Reviewerly/exHarmony huggingface dataset:
A Context-Aware Reminder System for Tracking SMS in Mobile Device Environment Using Artificial Neural Network

Reminder systems have always been a major companion of human life. With the proliferation of mobile devices such as mobile phones, smartphones and tablets, users readily rely on them by using such applications as calendar, notes and alarm systems provided by these devices to prompt them of impending activities. One major communication system employed in almost all human endevours is short messages (SMS) which are used for sending reminders and notices. However, there is no mechanism to remind users of mobile devices information contained in SMS. This paper implements a context-aware reminder system using a trained Artificial Neural Network (ANN) based on gradient-descent delta rule backpropagation algorithm as the reasoning engine. SMS which are classified as activity context are abstracted and normalised based on users’ preferences. The abstracted data is used to develop a reminder system which prompts the user when the date and time contained in the SMS are due. The developed system effectively retrieves and classifies data that followed the specified formats with 95% accuracy level. The work revealed that simple intelligent systems that eliminate cumbersome rule set architectures whilst taking into consideration the dynamics of users’ preferences are better than the existing systems
Discussion on <i>“Some Notes on Fusion”</i> By <i>Paolo Fonda</i>

Abstract In his conference, Paolo Fonda highlights the concepts of “micro-area of fusion” and of “separation coefficient”, which makes it possible to think in terms of quantitative factors. The perspective of the gradualness of some mechanisms or phenomena implies the existence of a theoretical continuum between psychotic and neurotic functioning. Another aspect addressed concern symbolic component versus concrete elements of the communication, and ambiguity as a way of moderating reality. Authors of prose and poetry are brought to support this psychoanalytic thinking.
Exploring EFL Teachers’ Beliefs about English Language Learning and Teaching: Evidence from Indonesia Context

This study explores English as Foreign Language (EFL) senior high school teachers’ beliefs about foreign language learning and teaching in Indonesia context. A survey design was used to obtain data about Indonesian EFL teachers’ beliefs from 75 English language teachers (32 males and 43 females). A convenience random sampling technique was used to select the respondents from various geographical areas within Indonesia. An adapted instrument was administered online to gather the data from those samples, and to analyze the data, descriptive statistics was utilized. Indonesian EFL teachers’ belief about foreign language learning and teaching is not yet conclusively revealed. While the beliefs can determine teachers’ classroom teaching behaviour, and uncover EFL teachers’ view on how language learned and taught; therefore, study on EFL teachers’ belief about language learning and teaching is worth conducting. The findings revealed that EFL teachers possess different perceptions about foreign language learning talent, the challenges in language learning and the nature of language learning and teaching. Unlike in some other parts of the world, English in Indonesia is considered as a foreign language, therefore this view will affect the Indonesian EFL teachers’ beliefs and practices. Keywords: English as Foreign Language, English Language Learning and Teaching, Teachers’ beliefs.
A Framework for an Assessment of the Kernel-target Alignment in Tree Ensemble Kernel Learning

Kernels ensuing from tree ensembles such as random forest (RF) or gradient boosted trees (GBT), when used for kernel learning, have been shown to be competitive to their respective tree ensembles (particularly in higher dimensional scenarios). On the other hand, it has been also shown that performance of the kernel algorithms depends on the degree of the kernel-target alignment. However, the kernel-target alignment for kernel learning based on the tree ensembles has not been investigated and filling this gap is the main goal of our work. Using the eigenanalysis of the kernel matrix, we demonstrate that for continuous targets good performance of the tree-based kernel learning is associated with strong kernel-target alignment. Moreover, we show that well performing tree ensemble based kernels are characterized by strong target aligned components that are expressed through scalar products between the eigenvectors of the kernel matrix and the target. This suggests that when tree ensemble based kernel learning is successful, relevant information for the supervised problem is concentrated near lower dimensional manifold spanned by the target aligned components. Persistence of the strong target aligned components in tree ensemble based kernels is further supported by sensitivity analysis via landmark learning. In addition to a comprehensive simulation study, we also provide experimental results from several real life data sets that are in line with the simulations.
Paperless Library Management System P.

This paper is based on a very new and easy-to-handle feature of the college's library management system. It is designed using .NET, HTML, CSS as the front end and SQL as the backend. This web application manages the college information, students' information, lecturers' information, and library staff members' information to keep track of authorized users of the system. It has question papers and answer papers which are sorted by department and subjects for the betterment of students. It contains every detail of a book which is available in the library. Using a barcode scanner, details of the user can be obtained for further evaluation. The main purpose of this paper is to provide users with details of available books in the library, issue/return books, and notify the users of their respective due dates for the issued books through the mail. It also possesses an automated mailing system. This paper is fully automated.
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations

Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid federated learning (TT-HF), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. In TT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of O(1/t) while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions.
Research on Path Planning of Mobile Robot with Improved Ant Colony Algorithm

Aiming at the problems of poor convergence and local optimization in path planning of basic ant colony algorithm, the paper studies an improved ant colony algorithm to improve the effect of mobile robot path planning. First, modify the state transition probability of the ant colony algorithm, and increase the influence of the angle on the node selection by adding a new angle index heuristic function; Then fusion sorting and elite ant colony algorithm strategy, research a path length difference pheromone update method, improve the efficiency of ant colony algorithm planning the optimal path; Finally, by comparing with the basic ant colony algorithm simulation on MATLAB, it can be seen that the feasibility and effectiveness of the improved ant colony algorithm are verified.
Coherent Perfect Absorption in Tavis-Cummings Models

We theoretically study the conditions under which two laser fields can undergo Coherent Perfect Absorption (CPA) when shined on a single-mode bi-directional optical cavity coupled with two two- level quantum emitters (natural atoms, artificial atoms, quantum dots, qubits, etc.). In addition to being indirectly coupled through the cavity-mediated field, in our Tavis-Cummings model the two quantum emitters (QEs) are allowed to interact directly via the dipole-dipole interaction (DDI). Under the mean-field approximation and low-excitation assumption, in this work, we particularly focus on the impact of DDI on the existence of CPA in the presence of decoherence mechanisms (spontaneous emission from the QEs and the leakage of photons from the cavity walls). We also present a dressed-state analysis of the problem to discuss the underlying physics related to the allowed polariton state transitions in the Jaynes-Tavis-Cummings ladder. As a key result, we find that in the strong-coupling regime of cavity quantum electrodynamics, the strong DDI and the emitter-cavity detuning can act together to achieve the CPA at two laser frequencies tunable by the inter-atomic separation which are not possible to attain with a single QE in the presence of detuning. Our CPA results are potentially applicable in building quantum memories that are an essential component in long-distance quantum networking.
A Third Glance at a Stylometric Map of Native and Translated Literature in Polish

This chapter presents a large-scale quantitative approach to literary translation. Most-frequent-word-based stylometry is used to distant read over 3,000 texts in Polish: originals by Polish writers and translations into Polish from several European languages. The patterns observed in network analysis of cluster-analysed distance measures suggest that texts translated into a language differ in most-frequent-word usage frequency from texts originally written in that language and from translations from other languages. This is an interesting stylometric manifestation of the phenomenon of translationese. Despite this difference, however, texts translated from some national literatures (French or English/American) exhibit an interesting parallelism to those periods in Polish literature which saw a heightened impact of either one or the other.
Thesauri and Terminologies as Sources for the Interoperability of Archaeological Data: The Case of Prospection Vocabulary

– Vocabulary-based approach of interoperability for archaeological data. – Use of thesaurus (ISO 25964) as semantic tools for building a “hub” between multiple scientific vocabularies and perennial librarian repositories, to allow “machine to machine” dialogue. – A bottom-up (from ground vocabulary to abstraction) and collective approach to build a disciplinary consensus.
SISTEM INFORMASI BIMBINGAN BELAJAR NUMBER ONE MEDAN BERBASIS WEB

During increasingly rapid technological developments, one of which is the internet, it is time for manual information processing to be gradually replaced with technological information management. With the development of gadgets and smartphones, which are generally owned by everyone, the availability of information can be obtained through the website. The Web-Based Number One Medan Tutoring Information System is needed as a means of storing information for parents and students who want to know the number one tutoring in the city of Medan. This information system is also a form of promotion, where one of the intermediary media is computers and smartphones. This website can also make it easier for prospective students and parents of students to just know the profile of the number one field guidance. The more parents who register their children to study in the number one field guidance, the faster the number one field guidance can be known by many people.
Research on Parameter Selection of Multi-layer Kernel Extreme Learning Machine Based on Differential Evolution

The multi-layer kernel extreme learning machine (MKELM) is an improved multi-layer extreme learning machine(ML-ELM) based on kernel method, which has the advantages of no need to manually adjust the number of nodes in each hidden layer, no need to randomly initialize weights and offsets. However, due to the introduction of kernel method, it is necessary to select appropriate kernel parameters to obtain a model with better generalization performance, and manually adjust the parameters in kernel method according to prior knowledge, which is undoubtedly a very time-consuming process. In this paper, a self-adaptive differential evolution multi-layer kernel extreme learning machine (SaDE-MKELM) is proposed. The main idea of this algorithm is to use the self-adaptive differential evolution(SaDE) to automatically optimize the parameters in MKELM, take the parameter vector composed of the kernel parameter &#x03C3; and penalty factor c of MKELM as an individual in the differential evolution algorithm, and build a MKELM model for each individual. The average root mean square error obtained by 10-fold cross-validation is used as the fitness of the corresponding individual, and the individuals in the population are selected by evolution within a certain optimization range and iteration times, and finally the optimal parameter combination suitable for the current model is obtained. The proposed algorithm is tested on several UCI simulation datasets. The experimental results show that the classification prediction accuracy of SaDE-MKELM is improved by at least 2% compared with MK-ELM and SaDE-ELM, and it has better stability.
Managing Relevance Across Mixed Content Types Answer Sets: A Case Study

At LexisNexis our products typically span a range of content types which are searched separately, returning discrete answer sets which are optimized for each content type. Using the UK’s Lexis®PSL as a case study, this talk looks at a different approach, which returns to the user a single result list spanning multiple content types. The Lexis®PSL service enables users to search for and view research and practical guidance content associated with legal matters, as well as primary content such as cases and legislation if searched for specifically. A key feature of this service is the presentation of a single results list which spans research and practical guidance content types. We first look at the make-up of a mixed content type answer set in the legal domain as well as the drivers behind this approach from the user’s perspective – i.e., why does it matter?Having established the what and the why, we then share learnings from the Lexis®PSL experience. We will look at approaches and challenges to tuning relevance across a diverse answer set, which will include methods of measuring relevance quality particularly in the context of query intent.
PDCA, Review, Institution, Effic Implementasi Metode PDCA di Berbagai Organisasi : Kajian Literatur

This article will review several articles that use the PDCA method in overcoming production problems in various institutions. The method used is qualitative with a literature review approach. PDCA method is an appropriate method in identifying problems in various institutions. The application of the PDCA method to service institutions is not only appropriate in assessing service quality, but can also be applied to program assessments such as the Blended Learning program at educational institutions. The results of applying the PDCA method make the company more efficient, effective and can be apply in a sustainable manner in controlling the production process.
Moderating effect of culture on the relationship between knowledge management and organizational performance in the university context.

The huge amount of information in our daily life and the continuous work to organize and use it in the best possible way has led to the emergence of knowledge management. This work has two main objectives, first we test the existence of a correlation relationship between the application of knowledge management and organizational performance of Abdelmalek Essaadi University, and secondly, we verify the moderating role of organizational culture on the intensity of this relationship, by collecting the views of the Abdelmalek Essaadi University teacher-researchers, through a hypothetico-deductive reasoning approach and a quantitative working method. Our questionnaire was administered to a representative sample of 88 teacher-researchers from the different institutions of the university under study. The results obtained show a strong correlation between knowledge management and organizational performance. Also, the results confirm the moderating and positive role of organizational culture, on the intensity of the relationship between the application of the K.M and (Training, research, publication, and governance) as indicators of organizational performance retained in this work for the university in question.
THE EFFECT OF PROJECT-BASED COLLABORATIVE LEARNING ON CONCEPT MASTERY OF MUSHROOM: THE CASE OF SENIOR HIGH SCHOOL STUDENTS IN INDONESIA

Biology learning is highly recommended to be presented contextually following daily experience and phenomena. This study, a quasi-experimental research using a pretest-posttest non-equivalent control group design, aimed to understand the effect of project-based collaborative learning towards the concept mastery of mushrooms. The subjects of the study were 75 tenth grade of high school students in Surakarta City, Indonesia, divided into two classes: 38 students in the experimental class 37 in the control class. In the experimental class, the students were treated with project-based collaborative learning strategies. In project-based collaborative learning strategy, students were challenged to create a project on oyster mushroom cultivation (Pleurotus ostreatus) With the media garden waste such as sawdust. In the control class, the students were treated with direct instruction. The instrument used was a multiple-choice test and essay developed by researchers to measure students' concept mastery towards mushrooms. Research data were analyzed by independent sample t-test. The results found that the average n-gain score for the experimental and the control classes were 63.09% and 45.73%, respectively. Moreover, all indicators of mushroom concept mastery showed the n-gain scores for the experimental class higher than the control class. Analysis of independent sample t-test proved that the significant differences existed between direct instruction and project-based collaborative learning in improving the concept mastery of mushroom. Finally, This research concluded that project-based collaborative learning is more effective in enriching the concept mastery than direct instruction.
Some Proposed Problems on Permutation Polynomials over Finite Fields

From the 19th century, the theory of permutation polynomial over finite fields, that are arose in the work of Hermite and Dickson, has drawn general attention. Permutation polynomials over finite fields are an active area of research due to their rising applications in mathematics and engineering. The last three decades has seen rapid progress on the research on permutation polynomials due to their diverse applications in cryptography, coding theory, finite geometry, combinatorics and many more areas of mathematics and engineering. For this reason, the study of permutation polynomials is important nowadays. In this chapter, we propose some new problems in connection to permutation polynomials over finite fields by the help of prime numbers.
‘저녁돌봄’을 이용하는 직장맘의 여유시간이 삶의 질에 주는 의미 탐구

The purpose of this study was to explore the meaning of ‘Night-care’ obtained by using narrative methods to the quality of life. For this, we selected five working mothers who were using ‘Night-care’ services. First of all, the change made by using “evening in the evening” was psychological stability that made a mother feel safety just because of its presence. In the case of children, the stress had been eased through enough playing. Second, ‘Night-care’ was providing parents with a chance to free time and to develop their skills. Third, working mothers wanted to receive information about their children through education and to have a long and lasting education program. This study showed some characteristics. Parents are growing with their children. The education community for raising children has been expanded and transformed into a “parent erudition community.” Using the topics derived from this inquiry, we expect to expand discussions of various ‘Night-care’ services considering local characteristics and context.
Artificial Intelligence in the Global South (AI4D): Potential and Risks

Artificial intelligence is becoming more widely available in all parts of the world. This has created many previously unforeseen possibilities for addressing the challenges outlined in the Sustainable Development Goals in the Global South. However, the use of AI in such contexts brings with it a unique set of risks and challenges. Among these are the potential for Governments to use such technologies to suppress their own people, and the ethical questions arising from implementing AI primarily designed and developed in the Global North into vastly different social, cultural, and political environments in the Global South. This paper examines the key issues and questions arising in the emerging sub-field of AI for global development (AI4D) and the potential and risks associated with using such technologies in the Global South. We propose that although there are many risks associated with the use of AI, the potential benefits are enough to warrant detailed research and investigation of the most appropriate and effective ways to design, develop, implement, and use such technologies in the Global South. We conclude by calling for the wider ICT4D community to continue to conduct detailed research and investigation of all aspects of AI4D.
Perbandingan Algoritma Klasifikasi Data Mining untuk Penelusuran Minat Calon Mahasiswa Baru

During the AMIK HASS pandemic, it was difficult to determine new student candidates. So that to attract public interest, the Marketing Department has implemented several strategies to attract prospective students to become new students. The data mining technique used in predicting is a classification that includes Naïve Bayes, J48 Decision Tree, and K-Nearest Neighbor. This study aims to perform a comparative analysis of data mining classification algorithms using WEKA tools. The method used in this study, using CRISP-DM. The dataset used by the three classifications is 5,934 records with split mode, the percentage of testing is 70% as much as 4154 as training data and 30% as much as 1780 data as test data. Based on the test results on the three classification models, the highest accuracy value is obtained in the J48 Decision Tree classification, which has a value of 90.3%. While the K-Nearest Neighbor classification has a lower accuracy of 87.52% and the Naïve Bayes classification has an accuracy of 87.24%. The comparison of the AUROC J48 Decision Tree test results has the highest value of 0.9654 while the Naïve Bayes results are 0.9461 and the K-Nearest Neighbor results are 0.9343. The three classifications with ABK scores above 0.90 are included in the excellent classification category