Reviewerly/exHarmony huggingface dataset:
Energy consumption-based services composition optimization for internet of things

Internet of Things (IoT) services are directly deployed on resource-constrained smart devices. Smart devices are characteristic by spatial and temporal constraints and energy limitations. A single IoT service cannot meet the complex requirements of users, so multiple IoT services need to be combined to provide services to users. As more and more smart devices are deployed in IoT, how to select IoT services with lower energy consumption and better quality of service (QoS) for service composition becomes a challenging problem. Combined with the characteristics that the data information of IoT is closely related to geographical location, the GeoHash algorithm is used to locally screen services based on geographical location, so as to narrow the range of candidate services. For smart devices with energy constraints, this paper proposes a combined optimization model. The model considers not only the transmission energy consumption and switching energy consumption, but also the execution energy consumption when the device provides services. In order to balance QoS attributes and energy consumption, the composition problem is regarded as a multi-objective optimization problem and solved using a genetic algorithm (GA). The simulation results show that service composition scheme selected by this service composition optimization model has lower energy consumption and higher service quality.
Locally Linear Embedding Based on t- Distribution Sailfish Optimization Algorithm

In the field of machine learning (ML), locally linear embedding (LLE) algorithm, as a nonlinear dimensionality reduction method of manifold learning, has become an indispensable tool, is very effective in handling high-dimensional data arrays. The core idea of LLE algorithm is to find the proper mapping of data points from high-dimensional space to low-dimensional space, with their algebraic topology unchanged. LLE is usually transformed into two optimization problems, constrained optimization and unconstrained optimization, which are usually solved by gradient descent (GD) method or stochastic gradient descent (SGD) method. However, gradient dependent methods often fall into the trap of local minimum and cannot find the global optimal solution. Based on the idea of employing swarm intelligence algorithm, in this paper, a t-distribution sailfish optimization algorithm has been introduced to overcome the problem of global optimization in the original LLE method. Via numerical experiment, it has been proved that the proposed sailfish optimization algorithm can improve the robustness and the effectiveness of the algorithm.
Thematic grouping for messages in major events

The process of information evaluation may compete with the decision making process by requiring the limited cognitive resources. In the case of Major Events, such as the Football World Cup or the Olympic Games, the thematic grouping of every information may be overwhelming. The theme switching caused by information associated with a different thematic group may be modelled as an interruption in multitasking set-ups. Thus, automating the thematic grouping of information may facilitate the decision making process by reducing the theme switching for a decision maker when he reads a set of messages. In this paper were used clustering techniques with multi-criteria to group the messages in themes. These criteria were implemented as configurable operators. To achieve a better comprehension of those parameters, we introduced the concepts of Thematic Strength (TS) and Thematic Density (TD). The evaluation of the strategy was made over a set of operational messages available in the Pacificador system during the 2014 FIFA World Cup.
Trends in the Development of Labor Migration in the Educational Space of the Russian School in the Context of COVID-19

The presented article is aimed at identifying the leading trends characteristic of the processes of professional migration in the educational space of the Russian school. The relevance of this work lies in the fact that the noted trends are revealed during a new (regular) wave of the spread of coronavirus infection. The paper shows that covid-19 significantly intensified the processes of labor migration in the modern Russian school. These processes are analyzed based on data obtained during a sociological survey conducted in August-September of 2021. The intensification of labor migration processes has resulted in the fact that a significant number of teachers who have reached retirement age are leaving school today. Content analysis showed that this is due, firstly, to the concern of this category of teachers for their health, and, secondly, to a weak level of knowledge of modern information technologies, on which distance learning is mainly based. Another revealed trend of professional migration in the educational space of the Russian school in the conditions of the spread of covid-19 was that new teachers come to the place of outgoing teachers of retirement age – pedagogical workers who either do not have a systematic basic pedagogical education in a significant number, because they received it through professional retraining courses, or yesterday's graduates of pedagogical areas of training in the higher education system, due to natural reasons, having no experience in school. The consequences of the identified trends of professional migration for the system of general (school) education in Russia have been analyzed at the end of the work.
Resume Screening and Recommendation System using Machine Learning Approaches

Candidates apply in large numbers for jobs on web portals by uploading their resumes, due to the rapid growth of online-based recruitment systems. On the other hand, the resume has its formatting style, data blocks, and segments, as well as a variety of data formatting options such as text alignment, color, font type, and font size, making it an excellent example of unstructured data. As a result, filtering applicants for the appropriate position in an organization becomes a difficult task for recruiters. We can use Natural Language Processing (NLP) techniques to extract the relevant information from the resume to save time and effort. Also, a Machine Learning (ML) model is trained to check whether a candidate’s skills, experiences, and other aspects are suitable for that particular role. In addition to that, our system will also recommend the other available job roles based on the candidate’s skillset.
EARThD: an effort to make East African tephra geochemical data available and accessible

&amp;lt;div&amp;gt;Tephra deposits are excellent chronostratigraphic markers that are prolific and widespread in portions of the East African Rift (EAR). Arguably one of the most powerful applications of tephrochronology is the establishment of regional chronological frameworks, enabling the integrated study of the timescales and interaction of the geosphere, hydrosphere, and biosphere. In order for these disparate disciplines to integrate and fully utilize the growing number of available tephra datasets, infrastructural efforts that centralize and standardize information are required. Of particular importance to these efforts is digitizing and standardizing previously published datasets to make them discoverable in alignment with current FAIR data reporting practices. &amp;amp;#160;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;lt;br&amp;gt;EARThD is a NSF funded data compilation project that has integrated and standardized geochemical and geochronological data from over 400 published scientific papers investigating tephra datasets from the East African Rift. Our team has trained 15 undergraduate students in spreadsheet data entry and management, data mining, scientific paper comprehension, and in East African tephrochronology. We utilize an existing NSF-supported community-based data facility, Interdisciplinary Earth Data Alliance (IEDA), to store, curate, and provide access to the datasets. We are currently working with IEDA to ensure that data generated from EARThD is ingested into the IEDA Petrological Database (PetDB) and ultimately EarthChem, making it broadly available. Here we demonstrate our data entry process and how a user can locate, retrieve, and utilize EARThD tephra datasets. With this effort we aim to preserve available geochemical data for posterity, fulfilling a crucial data integration role for researchers working in East Africa --especially those working at paleontological and archeological sites where tephra dating and geochemical correlations are critical. The EARThD compilation also enables data synthesis efforts required to address new science questions.&amp;lt;/div&amp;gt;
Workshop Pengembangan Media Pembelajaran Interaktif Kreatif Dalam Melaksanakan Tri Dharma Perguruan Tinggi Di SMK Widya Nusantara Bekasi

SMK Widya Nusantara is one of the best vocational schools in the city of Bekasi whose students and teachers have the hope of creating independent and characterized people who are based on faith and piety. The main element to be able to realize the hopes of the Vocational High School is that it has collaborated with universities in training activities and improving knowledge from various fields, so that they can compete in the era of globalization. Currently strengthening the competence of students and teachers is the main thing, because later they will be able to compete with other developed countries. To strengthen these competencies, training and simulations on the development of creative interactive learning media were carried out at SMK Widya Nusantara. It begins with a survey, then it is analyzed and finally the training activities are carried out. The result of this community service is that students and teachers are able to understand the creation of creative interactive learning media such as the use of a mentimeter, classroom google, youtube and the use of powerpoint, examples and their application, so that students and teachers have special knowledge about this which is useful for increasing competence and global competition.&#x0D; &#x0D; Keywords: learning media, interactive, creative, mentimeter
Live Migration in HPC

In the wake of digitalization, the need for highly efficient and available application programs is increasing. Hence arises the need for better resource utilization, fault tolerance and, process optimization approaches to make reliable and secure systems. A possible solution to the given problem is proposed in the live migration. The chapter starts with giving a brief overview of live migration and then proceeds with discussing two of the live migration approaches - live virtual machine(VM) migration and live container migration. Starting with live VM migration, the performance metrics concerning live migration along with various live VM migration techniques are explained. Next, it discusses the challenges in this approach and then reviews the security aspects associated with it. Then the chapter turns over to the second approach, live container migration. It begins with explaining different types of migration techniques and various case studies of implementation. This is then followed by understanding the performance measures and issues and then does a fair comparison between the two methods. Finally, the chapter concludes with the discussion on the various security problems and then gives its solution to take a step towards protection.
Psychological and Pedagogical Support of Professional Development of Students in an Inclusive University Environment

The article reflects the problem of psychological and pedagogical support of the educational process in an inclusive environment of the university, presents the results of experimental work aimed at the formation of professional and pedagogical motivation of students during university education, in which the study of disciplines of socio-psychological orientation that contribute to their successful professional development.
A Template To Obtain Information On Gravitational Mass Movements From The Spectrograms Of The Seismic Signals Generated

&amp;lt;p&amp;gt;We present a method to obtain a parameter (&amp;lt;em&amp;gt;b&amp;lt;/em&amp;gt;) that allows to analytically reproduce the shape of the increase in amplitude at high frequencies in time of the SON (Signal Onset) section of the spectrogram of seismic signals generated by gravitational mass movements (snow avalanches, lahars and debris flows) descending a slope and approaching a seismic sensor. This increasing shape is a consequence of the appearance of energy at high frequencies as the gravitational mass approaches the seismic sensor. The developed method to obtain the parameter (&amp;lt;em&amp;gt;b&amp;lt;/em&amp;gt;) allows to analytically reproduce the increasing shape of the SON section. Since this shape is related to the speed of the avalanche and the characteristics of the terrain, the parameter allows us to &amp;quot;classify&amp;quot; the mass movement with only one sensor. This methodology includes a link between the propagation properties of seismic waves and the results of the application of an image processing using the Hough transform.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Depending on the type of event, differences are obtained in the order of magnitude of the values of &amp;lt;em&amp;gt;b&amp;lt;/em&amp;gt;. The mean value of &amp;lt;em&amp;gt;b&amp;lt;/em&amp;gt; for lahars is around 0.003 s&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;, that for debris flows is an order of magnitude greater (0.017 s&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;) and an order of magnitude less than that for avalanches (0.12 s&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;). Furthermore, differences in &amp;lt;em&amp;gt;b&amp;lt;/em&amp;gt; are observed within each type of event. This fact allows us to create a template with different values of parameter &amp;lt;em&amp;gt;b &amp;lt;/em&amp;gt;to help in the classification within each type of mass movement by only superimpose graphically the corresponding spectrogram with the appropriate template when they are at the same scale.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Once the value of &amp;lt;em&amp;gt;b&amp;lt;/em&amp;gt; has been determined, the characteristics of the mass movement should be set according to the judgment of experts. This must be done for each site and for each type of gravitational mass movement. The application to one lahar and one debris flow is presented as an example.&amp;lt;/p&amp;gt;
Kidney and Kidney Tumor Segmentation Using a Two-Stage Cascade Framework

Automatic segmentation of kidney tumors and lesions in medical images is an essential measure for clinical treatment and diagnosis. In this work, we proposed a two-stage cascade network to segment three hierarchical regions: kidney, kidney tumor and cyst from CT scans. The cascade is designed to decompose the four-class segmentation problem into two segmentation subtasks. The kidney is obtained in the first stage using a modified 3D U-Net called Kidney-Net. In the second stage, we designed a fine segmentation model, which named Masses-Net to segment kidney tumor and cyst based on the kidney which obtained in the first stage. A multi-dimension feature (MDF) module is utilized to learn more spatial and contextual information. The convolutional block attention module (CBAM) also introduced to focus on the important feature. Moreover, we adopted a deep supervision mechanism for regularizing segmentation accuracy and feature learning in the decoding part. Experiments with KiTS2021 testset show that our proposed method achieve Dice, Surface Dice and Tumor Dice of 0.650, 0.518 and 0.478, respectively.
A Study on the Cultivation Plan Service at the Preliminary production phase based on bigdata analysis

This focuses on the Cultivation Plan Service at the preliminary production phase is critical in that it supports agricultural producers’ decision by providing related information such as predicted crop production or expected profits for consulting or other agricultural information when they plan to cultivate. This paper describes the reference architecture of the farming sector will benefit immensely from the implementation of farming data in farming contents repository which will serve as the knowledge base for the Cultivation Plan Service at the pre-production stage based on Bigdata analysis.
Frequency Spectrum Modification Process-Based Anti-Collusion Mechanism for Audio Signals

The collusion attack combines multiple multimedia files into one new file to erase the user identity information. The traditional anti-collusion methods (which aim to trace the traitors) can defend the collusion attack, but they cannot well defend some hybrid collusion attacks (e.g., a collusion attack combined with desynchronization attacks). To address this issue, we propose a frequency spectrum modification process (FSMP) to defend the collusion attack by significantly downgrading the perceptual quality of the colluded file. The severe perceptual quality degradation can demotivate the attackers from launching the collusion attack. Because FSMP is orthogonal to the existing traitor-trace-based methods, it can be combined with the existing methods to provide a double-layer protection against different attacks. In FSMP, after several signal processing procedures (e.g., uneven framing and smoothing), multiple signals (called FSMP signals) can be generated from the host signal. Launching collusion attack using the generated FSMP signals would lead to the energy disturbance and attenuation effect (EDAE) over the colluded signals. Due to the EDAE, FSMP can significantly degrade the perceptual quality of the colluded audio file, thereby thwarting the collusion attack. In addition, FSMP can well defend different hybrid collusion attacks. Theoretical analysis and experimental results confirm the validity of the proposed method.
Penerapan Algoritma Naïve Bayes untuk Mengidentifikasi Strategi Marketing dalam Penjualan Deposit E-Money

This research is based on very rapid technological developments and in the current Covid-19 pandemic, all people are very careful in everything, keep their distance from other people and continue to reduce direct contact with other people, including when transacting. One option to help reduce direct contact with other people when transacting is to use E-Money. Currently, various kinds of E-Money products have started to appear, there are 4 E-Money which are quite widely used by the people of Indonesia, namely GOPAY, OVO, DANA and LINKAJA. In this study, the author wants to classify between the four E-Money, which one is the best-selling product in the sale of E-Money deposits. By using the help of the RapidMiner application, researchers process a Sales Performance data from one of the fintech Start-up applications. Of the total 67,971 data, divided into 2, namely 54,376 data used as training data and 13,595 data for testing data.
Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images

Kidney cancer is one of the top ten cancers in the world, and its incidence is still increasing. Early detection and accurate treatment are the most effective control methods. The precise and automatic segmentation of kidney tumors in computed tomography (CT) is an important prerequisite for medical methods such as pathological localization and radiotherapy planning, However, due to the large differences in the shape, size, and location of kidney tumors, the accurate and automatic segmentation of kidney tumors still encounter great challenges. Recently, U-Net and its variants have been adopted to solve medical image segmentation problems. Although these methods achieved favorable performance, the long-range dependencies of feature maps learned by convolutional neural network (CNN) are overlooked, which leaves room for further improvement. In this paper, we propose an squeeze-and-excitation encoder-decoder network, named SeResUNet, for kidney and kidney tumor segmentation. SeResUNet is an U-Net-like architecture. The encoder of SeResUNet contains a SeResNet to learns high-level semantic features and model the long-range dependencies among different channels of the learned feature maps. The decoder is the same as the vanilla U-Net. The encoder and decoder are connected by the skip connections for feature concatenation. We used the kidney and kidney tumor segmentation 2021 dataset to evaluate the proposed method. The dice, surface dice and tumor dice score of SeResUNet are 67.2%, 54.4%, 54.5%, respectively.
Deploying the ITU-T P.1203 QoE model in the wild and retraining for new codecs

This paper presents two challenges associated with using the ITU-T P.1203 standard for video quality monitoring in practice. We discuss the issue of unavailable data on certain browsers/platforms and the lack of information within newly developed data formats like Common Media Client Data. We also re-trained the coefficients of the P.1203.1 video model for newer codecs, and published a completely new model derived from the P.1204.3 bitstream model.
Supplemental Material: Fingerprinting the metal source and cycling of the world’s largest antimony deposit in Xikuangshan, China

Table S1: Sulfur isotopic compositions of stibnite in the Xikuangshan deposit.
A Holistic Approach for Enhancing English Speaking Proficiency

Speaking English is one of the essential skills required by students nowadays due to its significant advantages and influences in every aspect of life, especially in education. Most of the countries worldwide have implemented English as one of the school subjects in their curriculum having the purpose to escalate their language proficiency and hence, steering them to compete in the global network. This article aims to explore students’ speaking proficiency encompassing the nature of speaking, factors affecting students’ speaking problems, factors for high speaking proficiency, and teachers’ strategies in teaching English. The data are gathered from various sources such as research findings, articles, online books, and theses. The result showed that students’ speaking problems are caused by several factors which are divided into two groups; Internal and External Factors. Internal factors consist of students’ problems such as fear of making mistakes, hesitation, lack of motivation, and many others. External factors refer to students’ environmental problems including friends and other surroundings. Besides, this article also found that teachers’ strategies used in teaching English in the classroom and their efforts to become effective teachers assisted the students in improving their speaking skills. Overall, this article concludes that collaboration between teachers and students is a requirement to promote students’ speaking proficiency.
On the angle-dependent vibrational behavior of fiber composite plates and its implications for musical instrument making

Composite materials facilitate the control of specific properties in components while varying the type, angle, and order of individual fiber weaves in the laminate. This possibility of synthesizing material properties has aroused great interest in musical instrument making since the availability of synthetic fiber composites in the 1970s. However, when arranging plies, the combination of weave types and angles can lead to vibroacoustic effects which are unusual for makers used to working with wood. The mechanics behind these effects are described, starting with an outline of the theory of vibrations in thin plates. Further, the consequences of rotating fibers are theoretically derived and, subsequently, examined in a series of measurements on rectangular thin plates as well as assembled violin top plates. From the findings obtained, it can be concluded that the specific characteristics have to be taken into account for a successful use of composite materials in musical instrument making. This paper, therefore, concludes with easy-to-understand recommendations for musical instrument makers when using fiber composites.