Reviewerly/exHarmony huggingface dataset:
LGViT: A Local and Global Vision Transformer with Dynamic Contextual Position Bias Using Overlapping Windows

Vision Transformers (ViTs) have shown their superiority in various visual tasks for the capability of self-attention mechanisms to model long-range dependencies. Some recent works try to reduce the high cost of vision transformers by limiting the self-attention module in a local window. As a price, the adopted window-based self-attention also reduces the ability to capture the long-range dependencies compared with the original self-attention in transformers. In this paper, we propose a Local and Global Vision Transformer (LGViT) that incorporates overlapping windows and multi-scale dilated pooling to robust the self-attention locally and globally. Our proposed self-attention mechanism is composed of a local self-attention module (LSA) and a global self-attention module (GSA), which are performed on overlapping windows partitioned from the input image. In LSA, the key and value sets are expanded by the surroundings of windows to increase the receptive field. For GSA, the key and value sets are expanded by multi-scale dilated pooling to promote global interactions. Moreover, a dynamic contextual positional encoding module is exploited to add positional information more efficiently and flexibly. We conduct extensive experiments on various visual tasks and the experimental results strongly demonstrate the outperformance of our proposed LGViT to state-of-the-art approaches.
FCHM-stream: fast closed high utility itemsets mining over data streams

The high-speed, continuous and endless characteristics of data streams make it a challenging task to quickly mine high utility itemsets in limited memory space. The sliding window model, which focuses only on the most recent data, has received extensive research and attention as it can effectively adapt to the data stream environment. However, the presence of many communal batches in adjacent sliding windows causes the algorithm to repeatedly generate a large number of identical itemsets, which reduces the spatiotemporal performance of the algorithm. In order to solve these problems and provide users with a concise and lossless resultset, a new closed high utility pattern mining algorithm over data stream is proposed, named FCHM-Stream. A new utility list structure based on batch division and a resultset maintenance strategy based on skip-list structure are designed to effectively reduce identical itemsets repeatedly generated and thus reduce the running time of the algorithm. Extensive experimental results show that the proposed algorithm has a large improvement in runtime compared to the state-of-the-art algorithms.
Performance Analysis of the Thermal Automatic Tracking Method Based on the Model of the UAV Dynamic Model in a Thermal and Cubature Kalman Filter

Fixed-wing, solar-powered unmanned aerial vehicles (SUAVs) can use thermals to expand the duration of flight. Nevertheless, due to the demand for calculating the thermal state parameters of the SUAV during flight, the existing methods still have some shortcomings in their practical applications, such as an inaccurate location estimation of the thermal and an insufficient seeking efficiency. In this paper, by integrating the Gaussian distribution model of thermal updraft of the pitching and roll moment of SUAVs, it is demonstrated that the approach introduced is superior to the traditional methods, disregarding the pitching moment. The simulation indicated that the accuracy and convergence speed of the thermal state estimation, performed while employing the cubature Kalman filter (CKF), are significantly improved after the SUAVs pitching moment is considered. The proposed method improves the automaticity and intelligence of SUAVs autonomous thermal search and enhances the cognitive and decision-making capabilities of SUAVs.
An Adaptive Multipath Linear Interpolation Method for Sample Optimization

When using machine learning methods to make predictions, the problem of small sample sizes or highly noisy observation samples is common. Current mainstream sample expansion methods cannot handle the data noise problem well. We propose a multipath sample expansion method (AMLI) based on the idea of linear interpolation, which mainly solves the problem of insufficient prediction sample size or large error between the observed sample and the actual distribution. The rationale of the AMLI method is to divide the original feature space into several subspaces with equal samples, randomly extract a sample from each subspace as a class, and then perform linear interpolation on the samples in the same class (i.e., K-path linear interpolation). After the AMLI processing, valid samples are greatly expanded, the sample structure is adjusted, and the average noise of the samples is reduced so that the prediction effect of the machine learning model is improved. The hyperparameters of this method have an intuitive explanation and usually require little calibration. We compared the proposed method with a variety of machine learning prediction methods and demonstrated that the AMLI method can significantly improve the prediction result. We also propose an AMLI plus method based on the linear interpolation between classes by combining the idea of AMLI with the clustering method and present theoretical proofs of the effectiveness of the AMLI and AMLI plus methods.
Transferring CNN Features Maps to Ensembles of Explainable Neural Networks

The explainability of connectionist models is nowadays an ongoing research issue. Before the advent of deep learning, propositional rules were generated from Multi Layer Perceptrons (MLPs) to explain how they classify data. This type of explanation technique is much less prevalent with ensembles of MLPs and deep models, such as Convolutional Neural Networks (CNNs). Our main contribution is the transfer of CNN feature maps to ensembles of DIMLP networks, which are translatable into propositional rules. We carried out three series of experiments; in the first, we applied DIMLP ensembles to a Covid dataset related to diagnosis from symptoms to show that the generated propositional rules provided intuitive explanations of DIMLP classifications. Then, our purpose was to compare rule extraction from DIMLP ensembles to other techniques using cross-validation. On four classification problems with over 10,000 samples, the rules we extracted provided the highest average predictive accuracy and fidelity. Finally, for the melanoma diagnostic problem, the average predictive accuracy of CNNs was 84.5% and the average fidelity of the top-level generated rules was 95.5%. The propositional rules generated from the CNNs were mapped at the input layer by squares in which the relevant data for the classifications resided. These squares represented regions of attention determining the final classification, with the rules providing logical reasoning.
Spatial search algorithms on graphs with multiple targets using discrete-time quantum walk

Search algorithms based on discrete-time quantum walk (QW) can be considered as alterations of the standard QW: Use a different coin operator that distinguishes target and nontarget vertices, or, mark the target vertices first followed by the standard QW. Two most frequently used marking coins are [Formula: see text] and [Formula: see text] the negative identity operator and the negative Grover diffusion operator. We show that search algorithms corresponding to these four combinations can be reduced to two, denoted as [Formula: see text] and [Formula: see text], and they are equivalent when searching for nonadjacent multiple targets. For adjacent target vertices, numerical simulations show that the performance of the algorithm [Formula: see text] highly depends on the density of the underlying graph, and it outperforms [Formula: see text] when the density is large enough. At last, a generalized stationary state of both search algorithms on the graphs with even-numbered degree is provided.
Modified GAN with Proposed Feature Set for Text-to-Image Synthesis

Automated synthesis of practical images from the text could be useful and interesting; however, present AI systems are yet far from this objective. Nevertheless, in current years, powerful and generic Recurrent Neural Network (RNN) structures were introduced to train discriminative text feature representation. In the meantime, Deep Convolutional GANs have started producing highly convincing images of specified categories, like room interiors, album covers, and faces. In this research work, we plan to develop a new model for text-to-image synthesis, which contains three important phases: (i) feature extraction, (ii) text encoding, and (iii) optimal image synthesis. Initially, the text features such as improved TF–IDF, bag of words, and N-gram are extracted from the text and they are trained by Bi-LSTM. During the encoding of an image from text, cross-modal feature grouping is performed. Further, the image is synthesized using modified GAN (MGAN) with a new loss function. Here, for precise synthesis of images, the weights of GAN are optimized using Self-improved Social Ski-Driver (SI-SSD) optimization algorithm. Eventually, the superiority of the suggested model is examined via an assessment over existing schemes.
Multitarget Search Algorithm Using Swarm Robots in an Unknown 3D Mountain Environment

A multitarget search algorithm for swarm robot in an unknown 3D mountain environment is proposed. Most existing 3D environment obstacle avoidance algorithms are potential field methods, which need to consider the location information of all obstacles around the robot, and they easily fall into local optima, and their calculation is complex. Furthermore, they cannot well meet the requirements of real-time obstacle avoidance characteristics of swarm robots in multiobject searches. This paper first focuses on solving the obstacle avoidance problem of swarm robots in mountain environments. A new 3D curved obstacle tracking algorithm (3D-COTA) is designed by discretizing the mountains within the detection range of robot obstacles. Then, a task assignment model and virtual force model in 2D space are extended to 3D, and a particle swarm search model with kinematic constraints is constructed, which considers the kinematic constraints and the limitations of the communication ability of the robots. Finally, a new multitarget search algorithm for swarm robot in an unknown 3D mountain environment is proposed by means of the designed 3D surface obstacle tracking algorithm. Numerical simulation results demonstrate the effectiveness of the proposed algorithm.
Identical damped harmonic oscillators described by coherent states

Some aspects of quantum damped harmonic oscillator (DHO) obeying a Markovian master equation are considered in the absence of thermal noise. The continuity equation is derived and Bohmian trajectories are constructed. As a solution of the master equation, we take a single coherent state and compute analytically the relative entropy of coherence, $C_r$, in the energy, position and momentum bases. Although $C_r$ is constant in both the position and the momentum bases, it is a decreasing function of time in the energy basis becoming zero at long times, revealing its role as the preferred basis. Then, quantum coherence is computed for a superposition of two coherent states, a cat state, and also a superposition of two cat states in the energy basis as a function of separation, in the complex plane, between the two superposed states. It is seen that the quantum coherence increases with this separation. Furthermore, quantum coherence of superposition is compared to that of decomposed states in the superposition. Finally, considering a system of two non-interacting DHOs, the effect of quantum statistics is studied on the coherence of reduced single-particle states, the joint detection probability and the mean square separation of particles. Our computations show that the single-particle coherence for antisymmetric states is always less than that of symmetric ones. Furthermore, boson anti-bunching and fermion bunching is seen in this open system. This behavior of bosons is the matter-wave analogue of photon anti-bunching seen in a modified Hanbury Brown-Twiss (HBT) interferometer.
Words for Sale: Linguistic Complexity Associates with Higher Housing Prices in Online Realty Advertisements

Prior work suggests when instrumental goals are salient, linguistic complexity associates with positive behavioral outcomes compared to linguistic simplicity. The current work tested this idea using descriptions from over 1,000 online realty advertisements to associate with housing prices. The evidence suggested linguistic complexity (e.g., fewer common words, more analytic writing, and less readable writing) indeed associated with higher housing prices. These data explicated the contingent-on-effort hypothesis: linguistic complexity is favored when people value effort in a particular setting.
Presentations and Reports

This chapter offers advice on technical writing, and the inclusion of tables and graphs in reports. The purpose of a table or graph is to advance the narrative by drawing attention to the most important patterns or features in the data such as the nature and direction of various effects. A graph is helpful for presentation only if it illustrates an important effect clearly. A graph of residuals may be helpful for model checking, and may be mentioned in presentation, but, unless it is explicitly requested in a homework exercise, it is seldom included as part of the report. In most factorial designs, whether balanced or otherwise, one-way and two-way tables of averages are often useful as a partial summary of conclusions. The relevance of the graph to the conclusions must be spelled out in the accompanying narrative. Computer output: (i) How to adapt computer output for inclusion in the text; (ii) From the computer-generated analysis-of variance table, list only the parts that are relevant to your analysis. It is your job as author, statistician and expert to judge what is relevant and what is not. (iii) The number of decimal digits for regression coefficients, standard errors, F-ratios, p-values; (iv) Labelling of parameters and factor levels in tables; (v) Every parameter has a physical interpretation. Do not pass up the opportunity to remind the reader what the physical interpretation of the logistic coefficient $$\hat \beta = -0.684$$ is in the context of the problem. It means that the odds of disease in the treatment group are one half the odds in the control group. Grammar and word usage: substitute versus replace, use versus usage; verb tense; verbs for statistical and computational activities; Technical matters: (i) Response transformation must always be considered; (ii) Experimental units versus observational units; it is the number of experimental units that governs the degrees of freedom for treatment-effect contrasts; (iii) Beware the pitfalls of automated model-selection procedures in situations for which they are not designed; e.g. factorial models; (iv) How to report interactions. (v) p-values: best avoided, but if you must report one, be sure to state the null hypothesis being tested.
Social news aggregations and the bitcoin

Social media and financial markets are ecosystems bound to interact and overlap more and more. For example, cryptocurrencies, Bitcoin in the first place, have long been among the hottest topics on social media. Here we illustrate a methodology for correlating Bitcoin price trends and social media that operates on the aggregation of news from multiple social networks as typically occurs on dedicated channels in sites such as Reddit. For this purpose, we define general laws to map the financial fluctuations of Bitcoin in a space defined on three dimensions: content volume, sentiment and time. These laws provide the foundation upon which to build effective methodologies for social media-based prediction of Bitcoin performance.
Flexible execution of multi-party business processes on blockchain

The execution of multi-party business processes on blockchains has been more and more used in the last years in those contexts where trust among the involved parties is a crucial requirement that cannot be assumed by default. The immutability, transparency, and decentralised characteristics of the blockchain ensure the parties' responsibility in case of controversial actions. However, if on the one hand, the immutable nature of the blockchain brings many benefits from the trust perspective, on the other hand such rigidity leads to a lack of flexibility in the execution of blockchain-based business processes.
Nonsense or Riddle? Elusive Genre in Giuseppe Pitrè’s Sicilian Folktales

Research Article| August 01 2020 Nonsense or Riddle? Elusive Genre in Giuseppe Pitrè’s Sicilian Folktales Joseph Russo Joseph Russo Search for other works by this author on: This Site Google Italian Americana (2020) XXXVIII (2): 115–122. https://doi.org/10.5406/2327753X.38.2.05 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Joseph Russo; Nonsense or Riddle? Elusive Genre in Giuseppe Pitrè’s Sicilian Folktales. Italian Americana 1 August 2020; XXXVIII (2): 115–122. doi: https://doi.org/10.5406/2327753X.38.2.05 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressItalian Americana Search Advanced Search The text of this article is only available as a PDF. Copyright 2016 Carla Anne Simonini & Carol Bonomo Albright2016 Article PDF first page preview Close Modal Issue Section: ARTICLES You do not currently have access to this content.
Source Code Preprocessing Method Analysis in Unit Test Code Classification

Machine learning has been applied on datasets that consist of source codes to perform programming language classification. However, there has been no study that directly investigates the impact of applying various preprocessing methods. This paper aims to analyze the impact of different preprocessing methods and their usage combinations on the machine learning model performance datasets that consists of source codes. To simply the research and to focus on the preprocessing aspect, this research utilizes the preprocessing methods to build a simple code coverage classification (i.e., branch/non-branch). The study uses Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to build the models that will be analyzed. The preprocessing methods analyzed are several vectorizations including Vector Count, TF-IDF, and Word2Vec. The results show that the preprocessing methods and their combinations used significantly influences the performance of the machine learning model. Using SVM, TF-IDF vectorization showed the best results, while Count vectorization yielded the best results for MLP. The alphanumeric character extraction preprocessing method contributed the maximum results regardless of the model and vectorization.
Blockchain Solutions for Local Energy Market

Tools such as blockchain and smart contracts represent an interesting opportunity for various sectors including the electro-energy one. This paper describes the study and implementation of a Proof of Concept for the creation of local energy markets using blockchain technologies and smart contracts in order to ensure decentralization, transparency, trustworthiness and accountability.
Measurement Level of Information Security Awareness for Employees Using KAB Model with Study Case at XYZ Agency

XYZ Agency in its operations always maintains the security of its information. This research focuses on measuring the Information Security Awareness (ISA) of agency employees so that it can provide demographic information on employee understanding and solutions to information security. In assessing the level of security awareness and priority, the research uses the Knowledge, Attitude, Behavior (KAB) concept model. Meanwhile, the questionnaire was based on the Human Aspects of Information Security Questionnaire (HAIS-Q) and the Analytic Hierarchy Process (AHP) method. The results of the answers to the questionnaire were taken from about 30 employees in one of the agency's operational branches. There are a total of 21 parameters for measuring the results obtained, consisting of 12 parameters at the good level and 9 parameters at the average level with an overall value of 80.82% (satisfactory category). However, the 9 parameters which are divided into internet use, social network, incident reporting, and mobile computing need to be developed by means of the agency providing socialization and training, especially on these parameters.
An Overview of Machine Learning Approaches to Software Development Cost Estimation

Software cost estimation still presents a real challenge to software development firms and practitioners. One of the crucial activities in project management is software cost estimation. The accuracy of the estimated cost has a critical impact on the project’s success or failure. Overestimation and underestimation are serious challenges to project managers, especially in today’s competitive marketplace. Inaccurate estimation may lead to over budget, missing deadlines, business loss, and even project failure. The precise cost estimation will enable the project manager to forecast and allocate the necessary resources to deliver a successful product. Many researchers have published research papers in this area. In this paper, the study’s goal is to outline the recent machine learning approaches to software cost estimation. The study will address the problems with conventional cost estimation models and provide a comprehensive review of the researches regarding software cost estimation with machine learning. The current study will discuss the intelligence models for software cost estimation and highlight the models that excel in this area.
Blockchain Applications and Roadblocks in the Energy Transition: Joint Research Centre Testing Campaigns and Policy Recommendations

This paper summarizes the main outcomes of several experimental studies carried out by the Joint Research Centre on blockchain solutions for energy systems. The cases selected for testing and implementation are: (i) smart metering billing and security, (ii) fostering of energy communities, (iii) certification of origin of energy production, (iv) support the implementation of flexibility services, and (v) electro-mobility scenarios. Overall, our study found that there is a clear interest among energy and digital industries to exploit the potential of blockchain in the energy sector. Results confirmed the added value blockchain can bring to the sector (transparency, immutability, non-repudiation, integrity), highlighting many of its unfolding functionalities and capabilities. At the same time, our study identified a number of barriers and limitations blockchain technology might encounter, such as scalability and sustainability issues, but also low consumers engagement and involvement. Finally, we conclude this paper by presenting our considerations and recommendations for European policymakers regarding blockchain deployment across the energy value chain.