Reviewerly/exHarmony huggingface dataset:
A new hierarchical multi group particle swarm optimization with different task allocations inspired by holonic multi agent systems

Nowadays expert systems have been used in different fields. They must be able to operate as quickly and efficiently as possible. So, they need optimization mechanism in their different parts and optimization is a critical part of almost all expert systems. Because of difficulties in real world problems, traditional optimization techniques commonly cannot solve them. Therefore, stochastic algorithms are used to do the optimization in expert systems. Particle swarm optimization (PSO) is one the most famous stochastic optimization algorithms. But this algorithm has some difficulties like losing diversity, premature convergence, trapping in local optimums and imbalance between exploration and exploitation. To overcome these drawbacks, inspired by holonic organization in multi agent systems, a new hierarchical multi group structure for PSO is presented in this paper. Considering the particles in PSO as simple agents, PSO is a kind of multi agent system. Existence of different facilities and organizations in multi agent systems and their great impact on performance encouraged us to use them. So, inspired by holonic multi agent systems, a new structure for PSO is presented. This work has been done for the first time in the literature. Meanwhile, to promote exploration and exploitation ability of proposed structure and create a suitable balance between them, different tasks are assigned to different groups of this structure. So, a holonic PSO with different task allocations (HPSO-DTA) is created. It provides the opportunity to employ all aspects for empowering PSO including parameter settings, neighborhood topologies and learning strategies to enhance the ability of it unlike other versions of PSO that use only one of these aspects to improve their solutions. This structure provides a lot of advantages for PSO. It is a new topological structure that improves the performance of PSO. It provides several leaders with efficient information to guide the particles in the search space. Also, it helps to control suitable information flow between groups and particles in order to preserve diversity and prevent from trapping in local optimums. Meanwhile, with assigning different tasks to different groups of proposed structure, an appropriate balance between exploration and exploitation is created to enhance the performance of the algorithm. In each group, based on its assigned task, particles use different parameters settings, different dynamic neighborhood topologies and different learning strategies which are proposed in this paper to enhance the performance of algorithm. A set of thirty four benchmark functions are used to evaluate the performance of proposed structure. Proposed algorithm is compared with a set of well-known PSO algorithms that their efficiency have been proved. Experimental results and comparative analysis demonstrate good performance of HPSO-DTA compared to other algorithms. Its solution accuracy, convergence speed and robustness is completely appropriate especially in more complicated benchmarks.
AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation

Accurate segmentation of pediatric echocardiography images is essential for a wide range of diagnostic and pre-interventional planning, but remains challenging (e.g., low signal to noise ratio and internal variability in heart appearance).To address these problems, in this paper, we propose a novel Cardiac Attention-guided Dual-path Network (i.e., AIDAN).AIDAN comprises a convolutional block attention module (CBAM) attached to a spatial (i.e., SPA) and context paths (i.e., CPA), which can guide the network and learn the most discriminative features.The spatial path captures low-level spatial features, and the context path is designed to exploit high-level context.Finally, features learned from the two paths are fused efficiently using a specially designed feature fusion module (FFM), and these are used to predict the final segmentation map.We experiment on a self-collected dataset of 127 pediatric echocardiography cases which are videos containing at least a complete cardiac cycle, and obtain a Dice coefficient of 0.951 and 0.914, in the left ventricle and atrium segments, respectively.AIDAN outperforms other state-of-the-art methods and has great potential for pediatric echocardiography images analysis.
Driving amount based stochastic configuration network for industrial process modeling

Stochastic configuration network (SCN) that randomly assigns the weights connecting the input layer and the hidden layer with an inequality constraint can achieve a fast learning speed for dealing with regression tasks. In this paper, a driving amount based SCN (DASCN) is proposed to improve the performance in terms of generalization and structure compactness, which have gained considerable attention in the industrial process. In the proposed DASCN, the driving amount incorporated into SCN is used to further improve the structural parameters, especially the output weights, of SCN. The performance of DASCN is evaluated by function approximation, four benchmark datasets and practical application in the industrial process. The simulation results indicate that the DASCN has better generalization capability and a more compact network structure compared to other methods.
Towards explainable personalized recommendations by learning from users’ photos

Explaining the output of a complex system, such as a Recommender System (RS), is becoming of utmost importance for both users and companies. In this paper we explore the idea that personalized explanations can be learned as recommendation themselves. There are plenty of online services where users can upload some photos, in addition to rating items. We assume that users take these photos to reinforce or justify their opinions about the items. For this reason we try to predict what photo a user would take of an item, because that image is the argument that can best convince her of the qualities of the item. In this sense, an RS can explain its results and, therefore, increase its reliability. Furthermore, once we have a model to predict attractive images for users, we can estimate their distribution. Thus, the companies acquire a vivid knowledge about the aspects that the clients highlight of their products. The paper includes a formal framework that estimates the authorship probability for a given pair (user, photo). To illustrate the proposal, we use data gathered from TripAdvisor containing the reviews (with photos) of restaurants in six cities of different sizes.
Hate Speech Detection on Twitter Using Multinomial Logistic Regression Classification Method

In today's social media, especially Twitter is very important for the success and destruction of one's image due to the many sentences of opinion that can compete the users. Examples of phrases that mean evil refer to hate speech to others. Evil perspectives can be categorized in hate speech, which hate speech is regulated in Article 28 of the ITE Law. Not a few people who intentionally and unintentionally oppose a social media that contains hate speech. Unfortunately social media does not have the ability to aggregate information about an existing conversation into a conclusion. One way to draw conclusion from aggregation results is to use text mining. In this paper to classify whether the text in the sentence contains elements of hate speech or not. The author hopes in this paper can make how to classify element of hate speech in text by computer, which later speech of the can be recognized. By using Multinomial Logistic Regression method. The author hopes after this application the computer can know and classify the existence of hate speech on a text from social media Twitter. From the results of tests that have been done the average precision of 80.02, recall 82%, and accuracy of 87.68%.
Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records

About 44.4 million people have been diagnosed with dementia worldwide, and it is estimated that this number will be almost tripled by 2050. Predicting mild cognitive impairment (MCI), an intermediate state between normal cognition and dementia and an important risk factor for the development of dementia is crucial in aging populations. MCI is formally determined by health professionals through a comprehensive cognitive evaluation, together with a clinical examination, medical history and often the input of an informant (an individual that know the patient very well). However, this is not routinely performed in primary care visits, and could result in a significant delay in diagnosis. In this study, we used deep learning and machine learning techniques to predict the progression from cognitively unimpaired to MCI and also to analyze the potential for patient clustering using routinely-collected electronic health records (EHRs). Our analysis of EHRs indicates that temporal characteristics of patient data incorporated in a deep learning model provides increased power in predicting MCI.
Improved ORB Algorithm Using Three-Patch Method and Local Gray Difference

This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a three-pixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms.
Words are important: A textual content based identity resolution scheme across multiple online social networks

Identity resolution of a person using various online social networks can enable an interested party to have a better and holistic understanding of former’s behavior and personality. Major challenges in developing a reliable and scalable matching scheme for online identities include non-availability of required information or having contradictory information for the same user across these networks. In this study, we present a scheme for identity matching which utilizes important features extracted from contents generated by or shared with users across one’s online social networks. With the help of natural language processing and text mining techniques, we extract and process parts-of-speech, symbols, emoticons, numbers, and high frequency words in user’s posts, tweets, retweets, and URLs. On the basis of experiments with ground truth Twitter–Facebook real datasets, this method achieved 91.2 percent accuracy in matching user’s identity across the user’s profiles. The main contribution of this paper is that this proposes a novel method for identity matching, which utilizes only the publicly available content information of online social network users. This method can be used alone for identity matching, or can be used along with other identity resolution frameworks to enhance their accuracy.
Deterministic joint remote state preparation of arbitrary two-qubit state through noisy cluster-GHZ channel

We have investigated the effect of different quantum noises on deterministic joint remote state preparation of arbitrary two-qubit systems, carried out through the collective action on a coupled cluster-GHZ state without introducing auxiliary qubit. The present scheme is deterministic, in comparison to the existing probabilistic schemes. The selection of measurement bases plays a crucial role in the success of our scheme. We have demonstrated the effect of six distinct noises on information loss, quantified through the fidelity measure. The loss of information is found to be minimum for amplitude damping channel and is maximum for the bit-flip channel.
The Forthcoming Data Ecosystem for Business Measurement and Assurance

ABSTRACT A data ecosystem is a combination of relevant data and the technologies used to integrate information feeding a particular process or a set of functionalities. With the advent of substantially improved computing and storage capabilities, sophisticated algorithms, and easier access to exogenous data, the data ecosystem is facilitating increasingly automated and continuous business measurement and assurance processes that reflect the rhythm and nature of business events and data sources. We propose that the big data used in business measurement and assurance must first be taxonomized and integrated, and then be processed by piggybacked applications to complement human activities to more effectively achieve operational and economic goals. We argue that the most notable technologies for the current wave of change are Robotic/Intelligent Process Automation, Machine Learning, Blockchain, and Continuous Measurement and Assurance applications. Furthermore, we discuss the main effects of the data ecosystem on the measurement and assurance of business activities.
Approximation bounds for random neural networks and reservoir systems

This work studies approximation based on single-hidden-layer feedforward and recurrent neural networks with randomly generated internal weights. These methods, in which only the last layer of weights and a few hyperparameters are optimized, have been successfully applied in a wide range of static and dynamic learning problems. Despite the popularity of this approach in empirical tasks, important theoretical questions regarding the relation between the unknown function, the weight distribution, and the approximation rate have remained open. In this work it is proved that, as long as the unknown function, functional, or dynamical system is sufficiently regular, it is possible to draw the internal weights of the random (recurrent) neural network from a generic distribution (not depending on the unknown object) and quantify the error in terms of the number of neurons and the hyperparameters. In particular, this proves that echo state networks with randomly generated weights are capable of approximating a wide class of dynamical systems arbitrarily well and thus provides the first mathematical explanation for their empirically observed success at learning dynamical systems.
LISA: Language-Independent Method for Aspect-Based Sentiment Analysis

Understanding ''what others think'' is one of the most eminent pieces of knowledge in the decision-making process required in a wide spectrum of applications.The procedure of obtaining knowledge from each aspect (property) of users' opinions is called aspect-based sentiment analysis which consists of three core sub-tasks: aspect extraction, aspect and opinion-words separation, and aspect-level polarity classification.Most successful approaches proposed in this area require a set of primary training or extensive linguistic resources, which makes them relatively costly and time consuming in different languages.To overcome the aforementioned challenges, we propose an unsupervised paradigm for aspect-based sentiment analysis, which is not only simple to use in different languages, but also holistically performs the subtasks for aspect-based sentiment analysis.Our methodology relies on three coarse-grained phases which are partitioned to manifold fine-grained operations.The first phase extracts the prior domain knowledge from dataset through selecting the preliminary polarity lexicon and aspect word sets, as representative of aspects.These two resources, as primitive knowledge, are assigned to an expectation-maximization algorithm to identify the probability of any word based on the aspect and sentiment.To determine the polarity of any aspect in the final phase, the document is firstly broken down to its constituting aspects and the probability of each aspect/polarity based on the document is calculated.To evaluate this method, two datasets in the English and Persian languages are used and the results are compared with various baselines.The experimental results show that the proposed method outperforms the baselines in terms of aspect, opinion-word extraction and aspect-level polarity classification.
VPAMS: Verifiable and practical attribute-based multi-keyword search over encrypted cloud data

With fast development of cloud storage, people increasingly outsource their files to the cloud server. To protect the confidentiality of outsourced files, files often are encrypted before outsourced. However, the accurate location and search on encrypted files will be greatly limited. This paper mainly considers how to realize an accurate multi-keyword fine-grained search on the encrypted files. Firstly, we use multiple keywords and an improved k-nearest neighbor (k-NN) technology to improve the search accuracy. Secondly, our scheme can protect the privacy of the file keyword indexes and the search queries and further preserve the privacy of outsourced files and users. Thirdly, unlike other existing multi-keyword search schemes without considering the verification and decryption of search results, the data users in our scheme not only can check whether the returned files contain multiple queried keywords, but also can realize the authorized decryption of the search files by using attribute-based encryption. Finally, performance evaluations show the time costs of our search algorithm is a constant value and independent of the number of user attributes. Therefore, our scheme is more efficient and practical.
Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems

More and more software practitioners are tackling towards industrial applications of artificial intelligence (AI) systems, especially those based on machine learning (ML). However, many of existing principles and approaches to traditional software systems do not work effectively for the system behavior obtained by training not by logical design. In addition, unique kinds of requirements are emerging such as fairness and explainability. To provide clear guidance to understand and tackle these difficulties, we present an analysis on what quality concepts we should evaluate for AI systems. We base our discussion on ISO/IEC 25000 series, known as SQuaRE, and identify how it should be adapted for the unique nature of ML and Ethics guidelines for trustworthy AI from European Commission. We thus provide holistic insights for quality of AI systems by incorporating the ML nature and AI ethics to the traditional software quality concepts.
A Performance Review of Collision-Free Path Planning Algorithms

Path planning for mobile agents is one of the areas that has drawn the attention of researchers’, as evidenced in the large number of papers related to the collision-free path planning (CFPP) algorithm. The purpose of this paper is to review the findings of those CFPP papers and the methodologies used to generate possible solutions for CFPP for mobile agents. This survey shows that the previous CFPP papers can be divided based on four characteristics. The performance of each method primarily used to solve CFPP in previous research is evaluated and compared. Several methods are implemented and tested in same computing environment to compare the performance of generating solution in specified spatial environment with different obstacles or size. The strengths and weakness of each methodology for CFPP are shown through this survey. Ideally, this paper will provide reference for new future research.
Price discovery and microstructure in ether spot and derivative markets

Ethereum is an important blockchain, being the first and most popular public platform for the smart contracts underpinning financial transactions, time-stamping of supply chains, decentralized applications and initial coin offerings. Ethereum's cryptocurrency, ether, is actively traded on centralized exchanges, second only to bitcoin. It has attracted investor's interest primarily because of its intrinsic value – small units of ether called ‘gas’ are used, essentially, as the fuel driving smart contract transactions on the Ethereum blockchain. We ask whether off-chain trading on ether derivatives plays a dominant role in ether spot price discovery, thereby driving ether's utility value for on-chain activity. Using minute-by-minute data we find that the ether perpetual swap on BitMEX, an unregulated cryptocurrency derivative exchange, has dominant trading volume and price discovery over the major spot exchanges. Furthermore, we identify interesting hour-of-day and day-of-week effects in trading volume on the spot exchanges, and these indicate that more informed institutional players are trading ether spot and derivatives.
Deep‐Q learning‐based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud

Summary The complex and large‐scale scientific workflow applications are effectively executes on the cloud. The performance of cloud computing highly depends on the task scheduling. Optimal workflow scheduling is still a challenge that needs to be addressed due to the conflicting objectives and increasing demand for quality of service. Task scheduling is an NP‐hard problem due to its complexity. The newly introduced methods for resolving the problem of task scheduling are facing challenges to take the benefits of all aspects of cloud computing. In this article, we study the joint optimization of cost and makespan of scheduling workflows in infrastructure as a service clouds and propose a new workflow scheduling scheme using deep learning. In this scheme, a deep‐Q learning‐based heterogeneous earliest‐finish‐time (DQ‐HEFT) algorithm is developed, which closely integrates the deep learning mechanism with the task scheduling heuristic HEFT. The workflowsim simulator is used for the experiment of the real‐world and synthetic workflows. The experiment results demonstrate the efficiency of our proposed approach compared with existing algorithms. This technique can achieve significantly better makespan and speed metrics with a remarkably higher volume of data and can run faster compared with the existing workflow scheduling algorithms in cloud computing environment.