Reviewerly/exHarmony huggingface dataset:
Analisa dan Perbandingan Performa Tools Forensik Digital pada Smartphone Android menggunakan Instant Messaging Whatsapp

Technology services are currently developing rapidly, one of the services that rapidly increased is the development of Instant messaging (IM) technology which is increasingly sophisticated and cannot be separated from people's lives. However, the use of this technology is not always about activities that have a good impact but are also vulnerable to being used as a medium to do harmful things. One of the things that have a detrimental impact that often occurs is cybercrime by utilizing Instant messaging (IM) applications. One of the IM platforms that is often used as a media for cybercrime is WhatsApp. The message deletion feature can be one of the feature that uses criminals to hide evidence of a crime. Therefore, a digital forensic protocol is needed that can help the authorities to uncover evidence of crimes that have been tried to eliminate by criminals. Therefore, this study raises the issue of research utilizing the Whatsapp platform using two tools, namely MOBILedit and Oxygen Forensic. The final result of this research is a method and tool recommendation that can be the best alternative for digital forensics needs.
Are Large Pre-Trained Language Models Leaking Your Personal Information?

Are Large Pre-Trained Language Models Leaking Your Personal Information? In this paper, we analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal information. Specifically, we query PLMs for email addresses with contexts of the email address or prompts containing the owner's name. We find that PLMs do leak personal information due to memorization. However, since the models are weak at association, the risk of specific personal information being extracted by attackers is low. We hope this work could help the community to better understand the privacy risk of PLMs and bring new insights to make PLMs safe.
E-Learning adoption during COVID-19 crisis and its effect on achieving students’ performance: Evidence from Jordanian universities

This study aimed to know the effect of E-Learning with all its components on students’ performance at Jordanian Universities during at COVID-19 crisis. To achieve study goals, a questionnaire was distributed to the students at Jordanian Universities, (870) valid questionnaires were recovered. The study found that: The level of E- Learning and its components at Jordanian Universities were at middle rates, students’ performance also comes at a moderate level. E- Learning and its components (Technological facilities readiness, Electronic applications readiness, Databases &amp; E-resources readiness, People readiness, E-learning management system readiness, Evaluation system readiness, teaching methods diversity, Educational curricula and electronic content readiness) have a significant effect on students’ performance at Jordanian Universities at significance (α ≤ 0.05). Whereas subcomponents of (E-learning organizational environment readiness and legislations and regulations readiness) have no effect separately on students’ performance. The study recommended Jordanian Universities to improve E- Learning in all its components in the Universities, reinforce using E- Learning in education, and exploit the benefits of E- Learning to improve students’ performance because it is the main alternative to face emerging crises like Coronavirus.
Componential Analysis of English Verbs

Computational lexical resources such as WordNet, PropBank, VerbNet, and FrameNet are in regular use in various NLP applications, assisting in the never-ending quest for richer, more precise semantic representations. Coherent class-based organization of lexical units in VerbNet and FrameNet can improve the efficiency of processing by clustering similar items together and sharing descriptions. However, class members are sometimes quite different, and the clustering in both can gloss over useful fine-grained semantic distinctions. FrameNet officially eschews syntactic considerations and focuses primarily on semantic coherence, associating nouns, verbs and adjectives with the same semantic frame, while VerbNet considers both syntactic and semantic factors in defining a class of verbs, relying heavily on meaning-preserving diathesis alternations. Many VerbNet classes significantly overlap in membership with similar FrameNet Frames, e.g., VerbNet Cooking-45.3 and FrameNet Apply_heat, but some VerbNet classes are so heterogeneous as to be difficult to characterize semantically, e.g., Other_cos-45.4. We discuss a recent addition to the VerbNet class semantics, verb-specific semantic features, that provides significant enrichment to the information associated with verbs in each VerbNet class. They also implicitly group together verbs sharing semantic features within a class, forming more semantically coherent subclasses. These efforts began with introspection and dictionary lookup, and progressed to automatic techniques, such as using NLTK sentiment analysis on verb members of VerbNet classes with an Experiencer argument role, to assign positive, negative or neutral labels to them. More recently we found the Brandeis Semantic Ontology (BSO) to be an invaluable source of rich semantic information and were able to use a VerbNet-BSO mapping to find fine-grained distinctions in the semantic features of verb members of 25 VerbNet classes. This not only confirmed the assignments previously made to classes such as Admire-31.2, but also gave a more fine-grained semantic decomposition for the members. Also, for the Judgment-31.1 class, the new method revealed new, more fine-grained existing semantic features for the verbs. Overall, the BSO mapping produced promising results, and as a manually curated resource, we have confidence the results are reliable and need little (if any) further hand-correction. We discuss our various techniques, illustrating the results with specific classes.
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints

Accounting for inequality constraints, such as boundedness, monotonicity, or convexity, is challenging when modeling costly-to-evaluate black box functions. In this regard, finite-dimensional Gaussian process (GP) regression models bring a valuable solution, as they guarantee that the inequality constraints are satisfied everywhere. Nevertheless, these models are currently restricted to small dimensional situations (up to dimension 5). Addressing this issue, we introduce the MaxMod algorithm that sequentially inserts one-dimensional knots or adds active variables, thereby performing at the same time dimension reduction and efficient knot allocation. We prove the convergence of this algorithm. In intermediary steps of the proof, we propose the notion of multiaffine extension and study its properties. We also prove the convergence of finite-dimensional GPs, when the knots are not dense in the input space, extending the recent literature. With simulated and real data, we demonstrate that the MaxMod algorithm remains efficient in higher dimension (at least in dimension 20), and needs fewer knots than other constrained GP models from the state of the art, to reach a given approximation error.
FOAM: A Follower-aware Speaker Model For Vision-and-Language Navigation

The speaker-follower models have proven to be effective in vision-and-language navigation, where a speaker model is used to synthesize new instructions to augment the training data for a follower navigation model.However, in many of the previous methods, the generated instructions are not directly trained to optimize the performance of the follower.In this paper, we present FOAM, a FOllower-Aware speaker Model that is constantly updated given the follower feedback, so that the generated instructions can be more suitable to the current learning state of the follower.Specifically, we optimize the speaker using a bi-level optimization framework and obtain its training signals by evaluating the follower on labeled data.Experimental results on the Room-to-Room and Room-across-Room datasets demonstrate that our methods can outperform strong baseline models across settings.Analyses also reveal that our generated instructions are of higher quality than the baselines. 1
K-Prototypes Algorithm for Clustering The Tectonic Earthquake in Sulawesi Island

Natural disasters that had occurred in Indonesia consist of hydro-meteorology: floods, droughts, and landslides, geophysical: volcanic earthquakes and volcanic eruptions, and biological: epidemics. Regarding the tectonic earthquake on Sulawesi Island, there are at least 2 earthquake disasters that became national disasters, namely in Central Sulawesi and West Sulawesi in the range of 2017 to 2021. This study aims to cluster tectonic earthquakes on Sulawesi Island, from 2017 to 2020, as the basis for formulating disaster mitigation plans. This study used tectonic earthquake data from 2017 to 2020 obtained from BMKG Gowa, Indonesia. The variables used are magnitude, depth, and distance category. Because they are mixed variables, this study used a k-prototype algorithm. There are four clusters in 2017, six clusters in 2018, five clusters in 2019, and six clusters in 2020 based on the ratio of within-cluster distance against between-cluster distance. It can be related to the active fault on Sulawesi Island. The characteristics of clusters form each year are the greater magnitude of the earthquake, the deeper of deep and the category distance is dominated by the regional level.
Computational Analysis of Correlations between Image Aesthetic and Image Naturalness in the Relation with Image Quality

The main purpose of this paper is the study of the correlations between Image Aesthetic (IA) and Image Naturalness (IN) and the analysis of the influence of IA and IN on Image Quality (IQ) in different contexts. The first contribution is a study about the potential relationships between IA and IN. For that study, two sub-questions are considered. The first one is to validate the idea that IA and IN are not correlated to each other. The second one is about the influence of IA and IN features on Image Naturalness Assessment (INA) and Image Aesthetic Assessment (IAA), respectively. Secondly, it is obvious that IQ is related to IA and IN, but the exact influence of IA and IN on IQ has not been evaluated. Besides that, the context impact on those influences has not been clarified, so the second contribution is to investigate the influence of IA and IN on IQ in different contexts. The results obtained from rigorous experiments prove that although there are moderate and weak correlations between IA and IN, they are still two different components of IQ. It also appears that viewers' IQ perception is affected by some contextual factors, and the influence of IA and IN on IQ depends on the considered context.
Augmenting Scientific Creativity with Retrieval across Knowledge Domains

Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas \textit{outside} such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can `zoom in' to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration.
Influence of University Service Quality on Student Satisfaction and Loyalty in Bangladesh

Evaluating the service quality of higher educational institutions in Bangladesh has become crucial to ensure continuous improvement and strategy development for tertiary education sector. The present study has aimed to investigate the influence of higher education service quality dimensions on student satisfaction and student loyalty. Moreover, the study also tests the mediating impact of university reputation and external prestige between university service quality and satisfaction and loyalty. The research work has shown the performance of selected public universities in higher education service quality dimensions. Data has been collected through a structured questionnaire from a sample of 390 graduate and undergraduate students of two public universities of Bangladesh. The study reflects that the performance on service quality dimensions of selected public universities is not satisfactory. The results of the study have shown that Teaching, Support services, Library and lab facilities and internationalization have significant influence on student satisfaction except Administrative service and Hostel facilities. Teaching, Administrative services, Library and lab facilities and Internationalization have significant influence on student Loyalty. The mediating analysis has shown that student satisfaction partially mediates between service quality and student loyalty. The results of the study have also indicated that university reputation and external prestige partially mediate between service quality and student satisfaction and loyalty. The previous studies which measure service quality of Bangladeshi higher educational institutions have applied SERVQUAL model which is not absolutely suitable for an educational institution. However, this study has applied higher education service quality model which has been uniquely developed to measure service quality of higher educational institutions.
Web-Based Employee Work Scheduling Information System Design at PT Trafoindo Power Indonesia

This study aims to assist PT Trafoindo Power Indonesia in improving the description of the employee work scheduling website. So far, several departments of PT Trafoindo Power Indonesia use an application called Microsoft Excel to process employee work schedules and save a file on a public server where each employee has access to open and perform data engineering. The solution to the indication of the problem is to create a new system design that functions to process data in the form of employee work schedules with a one-way input system. The website is one of the choices that companies need if they want to present information both internal and external. A website would be better if there is a database or what is commonly known as a data warehouse, for the basis of reference for decision-makers tomorrow.&#x0D; In this case, the design of an employee work scheduling information system using Bootstrap as its platform. To obtain the data needed in conducting research, several methods were used, including interviews, observation, and literature study. The data obtained were then analyzed and described using the UML (Unified Modeling Language) method. The final result of the research is the design of a Bootstrap-based employee work scheduling information system using Visual Studio Code as the code editor.
Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram

Indonesia is an agrarian country, which is a sector that plays an important role most of the Indonesian population makes agriculture the main focus, but the function of rice fields into housing or industry has resulted in a decrease in rice production, in addition to pests, diseases, unfavorable weather, Irrigation is not smooth resulting in less than the maximum yield. For this reason, it is necessary to have technology that can implement the process of detecting rice leaf disease in order to provide information to farmers about rice leaf damage. The most modern approach today can be done with machine learning or deep learning by using various algorithms to improve recognition and accuracy in the detection and diagnosis of plant diseases. Based on this, this study aims to propose a method of classifying rice leaf diseases in order to provide information to farmers about rice leaves which are expected to reduce the disease by detecting the disease early so as to increase rice production. In this study, the classification process is carried out using the augmented image, then the Color Histogram feature extraction method is applied, and the classification is carried out using the Random Forest algorithm. In addition, this study also conducted several comparisons, including feature extraction and yahoo to get the results, and the highest results reached 99.65% of the proposed method. Keywords: Color Histogram; Rice Leaf Disease; Random Forest.
Dop-DenseNet: Densely Convolutional Neural Network-Based Gesture Recognition Using a Micro-Doppler Radar

Hand gesture recognition is an efficient and practical solution for the non-contact human–machine interaction in smart devices. To date, vision-based methods are widely used in this research area, but they are susceptible to light conditions. To address this issue, radar-based gesture recognition using micro-Doppler signatures can be applied as an alternative. Accordingly, the use of a novel densely convolutional neural network model, Dop-DenseNet, is proposed in this paper for improving hand gesture recognition in terms of classification accuracy and latency. The model was designed with cross or skip connections in a dense architecture so that the former features, which can be lost in the forward-propagation process, can be reused. We evaluated our model with different numbers of filter channels and experimented with it using the Dop-Net dataset, with different time lengths of input data. As a result, it was found that the model with 64 3 × 3 filters and 200 time bins of micro-Doppler spectrogram data could achieve the best performance trade-off, with 99.87% classification accuracy and 3.1 ms latency. In comparison, our model remarkably outperformed the selected state-of-the-art neural networks (GoogLeNet, Res- Net-50, NasNet-Mobile, and MobileNet-V2) using the same Dop-Net dataset.
Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters

A predictive model based on artificial neural networks (ANNs) for modeling primary settling tanks' (PSTs) behavior in wastewater treatment plants was developed in this study. Two separate ANNs were built using input data, raw wastewater characteristics, and operating conditions. The output data from the ANNs consisted of the total suspended solids (TSS) concentration and chemical oxygen demand (COD) as predictions of PSTs' typical effluent parameters. Data from a large-scale wastewater treatment plant was used to illustrate the applicability of the predictive model proposal. The ANNs model showed a high prediction accuracy during the training phase. Comparisons with available empirical and statistical models suggested that the ANNs model provides accurate estimations. Also, the ANNs were tested using new experimental data to verify their reproducibility under actual operating conditions. The predicted values were calculated with satisfactory results, having an average absolute deviation of <20%. The model could be adapted to any large-scale wastewater plant to monitor and control the operation of primary settling tanks, taking advantage of the ANNs' learning capacity.
THE IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON THE CUSTOMERS’ LOYALTY IN THE JORDANIAN BANKING SECTOR: THE MEDITATING ROLE OF CUSTOMER SATISFACTION

This study examines the impact of corporate social responsibility on customer loyalty in Jordanian commercial banks. The study aimed to examine the mediating role of customer satisfaction in the relationship between corporate social responsibility (ethical, philanthropic, and environmental) and customer loyalty. The descriptive-analytical method was used. Data were collected from customers of a Jordanian commercial bank in Amman. A total of 380 questionnaires were analyzed using smart PLS version 3.2.8. The relationship and mediation effects were investigated using partial least squares structural equation modeling (PLS-SEM). The research results indicated a significant impact of corporate social responsibility (CSR) on customer loyalty. Also, the result indicated that customer satisfaction mediated the relationship between the corporate social responsibility dimension (ethical responsibility, philanthropic responsibility, and environmental responsibility) and customer loyalty among the bank’s customers. Accordingly, the study recommends employing social initiatives in the bank with ethical and environmental responsibility because of their great resonance with customers to bridge the great competitive gap in the Jordanian banking sector. Furthermore, the current study supports the literary framework by examining the relationship between corporate social responsibility and customer loyalty. The novelty of this study establishes the context of highlighting the key dimensions of CSR that impact customer loyalty and satisfaction, thereby improving the performance of Jordanian banks. Finally, Jordanian banks may be able to increase their revenues and meet the organization’s objectives.
YouTube teaching materials with peer support to improve students' mastery of subject content learning

This study investigated the effectiveness of using YouTube teaching materials with peer support on students’ mastery of subject content learning, motivation, obstacles, and benefits they acquired in learning through this media. This study involved 60 undergraduate students attending the English Language Teaching Method Course (ELTMC) at a university in Banda Aceh, Indonesia. The study used mixed methods: quantitative and qualitative. A quasi-experimental with pre-test and post-test design was used to see the effectiveness of using YouTube teaching materials with peer support and without peer support on students’ mastery of the course. The instruments used to collect the data were tests and an open-ended questionnaire. The findings revealed that using YouTube teaching materials with peer support significantly improved students’ mastery of the subject content of ELTMC compared to those without peer support. The study also reported that only 13 of 30 students had problems learning the subject via the media in the experimental group with peer support. The problems were the speed of speaking by the native speakers, lengthy videos, different English accents that were difficult to absorb, and unclear video sounds. Meanwhile, the benefits include the presence of the native speakers in the videos who are creative and accurate in the language being learned (i.e., English), the joy and positive challenges they got from watching the videos and having discussions afterward with peers. The availability of peer assistance enabled students to comprehend the teaching material delivered through actual media on YouTube on a deeper level.