Reviewerly/exHarmony huggingface dataset:
A Privacy-Preserving Method Based on Artificial Immune Computing in MCS

Due to the widespread use of mobile intelligent terminal devices, Mobile Crowd Sensing (MCS) applications have gained significant research attention. However, ensuring users privacy remains a critical challenge, as it can hinder users’ willingness to participate actively in tasks. To address the limitations of existing differential privacy protection methods, this paper proposes a novel privacy protection approach based on Artificial Immune Computing (AICppm). Specifically, private information is concealed within a masking carrier, and data scrambling is avoided. The proposed method involves two main steps: first, a carrier preprocessing approach based on a high-pass filter bank is designed to identify candidate positions for perturbation. Then, a carrier steganography algorithm based on multi-objective optimization is used, transforming the perturbation position into an antibody using the artificial immune algorithm. By iteratively searching for antibodies with higher fitness, the optimal perturbation of the offspring population is identified using the improved Strength Pareto Evolution Algorithm (SPEA2). The experimental results demonstrate that the proposed algorithm can withstand the attacks of malicious steganalysis tools, preserving the integrity of the sensing data and enabling real-time processing of private information.
Fast SwT-based Deep Steganalysis Network for Arbitrary-sized Images

In this letter, a novel deep steganalysis network called SwT-SN is proposed by integrating directional difference adaptive combination (DDAC) followed by three residual blocks, convolutional spatial pyramid pooling equipped with size-independent detector (CSPP-SID) as well as a two-part of Swin transformer (SwT) structure suitable for steganalysis, aiming at enhancing the detection accuracy for arbitrary-sized images while significantly reducing training and test cost. In comparison to simply exploiting DDAC as preprocessing, DDAC + the residual structure can better improve the signal-to-noise ratio of the residual maps by suppressing the image content. In addition, CSPP-SID is innovatively proposed to convert feature maps of any size into feature vectors with fixed dimension, helping SwT-SN achieving high detection accuracy for arbitrary-sized images. Finally, a two-part structure of SwT with a fixed number of patches is firstly designed for image steganalysis to greatly reduce training cost by calculating the multi-head self-attention mechanism in the shifted window. Extensive experiments on two benckmark databases verify that SwT-SN has higher detection accuracy and shorter training cost compared to two prior state-of-the-art networks.
And How Enjoyable? Converting a User Experience Evaluation Questionnaire into a Voice Conversation

Voice interfaces have gained popularity, driven by the proliferation of intelligent assistants and the development of natural language processing. Given their progress, they could be used to implement self-reported instruments such as standardized user experience (UX) evaluation questionnaires, particularly in the response capture mechanism. This novel use of voice interfaces would make sense if the evaluations using the voice mechanism did not differ significantly from the evaluations carried out with the traditional written questionnaire. In addition, given different possible implementations of the voice mechanism, it must be analyzed whether the user experience is affected by the format of the instrument used. This paper compares two implementations of the User Experience Questionnaire in which the capture mechanism is implemented by voice. Results show that in both implementations the evaluations retain their validity, and that the use of conversational patterns improves the user experience and increases the quality of the answers, which present fewer inconsistencies.
On the Relationship Between Iterated Statistical Linearization and Quasi–Newton Methods

This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (IUKF), and filtering algorithms based on quasi-Newton (QN) methods, such as the QN iterated extended Kalman filter (QN-IEKF). Firstly, it is shown that the IUKF and the iterated posterior linearization filter (IPLF) can be viewed as QN algorithms, by finding a Hessian correction in the QN-IEKF such that the IPLF iterate updates are identical to that of the QN-IEKF. Secondly, it is shown that the IPLF/IUKF update can be rewritten such that it is approximately identical to the QN-IEKF, albeit for an additional correction term. This enables a richer understanding of the properties of iterated filtering algorithms based on statistical linearization.
Model Regresi Weibull Pada Data Kontinu yang Diklasifikasikan

Weibull Regression is a model of regression developed from Weibull distribution in which scale parameter is expressed in the regression parameters. The Weibull regression models discussed in this study are the Weibull survival regression, Weibull hazard regression and regression model for the mean. The Weibull survival regression model is a model of the probability that the Mahakam River water is polluted. The Weibull hazard regression model is a model of velocity of the polluted Mahakam River water, and the Weibull regression for the mean is the model used to predict the average value of BOD (Biochemical Oxygen Demand). The purpose of this study was to obtain the Weibull regression model on BOD water pollution indicator data in the Mahakam River basin, to determine the factors that influence the Weibull regression model. The parameter method is maximum likelihood estimation (MLE). Based on the parameter estimation results, the maximum likelihood estimator is obtained by using the method of Newton-Raphson iteration. The results of hypothesis testing, it is concluded that the factors that influence the Weibull regression model are pH, Total Dissolved Solid (TDS) and water discharge.
Peramalan Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Autoregressive Integrated Moving Average (ARIMA)

Autoregressive Moving Average (ARIMA) is a general model that is often used in time series modeling. One application of ARIMA can be used on the data foreign tourist visits to Indonesia. The tourism sector is one of the priority sectors in Indonesia's economic development. One of the determining factors in the tourism sector is the number of foreign tourist visits. Therefore, forecasting the number of foreign tourist visits is very necessary. The purpose of this study was to obtain a model and forecast results for the number of foreign tourist visits from March 2020 to October 2021 using the ARIMA model. The results of the analysis showed that the ARIMA model (0,1,1) was the best model with a MAPE of 6.23%. The forecasting results with the best model showed that the highest number of foreign tourist visits is in Agustus 2021 and the lowest is in December 2020.
Pengelompokan Kabupaten/Kota Di Pulau Kalimantan Berdasarkan Indikator Indeks Pembangunan Manusia Tahun 2020 Menggunakan Optimasi K-Means Cluster Dengan Principle Component Analysis (PCA)

Data mining is a technique or process to obtain useful information from a large database. Based on its functionality, one of the tasks of data mining is to group data. Cluster analysis is an analysis that aims to group objects based on the information found in the data. One of the cluster analysis methods is the K-Means cluster method, which is a non-hierarchical grouping method by dividing the data set into a number of groups that do not overlap between one group and another. This study aims to classify districts/cities on the island of Kalimantan based on indicators of the human development index and obtain the sillhoutte coefficient value from the optimal cluster analysis using the K-Means algorithm on principle component analysis. The data used is the 2020 human development index data in districts / cities on the island of Kalimantan and used 8 variables from the human development index indicator. The results of the optimal cluster formed in the grouping of regencies/cities on the island of Kalimantan using the K-Means cluster method on the principle component analysis are 4 clusters. Cluster 1 has 20 regencies/cities, cluster 2 has 3 regencies/cities, cluster 3 has 26 regencies/cities and cluster 4 has 7 regencies/cities. The sillhoutte coefficient value for data validation from district/city clustering on the island of Kalimantan using the K-Means cluster method on principle component analysis produces 4 clusters of 0.540 which states that the cluster structure formed in this grouping is a medium structure.
Peramalan Curah Hujan di Kota Samarinda Menggunakan Autoregressive Integrated Moving Average (ARIMA)

Autoregressive Integrated Moving Average (ARIMA) is a forecasting model for time series data analysis. In this study, the modeling and forecasting of monthly rainfall in Samarinda City was carried out using the ARIMA model. The results showed that the ARIMA (6, 1, 1) model was the best model . The results of forecasting rainfall for the period January to December 2022 in Samarinda City using the ARIMA (6, 1, 1) model show that rainfall tends to be constant every month. The lowest level of rainfall occurred in January 2022, which was 210.3869 mm. The highest level of rainfall occurred in April 2022, which was 271.5705 mm.
Penerapan Metode Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Kesejahteraan Rakyat Tahun 2020

Clustering is a method of grouping data into several clusters or groups so that data in one cluster has a high level of similarity and data between clusters has a low level of similarity. The clustering method used in this research is Fuzzy C-Means (FCM). FCM is a data grouping technique in which the existence of each data point in a cluster is determined by the degree of membership. To optimize the grouping results, it is necessary to validate the number of clusters using Partition Coefficient (PC). The purpose of this study is to obtain optimal grouping results from the FCM method using the PC validity indices from the people's welfare indicator data in 56 regencies/cities on the island of Kalimantan in 2020. Based on the results of the analysis, the conclusion is that the optimal number of clusters is three clusters. The first cluster consists of 24 regencies/cities on the island of Kalimantan, the second cluster consists of 17 regencies/cities on the island of Kalimantan, and the third cluster consists of 15 regencies/cities on the island of Kalimantan.
Psychosocial Assistance of Learners in the Limited Face-to-Face Learning Period at Elementary Schools in Southeast Maluku District

Abstract. Community Service Activities (PKM) in the form of mentoring are aimed at, 1) increasing students' reading and numeracy literacy skills, 2) increasing learning motivation and competence in facing face-to-face learning. The methods used during the training were lectures, interactive discussions and mentoring by experts. The results of this PkM activity are, after following the mentoring of students who were shy became more confident. Likewise, students who initially rarely interact or are passive with their friends can finally be more open to playing and learning together.Keywords: quality pursuit program, psychosocial, limited face-to-face learning
PECULIARITIES OF PROFESSIONAL-PSYCHOLOGICAL TRAINING OF LAW ENFORCEMENT EMPLOYEES

The article focuses on the peculiarities of the professional activities of law enforcement officers, the definition of components that affect the level of psychological readiness of professionals (strict legal regulation of professional conduct, authoritative and mandatory nature of professional powers, the extreme nature of law enforcement, procedural independence).&#x0D; Qualitative characteristics of law enforcement officers' activity are analyzed: normativeness, organization, preparedness and efficiency.&#x0D; The components of professional and psychological training of law enforcement officers for activities are presented: professional and psychological orientation and sensitivity, knowledge and skillful use of psychological tools and techniques, developed professional observation and memory, psychological stability.&#x0D; The article notes that professional and psychological training promotes the development of professional skills of law enforcement officers, ie readiness to act professionally in any conditions. It is aimed at the formation and development of psychological characteristics of the individual that meet the specific and important psychological characteristics of service activities and are one of the necessary prerequisites for its implementation.
Virtual reality as a drug-free treatment for depression in the elderly: Considerations of creating a bespoke software treatment

While the use of virtual reality as a drug-free medical treatment for pain has a history spanning decades, its use as a treatment for depression has a much shorter and less expansive history. Few studies have been published, and most of these have relied upon using existing, commercial games to treat depressed patients. However, commercially available games tend to be designed for a younger audience who are assumed to be in full mental health and often already comfortable and knowledgeable in the expectations of gameplay and with a familiarity or interest in technology. This article describes the creation of two short, experimental prototype virtual reality games designed specifically for the needs of elderly, profoundly depressed patients. The reasoning behind the creation of the prototypes are outlined, together with the difficulties and considerations of this approach, what was learned and how approaches to designing, animating and creating more advanced prototype games might be brought forward into the next stage of research. This article is also presented as an example of how artists, animators and designers can utilize their skills to bring richness and value to new applications and medical treatments through the medium of virtual reality and serious games.
One nation one subscription: an illusion or a reality?

Purpose The objective of the study is to highlight the new concept, i.e. One nation one subscription (ONOS), proposed by the Government of India (GOI). Under the proposal, the GOI will negotiate with the publishers at national level for all the electronic resources to provide access to its citizens. This study aims to make aware the library and information science professionals about the needs and advantages of the new concepts. Another purpose of this study is to highlight the steps needed for the successful implementation of the concept. Design/methodology/approach Literature on the “One nation and one subscription” concept, recently introduced by the GOI, has been studied to know the concept’s pros and cons. The authors identified the needs and advantages of the idea. They consulted SCOPUS to know the total and open access research publications contribution from India. They also identified the challenges and steps to convert the concept from illusion to reality. Consulted the latest correspondence of GOI to highlight the actions taken by it to implement the first phase. Findings The ONOS concept is challenging and aims to make all the e-resources required by Indian citizens accessible under a nationwide license. It is an extension of libraries’ consortia-level negotiation to a national level. The idea is helpful to those who cannot afford the subscription to these e-resources due to the lack of funds, especially in remote areas. The view will also help improve the quantity and quality of research publications from India. However, there are challenges. The GOI and other players must work together to address these challenges and ensure that the concept of ONOS becomes a reality. Originality/value The study is an original one and checked through Turnitin which shows 1% similarity.
A paradigm shift toward the application of blockchain in enhancing quality information management

Purpose Lack of trust and poor quality of construction deliverables have become a serious matter nowadays. This is due to the absence of a uniform and decentralized system for managing quality information. In Kuwait’s industry, many incidents have been recorded as a lack of confidence in the authenticity and integrity of the documented data in the system. This paper aims to shed the light on a framework that would tackle this matter. Design/methodology/approach A designed framework using Blockchain technology (Hyperledger Fabric) has been used to create a transparent and decentralized environment between the parties. A digitalized informative checklist referred to as “Smart Construction Inspection Checklist (SCIC)” has been initiated to enhance the poor information recorded between the parties. Findings The framework has provided a transparent, immutable, traceable and decentralized environment in which all parties are involved in transactions. In addition, the integration of the SCIC in the blockchain environment provided an advantage in which all the necessary criteria of inspection will be stated, checked by the consultant and validated by the client to approve the transaction. A preliminary testing has been conducted to support the proposed framework. Originality/value This study fulfils the gap in the state of art for further studies to practically apply the framework that will enhance the quality of information management in Kuwait’s industry.
The Influence of an SQD-based Practicum Experience on Student Teachers' Tpack-practical Development: Opportunities and Challenges

As a result of the COVID-19 pandemic, teacher education in Qatar, similar to many countries around the world, witnessed a succession of disruptions to the way it operated. The disruption continued throughout much of 2020, and the need to adapt to arising changes and concerns permeated all aspects of teacher education, particularly the practicum experience. The chapter presents our attempt to investigate the influence of an adapted practicum experience which was based on the synthesis of qualitative evidence (SQD) model on the development of six student teachers’ technology knowledge and skills. Using a qualitative case study research design, we collected data from multiple data sources, including pre–post-interviews and weekly reflection logs. Quantitative data collected from a pre–post-administration of the SQD survey and TPACK (Technological Pedagogical and Content Knowledge)-practical survey were used to triangulate the qualitative data. Findings from the thematic analysis and descriptive statistical analysis revealed evidence for participants’ increased TPACK-practical knowledge and skills, specifically in the domains of practical teaching and curriculum design. However, an emerging theme revealed that participants considered technology before pedagogy during instructional design. Findings also revealed two challenges to participants’ further development, namely working within a restricted learning environment and experiencing limited mentoring opportunities. We illustrated several implications for the design of the practicum experience and the required institutional support within the context of continued disruption to education and thereafter.
Developmental Strategy for Agriculture in Nigeria

There seems to be no controversy about Nigeria being an agricultural country with food sufficiency up till the late 1970s. However, in recent times the country is finding it very difficult to provide sufficient food for the teeming population which has resulted in the majority of the country’s citizens slipping into poverty. The ability of the country to provide sufficiently for the citizens was a result of a lack of reliable and effective developmental and transformational strategies in the agricultural sector of the country which is a major employer of labour in the rural community. To this end, this chapter mainly focuses on factors inhibiting the development of agricultural companies in Nigeria and how to overcome the developmental barriers in the agricultural sector in Nigeria. The findings from the review show that the bane of the agricultural sector in Nigeria is due to the lack of an agricultural regulatory framework and policy transmission mechanism and over-dependence on oil revenue amongst other things (Adams, 2016). It is therefore imperative for the country to embark on the development of a reliable agricultural framework and model that will aid food sufficiency in the country.
Students' Perceptions of Online Class Delivery During the COVID-19 Pandemic: The Case of the UAE

The COVID-19 pandemic caused an unprecedented crisis in all industries. In the education sector, the pandemic led to the immediate closure of university and college campuses and the migration to online delivery in more than 190 countries in order to prevent the spread of the virus and mitigate its impact. The current study explores students' perceptions of online accounting course delivery during the COVID-19 pandemic in the United Arab Emirates. Using an online survey, the findings show that student perceptions regarding online course delivery varied based on students' years in the program. Specifically, underclassmen (freshmen and sophomores) reported greater enjoyment than upperclassmen (juniors and seniors) from studying classes online and were more likely than upperclassmen to extol the benefits of online learning such as the ability to view classes anytime, anywhere as well as the enhanced opportunity to utilize technology in learning. These differences may result from the familiarity of upperclassmen with the traditional learning format. The analysis also reports differences in perceptions of online learning between students of different ages and gender. The results from this research can help inform institutional policies by providing insight into students' views on the efficacy and challenges associated with transitioning to a fully online learning environment in the midst of a major disruption of United Arab Emirates.
FLIPS

This paper presents the design and implementation of FLIPS, a middleware system to manage data and participant heterogeneity in federated learning (FL) training workloads. In particular, we examine the benefits of label distribution clustering on participant selection in federated learning. FLIPS clusters parties involved in an FL training job based on the label distribution of their data apriori, and during FL training, ensures that each cluster is equitably represented in the participants selected. FLIPS can support the most common FL algorithms, including FedAvg, FedProx, FedDyn, FedOpt and FedYogi. To manage platform heterogeneity and dynamic resource availability, FLIPS incorporates a straggler management mechanism to handle changing capacities in distributed, smart community applications. Privacy of label distributions, clustering and participant selection is ensured through a trusted execution environment (TEE). Our comprehensive empirical evaluation compares FLIPS with random participant selection, as well as three other "smart" selection mechanisms -- Oort [51], TiFL [15] and gradient clustering [27] using four real-world datasets, two different non-IID distributions and three common FL algorithms (FedYogi, FedProx and FedAvg). We demonstrate that FLIPS significantly improves convergence, achieving higher accuracy by 17-20 percentage points with 20-60% lower communication costs, and these benefits endure in the presence of straggler participants.