Reviewerly/exHarmony huggingface dataset:
New progress in international nanotechnology research in the past ten years – visual analysis based on CitesSpace

With the continuous development of high-tech industry, Moore’s law is close to the limit. People urgently need nano science and technology to trigger a new scientific and technological revolution to meet the needs of life, military and so on. Nanotechnology covers almost all industries and has made achievements in the industries such as medical, materials, manufacturing, and information technology. It has changed the production and life of human beings and subverted many industries. In recent years, more and more people have conducted data mining on nanotechnology research. By combing the literature, this paper summarizes the core authors, keyword changes, important authors and emergent words of the existing literature. Contributing to analyzing the research status of this field and revealing research hotspots in this field. It is of great significance for scholars to sort out the development process of nano field and predict the future development trend. Using CitesSpace bibliometric analysis software, 44002 pieces of literature about nanotechnology in SCI and SSCI journals in the core collection of the Web of Science database were analyzed in this paper. The results indicated that countries such as the United States, Germany, China, and Japan have issued more articles; However, the centrality of articles published in European countries such as the UK, Germany, and France was relatively strong; High-yield units mainly included Chinese Acad Sci and Russian Acad Sci; The main research scholars were Wei Wang, Peixuan Guo, Thomas J Webster, Hao Yan; Research emergent words primarily included polymer, particle, dynamics, mechanical properties and silver nanoparticle. On this basis, countermeasure suggestions and prospects are proposed.
Hierarchical Symbol Transition Entropy: A Novel Feature Extractor for Machinery Health Monitoring

This article develops a novel collaborative health monitoring framework based on hierarchical symbol transition entropy (HSTE) and 2-D-extreme learning machine (2-D-ELM) without fusion. In the proposed framework, a novel metric called symbol transition entropy (STE) is first presented to evaluate the dynamical complexity of time series through multistep transition and the joint probability distribution of the symbol state and its transition state. Compared with the existing entropy algorithms, STE has better robustness and captures more detailed dynamical changes. Subsequently, a new feature representation method called HSTE is proposed by combining STE with the hierarchical analysis. The two-order tensor features can be constructed for multichannel data by stacking HSTE values extracted from each single-channel data. Finally, 2-D-ELM is incorporated to identify the extracted two-order tensor features without vectorization. The feasibility of the proposed schemes is verified through simulation and experimental studies, and the final results confirm that the developed schemes have better performance than the existing entropy-based collaborative fault diagnosis methods.
Costume as a shared sensorial experience

This research report discusses the creation of a costume for the spectator that stimulates sensorial experiences of internalized feelings. Under the project title Sense my Thoughts , we constructed two wearable prototypes: one for the performer and the other one for the spectator. Wearable electronics embedded both in the performer’s and the spectator’s garments allow the spectator to become directly connected with the performer’s emotional experience through costume. This enables the spectators to identify with the onstage character through a personal connection and immerse themselves in the performance on a multi-sensorial level. The goal of our practice-as-research was to design wearables that measure and transmit the performer’s biometric data in a sensorial format, which the spectator would be able to feel on/in their own body in real time. We assembled one garment that captures performer’s input data, such as brainwaves, heartbeat and breathing, and interpret them as different emotional states. We then connected it wirelessly to another garment that we named the spectator’s costume , which receives the performer’s data and activates hidden vibrational motors, speakers, heaters and coolers as various combinations of physical stimuli. In this article, we focus on the latter garment and explain how the spectator’s costume can become an agent of the wearer’s empathic engagement through stimulating the suggestion of the performer’s embodied experiences. The work presented here is still in an early stage of development and, therefore, the conclusions are based on small-scale prototype research.
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling

Human conversations naturally evolve around different topics and fluently move between them.In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored.In this paper we introduce TIAGE, a new topic-shift aware dialog benchmark constructed utilizing human annotations on topic shifts.Based on TIAGE, we introduce three tasks to investigate different scenarios of topic-shift modeling in dialog settings: topic-shift detection, topic-shift triggered response generation and topic-aware dialog generation.Experiments on these tasks show that the topic-shift signals in TIAGE are useful for topic-shift response generation.On the other hand, dialog systems still struggle to decide when to change topic.This indicates further research is needed in topic-shift aware dialog modeling. 1
REACT-ION: A Model-based Runtime Environment for Situation-aware Adaptations

Trends such as the Internet of Things lead to a growing number of networked devices and to a variety of communication systems. Adding self-adaptive capabilities to these communication systems is one approach to reducing administrative effort and coping with changing execution contexts. Existing frameworks can help reducing development effort but are neither tailored toward the use in communication systems nor easily usable without knowledge in self-adaptive systems development. Accordingly, in previous work, we proposed REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. REACT addresses heterogeneity and distribution aspects of such systems and reduces development effort. In this article, we propose REACT-ION—an extension of REACT for situation awareness. REACT-ION offers a context management module that is able to acquire, store, disseminate, and reason on context data. The context management module is the basis for (i) proactive adaptation with REACT-ION and (ii) self-improvement of the underlying feedback loop. REACT-ION can be used to optimize adaptation decisions at runtime based on the current situation. Therefore, it can cope with uncertainty and situations that were not foreseeable at design time. We show and evaluate in two case studies how REACT-ION’s situation awareness enables proactive adaptation and self-improvement.
Optimization Method for Distributed Database Query Based on An Adaptive Double Entropy Genetic Algorithm

In a distributed database environment, multi-join query optimization is one of the key factors affecting database performance. Genetic algorithms have a good application in dealing with this type of problem. However, the traditional genetic algorithm has the problems of low efficiency and easily falls into the precocity when dealing with query optimization, which is mainly caused by the lack of population diversity. Therefore, this paper sets up a mathematical model for distributed database query optimization and proposes an adaptive genetic algorithm based on double entropy. We introduced a genetic algorithm with two types of entropy: genotype and phenotype. Genotype entropy was used to optimize the distribution of the initial population, ensuring that the initial population has good population diversity. Phenotype entropy is used to optimize the genetic strategy, which can be divided into individual entropy and population entropy. Individual entropy is used to optimize the selection strategy, and population entropy is used to optimize the crossover and mutation operators to maintain the population diversity in the iteration process and accelerate the speed of iteration. The experimental results show that the algorithm proposed in this paper is effective for query optimization of a distributed database.
An Efficient Approach for Mining Reliable High Utility Patterns

Utility mining is one of the most thriving research topics with a wide range of real-world applications. High utility pattern mining uses a utility function to extract all desired patterns that exceed a minimum utility threshold. However, a significant number of patterns will be generated if this threshold is set too low, which is an inherent limitation of these algorithms. This may cause the mining process to be inefficient as it would be difficult to analyze the patterns found. Furthermore, most of these patterns are unreliable and hard to be employed in making decisions. This paper proposed a novel problem of mining reliable high utility patterns by adapting the concept of reliability to mine a significant type of pattern called reliable high utility patterns. To address this issue, an efficient approach named RUPM (Reliable Utility-based Pattern Mining) is presented. RUPM introduces three novel measurements for estimating the reliability of utility-based patterns and proposes several strategies to efficiently handle reliable patterns with high utility values. Experimental results suggest that up to 99% of the patterns discovered by existing traditional high utility pattern mining algorithms were, in fact, unreliable. In contrast, the average reliability proportion in the resultant patterns obtained from the RUPM approach is at least 47.6% higher. Moreover, the proposed pruning strategies provide a reduction in both the runtime and memory usage.
A scale‐sensitive heatmap representation for multi‐person pose estimation

Multi-person pose estimation is a challenging vision task that can be seriously affected by keypoint scale variation. Existing heatmap-based approaches are devoted to reducing the effect by optimizing backbone architecture or loss functions, but the problem of an inaccurate heatmap representation with different keypoint scales still exists. A scale-sensitive heatmap algorithm is presented to generate reasonable spatial and contextual features for the network to predict more precise coordinates, by systematically considering the standard deviation, truncated radius, and shape of Gaussian kernels. Specifically, the scale-sensitive heatmap algorithm contains three parts: inter-person heatmap, limited-area heatmap, and shape-aware heatmap. The inter-person heatmap allocates different standard deviations for each human instance proportionally calculated by the keypoint-based method, the limited-area heatmap defines the truncated radius to limit the influence area of Gaussian kernels, and the shape-aware heatmap modifies the Gaussian kernels generated by some ellipse-shaped joints. Our scale-sensitive heatmap algorithm outperforms the baseline by a considerable margin on the COCO and CrowdPose benchmark datasets. The code and pretrained models are available at https://github.com/ducongju/Scale-sensitive-Heatmap.
Implementing Stable-Unstable Semantics with ASPTOOLS and Clingo

Normal logic programs subject to stable model semantics cover reasoning problems from the first level of polynomial time hierarchy (PH) in a natural way. Disjunctive programs reach one level beyond this, but the access to the underlying NP oracle(s) is somewhat implicit and available for the programmer using the so-called saturation technique. To address this shortcoming, stable-unstable semantics was proposed, making oracles explicit as subprograms having no stable models. If this idea is applied recursively, any level of PH can be reached with normal programs only, in analogy to quantified Boolean formulas (QBFs). However, for the moment, no native implementations of stable-unstable semantics have emerged except via translations toward QBFs. In this work, we alleviate this situation with a translation of (effectively) normal programs that combines a main program with any fixed number of oracles subject to stable-unstable semantics. The result is a disjunctive program that can be fed as input for answer set solvers supporting disjunctive programs. The idea is to hide saturation from the programmer altogether, although it is exploited by the translation internally. The translation of oracles is performed using translators and linkers from the ASPTOOLS collection while Clingo is used as the back-end solver.
Blockchain Based E-Voting System: Open Issues and Challenges

Blockchain technology has become very trendy and penetrated different domains, mostly due to the popularity of cryptocurrencies. Blockchain technology offers decentralized nodes for e-voting and is used to create e-voting systems, mainly because of their end-to-end verification benefits. This technology is an excellent replacement for traditional e-voting solutions with distributed performance, reliability and security. The following article provides an overview of e-voting systems based on blockchain technology. The main purpose of this analysis was to examine the current state of blockchain-based voting systems, as well as any associated difficulties in predicting future events.
An Hybrid Approach Based on Clustering and Synthetic Sample Generation for Imbalance Data Classification: ClustSyn

One of the major problems to be investigated in various application domains in the real world is the skewed/imbalanced distribution of data. The class imbalance problem turns out when the number of samples from one class significantly exceeds the number samples from the other class and when such data is trained on traditional learning algorithms tends to be bias towards the majority class (i.e. class having more number of samples), resulting in a significant deterioration in the classification performance. Recent studies have shown that the presence of other features, such as small disjuncts, overlapping, and noise in data, can make classification much more difficult. In this paper, we propose an effective hybrid method based on clustering and synthetic sample generation for imbalance data classification called ClustSyn. It consists of clustering algorithm along with the synthetic data generation using Mahalanobis distance. The reason for using the Mahalanobis distance is that it can minimize the probability of overlap and maintain the structure of covariance when providing synthetic samples for the minority class. ClustSyn efficiency is compared with existing methods such as AdaBoost, RUSBoost, SMOTEBoost, based on ensemble learning. We have performed experiments with different Imbalance Ratios (IR) on 11 datasets. Results show ClustSyn outperformed existing methods for imbalanced and small disjunct datasets.
Effective Image Representation using Double Colour Histograms for Content-Based Image Retrieval

Image representation is critical to the successful realisation of Content Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous, and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance.
SolType: refinement types for arithmetic overflow in solidity

As smart contracts gain adoption in financial transactions, it becomes increasingly important to ensure that they are free of bugs and security vulnerabilities. Of particular relevance in this context are arithmetic overflow bugs, as integers are often used to represent financial assets like account balances. Motivated by this observation, this paper presents SolType, a refinement type system for Solidity that can be used to prevent arithmetic over- and under-flows in smart contracts. SolType allows developers to add refinement type annotations and uses them to prove that arithmetic operations do not lead to over- and under-flows. SolType incorporates a rich vocabulary of refinement terms that allow expressing relationships between integer values and aggregate properties of complex data structures. Furthermore, our implementation, called Solid, incorporates a type inference engine and can automatically infer useful type annotations, including non-trivial contract invariants. To evaluate the usefulness of our type system, we use Solid to prove arithmetic safety of a total of 120 smart contracts. When used in its fully automated mode (i.e., using Solid's type inference capabilities), Solid is able to eliminate 86.3% of redundant runtime checks used to guard against overflows. We also compare Solid against a state-of-the-art arithmetic safety verifier called VeriSmart and show that Solid has a significantly lower false positive rate, while being significantly faster in terms of verification time.
An Approach for Variable Selection and Prediction Model for Estimating the Risk-Based Capital (RBC) Based on Machine Learning Algorithms

The risk-based capital (RBC) ratio, an insurance company’s financial soundness system, evaluates the capital adequacy needed to withstand unexpected losses. Therefore, continuous institutional improvement has been made to monitor the financial solvency of companies and protect consumers’ rights, and improvement of solvency systems has been researched. The primary purpose of this study is to find a set of important predictors to estimate the RBC ratio of life insurance companies in a large number of variables (1891), which includes crucial finance and management indices collected from all Korean insurers quarterly under regulation for transparent management information. This study employs a combination of Machine learning techniques: Random Forest algorithms and the Bayesian Regulatory Neural Network (BRNN). The combination of Random Forest algorithms and BRNN predicts the next period’s RBC ratio better than the conventional statistical method, which uses ordinary least-squares regression (OLS). As a result of the findings from Machine learning techniques, a set of important predictors is found within three categories: liabilities and expenses, other financial predictors, and predictors from business performance. The dataset of 23 companies with 1891 variables was used in this study from March 2008 to December 2018 with quarterly updates for each year.
A Blockchain Based Privacy-Preserving Incentive Mechanism for Internet of Vehicles in Satellite-Terrestrial Crowdsensing

Crowdsensing has been a popular technology recently and has broad application prospects. And Internet of Vehicles (IoV) refers to the effective utilization of static and dynamic information on the network platform through electronic equipment loaded on vehicles and the wireless technology, which means there are many devices generating huge amount of worthy data. However, preserving the privacy of workers and incentive them should be concerned. To this end, we apply the satellite-terrestrial system to solve the communication limitation among vehicles. We propose to use blockchain to act as the channel that requesters and vehicles communicate in. Smart contracts are the main designable parts to do the interactions among the participants. And blockchain is tightly related to payment so that we implement a total payment minimized auction algorithm on it. We carry out extensive simulations and run an experiment on blockchain to find the auction can be implemented as a smart contract and achieve privacy protection.
A rational quantum state sharing protocol with semi-off-line dealer

A rational quantum state sharing protocol with the semi-off-line dealer is proposed. Firstly, the dealer Alice shares an arbitrary two-particle entangled state with the players by Einstein–Podolsky–Rosen (EPR) pairs and Greenberger–Horne–Zeilinger (GHZ) states. The EPR pairs are prepared by Charlie instead of the dealer, reducing the workload of the dealer. Secondly, all players have the same probability of reconstructing the quantum state, guaranteeing the fairness of the protocol. In addition, the dealer is semi-off-line, which considerably reduces the information exchanging between the dealer and the players. Finally, our protocol achieves security, fairness, correctness, and strict Nash equilibrium.
An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes

Multiple Object Tracking (MOT) focuses on tracking all the objects in a video. Most MOT solutions follow a tracking-by-detection or a joint detection tracking paradigm to generate the object trajectories by exploiting the correlations between the detected objects in consecutive frames. However, according to our observations, considering only the correlations between the objects in the current frame and the objects in the previous frame will lead to an exponential information decay over time, thus resulting in a misidentification of the object, especially in scenes with dense crowds and occlusions. To address this problem, we propose an effectively finite-tailed updating (FTU) strategy to generate the appearance template of the object in the current frame by exploiting its local temporal context in videos. To be specific, we model the appearance template for the object in the current frame on the appearance templates of the objects in multiple earlier frames and dynamically combine them to obtain a more effective representation. Extensive experiments have been conducted, and the experimental results show that our tracker outperforms the state-of-the-art methods on MOT Challenge Benchmark. We have achieved 73.7% and 73.0% IDF1, and 46.1% and 45.0% MT on the MOT16 and MOT17 datasets, which are 0.9% and 0.7% IDFI higher, and 1.4% and 1.8% MT higher than FairMOT repsectively.
Current Problems of the Tertiary Education Modernization and Reform Practices

The sphere of postgraduate studies is the most governance reform-sensitive element of high school educational system in Russia.The article focuses on the governmental decisions taken to address difficulties in high school modernization sphere. The article posits that the mainstream modernization vector of educational system connected with the upgraded scientific and pedagogical personnel education is performed in accordance with transformational process set forth in Bologna declaration signed by the Russian Federation. The Covid-19 pandemic challenged the postgraduate reform results in Russia. The article provides a thorough and discourse analysis of scientific publications, expert interviews and normative documents. The study concludes that the set of fundamental approaches to further Russian system of postgraduate studies is currently semi-functional.Current reforms seek to overcome a destructive impact of the current postgraduate studies system.