Reviewerly/exHarmony huggingface dataset:
Monotonic Constrained Clustering: A First Approach

Clustering has been proven to produce better results when applied to learning problems that fall under semi-supervised paradigms, where only incomplete or partial information about the dataset is available to perform clustering. Classic constrained clustering and recent monotonic clustering problems belong to the semi-supervised learning paradigm, although a combination of both has never been addressed. This study aims to prove that the fusion of the background knowledge leveraged by the two aforementioned semi-supervised clustering techniques results in improved performance. To do so, a hybrid objective function combining them is proposed and optimized by means of an expectation minimization scheme. The capabilities of the proposed method are tested in a wide variety of datasets with incremental levels of background knowledge and compared to purely monotonic clustering and purely constrained clustering methods belonging to the state-of-the-art. Bayesian statistical testing is used to validate the obtained results.
Parallel High Utility Itemset Mining

Association rule mining is a popular data mining task for finding relationships between values from the itemsets that co-occur frequently in a transactional database. Association rule mining has many applications but the "support-confidence" framework it depends on is inadequate for many cases. In recent years, a generalised task called high utility itemset mining (HUIM) has gained much popularity; it aims at discovering itemsets that yield a high revenue as measured by a utility function. However, when facing large data volumes, the running time of state-of-the-art HUIM algorithms often grows exponentially. In this work, we investigate parallel HUIM algorithms (PHUIM) and adapt two state-of-the-art sequential HUIM algorithms for parallel processing based on the Apache Spark in-memory data processing platform. Extensive experiments on several benchmark and synthetic datasets show that the proposed methods improve considerably the efficiency of the baseline HUIM algorithms.
Application of a Limit Theorem to the Construction of Japanese Crossword Puzzles

The generation of crossword puzzles is known to be NP-Complete. Optimization over structural characteristics is known to extend this into the NP-Hard regime. This paper discusses the application of a limit theorem to assist in optimizing the NP-Hard aspects of crossword puzzle generation of Japanese lexicons in particular. It is shown that the similarity of artificially enumerated lexicons to Japanese kana-based lexicons is greater than to English language lexicons. This greater similarity is exploited in the derivation of expressions for both the expected value of the crossword and the use of the central limit theorem to determine an empirical estimate on the upper limit of the associated optimization problem. Initial empirical outcomes attest to the expected efficacy of the central limit theorem application to the final expressions.
SAKE: A Graph-Based Keyphrase Extraction Method Using Self-attention

Keyphrase extraction is a text analysis technique that automatically extracts the most used and most important words and expressions from a text. It helps summarize the content of texts and recognize the main topics discussed. The majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher-order statistical methods. Supervised keyphrase extraction requires a large amount of labeled training data and has poor generalization ability outside the training data domain. Unsupervised systems have poor accuracy, and often do not generalize well. This paper proposes an unsupervised graph-based keyphrase extraction model that incorporates the words' self-attention score. Specifically, the proposed approach identifies the importance of each source word based on a word graph built by the self-attention layer in the Transformer and further introduces a new mechanism to capture the relationships between words in different sentences. The experimental results show that the proposed approach achieves remarkable improvements over the state-of-the-art models.
Serially Disentangled Learning for Multi-Layered Neural Networks

The present paper aims to propose a new type of learning method called "serially disentangled learning." This method is inspired by representation learning, but our focus is not on the production of the disentangled representation but on the computational procedures themselves to make it possible to produce the representation. We try to separate or disentangle several contradictory computational procedures and to force them to perform their tasks serially and independently, meaning that we try to resolve, though seemingly, the contradiction among computational procedures. This concept can be used to disentangle complex information control procedures, where information minimization and maximization procedures are forced to perform each procedure separately, independently, and serially. The method was applied to relations between the mission statements of companies and their profitability, where conventional learning methods could not show better generalization performance. The new method with de-hierarchical disentanglement by network compression produced compressed weights close to the point-biserial correlation coefficients between inputs and targets of the original data set. In addition, we could detect an input related to improved generalization.
A Comparison of Resource Data Framework and Inductive Logic Programing for Ontology Development

This study compares the expressive power of Resource Data Framework (RDF) and Inductive Logic Programing (ILP). While RDF and RDF Schema do not possess any rule language, ILP is a logic programing language that is fit for inferring facts. The research aims to identify and acknowledge the differences between RDF and ILP in terms of how much expressive power they hold within themselves and how efficient they infer knowledge when employed in ontologies. The paper represents ongoing work to compare RDF and ILP.
Detecting Use Case Scenarios in Requirements Artifacts: A Deep Learning Approach

Use case scenarios (UCS) written in natural languages like English are popular tools for requirements elicitation. They also play an important role in the model driven design process by being used as an initial input for many automated behavior modeling such as generating sequence diagrams and class diagrams. However, there is no unified approach used by the engineers to represent use cases and make it hard to identify use case statements from requirements artifacts without doing it manually by human experts, which is time consuming and tedious. In this paper, we propose a novel approach for automatically identifying use case scenarios within requirements documents written in English. We employ a machine learning based approach which is applicable to a wide range of specifications in various domains. We train and evaluate different state-of-the-art machine learning models including transformers over an independently labeled dataset to compare the performance of different models and select the best classifier based on the result. Our experimental result shows that ULMFiT outperforms other models with an accuracy of 95%. Moreover, we disclose our dataset as well as the source code to foster the discourse on the automatic use case detection within the research community.
Benchmarking Training Methodologies for Dense Neural Networks

Multi-Layer Perceptrons (MLP) trained using Back Propagation (BP) and Extreme Learning Machine (ELM) methodologies on highly non-linear, two-dimensional functions are compared and benchmarked. To ensure validity, identical numbers of trainable parameters were used for each approach. BP training combined with an MLP structure used many hidden layers, while ELM training can only be used on the Single Layer, Feed Forward (SLFF) neural network topology. For the same number of trainable parameters, ELM training was more efficient, using less time to train the network, while also being more effective in terms of the final value of the loss function.
Multi-class Sentiment Classification for Customers’ Reviews

The rise of e-commerce due to the Covid-19 situation is becoming more significant in 2021. It could lead to great demands to understand customers' opinions usually shown in their reviews. An e-commerce platform with the ability to be aware of its users' viewpoint can have a higher possibility of meeting customer expectations, attracting new users, and increasing sales. With the tremendous data in e-commerce platforms presently, sentiment analysis is a powerful tool to understand users. However, the sentiment in reviews data may contain more than two states, positive and negative, and then a binary sentiment classifier may not be helpful in practice. According to our knowledge, research on this subject is often restricted access. Therefore, this paper presents a multi-class sentiment analysis for Vietnamese reviews on a large-scale dataset, including 480,702 reviews. We collected these reviews from popular Vietnamese e-commerce websites and manually did the labeling process with three classes of sentiments (positive, negative, and neutral). To build a suitable classification model for the main problem, we propose a deep learning approach using different architectures (LSMT, GRU, TextCNN, LSTM + CNN, and GRU+CNN) and compare the performance among other ensemble techniques. The experimental results show the outperformance of the ensemble techniques on the multi-class sentiment classification problem, and the combination of chosen architectures using the attention mechanism could obtain the best F-1 score of $$73.64\%$$ .
An Optimization Algorithm for Extractive Multi-document Summarization Based on Association of Sentences

Designing of automatic summary extraction technology becomes more and more important as the number of documents increasing rapidly. At present, the indicators that are often used for summary evaluation including the coverage, redundancy, and relevance. However, the summary cannot be completely extracted using only these three evaluation indicators. Therefore, with the concept of centrality, we design an association-based centrality criterion firstly, which can be used to evaluate associations of sentences to reinterpret the centrality of summary. Based on the three commonly used and the designed centrality factors, an optimization algorithm is then proposed for obtaining the summary from the given multiple documents. Experiments were also made on the dataset from the Document Understanding Conference 2002 (DUC2002) to show the effectiveness of the proposed algorithm in terms of ROUGE scores.
Enhancing a Multi-population Optimisation Approach with a Dynamic Transformation Scheme

The adaptive multi-population optimisation (AMPO) algorithm is an intelligent meta-heuristic search method utilising multiple search groups to conduct a diversity of search strategies in evolutionary algorithms or swarm intelligence. With the careful design of different search operators, the AMPO algorithm has achieved outstanding performance in many optimisation problems including two sets of benchmark functions when compared to some latest approaches including the hybrid firefly and particle swarm optimisation for continuous optimisation. Yet there are still opportunities to enhance the adaptability of its search mechanism in various aspects. Therefore, a more adaptive AMPO (AMPO $$^{+}$$ ) algorithm is considered in this work in which the probability of the transformation between specific search groups can be more flexibly adjusted during the different stages of the search process. In this way, the AMPO $$^{+}$$ can better adapt its search efforts to specific search groups through revising its search strategies so as to effectively solve many challenging optimisation problems. To carefully examine the search effectiveness of the enhanced framework, the proposed AMPO $$^{+}$$ algorithm is evaluated against the original AMPO and other sophisticated meta-heuristic algorithms on a set of well-known benchmark functions of different dimensions in which impressive results are attained by the AMPO $$^{+}$$ . More importantly, the proposed adaptive search framework sheds light on many possible directions for further investigation.
Multiple targets tracking by using improved labeled multi-target multi-Bernoulli filter with FDA-MIMO radar

In the traditional multi-target multi-Bernoulli (MeMBer) filter, only the radar signal after constant false alarm rate (CFAR) is considered. In addition, it is also necessary to assume that the detection probability of the target and the covariance matrix of the measurement noise are known. In fact, the detection probability of the target and the covariance matrix of the noise are difficult to determine. Therefore, this paper proposes an improved labeled multi-target multi-Bernoulli (IL-MeMBer) filter that can avoid considering the detection probability of the target and the covariance matrix of the measurement noise. Firstly, the multi-target superposition measurement model of FDA-MIMO is given in the presence of false targets. Secondly, the target likelihood function under the superposition measurement model is derived by analyzing the characteristics of the traditional point measurement likelihood function and the Music spatial spectrum. Thirdly, considering the unknown and time-varying number of targets, the original echo signals without detection probability before (CFAR) processing, and no target tracks output by the traditional MeMBer filter, we put forward an analytical solution of the IL-MeMBer filter. Finally, a fusion strategy based on Euclidean distance is proposed to solve the problem that the same target can be estimated to have multiple tracks in the IL-MeMBer filter. In addition, the sequential Monte Carlo (SMC) implementation of the IL-MeMBer filter is provided. Numerical experiences verify the effectiveness and superiority of the proposed methods.
Intuitionistic propositional probability logic

Abstract We give a sound and complete axiomatization of a probabilistic extension of intuitionistic logic. Reasoning with probability operators is also intuitionistic (in contradistinction to other works on this topic), i.e., measure functions used for modeling probability operators are partial functions. Finally, we present a decision procedure for our logic, which is a combination of linear programming and an intuitionistic tableaux method.
Optimization assisted convolutional neural network for detection of thyroid

Summary This research work endeavor to suggest a new thyroid detection system that involves three main processes like segmentation, feature extraction, and detection. Initially, for the segmentation process, an improved watershed algorithm is proposed. Besides, from the segmented image, it extracted the texture features that comprises local binary patterns, modified GLCM feature, local tetra pattern features, together with the statistical features. Primarily, the extracted features are given as an input to convolutional neural network (CNN) for the final detection result. For making the detection more accurate, the training of deep CNN is performed via a new deer encircling included gray wolf optimization model for optimal weights tuning. The adopted hybrid algorithm combines gray wolf optimization and deer hunting optimization algorithm. At last, the outcomes of the adopted scheme is validated with the extant approaches under various metrics.
A cost‐aware approach for cloud federation formation

Abstract The number of computing resources required for high‐volume applications is ever‐increasing. As far as the users' requests generate workload variations over time and the resources of cloud servers that respond to the requests are limited, the quality of services (QoS) of the cloud server is experiencing fluctuations. The cloud federation is a place where the cloud service providers work together to overcome the problem of resource scarcity and poor service quality. As a result, the main aim of forming a cloud federation is to create an optimal one in a way that provides the required resources and QoS for customers and maximum profit for the providers. Since the service providers play a key role in cloud federation formation, and their absence deteriorates the QoS of the federation, it is required to form an optimal cloud federation that provides the best profit for service providers to encourage them to remain in the federation. In the present paper, first, a model will be introduced for evaluating the cloud service providers, inspired by the concepts of macroeconomics. Then a greedy algorithm will be presented for forming an optimal federation. The proposed algorithm uses the evaluation results to rank the service providers and selects the best federation based on this ranking. In addition, the reputation of providers is evaluated based on the proposed method, which is a criterion for introducing the best providers. Furthermore, the surplus shared resources of the providers will be determined using the evaluation results and its optimal value will be proposed to cloud service providers using the proposed method. Finally, a set of tests will be performed on the introduced system.
A Consensus Algorithm Based on Risk Assessment Model for Permissioned Blockchain

Blockchain is characterized by privacy, traceability, and security features as a novel framework of distributed ledger technologies. Blockchain technology enables stakeholders to conduct trusted data sharing and exchange without a trusted centralized institution. These features make blockchain applications attractive to enhance trustworthiness in very different contexts. Due to unique design concepts and outstanding performance, blockchain has become a popular research topic in industry and academia in recent years. Every participant is anonymous in a permissionless blockchain represented by cryptocurrency applications such as Bitcoin. In this situation, some special incentive mechanisms are applied to the permissionless blockchain, such as “mined” native cryptocurrency to solve the trust issues of the permissionless blockchain. In many use cases, permissionless blockchain has bottlenecks in transaction throughput performance, which restricts further application in the real world. A permissioned blockchain can reach a consensus among a group of entities that do not establish an entire trust relationship. Unlike permissionless blockchains, the participants must be identified in permissioned blockchains. By relying on the traditional crash fault-tolerant consensus protocols, permissioned blockchains can achieve high transaction throughput and low latency without sacrificing security. However, how to balance the security and consensus efficiency is still the issue that needs to be solved urgently in permissioned blockchains. As the core module of blockchain technology, the consensus algorithm plays a vital role in the performance of the blockchain system. Thus, this paper proposes a new consensus algorithm for permissioned blockchain, the Risk Assessment-based Consensus (RAC) protocol, combined with the decentralized design concept and the risk-node assessment mechanism to address the unbalance issues of performance in speed, scalability, and security.
Populism and the Virtues of Argument

This chapter argues that a virtue-theoretic account of argumentation can enhance our understanding of the phenomenon of populism and offer some lines of response. Virtue theories of argumentation emphasize the role of arguers in the conduct and evaluation of arguments and lay particular stress on arguers’ acquired dispositions of character, otherwise known as intellectual virtues and vices. One variety of argumentation of particular relevance to democratic decision-making is group deliberation. There are both theoretical and empirical reasons for maintaining that intellectual humility is pivotal to the practice of group deliberation. Several factors to which the rise of populism has been attributed may be understood as failures of the virtues of argumentation in general, and intellectual humility in particular.
On Wilderness, Carnivals, and Foolishness

Abstract Let’s start our pilgrimage by following one of the most significant signposts of all: the workplace should promote absurdity. Signpost 2 came out of the idea that our capability to be absurd gives us a competitive edge over logic-based algorithms in the future of work. Because of this, absurdity should be nurtured at the places where we work (Signpost 3).
66 loops

Abstract It took me 66 loops in the stormwater drain trail over the same number of days to complete the 774 km of El Camino online. A significantly slower pace when compared to the 905 km in 42 days of my walk to Sydney. However, this time around I didn’t have to put my life on hold and I squeezed the pilgrimage in alongside my daily life and unescapable household chores.
BH2I-GAN: Bidirectional Hash_code-to-Image Translation using Multi-Generative Multi-Adversarial Nets

Given the benefits of high retrieval efficiency and low storage cost, hashing method has received an increasing attention. In particular, deep learning-based hashing has been widely used in data mining and information retrieval. However, almost all the existing methods only achieve the goal of high retrieval precision, and limit the evaluation of hashing methods to objective aspect. In this paper, we propose a novel bidirectional hash_code-to-image translation model by using multi-generative multi-adversarial nets to reduce storage cost truly and obtain satisfactory user acceptance on the basis of high retrieval precision. Firstly, we propose supervised manifold metric to reduce Hamming distance between similar instances while increasing the Hamming distance between dissimilar instances, which have been proved to be helpful for high retrieval precision and good user acceptance. Then, we utilize multi-generative and multi-adversarial networks to construct hash mapping and inverse hash generation. During inverse generation, theoretical analysis is conducted to show that inverse hash network can avoid unstable training and mode collapse. Besides, we prove that Poisson distribution induced by hash codes can be initialized as generative distribution to fit real distribution. Experimental results show that our method outperforms several state-of-the-art approaches on three popular datasets. Specifically, ours yields average about 9.3% increment in Mean Average Precision(MAP) on three datasets, and achieves over 90% user satisfaction. Besides, it successfully reduces storage cost by 1,634 times in COCO 2017 large-scale dataset.
Jewellery Tryon using AR

As the world becomes more digital, so Now-a-days individual prefer online shopping rather than going to shop and buying it.When the pandemic had striked, people were more likely to shop online since it reduces their exposure to the outside world.Hence in case of jewellery shopping online from website is challenging because as we don't have a proper idea about exact finite design of jewels as it displays the 2D view.Jewellery are the important part of the Indian culture.Hand jewellery specially plays very vital role in day to day lifestyle for women's.Therefore, to resolve this issue we have proposed our system, "Jewellery Try on Using AR," which will provide customers an idea of how that jewellery will look on them.Our system focuses on enhancing user experience by providing hand jewellery Tryon.This system uses Augmented Reality and Python Media Pipe to recognize the user's hand and augment jewellery on the detected hand in real time.