Reviewerly/exHarmony huggingface dataset:
Pendampingan Konversi Sampah Menjadi Bernilai Investasi Dengan Menggunakan Aplikasi Berbasis Android di Desa Sumber Gedang Pandaan Pasuruan

Garbage is an object/residual substance that is not used from human life, and should receive serious attention and handling. However, due to a lack of public understanding, this waste problem has been neglected, as is the case in Sumber Gedang Village, Pandaan District - Pasuruan. One of the existing solutions from the Yudharta University student service group is to empower youth through the movement to convert waste into investment value by initiating an Android-based waste bank. Through the ABCD approach, the results of this service can contribute and make changes in waste management which so far have not been in accordance with environmentally sound waste management methods and techniques. In addition, this service has also changed the waste management system from burning to an integrated waste management system by sorting according to the category, so that it has a selling value and contributes to the social economy of the community
Knowledge Mapping Bidang Ilmu Perpustakaan Dengan Metode Nine Basics of Information Science

The purpose of this study is to analyze the knowledge of mapping in the field of Library Science with the Nine Basics of Information Science method and to analyze the development that has led to the Kemenristekdikti nomenclature. The research method used is quantitative research with a survey approach. The total population of 1,597 and a sample of 94. he validity test used construct validity and empirical reliability tests. Data analysis with mean analysis based on variables from all respondents and presented. The results showed that of the knowledge mapping the field of library science was categorized into three categories, namely the category of foundation, resources, and subject-based knowledge. he category of foundation shows that the use of theory used has varied, but it was found that several publications did not use theory. The method of publication is more inclined to use qualitative methods, and a relatively high level of sophistication publication. The category of resources is more inclined to use secondary information sources. The category of subject-based knowledge tends to be the subject of social issues, the subject of the technique and process of presenting information in the library, the subject of systems and application development, and the subject of technology. The results of the knowledge mapping in the field of library science in the publications on the Garuda website have also led to a new nomenclature, which is more directed at the field of social issues. Keywords : knowledge mapping, library science , library science and information science
Extensions of Schönemann's theorem in Galois rings

Let p be a prime and Fp be a finite field of p elements. For a1,...,ak,b∈Fp, let NFp(a1,...,an,b) denote the number of the solutions (x1,...,xn)∈Fpn to the following linear equationsa1x1+⋅⋅⋅+anxn=b with the constraint xi≠xj for all i≠j. In 1839, Schönemann obtained the exact formula of NFp(a1,...,an,0) provided that ∑i=1nai=0 and ∑i∈Iai≠0 for all I⫋{1,...,n}. Schönemann's theorem was generalized by Bibak, Kapron and Srinivasan from Fp to Z/mZ for any positive integer m. Recently, Li and Yu generalized Schönemann's theorem to the finite field Fq with general prime power q. In this article, we give a further extension of Schönemann's theorem in the Galois rings GR(p2,r).
Estimating Finger Joint Motions Based on the Relative Sliding of Layered Belts

This article introduces a new method for measuring finger joints’ motion based on the relative sliding motions of belts placed on the finger. Conventional mechanical methods used in wearable devices for measuring hand motions, such as a method using resistive sensors, require calibration for the difference in the fingers’ shapes of individuals. In contrast, the proposed method measures the metacarpophalangeal (MP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints flex motion of a finger except for the thumb without the above individual calibration using the difference in the sliding amount of belts on the finger surface. The simple equations show that the relative sliding amount of belts is proportional to joint angles with a factor of the belt thickness. Therefore, the method only need to know the belts’ thickness beforehand to calculate the joint angles from the relative belt positions but not the fingers’ shapes. Experiments with a prototype showed that the proposed method estimated the joint motion of the finger model driven by servomotors with an accuracy of less than 1° in three kinds of motions. The device was in an early stage of development as a data glove, but the accuracies were comparable to other wearable devices. The layered belts mechanism proposed here can provide an option to design a device to realize simplified hand motion measurement by eliminating cumbersome calibration processing for each execution. Furthermore, the simple measurement principle also has an advantage in robustness to noise, such as the drift seen in the devices using inertial measurement units (IMUs).
Challenges, Potentials and Success Factors in Automotive HMI Concept Development in the Early Phase in the Model of PGE – Product Generation Engineering

The importance of UX is widely recognized by product developers among automotive OEMs.A distinctive UX shaping the brand is an important goal in the development of a new product generation.Trends such as advancing digitalization and connectivity of products open up numerous innovation potentials for UX developers.This contribution focuses on in-vehicle UX in terms of designing interactions between the vehicle and the user.In academia, theories and models to explain the phenomenon of user experience are widespread.Furthermore, numerous research approaches in the field of engineering sciences pursue the goal of making user experience measurable and evaluable.However, in the course of our literature review, we found that there is currently a lack of tools and methods that support product developers in eliciting user needs and subsequently synthesizing concepts.For this purpose, particularly the early phase of product development offers required creative freedom.However, this phase is also characterized by a high degree of uncertainty.These circumstances raise the question of which challenges product developers face when designing interactions.This paper aims to contribute to this question through a two-step descriptive analysis in cooperation with a leading German automotive group.Based on a literature review, challenges and strategic potentials were first discussed with UX experts in a workshop (n=11).From these results, a total of 28 hypotheses were derived, which were then evaluated in a second step in an online survey (n=45) by a broad set of experts.Subsequently, the challenges identified in this way were translated into four qualitative success factors to provide product developers with concrete recommendations for action.Additionally, a total of seven requirements were extracted from these factors to help product development research close the identified research gap in further prescriptive studies.
Multiscale Feature Fusion Attention Lightweight Facial Expression Recognition

Facial expression recognition based on residual networks is important for technologies related to space human-robot interaction and collaboration but suffers from low accuracy and slow computation in complex network structures. To solve these problems, this paper proposes a multiscale feature fusion attention lightweight wide residual network. The network first uses an improved random erasing method to preprocess facial expression images, which improves the generalizability of the model. The use of a modified depthwise separable convolution in the feature extraction network reduces the computational effort associated with the network parameters and enhances the characterization of the extracted features through a channel shuffle operation. Then, an improved bottleneck block is used to reduce the dimensionality of the upper layer network feature map to further reduce the number of network parameters while enhancing the network feature extraction capability. Finally, an optimized multiscale feature lightweight attention mechanism module is embedded to further improve the feature extractability of the network for human facial expressions. The experimental results show that the accuracy of the model is 73.21%, 98.72%, and 95.21% on FER2013, CK+ and JAFFE, respectively, with a covariance of 10.14 M. Compared with other networks, the model proposed in this paper has faster computing speed and better accuracy at the same time.
Jackknife Empirical Likelihood Methods for Testing the Distributional Symmetry

In this talk, we consider a general k-th correlation coefficient between the density function and distribution function of a continuous variable as a measure of symmetry and asymmetry.We make statistical inference of the k-th correlation coefficient by using jackknife empirical likelihood (JEL) and its variations to construct confidence intervals.The JEL statistic is shown to be asymptotically a standard chi-squared distribution.We compare our methods to the previous empirical likelihood (EL) techniques of [1] and show the JEL possesses better small sample properties compared with existing methods.Simulation studies are conducted to examine the performance of the proposed estimators.We also use our proposed methods to analyze two real datasets for illustration.
Augmented and Virtual Reality in Education

Technological advances have enabled the sustainability and desirability of augmented and virtual reality in many domains. When it comes to their application in education, it should be noted that they provide new models of learning that better meet the needs of modern society. The paper presents an overview of previous research on augmented and virtual reality in education. By reviewing and analyzing related works, relevant results were obtained on the application, benefits, and impact of augmented and virtual reality on the educational process. The paper aims to examine the importance of introducing augmented and virtual reality in education based on the analysis of the results of previous research and to present the reasons for the justification of their application, which will become a reality shortly.
The Structural Effects of Strategic Human Resource Management in Digital Transformation on the Task Performance of Travel Agency Employees

COVID-19 has accelerated digital transformation for many organizations but has caused human resource management problems. In particular, travel agencies need to discuss human resource management as a small number of people are performing many tasks, shifting many tasks online, and using more software or automation due to restructuring after COVID-19. Therefore, in this study, the AMO (Ability-Motivation-Opportunity Model) model, which helps explain human resource management, was applied to investigate the effect of strategic human resource management according to digital transformation on the task performance of the travel agency employee. The results showed that human resource management, according to digital transformation, has a partial positive effect on ability, motivation, and opportunity. Motivation and opportunity significantly affected task performance, but ability did not. These results suggest that continuing education should be carried out because strategic human resource management according to digital transformation is an essential factor as it is linked to task performance.
Facebook Exposure on Students’ Essay Writing Proficiency

The purpose of this study was to determine the effect of Facebook Exposure on Students’ Essay Writing Proficiency to the Science Technology Engineering and Mathematics (STEM) students of Palompon Institute of Technology (PIT). The researchers conducted a survey on the randomly selected 150 STEM students. The findings showed that students needs improvements on their essay writing skills. In addition, students should limit the number of hours they spent on Facebook and focus more on learning or use Facebook as a way or tool for education and not just for entertainment.
A Weather-Type Classification Strategy and its Forecasting Method for the Day-Ahead Prediction of Photovoltaic Output

Aim at more accurate weather information for PV-output prediction, this paper proposed a framework of reversely determining weather types from historical PV-output data. The framework includes the historical weather-type classification and its day-ahead forecast. First, the detrended sequence is extracted from the original sequence of the PV output. The original sequence and the detrended sequence constitute the volatility characteristic-sequences of PV output, and the symbol-squence histograms (SSH) are used to describe the volatility of the characteristic-sequences in a coarse-grained manner. Then, the SSHs are partitionally clustered and a rule of weather-type classification based on the SSHs clustering is proposed to label the historical weather types. Next, a prediction method combining phase-space reconstruction with an ELM-based single-layer forward net is developed to predict the SSH. Finally, according to the rule of weather-type classification and the predicted SSH, the day-ahead weather type is obtained. Simulation experiments were carried out on the weather-type classification and forecasting using a PV system in a campus microgrid in East China. The results show that, compared with weather forecasts from a weather-service supplier, the forecasted weather types provided by the proposed method is significantly helpful for improving the prediction accuracy of PV output.
Aobert: All-Modalities-In One Bert for Multimodal Sentiment Analysis

Multimodal sentiment analysis utilizes Text and various modalities such as Vision or Speech to predict sentiment. Given that each modality has its characteristics, an effective fusion method is essential; however, previous fusion methods have a severe problem, which results in loss of intra-modality and inter-modality. This paper introduces the single-stream transformer, AOBERT (All-modalities-in-One BERT), to address the existing fusion method problem. The model is pre-trained on two tasks simultaneously: Multimodal Masked Language Modeling (MMLM) and Alignment Prediction (AP). The dependency and relationship between modalities can be determined using two pre-training tasks, and state-of-the-art results on the CMU-MOSI, CMU-MOSEI, and UR-FUNNY datasets were achieved. In addition, ablation studies such as combinations of modalities or the effects of MMLM and AP show the superiority of the proposed model.
ADDRESS TERMS USED FOR BIRTHDAY WISHES: A CASE OF STUDENTS AND LECTURERS OF NWAFOR ORIZU COLLEGE OF EDUCATION, NSUGBE

This research investigated the address terms used by WhatsApp group members in expressing birthday wishes among its members. The WhatsApp groups constitute the undergraduates’ WhatsApp platform and the College of Education Academic Staff Union (COEASU) WhatsApp platform, all in Nwafor Orizu College of Education, Nsugbe, Anambra State. Address terms is a phenomenon that exists in our day-to- day communication and are bound to vary as a result of many social factors. Therefore, this paper focuses on the address terms used by the members of these groups when expressing birthday wishes to their fellow members, as well as the factor(s) that may be the face behind the mask of choice of address terms. The address terms are classified under the following: first name (FN), last name (LN), title (T), special nickname (SN), pet name (PN) and without address term (WAT). This study revealed that the combination of two or more address terms neutralises the original concept of one address term or the other. The without address term depicts more of solidarity than degree of intimacy and familiarity. Furthermore, the use of dual language in the address terms portrays a high degree of intimacy and the use of codes and abbreviations depict the highest degree of intimacy between the addresser and the celebrant or addressee. Finally, this simply means that the context in which an address term is used and the combinations of other address terms can neutralise the concept of a particular address term. Also, some address terms are status specific and the proposition of Wardhaugh (2010) that title (T) is the least intimate address term does not hold water in this paper, instead, it is without address term (WAT).&#x0D; Keywords: Address Terms, Birthday Wishes, Students, Lecturers, Whatsapp Group.
Power, Performance, and Area Analysis of Hardware Design Techniques for GF(2<sup>k</sup>) Greatest Common Divisor computation

Computation of the greatest common divisor (GCD) for two non-zero polynomials plays a vital role in several applications including cryptography, error-correcting codes, linear systems, and control systems. Several of these applications that involve finite-field arithmetic, use classical algorithm such as Euclid’s algorithm for the computation of GCD. This paper presents a new algorithm called successive polynomial XOR (SPX) to compute the GCD. Two implementation techniques: (1) pure combinational logic, and (2) finite state machine with data-path (FSMD), are used to implement the classical Euclid’s algorithm and the SPX algorithm. Both Euclid and SPX are implemented using 28 nm CMOS process; and a detailed power/ area/ performance analysis is performed using Synopsys design tools. Results show that the combinational implementation of the SPX algorithm utilizes significantly lesser area than the Euclid’s algorithm. For example, for degree six polynomial, the combinational SPX consumes approximately $255. 43 \mu m^{2}$ less area as compared to the combinational Euclid. The FSMD implementation of the SPX algorithm reduces both dynamic and leakage power consumption. The proposed SPX algorithm is also found to be significantly faster in computing the GCD than the Euclid’s algorithm by a factor of 4.7 and 2.1 for combinational and FSMD logic respectively.
INNOVATION OF ESP DESIGN TO IMPROVE STUDENTS’ ENGLISH LEARNING ACHIEVEMENTS IN NON-ENGLISH DEPARTMENT

Designing and providing learning materials that suit the needs of learners is one of the important roles of a lecturer. This study is conducted to examine the appropriate English learning design in non-English Education Study Programs at UMSU with an orientation to the English for Specific Purpose (ESP) approach to analyze the factors that become ESP learning needs and analysis of ESP English learning model design in non-English Education Study Programs. This study involved eight lecturer respondents who teach English in non-educational study programs. This study used a qualitative research design using questionnaires and interviews as research instruments. The results of data analysis showed that factors that underlined the need for ESP in teaching and learning English in non-English Education department were going under two categories, namely the short-term factor and the long-term need factor. The design of the ESP English learning model, then, was concerned to the students’ need of English after their study period. This fact is in accordance with the theory of ESP learning approach under the skill-centered course design which aims to help learners develop skills and strategies that will continue after the ESP learning. The combination of both (Learning-centered approach and skill-centered course design) emerged as an innovation of the ESP course design for students of non-English study program.
Wi-Fi and Bluetooth Contact Tracing Without User Intervention

Previous contact tracing systems required the users to perform many manual actions, such as installing smartphone applications, joining wireless networks, or carrying custom user devices. This increases the barrier to entry and lowers the user adoption rate. As a result, the contact tracing effectiveness is reduced. Unlike the systems above, we propose a new privacy preserving Wi-Fi and Bluetooth (BLE) contact tracing system that does not require smartphone applications, joining wireless networks, or custom user devices. Our specially built routers seamlessly track smartphones, laptops, smartwatches, BLE headphones, and tablets without any user action, but do not trace user identity. Mapping between devices and users is only carried out for confirmed cases and suspected contacts. Moreover, we can track the absolute positions of user devices within 1.0 m due to using bidirectional long short-term memory neural networks that are trained with data pre-collected by an autonomous robot. This allows public health authorities to track indirect droplet and surface transmissions that other contact tracing systems often overlook.
Energy-Efficient Federated Learning for Wireless Computing Power Networks

In the 6G era, the proliferation of data poses unprecedented challenges on the current computing networks. The collaboration among cloud computing, edge computing and networking is imperative to process such massive data, eventually realizing ubiquitous computing and intelligence. In this paper, we propose a Wireless Computing Power Networks (WCPN) by orchestrating the computing and networking resources of heterogeneous nodes towards specific computing tasks. To enable collaborative intelligence in WCPN, we design an energy-efficient federated learning model, which minimizies the sum energy consumption of all nodes by the joint optimization of the computing capability and the collaborative learning strategy. Based on the solution of the optimization problem, the neural network depth of computing nodes and the collaboration frequency among nodes are adjustable according to specific computing task requirements and resource constraints. Numerical results show that the proposed scheme outperforms the existing work in terms of convergence rate, learning accuracy, and energy saving.
Universal Approximators: New Approach for the Curve Fit of the COVID-19 Infected Population

Fuzzy systems that include Takagi-Sugeno inference method with linear outputs, are widely known to have the ability to uniformly approximate any polynomial with high precision and, as a consequence, any continuous function, by applying the approximation theorem of Weierstrass. There is one more advantage for these methods which is to obtain an explicit expression of the defuzzified output as a function of the system’s inputs. The purpose of this study is to describe the dynamics of a data set collected through the behavior of the tangential envelope and the local concavity of a curve to be adjusted. The functions that define the envelope and its concavity are identified by means of a hybrid system that combines a fuzzy clustering with the qualities of the Takagi-Sugeno inference method. The analyzed data set represents the world population of confirmed infected people by the infectious disease caused by the coronavirus of severe acute respiratory syndrome, named COVID-19. The proposed fuzzy method, in two versions, first and second order, are compared with curves built through the least square method with the maximum of the absolute value of the difference between the fit values and the data, normalized at each instant. In these comparisons, both fuzzy approaches proposed in this study are the ones that best match the data collected, being the fuzzy approximation of second order the best of all.