Reviewerly/exHarmony huggingface dataset:
Softmax Regression Based on Bacterial Foraging Optimization Algorithm with t-Distribution Parameters

Softmax regression is a supervised multi-class nonlinear classification algorithm in machine learning. Sometimes it is also used in regression problems. It is mainly used in transfer learning, knowledge distillation and meta learning in artificial intelligence. Softmax optimizer is gradient descent method or random gradient descent method, but its loss function is a multi peak, strong nonlinear function, and the optimal solution is only the local optimal solution rather than the global optimal solution, and depends on the gradient. So we use a swarm intelligence optimization algorithm which is independent of gradient and can find the global optimal solution to optimize. This article will introduce the bacterial foraging optimization algorithm.
A User-Centered Approach for Recognizing Convenience Images in Personal Photo Collections

Personal photo collections today usually include a huge number of convenience images (i.e. images with a practical character, such as handwritten notes, flipchart drawings, WI-FI logins or car parking positions). This paper proposes an iterative approach for the automatic recognition of convenience photos combining the methods from user-centered design and deep learning. In the first stage, we conducted a study to specify the categories of photos typically perceived by users as convenience photos. We then employed the specified categories to generate semi-automatically a dataset for training and testing a convolutional neural network model. After an evaluation of this model using from owners self-rated personal photo collections, we were able to improve both the quality of the dataset and our model. This paper therefore demonstrates the importance of integrating users at different stages of the development of deep learning solutions.
Study 3: Beyond the Call of Duty: The Impact of Innovative Service Behavior by Robots on Customer Delight

Innovative service behaviors offer a promising way to excite customers during service encounters; such behaviors increasingly are being performed by frontline service robots (FSRs), functioning as service representatives. This research tests whether innovative service behavioral cues provided by an FSR instead of a human service representative might be able to delight customers too. The empirical findings result from two experimental studies in a hotel setting with either an FSR or a human frontline employee (FLE) as a service representative.
Updating Service-Based Software Systems in Air-Gapped Environments

Contemporary component-based systems often manifest themselves as service-based architectures, where a central activity is management of their software updates. However, stringent security constraints in mission-critical settings often impose compulsory network isolation among systems, also known as air-gap; a prevalent choice in different sectors including private, public or governmental organizations. This raises several issues involving updates, stemming from the fact that controlling the update procedure of a distributed service-based system centrally and remotely is precluded by network isolation policies. A dedicated software architecture is thus required, where key themes are dependability of the update process, interoperability with respect to the software supported and auditability regarding update actions previously performed. We adopt an architectural viewpoint and present a technical framework for updating service-based systems in air-gapped environments. We describe the particularities of the domain characterized by network isolation and provide suitable notations for service versions, whereupon satisfiability is leveraged for dependency resolution; those are situated within an overall architectural design. Finally, we evaluate the proposed framework over a realistic case study of an international organization, and assess the performance of the dependency resolution procedures for practical problem sizes.
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations

A Bayesian network is a probabilistic graphical model that consists of a directed acyclic graph (DAG), where each node is a random variable and attached to each node is a conditional probability distribution (CPD). A Bayesian network can be learned from data using the well-known score-and-search approach, and within this approach a key consideration is how to simultaneously learn the global structure in the form of the underlying DAG and the local structure in the CPDs. Several useful forms of local structure have been identified in the literature but thus far the score-and-search approach has only been extended to handle local structure in form of context-specific independence. In this paper, we show how to extend the score-and-search approach to the important and widely useful case of noisy-OR relations. We provide an effective gradient descent algorithm to score a candidate noisy-OR using the widely used BIC score and we provide pruning rules that allow the search to successfully scale to medium sized networks. Our empirical results provide evidence for the success of our approach to learning Bayesian networks that incorporate noisy-OR relations.
Compressing the Scenes of Medical Video by Using Eigenvector

With the technological development, it has become easy to use digital data, and thus medical institutions have turned to analyzing images and video content. Medical video is recorded in order to collect experimental information about the use of technologies and integrated data that consists of a series of doctors with video recording, and this leads to user interaction with computers. Using the coding methodology, the video is analyzed for a medical diagnosis that contains important information about the patient's condition with the help of computers, as well as performing surgeries in a medical environment to conduct clinical trials and diagnostics. This leads to software development, reduction in medical effort real-time quality measurement and utilization of available treatments for patient medical diagnosis Medical video has become one of the leading and most common fields, in addition to medical images for diagnosing heart and lung diseases, brain and digestive diseases, and this diagnosis is made by using ultrasound and magnetic resonance imaging in addition to a CT scan. All this helped to provide health care through new methods of research and experiments clinical. Lately, Compression technologies developed video and images are widely used, and by using these effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected, major target of compression is to enable data to get saved or transmit in the better form types of compression in various manners like the lossless and the lossy. The aim of this study is to compress video film to lower the irrelevance and the redundancy of data, principle component analysis (PCA) is used in this research, the main component analysis of medical video compression based on self-matrix analysis that all readers will be able to understand the PCA method. This paper presents a suitable technique for medical video compression using Principle Component Analysis PCA, compressed the frames of medical video with eigenvalue and eigenvector techniques. Experimental results shown the efficient and quick technique for medical video frames.
Adapting knowledge workers with knowledge Management System and its use in organization / Alwi Mohd Yunus … [et al.]

A Knowledge Management System (KMS) is very important for organization to applying and using knowledge management practices. These include data-driven object around business productivity, a competitive business model, business intelligence analysis, and more. A KMS is made up of different software modules served by a central user interface. Some of these features can allow for data mining on customer input and histories, along with the provision or sharing of electronic documents. The purpose of the case study is to identify the adaptation of knowledge worker when using the KMS in organizations. Studies are made on critical aspects of Knowledge Management System research, case study, general review, and conceptual papers that were written by researchers in the topic of interest
Validated Undergraduates’ Misconceptions about Software Engineering

(Pre-)Conceptions are of central importance when it comes to learning in a (social) constructivist meaning. This can be explained by the fact that new information gets linked with existing knowledge and prior experiences. Therefore undergraduates do not enter university or a specific course as “blank sheets” or “empty vessels”. This is why lecturers should take prior knowledge into consideration for designing as well as implementing teaching and learning arrangements. To focus especially on Misconceptions <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> as potential learning obstacles, the first step is to identify these (Mis-)conceptions. Therefore, the paper covers a first catalogue of Misconceptions in Software Engineering, as the result of a mixed-methods approach enclosing Grounded Theory Methodology to elicit undergraduates' statements about Software Engineering, its tools, methods, and processes and reconstruct the (Mis-)conceptions included; as well as a subsequent expert validation of the Misconceptions found using qualitative data.
Overview of the PLEXIL Plan Execution Technology and its Applications in Autonomous Piloting Projects at NASA

Automated planning is a key Artificial Intelligence technology enabling Unmanned Aerial Systems (UAS) and the eminent reality of Urban Air Mobility (UAM). It produces plans, which formalize procedures often performed by humans. Plans differ from other kinds of computer programs in their ability to react and interact with a dynamically changing environment. Aviation plans must encode the procedural knowledge, reasoning capability, and capacity for multi-tasking held by competent human pilots. Correct execution of these plans (performed by software called an executive) in the dynamic airspace environment is vital to the success of each automated flight, and the safety of the vehicle and all things in its path. In the early 2000s NASA developed a plan representation language and executive called PLEXIL (Plan Execution Interchange Language) that has successfully been applied in several NASA aviation and UAS projects. Autonomy Operating System (AOS), Cockpit Hierarchical Automated Planning and Execution (CHAP-E), and ICAROUS are all projects that have used PLEXIL to help encode and automatically execute flight procedures, some normally performed by human pilots. AOS also automates a subset of pilot/Air Traffic Control communication towards enabling UAS entry into the National Airspace. PLEXIL has been open-source software since 2008 and has seen usage in a wide range of prototypical autonomy applications in academia, government, and industry. In this presentation, we describe PLEXIL and highlight its significant accomplishments in the aviation domain.
What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts

According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people's everyday life and career.We benchmark the novel task of automatically detecting those needs on short posts in English, by modelling it as a ternary classification task, and as three binary classification tasks.A detailed manual analysis shows that the latter has advantages in the real-world scenario, and that our best models achieve similar performances as a trained human annotator.
Investigation of the pre-eruptive processes of the 2014/15 Holuhraun eruption based on extracted volcanic tremor signals

&amp;lt;p&amp;gt;During volcanic unrest, multiple subsurface processes can happen simultaneously and may lead to an eruption. The analysis of seismic records in an unrest period before an eruption reveals information about the pre-eruptive processes and might be able to provide hints for a possible future eruption.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The 2014&amp;amp;#8211;2015 Holuhraun eruption was the largest one in Iceland in 230 years. It was extensively monitored and studied in a variety of multidisciplinary research approaches. Intense seismicity and ground deformation were interpreted as magma propagation from B&amp;amp;#225;r&amp;amp;#240;arbunga volcano 48 km laterally at &amp;amp;#8764;6 km depth over two weeks before an eruption started at Holuhraun. Different processes including vertical and lateral magma migration, dike propagation, caldera subsidence, and subglacial eruptions happened in this period and some models linking these processes are suggested. In the two-week interval preceding the eruption, there is still no clear connection between the observed tremor and pre-eruptive processes. Both the tremor source location and tremor generation process are not well understood yet. While cauldrons as a sign of subglacial eruptions were identified on the glacier surface from aerial photos, these cauldrons might have been formed earlier and there is hence an uncertainty of a few days. A tremor location might help to constrain these dates. However, the simultaneous occurrence of intense seismicity and tremor hinders the study and location of tremor. Here, we use a recent volcanic tremor extraction algorithm (Zali et al., 2020) and extract pre-eruptive tremor signals in order to better locate them using the Seismic Amplitude Ratio Analysis (SARA) method. Furthermore, the occurrence of the tremor could open new insights into ascending magma and fluid migration as well as the timing and duration of the subglacial eruptions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We also observed short-lived tremors before the eruptions on August 29 and 31, which could be considered as eruption precursors. The primary investigation on the extracted tremor signals is promising while further analysis is on-going.&amp;lt;/p&amp;gt;
KEMAMPUAN MENULIS CERPEN DENGAN METODE SUGESTOPEDIA PADA SISWA KELAS XI SMK NEGERI 2 BANDARLAMPUNG TAHUN PELAJARAN 2018/2019

The problems raised in this study are 1) theme or content, 2) character and characterization, 3) background, 4) plot, and 5) mandate. This research was conducted with the aim to find out and describe the ability to write short stories with suggestive method. The research method used is descriptive quantitative method. The population of the study was 42 students of SMK Negeri 2 Bandar Lampung totaling 42. The research samples were 20 taken by the clusster random sampling technique. The technique of collecting data uses performance techniques that are assigning students to write short stories. The results showed that the ability to write short stories using the sugestopedia method for class XI of SMK Negeri 2 Bandar Lampung Academic Year 2018/2019 was sufficient because the final value of all samples showed 67. The final results of each aspect assessed were the ability to apply aspects of the theme and content to achieve average scores the final score of 71 with sufficient level of ability, the ability to apply aspects of character and characterization reached an average final value of 70 with sufficient ability, the background aspect reached the final average of 64 with sufficient ability level, the flow aspect reached an average value the end is 61 with sufficient level of ability, and the mandate aspect reaches the final average value of 69 with sufficient level of ability.
The Interaction Between Probe and Cavity Field Assists Quantum Synchronization

As an important technology of the quantum detection, the quantum synchronization detection is always used in the detection or measurement of some quantum systems. A detection schemes are key to the study of quantum system. The quantum synchronization detection which is presented between the probe and the quantum system is always used in detection application. A probing model is established to describe the probing a qubit system in the cavity field and to reveal the effect of the environment (cavity) on the quantum synchronization occurrence as well as the interactions among environment, a qubit system and probing equipment. By adjusting the frequency of the probe, the in-phase and out-of-phase synchronization can be achieved. So the information of the probe can be used to obtain the quantum system. Simultaneously, the effect of $${\gamma }_{3}$$ which describes the interaction strength between the probe and environments for quantum synchronization is discussed under different the values of Ohmic dissipation index s. Finally, the machine learning method is applied to present an optimization for classification and regression of synchronization transition dependent on s and $${\gamma }_{3}$$ . This opens the way for studying the generalized form of quantum synchronization through machine learning algorithms (Artificial neural network) in the future.
Apollo: Modular and Distributed Runtime System for Serverless Function Compositions on Cloud, Edge, and IoT Resources.

This paper provides a first presentation of Apollo, a runtime system for serverless function compositions distributed across the cloud-edge-IoT continuum. Apollo's modular design enables a fine-grained decomposition of the runtime implementation(scheduling, data transmission, etc.) of the application, so that each of the numerous implementation decisions can be optimized separately, fully exploiting the potential for the optimization of the overall performance and costs. Apollo features (a) a flexible model of the application and the available resources and (b) an implementation process based on a large set of independent agents. This flexible structure enables distributing not only the processing, but the implementation process itself across a large number of resources, each running an independent Apollo instance. The ability to flexibly determine the placement of implementation actions opens up new optimization opportunities, while at the same time providing access to greater computing power for optimizing challenging decisions such as task scheduling and the placement and routing of data.
MODERN CONDITIONS FOR SOFT SKILLS FORMATION AMONG STUDENTS OF SOCIONOMIC PROFESSIONS

Modern higher professional education only stands on the threshold of reforms, related to the educational guidance aimed at the development of soft skills which are in demand in the international labour market. The basis of modernization consists of innovative transformations of constituents of the educational system, including continuous on-line education, blended learning. The development of universal «Soft skills» among the students of socionomic professions enables developing in a professional direction, building a career, and afterwards getting work in a successful organization.
Low-cost thermal explorer robot using a hybrid neural networks and intelligent bug algorithm model

Autonomous navigation requires an artificial agent able to independently move adapted to the environment. The robot sensors analyse the surrounding environment and learn from successful exploration experiences, to plot the best routes and avoid obstacles. This paper proposes a new algorithm for navigation being an adaptation of the Intelligent Bug Algorithm (IBA) combined with artificial neural networks. In addition, this approach also aims to reduce costs using low-cost sensors and a proposed thermal measurement system composed of a matrix infrared sensor superposed with a regular camera. The experimental results show that the novel algorithm is efficient, the prototype avoids collisions and manages to optimise the route, and thermal camera demonstrates accuracy in measuring temperatures and identifying different thermal zones. Moreover, the robot cost reduction and simple operation characteristics make possible its use in destructive missions in inhospitable terrain for humans, facilitating its implementation for research and testing.
A double-controller fuzzy scheme for intelligent resource discovery on IaaS cloud systems

The resource discovery process discovers appropriate computing resources (e.g., shared memory) in infrastructure as a service (IaaS) cloud systems according to user requirements. Since fuzzy systems use intelligent procedures based on humanistic experiences, they can be used in cloud computing to increase the number of completed jobs and improves the success rate. This paper proposes a double-controller fuzzy scheme for intelligent resource discovery in IaaS cloud systems, called DOCFIR. This scheme applies two fuzzy controllers to perform the intelligent resource discovery across the network. The first controller determines the number of virtual machines in the deployment phase based on the most important characteristics of the physical machines. The second controller discovers the appropriate computing resources for the user's job in the service phase based on characteristics of the physical machines and user requirements. The simulation results show that the proposed scheme surpasses some of the existing related works in terms of the number of completed jobs and success rate.
Factors Affecting Transfer of On-The-Job Training at workplaces in Pakistan

Purpose: This research is focused on examining the factors that influence transfer (effectiveness) of On-The-Job training at workplaces in Pakistan. The study identifies the factors that affect the transfer of OJT training included independent variables such as motivation to learn, Perceived Organizational Support and Training Design and their impact on employees’ performance i.e. Transfer of Training.Methodology: The study adopted quantitative research and data was collected through a well-structured questionnaire. The sample of 200 participants was selected throughout Pakistan using a convenience sampling technique. Smart PLS software was used to analyze the data through descriptive statistics, structural equation modeling and confirmatory factor analysis.Findings: The result revealed that there is a strong and significant impact of motivation to learn and training design on the transfer of OJT training at workplaces in Pakistan. However, the results do not show any significant impact of Perceived Organizational Support in organizations on the transfer of OJT training.Conclusions: This shows that even though if the organization's support is not present, the motivation to learn new things and to implement it to the workplace and effectively designed training programs can overcome the issues.Practical Implications: Organizations can use the result in favor of their organizational effectiveness, to consistently design effective training programs, and to consistently motivate their employees and to improve support and guidance by top management in the organizations.
Integrating Knowledge Compilation with Reinforcement Learning for Routes

Sequential multiagent decision-making under partial observability and uncertainty poses several challenges. Although multiagent reinforcement learning (MARL) approaches have increased the scalability, addressing combinatorial domains is still challenging as random exploration by agents is unlikely to generate useful reward signals. We address cooperative multiagent pathfinding under uncertainty and partial observability where agents move from their respective sources to destinations while also satisfying constraints (e.g., visiting landmarks). Our main contributions include: (1) compiling domain knowledge such as underlying graph connectivity and domain constraints into propositional logic based decision diagrams, (2) developing modular techniques to integrate such knowledge with deep MARL algorithms, and (3) developing fast algorithms to query the compiled knowledge for accelerated episode simulation in RL. Empirically, our approach can tractably represent various types of domain constraints, and outperforms previous MARL approaches significantly both in terms of sample complexity and solution quality on a number of instances.