuracy in image recognition and that of state-of-art approaches. We propose a BLS-based model that combines CNN and Adam algorithm. At first, we build a BLS-based classifier, and then we build a CNN-based feature extraction. After that, we take features extracted as inputs to the classifier. Feature extraction through convolution and pooling operations can effectively preserve key features of the image. We use Adam algorithm to update the weights of the feature extraction. By retaining key features and removing factors that affect image recognition, it helps to improve the accuracy of the classification results. Therefore, through the above operations, the speed and accuracy of our proposed lightweight network structure haves been improved and reflected in the dataset of the MNIST and Cat or Dog for Kaggle competitions. We can choose different convolution layers, pooling layers and optimization algorithms according to the different sized data sets.
Towards reducing delegation overhead in replication-based verification: An incentive-compatible rational delegation computing scheme

We consider the verifiable problem of delegation computing in cloud, which means a client needs to outsource a computing function to untrusted servers, and verify the returned computational results. A recently related result of Dong et al. (CCS 2017) shows a client outsources the same computation task to two different servers, which achieves verifiability by simply cross-checking. In Dong’s replication-based scheme, although the expensive cryptographic algorithms for verification is not needed, the delegation overhead is double. In order to reduce delegation overhead of Dong’s scheme, we propose a novel incentive-compatible rational delegation computing scheme. Specifically, the client sends duplicate tasks to some servers for cross-checking, meanwhile, each sever only knows the probability distribution of receiving a duplicate task. Furthermore, considering rational delegation computing as a dynamic game with imperfect information, we design reasonable and effective incentive mechanisms. Afterwards, we seek a unique sequential equilibrium in each game, and strictly prove that rational players still have no motivation to deviate from honest behavior under a condition of lower delegation overhead. Detailed analysis indicates that the lowest delegation overhead achieved by our scheme is only n2n-2 of that achieved by Dong’s scheme, where n means the number of servers.
On Pareto-Optimal Boolean Logical Patterns for Numerical Data

This paper clarifies the difference between intrinsically 0–1 data and binarized numerical data for Boolean logical patterns and strengthens mathematical results and methods from the literature on Pareto-optimal LAD patterns. Toward this end, we select suitable pattern definitions from the literature and adapt them with attention given to unique characteristics of individual patterns and the disparate natures of Boolean and numerical data. Next, we propose a set of revised criteria and definitions by which useful LAD patterns are clearly characterized for both 0–1 and real-valued data. Furthermore, we fortify recent pattern generation optimization models and demonstrate how earlier results on Pareto-optimal patterns can be adapted in accordance with revised pattern definitions. A numerical study validates practical benefits of the results of this paper through optimization-based pattern generation experiments.
11 Years with Wearables

The role of wearable technology in our daily lives is rapidly growing and many users are cumulatively becoming dependent on it. To provide insight into the future of wearable technologies and various community attitudes towards them, we implemented an in-depth quantitative investigation of opinions from academic texts (DBLP and PubMed), social media (Twitter), news media (Google News and Bing News), and entrepreneurship communities (Kickstarter and Indiegogo) over a 10-year period. Our results indicate that unlike academia, the news media, entrepreneurship communities, and social media all hold overall positive attitudes towards wearable technologies. Secondly, there are diverse perspectives towards various wearable products across different platforms. Specifically, "XR" technologies received the most attention, while "Exoskeleton" ignited the most heated debates. Thirdly, we discovered that the lifetime of a hyped wearable technology lasts approximately three years. Furthermore, the news media and entrepreneurship community's attitudes towards wearable technologies did not have a strong impact on public opinion. Finally, among all types of wearable technologies, "fashion design" and "healthcare" products were the most enlightening for the market.
Grey Wolf Optimization algorithm with Discrete Hopfield Neural Network for 3 Satisfiability analysis

Abstract An optimal learning algorithm contributes to the quality of the neuron states in the form of 3 Satisfiability logical representation during the retrieval phase of the Discrete Hopfield Neural Network. Based on that basis, we proposed a modified bipolar Grey Wolf Optimization algorithm with a Discrete Hopfield Neural Network for Boolean 3 Satisfiability analysis by manipulating the different levels of complexities. This work concerns the improvement in the learning phase which requires a robust iterative metaheuristic algorithm in minimizing the cost function of 3 Satisfiability logical representation with less iteration. Under some reasonable conditions, the proposed hybrid network will be assessed by employing several performance measures, in terms of learning errors, minimum energy evaluations, variability, and similarity analysis. To verify the compatibility of the Grey Wolf Optimization algorithm as a learning paradigm, the comparison was made with the hybrid model with an Exhaustive search. Thus, the results proved the capability of the proposed learning algorithm in optimizing the learning and generating global minimum solutions for 3 Satisfiability logic based on the analysis obtained via various performance metrics evaluation.
Market-oriented job skill valuation with cooperative composition neural network

The value assessment of job skills is important for companies to select and retain the right talent. However, there are few quantitative ways available for this assessment. Therefore, we propose a data-driven solution to assess skill value from a market-oriented perspective. Specifically, we formulate the task of job skill value assessment as a Salary-Skill Value Composition Problem, where each job position is regarded as the composition of a set of required skills attached with the contextual information of jobs, and the job salary is assumed to be jointly influenced by the context-aware value of these skills. Then, we propose an enhanced neural network with cooperative structure, namely Salary-Skill Composition Network (SSCN), to separate the job skills and measure their value based on the massive job postings. Experiments show that SSCN can not only assign meaningful value to job skills, but also outperforms benchmark models for job salary prediction.
Graphics processing unit acceleration of the island model genetic algorithm using the CUDA programming platform

Abstract Genetic algorithms are a practical approach for finding near‐optimal solutions for nondeterministic polynomial‐hard problems. In this work we exploit the parallel processing capability of graphics processing units and Nvidia's CUDA programming platform to accelerate the island model genetic algorithm by modifying the evolutionary operations to fit the hardware architecture and have successfully achieved significant computational speedups.
Linear combination estimator of multiple-outcome detections with discrete measurement outcomes

Finding the optimal estimator capable of saturating the Cram\'er-Rao bound is important for quantum parameter estimation. The maximum likelihood estimator (MLE) is well known to be asymptotically optimal, but it is usually nonanalytic and hence requires extensive numerical computation. On the other hand, the inversion estimator is widely used in experiments due to its simplicity, but it is usually suboptimal. Here we derive an estimator based on the linear combination of multiple inversion estimators associated with the occurrence probability of different measurement outcomes. This linear combination estimator shares the asymptotic optimality of the MLE and the simplicity of the inversion estimator and is applicable to general multi-outcome measurements, irrespective of the specific system and the noise in the system. We demonstrate this estimator for the intensity-difference measurement to a six-photon twin-Fock input state.
The effect of parameters and irradiance estimation techniques on PV potential quantification: The case of Mexico

The impact of some irradiance estimation techniques and model parameters in the assessment of solar potential has not yet been clarified, therefore, the case of Mexico is used in this work is to reduce such a gap. Firstly, a universe of 1629 weather stations and 108 models were used to study the effect of model-based and interpolation techniques on the estimation of the resource potential. Secondly, a centralized and two decentralized geographic potential scenarios were analyzed. Thirdly, the effect of parameters related to PV solar harvesting and production costs was evaluated. Our results revealed dramatic differences in the resource potential when heuristics models were used; differences up to 10% were found for the same location; in contrast, the evaluated interpolation techniques provided very close results with sensibility indexes (SI) below of 2.11%. Differences up to 15% were found in the average daily irradiance when the available area for PV installation is considered, which revealed the importance of the determination of suitable areas for harvesting. Changes up to %27 in the technical potential were found for different c Si-Poly modules. The parameters affecting most significantly the cost were the annuity factor (SI = 75.89%), the balance of the system (SI = 56.95%) and the PV module investment cost (39.28%). Our results provide reasonable results when compared with actual prices and provide evidence of the role of government policies to lower the costs.
Overview of phishing landscape and homographs in Arabic domain names

Abstract In this paper, we summarize the latest social engineering phishing attack types with the focus on domain name manipulation. Providing a fake domain is a crucial part of phishing attacks that can be carried out with different techniques such as cybersquatting, typosquattings, or homographs. We argue that homographs with special International Domain Names can be very serious threat for many users that can hardly be identified. We present the analysis of phishing attacks with Arabic domains and conclude that because of the linguistic complexity of the Arabic language and the missing support by the industrial tools expose millions of users to sophisticated domain manipulation based phishing attacks. To reduce the future risk of homograph attacks originating from Arabic internationalized domain names (IDNs) we suggest a change to the registration policy. We also present a browser extension to assist against homographs in current Arabic IDN namespace.
Dual-Domain Generative Adversarial Network for Digital Image Operation Anti-Forensics

In this letter, we propose a general digital image operation anti-forensic framework based on generative adversarial nets (GANs), called dual-domain generative adversarial network (DDGAN). To tackle the issue of image operation detection, the proposed framework incorporates both operation specific forensic features and machine-learned knowledge to ensure that the generated images exhibit better undetectability performance against various detectors. The DDGAN consists of a generator and two discriminators working on different domains, i.e., the operation-specific feature domain which helps to conceal the artifacts from the perspective of forensic analysis for the target task, and the spatial domain which facilitates to take advantage of machine-learned features from the scratch as a supplementary. Through the experiments on median filtering and JPEG compression anti-forensics, we show the superior performance of the proposed DDGAN compared with state-of-the-art anti-forensic methods in terms of undetectability and visual quality.
Re-assessing the “Classify and Count” Quantification Method

Learning to quantify (a.k.a. quantification) is a task concerned with training unbiased estimators of class prevalence via supervised learning. This task originated with the observation that "Classify and Count" (CC), the trivial method of obtaining class prevalence estimates, is often a biased estimator, and thus delivers suboptimal quantification accuracy. Following this observation, several methods for learning to quantify have been proposed and have been shown to outperform CC. In this work we contend that previous works have failed to use properly optimised versions of CC. We thus reassess the real merits of CC and its variants, and argue that, while still inferior to some cutting-edge methods, they deliver near-state-of-the-art accuracy once (a) hyperparameter optimisation is performed, and (b) this optimisation is performed by using a truly quantification-oriented evaluation protocol. Experiments on three publicly available binary sentiment classification datasets support these conclusions.
Constructing MDS Galois self-dual constacyclic codes over finite fields

As a further development and deepening of two examples in the paper (Fan and Zhang, 2017), we construct MDS Galois self-dual constacyclic codes over finite fields. First, we give the definition of normal MDS constacyclic codes and explore their existence conditions. Further, we give existence conditions for normal MDS Galois self-dual constacyclic codes. Based on the latter conditions, we classify and construct all normal MDS Galois self-dual λ-constacyclic codes over Fq of length n, where q=pe is an odd prime power, gcd(q,n)=1 and λ∈Fq∗.
The Effect of Video Lecture Design and Production Quality on Student Outcomes: A Quasi-Experiment with Implications for Online Teaching During the Covid-19 Pandemic

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Spatial-Temporal Attention Network for Temporal Knowledge Graph Completion

Temporal knowledge graph completion, which aims to predict missing links in temporal knowledge graph (TKG), is an important research task due to the incompleteness of TKG. Recently, TKG embedding methods have proved to be effective for this task. However, most of existing methods regard TKG as a set of independent facts and consequently ignore the implicit relevance among facts. Actually, as a kind of dynamic heterogeneous graph, the evolving graph structure of TKG is able to reflect a wealth of information. To this end, in this paper we regard temporal knowledge graph as heterogeneous and discrete spatial-temporal resource, and propose a novel spatial-temporal attention network to learn TKG embeddings by modeling spatial-temporal property of TKG while considering its special characteristics. Specifically, our model employs a Multi-Faceted Graph Attention Network (MFGAT) to extract rich structural information from the egocentric network of each entity. Additionally, an Adaptive Temporal Attention Mechanism (ADTAT) is utilized to flexibly model the correlation of entity representations in the time dimension. Finally, by combing our obtained representations with existing static KG completion methods, they can be extended to spatial-temporal versions to predict missing links in TKG while considering its inherent graph structure and time-evolving property. Experimental results on three real-world datasets demonstrate the superiority of our model over the state-of-the-art methods.
Automatic morphological extraction of fibers from SEM images for quality control of short fiber-reinforced composites manufacturing

The properties of fiber-reinforced composite materials greatly depend on the morphology of reinforcing fibers within the base materials, i.e., spatial uniformity, orientation, and length distribution. Accurately extracting this information in an automated manner from SEM images is essential for quality assessment, quality control, and process optimization. However, due to overlapping or cross-linking issue, morphological fiber extraction is very challenging and has not been well addressed in the existing literature. This paper takes into account this research gap and proposes five different methods, namely, the opening method, simple Hough transform (HT), partitioning HT, gradient-based HT, and break-merge method to automatically extract the straight fibers from SEM images to facilitate the morphological analysis. The robustness and accuracy of the proposed methods are evaluated and compared through both simulation and real case studies.
Contextual Bandit Approach-based Recommendation System for Personalized Web-based Services

In recent years, recommendation systems have started to gain significant attention and popularity. A recommendation system plays a significant role in various applications and services such as e-commerce, video streaming websites, etc. A critical task for a recommendation system is to model users' preferences so that it can attain the capability to suggest personalized items for each user. The personalized list suggested by a suitable recommendation system should contain items highly relevant to the user. However, many a times, the traditional recommendation systems do not have enough data about the user or its peers because the model faces the cold-start problem. This work compares the existing three MAB algorithms: LinUCB, Hybrid-LinUCB, and CoLin based on evaluating regret. These algorithms are first tested on the synthetic data and then used on the real-world datasets from different areas: Yahoo Front Page Today Module, Lastfm, and MovieLens20M. The experiment results show that CoLin outperforms Hybrid-LinUBC and LinUCB, reporting cumulated regret of 8.950 for LastFm and 60.34 for MovieLens20M and 34.10 for Yahoo FrontPage Today Module.
Constraints and Benefits of the Blockchain Use for Real Estate and Property Rights

Purpose - Many recent social media posts and news may create a perception of big success in the use of blockchain for the real estate industry, land registration and protection of titles and property rights. A sobering outlook is crucial because misleading concepts may bury the whole idea of blockchain use. The paper aims to research the possibilities of blockchain and other distributed ledger technologies (DLT) and applicability of these technologies for different purposes in real estate, property rights and public registries.Design/methodology/approach – This research is framed with policy studies and focuses on property rights, land registration regulatory framework and ICT innovations. The context of this paper is decentralization which has been developed in political science studies and the role of blockchain and DLT in it. Therefore, the provided analysis of blockchain and DLT is interdisciplinary research to interpret the facets of DLT technologies in the context of real estate and land title registration. Findings - Permissioned and private DLT systems cannot be considered a significant evolutionary step in government systems. Blockchain, which is distinguished from permissioned systems as the technology of the immutable ledger that does not require authorities, is a new word in governance. However, this technology has some principal features that can restrain its implementation at the state level, and thus require further research and development. The application of blockchain requires a proper architecture of overlaid technologies to support changes of outdated and mistaken data, address issues of digital identity and privacy, legal compliance and enforceability of smart contracts, hardforks and scalability of the ledger.Originality/value - This paper shows the constraints of the technology’s properties which were not explained before in the context of title rights and land registration even though technological limits are known in more specific technical sources. Along with the known benefits this meant to help to avoid misinterpretation of some DLT features by non-technical people. A multidisciplinary approach in analysing the technology and laws helped to better understand what can and cannot be beneficial for public registries and the protection of property rights. The presented outcomes can be laid down as requirements for the technical protocols aimed at addressing the issues of DLT and public policies to put blockchain at the service of society.
Group Reassignment for Dynamic Edge Partitioning

Graph partitioning is a mandatory step in large-scale distributed graph processing. When partitioning real-world power-law graphs, the edge partitioning algorithm performs better than the traditional vertex partitioning algorithm, because it can cut a single vertex into multiple replicas to apportion the computation. Many advanced edge partitioning methods are designed for partitioning a static graph from scratch. However, the real-world graph structure changes continuously, which leads to a decrease in partition quality and affects the performance of the graph applications. Some studies are devoted to offline repartitioning or batch incremental partitioning, but how to deal with dynamics in real-time is still worthy of in-depth study. In this article, we discuss the impact of dynamic change on partition and discover that both insertion and deletion will lead to local suboptimal partitioning, which is the reason for the degradation of partition quality. As a solution, a dynamic edge partitioning algorithm is proposed to partition dynamics in real-time. Specifically, we deal with dynamics by a distributed stream and improve partition quality by reassigning some closely connected edges. Experiments show that it is robust to initial partition quality, dynamic scale and type, and distributed scale. Compared with the state-of-the-art dynamic partitioner, it can reduce vertex-cuts by 29.5 percent. Compared with the repartitioning algorithms, it can save the partitioning time by 91.0 percent. Applied on the graph task, it can reduce the increase of communication cost and the increase of the total time of task by 41.5 and 71.4 percent.
A remote sensing image target recognition method based on improved Mask-RCNN model

As an important branch in the field of machine vision, target detection and recognition technology have important applications in various fields. In view of the fact that the general target detection and recognition model does not perform well in remote sensing images, and there're large amount of different size targets need to be recognized. On the basis of Mask R-CNN model, one of the classic target detection and recognition model, this paper proposes A target recognition and detection method that integrates data enhancement processing and improves the Mask R-CNN backbone network FPN to RFP. When inputting image data, perform random enhancement processing on the data, randomly cut the image, and randomly merge; when extracting features, improve the FPN in the original Mask R-CNN model to RFP to make the extracted features better Reflecting the original picture information, experiments have shown that this model has a good performance in fine-grained target recognition in remote sensing images. On the same data set, its evaluation indicators are superior to other comparison algorithms in all aspects.