Reviewerly/exHarmony huggingface dataset:
Knowledge Graph-based User Portrait Construction for Electricity Enterprise Suppliers

Common user profile construction methods usually analyze user data from a single perspective to extract feature attributes and build user profiles. Since the correlation between different attribute features of users and the deviation of analysis perspectives are ignored, the constructed user portraits are low in granularity and poor in accuracy. To address the above problems, the study constructs the user portrait of electric power enterprise suppliers based on knowledge graph. The supplier knowledge graph is built by integrating and calculating the basic information data of suppliers. The basic information of power suppliers and the data in the supply process are used as the basic data sources, and after data processing, evaluation indexes are selected and the power suppliers are evaluated by using hierarchical analysis method. Combining the built knowledge map and evaluation results, a user profile labeling system is established to complete the construction of user portraits. The simulation test results show that the recall rate of the constructed user portrait can reach 98.87%, and the constructed user portrait has high granularity, which is better for practical application.
Research Infographic: An Innovative Instructional System Design Teaching Steps In Writing a Research Paper

Research is viewed as a complex subject matter for students of all levels and various disciplines, as observed by the Research Center of the Dalubhasaan ng Lunsod ng San Pablo (DLSP). According to the pre-test conducted, 62% of the Grade 11 students in DLSP Integrated School answered that they do not like research as a subject. Moreover, 94% of students answered that they do not wish to learn more about research. Naturalistic observation suggests a better alternative to conventional classroom teaching strategies. An infographic ISD module was conceptualized using the ADDIE Model. After being exposed to the ISD module, more of the target learners were able to identify and analyze the steps in writing a research paper. Using McNemar's test, there was a significant difference in the target learners' idea in steps in making a research paper, with a staggering result of p= .000. A significant difference was detected in the student's desire to learn more about research after viewing the ISD module with the pre-and post-intervention of p= .017. Some of the generalizations are; there is nothing to improve in the ISD module because it is what they need to learn about research, and it is informative and easy to understand. An additional criterion of the ISD module effectivity was contributed by the reported performance evaluation of the target learners. Their final research outputs scored 85% - 93% as an average rating.
BLOCKSET (Block-Aligned Serialized Trees)

We present methods to serialize and deserialize gradient-boosted trees and random forests that optimize inference latency when models are not loaded into memory. This arises when models are larger than memory, but also systematically when models are deployed on low-resource devices in the Internet of Things or run as cloud microservices where resources are allocated on demand. Block-Aligned Serialized Trees (BLOCKSET) introduce the concept of selective access for random forests and gradient boosted trees in which only the parts of the model needed for inference are deserialized and loaded into memory. %BLOCKSET combines concepts from external memory algorithms and data-parallel %layouts of random forests that maximize I/O-density for in-memory models. Using principles from external memory algorithms, we block-align the serialization format in order to minimize the number of I/Os. For gradient boosted trees, this results in a more than five time reduction in inference latency over layouts that do not perform selective access and a 2 times latency reduction over techniques that are selective, but do not encode I/O block boundaries in the layout.
Exploration and Practice on Professional Development in Vocational Schools under New Circumstance

With the implementation of “Double High-quality Plan”, vocational schools are on their new journey to explore and practice ways of professional development which includes formulation of talent training plans, faculty development and curriculum design. It is a long-lasting and systematic project which is based on both national and regional economic development, and should demonstrate the characteristics of vocational education. The continuous exploration and implementation of professional development under new circumstance can improve the vitality and competitiveness of vocational schools, making them more adaptive to social development and economic development and with more prominent characteristics.
Deep Dive into Directory Traversal and File Inclusion Attacks leads to Privilege Escalation

In Modern Web application directory traversal vulnerability that can potentially allow an attacker to view arbitrary files and some sensitive files. They can exploit identified vulnerabilities or misconfigurations to obtain root privileges. When building the web application, ensure that some arbitrary file is not publicly available via the production server. when an attacker can include. Traversal vulnerabilities this vulnerability exploits the dynamic file include a mechanism that exists in programming frameworks a local file inclusion happens when uncontrolled user input such as form values or headers for example are used to construct a file include paths. By exploiting directory traversal attacks in web servers, they can do anything and with chaining with code injection they can upload a shell into a web server and perform a website defacement attack. Path-traversal attacks take advantage of vulnerable Website parameters by including a URL reference to remotely hosted malicious code, allowing remote code execution and leads to privilege escalation attack.
Intelligent Classification Method for Web page Manipulation Detection

Abstract In this paper, we endorse fake detection, a questionable Twitter URL identity system. Our system looks at URL links isolated from certain tweets. Given that aggressors have limited assets and usually reuse them, the URLs of their distracting chains often share similar URLs. We construct techniques to detect and evaluate their doubt by using the frequently exchanged URLs. We acquire numerous public Twitter tweets and build an observable classification model for them. Assessment results indicate that the classifiers identifies questionable URLs correctly and efficiently. Furthermore, we pose fake DETECTION information to circulate within the Twitter as a connected to ongoing mechanism to order dubious URLs.
Workshop for Improvement of Learning by Myself (Mechanics of Materials)

It was thought that students would improve their learning habits by themselves, by recognizing differences in interpretations of learning between students. Therefore, a group work was carried out to recognize the interpretation of learning. In the group work, students promoted learning by themselves from the diversity of interpretations of learning. The activation of learning led to the formation of an identity for engineer. The formation of the identity for an engineer led to the learning of problem-solving ability.
Using Distribution Divergence to Predict Changes in the Performance of Clinical Predictive Models

Clinical predictive models are vulnerable to degradation in performance due to changes in the distribution of the data (distribution divergence) at application time. Significant reductions in model performance can lead to suboptimal medical decisions and harm to patients. Distribution divergence in healthcare data can arise from changes in medical practice, patient demographics, equipment, and measurement standards. However, estimating model performance at application time is challenging when labels are not readily available, which is often the case in healthcare. One solution to this challenge is to develop unsupervised methods of measuring distribution divergence that are predictive of changes in performance of clinical models. In this article, we investigate the capability of divergence metrics that can be computed without labels in estimating model performance under conditions of distribution divergence. In particular, we examine two popular integral probability metrics, i.e., Wasserstein distance and maximum mean discrepancy, and measure their correlation with model performance in the context of predicting mortality and prolonged stay in the intensive care unit (ICU). When models were trained on data from one hospital’s ICU and assessed on data from ICUs in other hospitals, model performance was significantly correlated with the degree of divergence across hospitals as measured by the distribution divergence metrics. Moreover, regression models could predict model performance from divergence metrics with small errors.
Implications of Abraham Maslow’s Hierarchy of Needs Theory on Counselling Practice in Nigeria

The paper examines Abraham Maslow’s hierarchy of needs theory and its implications on counselling practice in Nigeria. It argues that people are born with the same instinctive needs that enable them to grow, develop, and fulfill their potentials. However, counselling practice enables individuals to set goals for themselves and achieve such goals by mobilizing their potentials. It stresses free will, which is the ability of human to make choices by freely choosing to live more creative, meaningful and satisfying lives. It is the contention of this that Maslow’s theory is applicable to counselling in Nigeria since it enables parents, guardians and other stakeholders to detect and provide the basic needs of their children for an enhanced personality development. It also guides counsellors in studying human needs in relation to their behavior, and provides a framework to detect and provide the needs of students to enhance academic performance among others. The paper therefore, recommends that, government and non-governmental organizations should consider initiating enlightment programmes that would encourage people to willingly seek professional help in times of mal-adjustment; schools and other government agencies should periodically organize seminars and workshops to sensitize parents, guidance and caregivers to help them detect the basic needs of their children; and counsellors at all times should study the behavior of individuals in relation to their needs and desire in the course of providing professional help to them, among others.
Research on Performance Scalability of State Grid chain-Data Side chain

Blockchain is a global research hotspot with the technical characteristics of building trust at low costs in open networks. However, the blockchain scalability problem has become the bottleneck of further application development, with the fast and widely spreading applications. This paper first analyzes the endogenous reasons that restrict the performance improvement of blockchain, and then summarizes the technology principles of three-layer blockchain scalability proposed by the current academia and enterprises, and makes a comparative analysis on the scalability effect, technical difficulty and practical application. Finally, we propose a distributed autonomous management solution for area partition and business channel, which guides an easy and executable scalability route for the State Grid chain-data side chain (SG-DSC).
A Literature Review on: Handwritten Character Recognition Using Machine Learning Algorithms

Due to wide range of applications such as banking, postal, digital libraries, handwritten character realization is precise sizzling. The maturation comedian in the area of handwritten character processing is application forms processing, digitizing old articles, postal code processing and bank transaction processing as well as many various others applications. The device’s handwritten recognition interprets the handwritten characters or phrases of the user into a format understandable by the computer machine. For efficient recognition, many machine learning and deep learning approaches have been planned. We have demonstrated a thorough study of handwritten character recognition phases and various strategies and methods for machine learning and deep learning in this paper. The primary oblique of this paper is to ascertain efficient and trustworthy motion to handwritten character recognition. For character approval, several machines have been used to learn algorithms such as support vector machine, Naive Bayes, KNN, Bayes Net, random forest, logistic regression, linear regression and random tree.
MUSICAL CONTENT AS A MEDIA TEXT

The relevance of the chosen subject area is determined primarily by the complexity of structural, semantic and communicative organization of the media text phenomenon due to its sign character, plurality of sign systems, correlation of the author and his audience in terms of understanding and decoding textual information, lying in the planes of different approaches to defining the media text concept as well as its analysis. From the viewpoint of modern semiotics, each sign holds a special meaning, is used to store and transmit information, functions in a sign situation, interacts with other elements of the sign system, has clearly defined boundaries, which allows it to be separated from other signs, while musical content is a system of signs expressed by notes that reflect the pitch and duration of the sound, where the note (from Latin, nota, «sign») is a conventional graphic sign, located on the note state and indicating the pitch and relative duration of any sound (in some cases it is also a playback method). Musical semiotics emerged later than other areas of semiotics. The researchers are still debating whether music is a means of transmitting, storing and processing information. Nowadays, musical content as a media text constitutes an integral part of the sound space, in which the role of music does not fundamentally change, as it continues to fulfill a specific function of conveying artistic information that can be emotionally and intellectually consumed, processed and compiled by using modern media technology. Although any media text has a clear focus on the given audience and the specific result, which leaves no room for perception options, it still induces changes in the individual worldview precisely because of the impact on the recipient’s emotions. The only peculiarity of the sound of musical content in the media is that it usually ceases to look like an integral work, and often has a quotation form to influence the viewer or listener as much as possible, in order to form a positive/negative perception of visual/audio object. The variety of modern media makes it possible to broadcast autonomous music (integral, complete musical works, primacy) and applied (discrete, secondary, compiled and subordinate to the text), which carries artistic information, models behavior and influences emotions.
The Predictive Genetic Algorithm (GA) Load Management Mechanism for Artificial Intelligence System Implementation (AI)

The next generation of cloud infrastructure will allow the network more versatile and useable resources effectively. Load balancing is one of the key issues of cloud computing which distributes the task over many nodes to insure that no one tool becomes overwhelmed which underused. The user must be willing to guarantee that all criteria are fulfilled in a limited span of time for optimal performance for applications which are cloud dependent almost every day. The cloud load genetic algorithm (GA) approach is given in this article. Depending on the population initialization period, the urgency of the proposal is considered. The emphasis is on the idea of imaging the universe in question. Systems for real-life situation have other targets for our algorithm to be mixed. The suggested method is modeled using cloud analyst. A simulation of cloud infrastructure is feasible. The end result will reveal the viability of a quantitative workload management approach that would help manage working loads with an improved use of computational capital. This article offers a new approach to genetic algorithm (GA) load control. To order to minimize the difficulty of a single task, the algorithm handles the cloud storage load. The proposed load balancing strategy was evaluated by a program analyst model. The findings of simulations for a typical sample system show that the suggested algorithms exceeded real methods like FCFS, round robin (RR), and the local search algorithm of stochastic hill climbing (SHC).
A detailed review on Prevailing Image Captioning Methods using Deep Learning Techniques

The availability of a camera within the smartphone has amplified the use of multimedia to staggering levels. A vast number of images raises technological demand for automatic image understanding, leads to index, classify and search these images better. The big challenge lies in obtaining semantic information from images and translating that into the human language using the machine. The interaction of computer vision and natural language processing further increase the complexity of Image Captioning. Various approaches are available to solve and narrow down the semantic gap effectively. A study is carried out to identify the state-of-the-art techniques for image captioning. In this paper, existing deep learning methods discussed systematically. The quantitative evaluations of representative approaches to benchmark datasets also presented and discussed.
Marchetti type of aircraft engine error detection system using hill climbing heuristic

Transportation has become a human need today, one of which is air transportation, namely airplanes. In order for the aircraft to be used properly and comfortably, it is necessary to carry out routine maintenance, especially before flying. Problems caused by aircraft engines are sometimes small problems that do not require a high level of knowledge. To overcome this, maybe someone who has very basic knowledge of aircraft engines can do it. But sometimes these problems also require a high level of expertise regarding these components so that they require technicians or experts to repair them. Through this application, users can consult the system such as consulting with experts to find out the symptoms that occur on the plane and find solutions to the problems encountered. The expert system built using the Hill Climbing Heuristic method resulted in 72.67% when tested on respondents.
Sumy region wind music festivals from the standpoint of cultural and artistic traditions of the region

The article considers such kind of musical activity as festivals of wind music. As a result of research, the emergence and development of some orchestral festivals held in Sumy region at different levels of performance have been traced. Their role in aesthetic and patriotic education is determined, as well as the need for such measures to promote orchestral wind music, preservation and further development of orchestral traditions, which are important both in military rituals and in public life of the region. It has been found that nowadays wind music festivals are a major factor in the development of this genre of performance. Despite the rather wide coverage of the festivals, the events in the genre of orchestral wind music taking place at different levels of performance in Sumy region, did not receive due attention. Coverage of this issue is necessary for proper understanding and analysis of the state of performance on wind instruments in the region. The material of the article will be useful for scholars who study the festival movement and develop issues of history and development of wind music in our country.
FastPRS: Augmenting Fast and Hidden Query in EPR Systems via Online/Offline Puncturable Search

Electronic patient record (EPR) gains popularity in the health care system with easy availability and usually employs searchable encryption to guarantee both record confidentiality and record retrieval. However, traditional searchable encryption schemes cost a linear complexity of database and even reveals the query index. Both problems hinder EPR applications thereby balancing privacy and search efficiency becomes challenging. In this article, we design a cryptographically searchable EPR system named FastPRS which supports strong privacy and high performance. FastPRS is built on top of online/offline puncturable search (OOPS), a newly defined primitive which splits the search process into the offline phase and the online phase and embeds an index in puncturable pseudorandom sets. Leveraging a combination of linearly homomorphic encryption and oblivious transfer, the query index is punctured. OOPS, therefore, hides the query index obliviously and reduces the search complexity to a sublinear size. We also implement a FastPRS prototype system that supports more than 60 000 keywords and evaluation results show that FastPRS optimizes the search efficiency to 417 search operations per millisecond.
Deep Neural Networks for End-to-End Spatiotemporal Video Quality Prediction and Aggregation

We propose an end-to-end deep neural network-based approach for full-reference video quality assessment (VQA). Many VQA methods predict local quality first and then apply a pooling mechanism to get a global score. However, these two steps are mostly independent of each other thus could not consider spatial and temporal information of a video simultaneously. The proposed method combines local quality prediction and aggregation together with a unified network that is trained in an end-to-end manner. To be specific, we predict the quality of local spatiotemporal segments with an attention-based convolutional neural network. Furthermore, we propose a spatiotemporal aggregation network (STAN) to allocate adaptive weights to localized quality scores. The aggregation network adopts Convolutional 3D (C3D) kernels to provide a better representation to characterize the quality of videos. Experiment results demonstrate that our method achieves superior performance in comparison with state-of-the-art methods on commonly used video quality datasets.
The Relation between the Administrative Empowerment and Creative Behavior at Jordanian Private Universities

The purpose of this study is to identify the relation between the administrative empowerment and creative behavior at Jordanian private universities, the objectives of this study focus on determining the level of administrative innovation in Jordanian private universities from the employee’s perspectives. Further, knowing of the impact of the administrative empowerment on the creative behavior and discrimination in the development of work from workers point of view Jordanian private universities.The study relies on a descriptive analytical method. The questionnaire was designed by researchers, and (150) questionnaires were collected from departments and people in Jordanian private universities. After that, the appropriate statistical analysis was applied, standard deviations, and choose multiple regression. The results showed that the level of application of empowerment and administrative creatively were moderate and showed that same of empowerment in the application level of administrative innovation in all its components. It also showed an effect of years of experience at the level of application of empowerment. The study resulted presenting several recommendations including: strengthening the application of empowerment and activating through appropriate training, and enhancing the ability to take risks, in addition to deploying it and effective means of communication between the employees and the importance of delegation of authority between the administrative levels.