Reviewerly/exHarmony huggingface dataset:
Scientometric Assessment of Publications on Electronic Resource Management and Electronic Resource Management System during 2000-2019

Electronic Resource Management is an eminent research area, and Electronic Resource Management System has become now an inevitable part of any academic library system. As such, considerable literature has been published on ERM and ERMS. Therefore, this article attempts to analyze the growth of scholar’s research output in the areas of electronic resource management, electronic resource management system during the period 2000 to 2019. This article examines 347 publications across the globe on the topic of “ERM” and “ERMS”. The analysis on these topics has been done, taking into account the total research output of these main areas as reflected in the SCOPUS database. The present study analyses the year-wise cumulative growth of research publications, list of top highly cited articles, list of core journals, the pattern of authorship, most prolific authors, types of publications preferred by the authors in the field of electronic resource management and electronic resource management system.
Organizador visual facilita la conceptualización en aprendizaje cooperativo de estudiantes de primer ciclo de instituto

&nbsp;This descriptive research, worked under the four phases of the design of an action research, uses the qualitative technique of direct observation and as an instrument the checklist. It contributes to improve classroom learning practice in the creation of the concept, in the face of confusion of ideas due to lack of understanding of the objective and diversity. The population intervened are the first cycle students of a section of the Creativity course of an institute. Cooperative learning and the visual organizer tool were used together with the visual and auditory learning style. The unintervened population; another section of the same course worked cooperatively with a concept generation technique. It concludes; In the section that used the co-instructional tool, he facilitated the understanding of the objective and the creation of the concept, culminating his academic work satisfactorily, unlike the unintervened section.
Image Authentication System using Ring Partition and GLCM

An image hashing for content authentication has been paid large attention from researchers. But, simultaneous achievement of robustness to geometric distortions, good discrimination, and identifying the areas of tampered regions in an image is still an open issue. To resolve the above issue, the proposed system includes ring partition with Gray level cooccurrence matrix ( GLCM), an exemplar-based saliency detection, and a blind geometric correction. First, the global features are extracted based on GLCM from rotations invariant regions, i.e., via ring partitions. Next, the local features are extracted using an exemplar-based saliency detection method. The two features are concatenated to form a final hash. At the time of image authentication, the geometric transformations are mitigated via a blind geometric transformation correction approach. The experiment results carried out on large standard image pairs show that the proposed provide better robustness, good discrimination, and identified the tampered areas. The efficacy of the proposed is shown using a true positive rate (TPR) and false positive rate (FPR).
GDPR-inspired IoT Ontology enabling Semantic Interoperability, Federation of Deployments and Privacy-Preserving Applications

Testing and experimentation are crucial for promoting innovation and building systems that can evolve to meet high levels of service quality. IoT data that belong to users and from which their personal information can be inferred are frequently shared in the background of IoT systems with third parties for experimentation and building quality services. This sharing raises privacy concerns, as in most cases, the data are gathered and shared without the user's knowledge or explicit consent. With the introduction of GDPR, IoT systems and experimentation platforms that federate data from different deployments, testbeds, and data providers must be privacy-preserving. The wide adoption of IoT applications in scenarios ranging from smart cities to Industry 4.0 has raised concerns for the privacy of users' data collected using IoT devices. Inspired by the GDPR requirements, we propose an IoT ontology built using available standards that enhances privacy, enables semantic interoperability between IoT deployments, and supports the development of privacy-preserving experimental IoT applications. We also propose recommendations on how to efficiently use our ontology within a IoT testbed and federating platforms. Our ontology is validated for different quality assessment criteria using standard validation tools. We focus on “experimentation” without loss of generality because it covers scenarios from both research and industry that are directly linked with innovation.
FPGA deep learning acceleration based on convolutional neural network

In view of the large amount of calculation and long calculation time of convolutional neural network (CNN), this paper proposes a convolutional neural network hardware accelerator based on field programmable logic gate array (FPGA). First, through in-depth analysis of the forward operation principle of the convolutional layer and exploration of the parallelism of the convolutional layer operation, a hardware architecture of input channel parallelism, output channel parallelism and convolution window deep pipeline is designed. Then in the above architecture, a fully parallel multiplication-addition tree module is designed to accelerate the convolution operation and an efficient window buffer module to implement the pipeline operation of the convolution window. The final experimental results show that the energy efficiency ratio of the accelerator proposed in this article reaches 32.73 GOPS/W, which is 34% higher than the existing solution, and the performance reaches 317.86 GOPS.
Sistem Pakar Diagnosa Penyakit Tanaman Rambutan dengan Metode Forward Chaining dan Certainty Factor Berbasis Android

The purpose of this research is to create an expert system application to diagnose diseases in the rambutan plant that can be accessed anytime and anywhere so that the handling of the disease can be done appropriately. The Forward Chaining and Definity Factor methods are used because they are very suitable for expert systems that diagnose something that is uncertain so that the manual calculation results from the symptom data and the existing expert values result in a 98.91% confidence level percentage, and solutions are given to treat disease-stricken plants. The implementation uses Android Studio so that the application can run dynamically on various devices so that farmers can immediately find out what is happening with the conditions of their rambutan plants directly.Keywords:Rambutan Plant, Expert System, Android, Forward Chaining, Certainty Factor.
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation

Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame. This results in high-quality segmentation across challenging scenarios such as changes in appearance and occlusion. But it also leads to unnecessary computations for stationary or slow-moving objects where the change across frames is minimal. In this work, we exploit this observation by using temporal information to quickly identify frames with minimal change and skip the heavyweight mask generation step. To realize this efficiency, we propose a novel dynamic network that estimates change across frames and decides which path -- computing a full network or reusing previous frame's feature -- to choose depending on the expected similarity. Experimental results show that our approach significantly improves inference speed without much accuracy degradation on challenging Semi-VOS datasets -- DAVIS 16, DAVIS 17, and YouTube-VOS. Furthermore, our approach can be applied to multiple Semi-VOS methods demonstrating its generality. The code is available in https://github.com/HYOJINPARK/Reuse_VOS.
Methods and forms of educational process that ensure the formation of professional and value orientations of future technical specialists

Methods and forms of the educational process, which ensure the formation of professional and value orientations of future technical specialists are researched and theoretically grounded in the artticle. According to the classification by sources of knowledge, the following teaching methods are identified: verbal methods; visual methods; practical methods. The group of methods based on the word includes methods of storytelling, lectures and discussions, as well as methods of using educational and special scientific literature. The use of these methods should be combined with methods of education that are similar in nature: verbal, ethical narration, debate, ethical conversation, etc.&#x0D; Professional and value orientations of future professionals are one of the most important components of the personality structure, which show the attitude to the values of a profession and find expression in the interests, needs, views, assessments, motives of social and professional activities.&#x0D; The university forms in students not only basic professional knowledge, but also clear guidelines for life, teaches to separate true values from conventional ones, to perceive and evaluate complex phenomena and processes of the modern world. As during the student years there are the development of value orientations of the individual, the accumulation of knowledge, professional experience, awareness of their abilities, capabilities, there is a value of self-determination and self-improvement.&#x0D; Extracurricular work is proved to perform a leading function in the educational aspect in the process of formation of professional and value orientations of students of technical universities, as most of the methods and forms, due to lack of study time, it is advisable to conduct after classess. The system of extracurricular work is considered as a set of educational influences that provide purposefulness, systematicity, consistency, combination of pedagogical guidance with the initiative and initiative of students. The basic principles of this system are the professional and pedagogical orientation of the educational process, the relationship and interdependence of teaching and educating students; the connection of the educational process of a higher educational institution with pedagogical practice.
Unit Under Test Identification Using Natural Language Processing Techniques

Abstract Unit under test identification (UUT) is often difficult due to test smells, such as testing multiple UUTs in one test. Because the tests best reflect the current product specification they can be used to comprehend parts of the production code and the relationships between them. Because there is a similar vocabulary between the test and UUT, five NLP techniques were used on the source code of 5 popular Github projects in this paper. The collected results were compared with the manually identified UUTs. The tf-idf model achieved the best accuracy of 22% for a right UUT and 57% with a tolerance up to fifth place of manual identification. These results were obtained after preprocessing input documents with java keywords removal and word split. The tf-idf model achieved the best model training time and the index search takes within 1s per request, so it could be used in an Integrated Development Environment (IDE) as a support tool in the future. At the same time, it has been found that, for document preprocessing, word splitting improves accuracy best and removing java keywords has just a small improvement for tf-idf model results. Removing comments only slightly worsens the accuracy of Natural Language Processing (NLP) models. The best speed provided the word splitting with average 0.3s preprocessing time per all documents in a project.
Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings

While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an important role in tasks where words need to be modelled in the absence of linguistic context. In this paper, we explore how the contextualised embeddings predicted by BERT can be used to produce high-quality word vectors for such domains, in particular related to knowledge base completion, where our focus is on capturing the semantic properties of nouns. We find that a simple strategy of averaging the contextualised embeddings of masked word mentions leads to vectors that outperform the static word vectors learned by BERT, as well as those from standard word embedding models, in property induction tasks. We notice in particular that masking target words is critical to achieve this strong performance, as the resulting vectors focus less on idiosyncratic properties and more on general semantic properties. Inspired by this view, we propose a filtering strategy which is aimed at removing the most idiosyncratic mention vectors, allowing us to obtain further performance gains in property induction.
Image Annotation based on Deep Hierarchical Context Networks

Context modeling is one of the most fertile subfields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extrinsic relationships. However, the potential of context modeling is currently underexplored and most of the existing solutions are either context-free or restricted to simple handcrafted geometric relationships. We introduce in this paper DHCN: a novel Deep Hierarchical Context Network that leverages different sources of contexts including geometric and semantic relationships. The proposed method is based on the minimization of an objective function mixing a fidelity term, a context criterion and a regularizer. The solution of this objective function defines the architecture of a bi-level hierarchical context network; the first level of this network captures scene geometry while the second one corresponds to semantic relationships. We solve this representation learning problem by training its underlying deep network whose parameters correspond to the most influencing bi-level contextual relationships and we evaluate its performances on image annotation using the challenging ImageCLEF benchmark.
Decentralized and Fault Tolerant Cloud Service Orchestration

This paper proposes a decentralized framework for the orchestration of Cloud Services using heterogeneous resources residing in the homes of private individuals or small-scale clusters. The framework makes use of Ethereum Smart Contracts to provide a decentralized mechanism for discovering the different interfaces exposed by Cloud Components. The paper introduces a novel concept of Component Administration Networks, which are peer-to-peer networks that monitor and ensure the availability of the software components. The concept applied for the Orchestration process to ensure that the deployment of an Application continues in the presence of Orchestrator component failure. Checkpoints are used to address the continuity of the Management components, in general, and of the Orchestrator, in particular. In our proposal, checkpoint metadata is stored in a Smart Contract to assess the execution time of a Service to reimburse the participants that ensure its execution.
Role of Public and Children's Libraries in Egypt Facing the Phenomenon of Reading Difficulties (DYSLEXIA) : Exploratory Study

The study aims to explore the role of public and children's libraries and the extent of their contribution in dealing with the phenomenon of Reading Difficulties (Dyslexia) in Egypt and the necessary measures to provide appropriate and necessary services to dyslexic children, and to identify the challenges that prevent this from being achieved .The study relied on the field approach that uses the descriptive and analytical method as a tool for it, and the checklist was relied upon as a methodological tool for collecting data from the libraries under study, which numbered ten libraries, in addition to the interview their managers .The study concluded the following results: Absence of awareness in all libraries under study of dyslexic Children and their needs, and the apparent deficiency in providing activities and services for this category, lack of vision for future plans for these libraries to provide services for Dyslexic children. Accordingly, the researcher presented a set of suggested recommendations for public and children's libraries subject of study - in light of IFLA's guiding instructions - to lay the foundations for providing an excellent service to Dyslexic Children .
The Influence of the Inquiry Model on the Social Studies Content Learning Outcomes of Class IV Students: Study of literature:

This research is motivated by the problem, namely the low social studies learning outcomes of elementary school students. This study aims to determine how the inquiry learning model affects the learning outcomes of social studies content in grade IV elementary school students. The research method used is literature study research. This means reviewing research materials by collecting library data, reading, and recording data from research material sources that have been carried out previously. In this literature study research, the data collection was collected by reference, including 13 journals that were related to the inquiry learning model. From the results of the research that has been obtained, the inquiry model has an effect on the social studies learning outcomes of elementary school students, so that the three main results of the study were found, namely, encouraging student activity, fostering student motivation, and increasing student learning outcomes.
The Model of Distance Education of the Designer in the Frames of Computer Project Graphics

The article considers the potential of computer graphic modeling based on the experience of distance learning in design and art disciplines at the Department of Environmental design of the Stroganov Academy. An analysis of both practical and theoretical work of the Department during the semester made it possible to conclude that the model for the integration of full-time and distance learning, the individual and interactive approaches to the student is optimal for schools of creative arts. The experiment revealed the greatest flexibility and adaptability to the online format of the methodological material of the disciplines of graphic tools, its maximum openness to self-study, its role in innovative forms of design work, of the creative search for student’s own learning style.
State Representation Learning from Demonstration

Robots could learn their own state and universe representation from perception, experience, and observations without supervision. This desirable goal is the main focus of our field of interest, State Representation Learning (SRL). Indeed, a compact representation of such a state is beneficial to help robots grasp onto their environment for interacting. The properties of this representation have a strong impact on the adaptive capability of the agent. Our approach deals with imitation learning from demonstration towards a shared representation across multiple tasks in the same environment. Our imitation learning strategy relies on a multi-head neural network starting from a shared state representation feeding a task-specific agent. As expected, generalization demands tasks diversity during training for better transfer learning effects. Our experimental setup proves favorable comparison with other SRL strategies and shows more efficient end-to-end Reinforcement Learning (RL) in our case than with independently learned tasks.
Current Situation and Enlightenment of Trace Evidence Identification Technology in the Age of Artificial Intelligence

Trace evidence; Identification technology; Artificial intelligence
Target/Action in SwiftUI

In the previous chapter, we used a Coordinator as our delegate for a UITextView. But we also realized the coordinator setup is just a helpful mechanism.
ANALISIS PENGGUNAAN CAMPUR KODE DALAM CERAMAH K.H. BAHAUDDIN NUR SALIM

This research is a study of the use of code mixing in K.H's lectures. Bahauddin Nursalim downloaded on YouTube. The background of this research is the lecture has an important role in presenting oral explanations to the audience both for the present or the future. Communities will easily accept ideas from those who are adept at speaking in a variety of ways by lecturers. The purpose of this research is to describe the form of code mixing, types of code mixing, and factors for the occurrence of language code mixing in K.Hâ€™s lectures. Bahauddin Nursalim. This type of research is qualitative. The subject of this research is the speech K.H. Bahauddin Nursalim, while the object of research is mixed language code used by K.H. Bahauddin Nursalim in the lecture. Data obtained by downloading videos from YouTube channel Serambi Narukan as many as five episodes. Data collection was carried out by listening methods followed by tapping techniques, competent free involve listening techniques and note taking techniques. The method of data analysis was carried out interactively including, (1) Data reduction, at this stage the researcher listened to K.H's lecture. Bahauddin Nursalim which has been downloaded from YouTube to collect data mixed language codes. (2) Presentation of data, i.e. in this study the data is presented in tabular form so that it is easy to understand, then the data is narrated. (3) Withdrawal of Conclusions, i.e. the data are inferred based on the research objectives in a descriptive form. The results showed that (1) The form of code mixing in K.H's lectures. Bahauddin Nursalim in the form of words, phrases and clauses. (2) the type of code mix used in K.H's lecture. bahauddin Nursalim in the form of mixed code in, mixed code out, and mixed code mixed. (3) Factors of code mixing in K.H.'s lecture Bahauddin Nursalim includes speakers and speech partners in relaxed situations, speakers to show their intelligence, and the need for foreign language expressions because there is no appropriate language equivalent in use
The Time Has Come... To Move Many Things: Inventorying and Preparing a Collection for Offsite Storage

In the spring of 2019, the Montana State University (MSU) Library embarked on a large-scale inventory project that involved weeding and moving portions of their collection to an offsite storage facility within six months in order to create more student study space in the Library. The department primarily responsible for leading the project, Collections Access &amp; Technical Services, the result of two departments merging, was also simultaneously navigating their new structure and a remodel of their workspace thus adding further challenges to the project. This poster session demonstrated how MSU Library approached and completed this project by advocating to their Library Administration for additional resources, including hiring a project manager and third-party companies to assist with the inventory and moving of the collection. It also discussed the types of work groups formed to identify new workflows (i.e., retrieval of offsite items) and modify existing ones, involving student employees in the project, and internal and external collaborations that took place. Additionally, presenters shared strategies used to communicate to their campus community, and the impact this project has had on our patrons. They also included statistics that were gathered during the project including deselection figures, the number of materials that did not have barcodes and were not accounted for in the Library’s catalog and discovery layer (Ex Libris’ Alma and Primo), and what subject areas currently remain in the main library building.