Reviewerly/exHarmony huggingface dataset:
Multi-network Transmission Using Socket Programming to Support Command and Control Systems

Information systems are needed to provide data and information anytime and anywhere to support the process of policy and decision making. In practice, Indonesia–Malaysia security border areas in West Borneo, each Indonesia military unit runs its own when monitoring the border without any interoperability or information sharing. Interoperability is needed to facilitate intermilitary unit of the information obtained by making it more efficient and effective for monitoring the Indonesia–Malaysia border in West Borneo, Indonesia. Also, intermilitary unit can be integrated with each other while facing the threats that might happen. The solution is to design a multi-network transmission system using socket programming as a communication media. Socket enables communication between one host and another using the same protocol. The protocol used in this application is TCP/IP because it has the advantage of guaranteeing data received its destination and no data duplication. The socket application model is based on a client–server which consists of one server and several clients. This is one of the advantages of a socket where the socket is able to handle multiple clients at the same time (multiple clients).
Introduction to Blockchain Evolution, Architecture and Application with Use Cases

Blockchain is not a hype, it is here to stay. Before you dive deeper into the business applications of the Blockchain, you have to understand the fundamentals and the functionalities of the Blockchain. This chapter will guide you to understand both non-technical and technical working principles, evolution, architecture, types, use cases, applications, and tools for implementation of Blockchain. Although there is no technical prerequisite of the chapter, a basic knowledge of network systems and cryptography is required for better understanding.
A Flexible Billing Life Cycle for Cloud Services Using Augmented Customer Agreements

Cloud computing constant evolution requires dynamic adjustments to service pricing and billing terms, considering provider infrastructures, customer requirements, and discount policies, among others. In this context, Customer Agreements (CA) are used to regulate the service provision including, among other information, the agreed service level in SLAs, pricing, and billing terms. Although many existing proposals and industrial tools support the definition of pricing and billing of cloud services, there is a lack in terms of customisation and automated monitoring tools integrated with the billing process. In this article, we provide a flexible billing proposal supporting CA that considers not only SLA terms but also customisable pricing and billing terms, possibly including compensations (i.e. discounts or overcharges) that apply when specified conditions are met. In addition, we developed an automated monitoring and analysis tool that we validate in a real-world industrial scenario. Moreover, we analyse and compare more than 50 existing industrial tools with our proposal, highlighting the advantages of its rule-based approach.
Interactive Editing of Circuits in a Step-based Tutoring System

Thompson serves as the Director for the Laboratory for Tactical and Communication Systems, which focuses on research for the design, implementation, and security of advanced wireless embedded systems.Dr
Towards Smart Farming? Mobile Technology Trends and Their Potential for Developing Country Agriculture

Mobile phones have reached some of the most remote parts of the globe. They offer opportunities to improve the delivery of services to farmers in developing countries, for instance by facilitating access to information, financial services or output markets. A review of the global situation suggests that much of the focus has been on offering services through SMS and voice-based interfaces to cater for users with low-tech phones which greatly limits the sophistication of the services that can be provided. This chapter assesses how current mobile technology trends may allow for the provision of more advanced agricultural services in the future. Three trends are identified, i.e. the growing diversity of mobile connected devices to access the services; the 'Internet of Things' which links objects and people through the network; and the increasing ubiquity of mobile networks and expanding user base which facilitate the collection and sharing of data and knowledge. The chapter presents two scenarios for the evolution of these trends based on a number of assumptions and highlights possible implications for the provision of services to farmers.
Automatic determining optimal parameters in multi-kernel collaborative fuzzy clustering based on dimension constraint

Most cluster assignments using the traditional kernel clustering method strongly depend on the selection of the initial values. Under this scenario, directly using dimension reduction methods to deal with high-dimensional heterogeneous data in preprocessing might destroy the integrity of the original data. To overcome these limitations, this study constructs a high-dimensional kernel feature space that the high-dimensional heterogeneous data is mapped into this space. The proposed method implements kernel dimension constraint (KDC) on the mapped data, from which the data processed by the KDC forms the maximize hyper-plane data. Then we use the multi-kernel collaborative fuzzy clustering based on kernel learning to partition the maximize hyper-plane, called KL-MK-CoC, thus ensures the integrity of original data and also reduces the running time. It determines the optimal parameter in the multi-kernel clustering process, and we also embed the feature weight computation into the clustering procedure. Therefore, the combination of multiple kernels and the automatic adjustment of kernel weights renders the proposed algorithm more immune to unreliable features. Experimental results and comparisons demonstrate the excellent performance of KL-MK-CoC with its effectiveness in practice.
Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

The explosion of mobile broadband as an essential means of Internet connectivity has made the scalable evaluation and inference of quality of experience (QoE) for applications delivered over LTE networks critical. However, direct QoE measurement can be time and resource intensive. Further, the wireless nature of LTE networks necessitates that QoE be evaluated in multiple locations per base station as factors such as signal availability may have significant spatial variation. Based on our observations that quality of service (QoS) metrics are less time and resource-intensive to collect, we investigate how QoS can be used to infer QoE in LTE networks. Using an extensive, novel dataset representing a variety of network conditions, we design several state-of-the-art predictive models for scalable video QoE inference. We demonstrate that our models can accurately predict rebuffering events and resolution switching more than 80% of the time, despite the dataset exhibiting vastly different QoS and QoE profiles for the location types. We also illustrate that our classifiers have a high degree of generalizability across multiple videos from a vast array of genres. Finally, we highlight the importance of low-cost QoS measurements such as reference signal received power (RSRP) and throughput in QoE inference through an ablation study.
Real-to-virtual domain transfer-based depth estimation for real-time 3D annotation in transnasal surgery: a study of annotation accuracy and stability

Abstract Purpose Surgical annotation promotes effective communication between medical personnel during surgical procedures. However, existing approaches to 2D annotations are mostly static with respect to a display. In this work, we propose a method to achieve 3D annotations that anchor rigidly and stably to target structures upon camera movement in a transnasal endoscopic surgery setting. Methods This is accomplished through intra-operative endoscope tracking and monocular depth estimation. A virtual endoscopic environment is utilized to train a supervised depth estimation network. An adversarial network transfers the style from the real endoscopic view to a synthetic-like view for input into the depth estimation network, wherein framewise depth can be obtained in real time. Results (1) Accuracy: Framewise depth was predicted from images captured from within a nasal airway phantom and compared with ground truth, achieving a SSIM value of 0.8310 ± 0.0655. (2) Stability: mean absolute error (MAE) between reference and predicted depth of a target point was 1.1330 ± 0.9957 mm. Conclusion Both the accuracy and stability evaluations demonstrated the feasibility and practicality of our proposed method for achieving 3D annotations.
Foreign Language Writing Anxiety: The Perceptions and Experiences of Freshmen Lebanese University Students

Writing is an essential skill for language production. However, many English as Foreign language learners (EFL) face many difficulties in writing. This study aims to shed light on the psychological aspect of writing; more specifically writing anxiety. Consequently, the main purpose of this exploratory mixed-method study is to explore and investigate the sources and manifestations of foreign language writing anxiety among Lebanese university students. The participants were Lebanese EFL university students (N=87). Data for this study was gathered from the use of the Second Language Writing anxiety scale(SLWAI), Sources of Writing Anxiety Inventory(SWAI), and semi-structured Focus group interviews (FG). Focus interviews with students were used to triangulate the derived data from the inventories. This study provides numerical data regarding the level of writing anxiety among students. The data from the FG interviews were transcribed and uploaded for thematic coding and further analysis. The results from this study shed light on the sources and manifestation of the writing anxiety among students. It also helps to disseminate several practical recommendations for the alleviation of writing anxiety among students. It is assumed that the findings will address the perceived psychological needs of Lebanese EFL learners and provide grounds for further research.
Extract Executable Action Sequences from Natural Language Instructions Based on DQN for Medical Service Robots

The emergence and popularization of medical robots bring great convenience to doctors in treating patients. The core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. Language is the most convenient way for people to communicate with each other, so in this paper, a DQN agent based on long-short term memory (LSTM) and attention mechanism is proposed to enable the robots to extract executable action sequences from doctors’ natural language instructions. For this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. We evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. The results show that our agent can perform better than similar agents. And our agent has a better ability to handle long texts than previous works.
Causal Mapping for Strategy Execution: <scp>Pitfalls and Applications</scp>

Organizations can attempt to improve strategy implementation by developing strategy execution maps, which aim to translate strategic objectives into specific activities and provide sufficient clarity to inform employees’ decisions and actions. However, managers often encounter pitfalls, both in framing the process and in developing maps. This article suggests how to overcome these pitfalls, describes several applications of causal maps to further enhance strategy execution, and illustrates strategy execution maps for organizations with distinctive strategies.
Challenges and reflections on teacher training in times of Covid-19: an analysis from educational psychology

Professional practices in teacher training are a crucial moment for the development and strengthening of competencies associated with teaching-learning from the discipline. Before 2020, most of the professional practices were developed in-person mode, a situation that changed in the health contingency of COVID-19, causing a demand for sudden adaptation on the part of trainers and teachers in training, for the development of the professional practices in virtual environments. The present investigation developed through the methodology of systematization of experiences had as objective, to characterize the teaching-learning strategies that favored the competences in professional practices of teachers in coherence with the need for mental health care and quality maintenance. life of learners. The results show the different emerging challenges such as the weak command of information and communication technologies, empathy as a cognitive-emotional teacher competence, the constant maintenance of student motivation and the application of curricular flexibility strategies. Likewise, prevention and support actions developed based on an adaptive educational environment are identified, which favors successful training in times of social distancing, quarantine and isolation.
Point cloud super‐resolution based on geometric constraints

Among all digital representations we have for real physical objects, three-dimensional (3D) is arguably the most expressive encoding. But due to the limitations of 3D scanning equipment, point cloud often becomes sparse or partially missing. A point cloud super-resolution (PCSR) method based on geometric constraints is proposed to solve the sparse problem of point clouds: it allows dense point clouds to be generated by sparse point clouds. The method is based on the conditional generative adversarial network including redesigned generator and discriminator for point cloud data specially. Moreover, the method can maintain the shape of the dense point cloud by adding geometric constraints. The contributions of our work are as follows: (1) a PCSR method based on geometric constraints is proposed; (2) add a module for obtaining point cloud neighbourhood information in the generator, called K-nn operation module; and (3) feature aggregation is performed using the weighted pooling to process the neighbourhood information obtained by the K-nn operation module. Extensive experimental results demonstrate the effectiveness of the proposed method.
Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation

Many processes no matter what kind are regularly changing over time, adapting themselves to external and internal circumstances. Analyzing them in a streaming context is a very demanding task. Particularly the detection and classification of significant deviations is important to be able to re-integrate these possible micro-processes. Assuming a deviation of a certain process, the significance is implicitly given when a high number of instances contain this deviation similarly. To enhance a process the integration of or preventive measures against those anomalies is of high interest for all stakeholders as the actual process core gets discovered more and more in detail. Considering various areas of application, we focus on previously neglected but potentially significant anomalies like small changes in the disease process of a virus infection that has to be discovered to develop an appropriate reaction mechanism. We concentrate on non-conforming traces of a stream on which we compute a local outlier factor. This allows us to detect relations between traces based on changing outlier scores. Hence, hereby connected traces are clusters with which we achieve the detection of concept drift. We evaluate our approach on a synthetic event log and a real-world dataset corresponding to a process representing building permit applications which emphasizes the extensive applicability.
Keyphrase Generation Based on Self-Attention Mechanism

Keyphrase greatly provides summarized and valuable information. This information can help us not only understand text semantics, but also organize and retrieve text content effectively. The task of automatically generating it has received considerable attention in recent decades. From the previous studies, we can see many workable solutions for obtaining keyphrases. One method is to divide the content to be summarized into multiple blocks of text, then we rank and select the most important content. The disadvantage of this method is that it cannot identify keyphrase that does not include in the text, let alone get the real semantic meaning hidden in the text. Another approach uses recurrent neural networks to generate keyphrases from the semantic aspects of the text, but the inherently sequential nature precludes parallelization within training examples, and distances have limitations on context dependencies. Previous works have demonstrated the benefits of the self-attention mechanism, which can learn global text dependency features and can be parallelized. Inspired by the above observation, we propose a keyphrase generation model, which is based entirely on the self-attention mechanism. It is an encoder-decoder model that can make up the above disadvantage effectively. In addition, we also consider the semantic similarity between keyphrases, and add semantic similarity processing module into the model. This proposed model, which is demonstrated by empirical analysis on five datasets, can achieve competitive performance compared to baseline methods.
Pedagogical vocational guidance has its own specifics

Pedagogical vocational guidance has its own specifics, unlike other types, and this must be taken into account by teachers when organizing work. Interest in the teaching profession arises among some schoolchildren even in the elementary grades, so certain elements of vocational guidance work should find a place in these classes as well.
SOFT MODELS FOR ASSESSING THE STATE OF INFORMATION ECOLOGY OF THE UNIFIED GEOINFORMATION SPACE OF A REGION

The principles of formation of a system of assessments of information ecology of a single information space of the region are considered. A technique is proposed for obtaining fuzzy properties of the layers of this space, as well as the principles of aggregating these properties. An approach to modeling the intellectual space of the region as one of the layers of a single geoinformation space is proposed.
Research on the Function of Visual Phonetic Software Praat in Vocational English Phonetics Teaching

Abstract Phonetics teaching is the most difficult part of English majors in higher vocational colleges. Many students are affected by their mother tongue pronunciation habits and the lack of systematic voice training at the middle school stage, resulting in weak phonetic foundations. There are problems such as inability to correctly distinguish between long and short vowels, voiced consonants, emphasized and light pronunciation, and intonation. To this end, the introduction of the visual speech software Praat, using its visual and intuitive image characteristics can assist teachers in class, help students find the pronunciation errors in time and correct them in time. Based on this, this article will combine the status quo of English phonetics teaching in higher vocational colleges and analyze the role of Praat phonetics learning software in phonetics teaching and its specific application implementation.
Research and Application of Intersection Clustering Algorithm Based on PCA Feature Extraction and K-Means

Abstract Traffic jam and traffic route design are important issues of people’s livelihood. In this paper, machine learning algorithm is used to cluster intersection features. Based on the collected traffic intersection data, PCA dimension reduction technology is used for feature extraction, and then K-Means clustering algorithm is used to sum up the number of entrances, and each intersection is clustered. Thus, a new intersection clustering algorithm is established, studied and applied. The algorithm can match similar intersections, which provides theoretical support for the transportation department and related technical personnel.
Binocular DIC system for 3D correlation measurements

A novel, to the best of our knowledge, mirror-assisted binocular stereo digital image correlation (DIC) system is proposed for the reconstruction of the overall contour, thickness, and strain measurement of the object. First, the angle between the two plane mirrors is adjusted until two virtual images and two real images can be formed in the mirrors. Then, the adjustable speckle size and definition characteristics of the projection speckle technology are fully utilized to realize the precise measurement of the mirror plane. Finally, a 3D contour reconstruction experiment and a dynamic stretching experiment are conducted to verify the proposed method. Experimental results show that the proposed method can achieve a 360° omnidirectional deformation measurement, and the 3D reconstruction of the object with complex contours has a relatively ideal reconstruction effect. According to the virtual image, the thickness of the conventional specimen can be completed easily, and the coordinates of the front and rear surfaces need not be subtracted. The dynamic strain can be calculated separately from the front and rear surfaces of the standard specimen and can be realized in the dynamic tensile experiment. Compared with the existing binocular DIC system, the proposed method can provide more valid data with guaranteed excellent results. It provides a better implementation method for omnidirectional measurement, thickness, and stress–strain calculation of the object.
A Contextual Word Embedding for Arabic Sarcasm Detection with Random Forests

Sarcasm detection is of great importance in understanding people’s true sentiments and opinions. Many online feedbacks, reviews, social media comments, etc. are sarcastic. Several researches have already been done in this field, but most researchers studied the English sarcasm analysis compared to the researches are done in Arabic sarcasm analysis because of the Arabic language challenges. In this paper, we propose a new approach for improving Arabic sarcasm detection. Our approach is using data augmentation, contextual word embedding and random forests model to get the best results. Our accuracy in the shared task on sarcasm and sentiment detection in Arabic was 0.5189 for F1-sarcastic as the official metric using the shared dataset ArSarcasmV2 (Abu Farha, et al., 2021).