Reviewerly/exHarmony huggingface dataset:
The Role of Participatory Budgeting Management in Reducing the Budget Slack in an Islamic Education Institution

This study aims to explore participatory-based budgeting procedures and their role in reducing budgetary slack in Islamic boarding schools. Using a qualitative approach, the research method was conducted at a pesantren-based madrasah in the City of Kudus involving foundation administrators, school principals, leaders, teachers, and plan participants. Primary and secondary data were collected through interviews, observation, and documentation. The data were then analyzed using descriptive analysis. The findings of this study reveal that budgeting in Islamic boarding schools is based on participatory budgeting. Bottom-up budgeting is done from the lowest unit in the organization. The lower unit is the party with a deeper understanding of the smallest practical problems. Having the same responsibility for preparing the budget, lower units are expected to allocate budget items proportionally. Even though we hope that the participatory pattern will not cause slack, the facts show the potential for budgetary slack. This relaxation occurred because of a conservative attitude to anticipate uncertain conditions.
Analysis of Reading Difficulties of Muslim Student in Selangor, Malaysia

The ability to read is one of the initial abilities that students from an early age must possess. This aims to improve students' ability to understand the information conveyed as well as knowledge in the learning process. However, there are still many problems with students' initial reading skills that limit students' ability to understand the information and knowledge provided, one of which is the problem of beginning reading skills faced by students in a guidance center in Hulu Langat, Selangor, Malaysia. This study aims to observe the problems of students' beginning reading skills in guidance workshops in Hulu Langat, Selangor, Malaysia. This study uses a type of qualitative research with a descriptive approach and data collection techniques through observation, interviews, questionnaires, and documentation studies. Data analysis was performed using data reduction techniques, data display, conclusions, and data confirmation. The results of the study showed that many students had difficulties in beginning reading. Some students cannot read the letters of the alphabet or spell words, while others do not know syllables, cannot read sentences, and cannot write letters of the alphabet A-Z. Internal and external factors that affect students' reading ability are also observed. Internal factors include students' ability to read and write, while external factors include the lack of parental attention and the lack of sufficient guidance. In conclusion, there needs to be more intensive efforts in helping students overcome initial reading difficulties.
The Positive Impact of Extensive Reading Through Wattpad for Students in Learning English as A Foreign Language (EFL)

This study aims to determine students' experiences with comprehensive reading strategies using Wattpad. This study uses a qualitative descriptive research method. Data are collected using interviews, observation, documentation, and questionnaires. The analysis of this research uses a triangulation technique which consists of steps; data reduction, data display, and ultimately conclusion to analyze the data. The results of this study indicate that extensive reading through Wattpad can be used for pleasure, extensive reading through the Wattpad platform can reduce stress, extensive reading through Wattpad can increase motivation, and extensive reading through Wattpad can increase vocabulary. All of these activities can have a positive impact on students. Plus, Wattpad's platform is great for in-depth reading. Therefore, one of the best ways to learn English is to read extensively on the Wattpad platform. Teachers should realize that because Wattpad is a practical reading tool that can be accessed anywhere and at any time, all the stories students read must be age-appropriate.
Empirical Comparison of Face Verification Algorithms from UAVs

Face verification use cases have recently gained momentum in the increasingly digitalised society, and thus the need arises significantly to integrate this technology in wireless/mobile networked systems such as 5G and applications such as Unmanned Aerial Vehicle (UAV) based public safety services. However, there is no benchmarking result for the evaluation of the various existing face verification algorithms for such UAV applications. This paper is concerned with such new use cases (e.g., the Drone Guard Angel in the EU H2020 project ARCADIAN-IoT and the surveillance network applications in the EU H2020 project 5G-INDUCE), and provides an empirical comparison among three popular state-of-the-art face verification algorithms for this use case. To this end, a face verification pipeline is presented. These algorithms are then compared in terms of their inference time, and the distribution of the similarity indexes for different distances in UAV-based use cases. Their strengths and weaknesses are analysed, leading to an insightful recommendation on their applicability scenarios for UAVs.
Convolutional Transformer-Based Image Compression

In this paper, we present a novel transformer-based architecture for end-to-end image compression. Our architecture incorporates blocks that effectively capture local dependencies between tokens, eliminating the need for positional encoding by integrating convolutional operations within the multi-head attention mechanism. We demonstrate through experiments that our proposed framework surpasses state-of-the-art CNN-based architectures in terms of the trade-off between bit-rate and distortion and achieves comparable results to transformer-based methods while maintaining lower computational complexity.
Action Localization and Recognition through Unsupervised I3D and TSN

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data.
The reading recognition algorithm of pointer meter in unmanned pumping station based on YOLOv5

The purpose of this paper is to study the reading recognition algorithm of pointer meters in unmanned pump stations based on YOLOv5. The algorithm includes two key stages: dial area detection, pointer recognition and reading conversion. In the stage of dial area detection, YOLOv5 algorithm is used to accurately detect the dial area in the pump station instrument image. In the stage of pointer recognition and reading conversion, Canny edge detection, Hough line detection and angle method are combined to convert the detected pointer information into instrument reading. Through experimental verification, the algorithm presented in this paper shows excellent performance in the reading recognition task of pointer meters in unmanned pump stations. The experimental results show that the algorithm can detect the dial area of pump station instruments in real time and with high accuracy, and successfully recognize and convert pointer information into readable instrument readings. This provides a feasible solution for the automatic management of the operation of the unmanned pump station.
Research of Rolling Bearing Fault Diagnosis Method Based on BO-DK-ELM

Aiming at solving the problems that the parameters of the traditional Extreme Learning Machine (ELM) model have significant impact on the fault diagnosis accuracy while it is applied in rolling bearings, a rolling bearing fault diagnosis method based on Bayesian Optimization Deep Kernel Extreme Learning Machine (BO-DK-ELM) was proposed in this paper. Firstly, a model based on Deep Kernel Extreme Learning Machine (DK-ELM) algorithm is constructed by combining the Auto Encoder (AE) with the traditional Kernel Extreme Learning Machine (K-ELM); Secondly, the Bayesian Optimization (BO) algorithm is applied to find the optimal hyper-parameters in the above proposed DK-ELM model, in order to minimize the sum of classification error rates of the training and validation sets in the DK-ELM model. And then the testing set would be fed into the trained BO-DK-ELM model for fault diagnosis. Finally, our proposed method was validated using the Case Western Reserve University bearing fault dataset, and the final results show that its fault diagnosis accuracy can reach 99.0%. Meanwhile, compared with other traditional intelligent fault diagnosis algorithms, the proposed method has higher fault diagnosis accuracy.
Abelian and Non-Abelian Quantum Two-Block Codes

We discuss quantum two-block codes, a large class of CSS codes constructed from two commuting square matrices. Interesting families of such codes are generalized-bicycle (GB) codes and two-block group-algebra (2BGA) codes, where a cyclic group is replaced with an arbitrary finite group, generally non-abelian. We present code construction and give several expressions for code dimension, applicable depending on whether the constituent group is cyclic, abelian, or non-abelian. This gives a simple criterion for an essentially non-abelian 2BGA code guaranteed not to be permutation-equivalent to such a code based on an abelian group. We also give a lower bound on the distance which, in particular, applies to the case when a 2BGA code reduces to a hypergraph-product code constructed from a pair of classical group codes.
Decomposing the Training of Deep Learned Turbo codes via a Feasible MAP Decoder

Most deep-learned error-correcting codes (DL-ECCs) use binary cross-entropy (BCE) between the true input bits and the soft decoded outputs of the learned encoder/decoder pair as a loss function during training. It is known that this decomposes into two terms which in the case of a DL-ECC correspond to: a Kullback-Leibler (KL) divergence between the true and estimated posteriors on the input bits given the noisy channel outputs, and the conditional entropy (CE) of the input bits given the channel outputs. We use this decomposition to explore the training process of one particular DLECC termed TurboAE, which replaces constituent codes in the encoders/decoders of a Turbo code with learned convolutional neural networks. Evaluating each term in the BCE decomposition of TurboAE is facilitated through the junction tree algorithm for exact inference on graphs, yielding a MAP decoding of TurboAE-like codes of up to 40 input, 120 output bits (vs. the original 100-bit inputs). Plots of this decomposition over training offer insight into the alternating training process used in TurboAE and other DL-ECCs. We add to the growing body of work on interpreting DL-ECCs by providing a new lens through which to view any DL-ECC training process that uses BCE as a loss function.
Are Neighbors Alike? A Semisupervised Probabilistic Collaborative Learning Model for Online Review Spammers Detection

Online platforms continually face the menace of review spammers who manipulate ratings and comments for personal gain, eroding trust in online reviews. Despite various methods proposed to tackle this issue, challenges, like collusion between spammers, scarce labels, and skewed data distribution, persist. This paper introduces a practical solution to these problems by integrating individual behavioral features with a reviewer network, and it proposes a novel semisupervised collaborative learning model. Imagine our approach as a detective tool for online platforms. It works by investigating who is reviewing what and how they are linked within the reviewer network, which is like uncovering hidden relationships among reviewers to catch spammers. Through this method, our model significantly boosts the accuracy and transparency in identifying spammers. We tested this approach on real-world review data from different types of online platforms, and it outperformed other methods. Importantly, the model is resilient, even when you have limited data or when some labels might not be perfect. By using our approach, online platforms can spot fake reviews and spammers in a more cost-effective way, ensuring that the reviews are more reliable and that it is easier for consumers to make informed decisions. It is a practical step forward in the fight against fake reviews online.
Development of IoT Control System Prototype for Flood Prevention in Bandung Area

Bandung is one of the areas with high rainfall that can increase the volume of river water, which, if not handled properly, has the potential for significant floods that can cause material damage and loss of life. With this problem, the authors' rationale for designing a control system for flood prevention. This system develops prototypes using Internet of Things technology and fuzzy logic. For Internet of Things technology, the author uses Arduino, which controls sensors and actuators, while Raspberry Pi is used to process data. In addition, the author uses ultrasonic sensors to measure the water level and a water pump to control the water level. So, if the water level exceeds the specified limit, the pump will move the water to another place, in this prototype, using an aquarium. For fuzzy logic, the criteria used are dry, filled, and full. In addition, this system is equipped with a website-based dashboard used to monitor real-time data from the sensor. The results of this study indicate the system is running well, with an average error of 32.2%. This indicates that the system has been well designed because the errors obtained are feasible to be minor, although there are several influencing factors, such as prototype construction and sensor readings. Thus, this prototype can be applied as a reference for making a real system for flood control.
Extreme Gradient Boosting Algorithm to Improve Machine Learning Model Performance on Multiclass Imbalanced Dataset

Unbalanced conditions in the dataset often become a real-world problem, especially in machine learning. Class imbalance in the dataset is a condition where the number of minority classes is much smaller than the majority class, or the number is insufficient. Machine learning models tend to recognize patterns in the majority class more than in the minority class. This problem is one of the most critical challenges in machine learning research, so several methods have been developed to overcome it. However, most of these methods only focus on binary datasets, so few methods still focus on multiclass datasets. Handling unbalanced multiclass is more complex than handling unbalanced binary because it involves more classes than binary class datasets. With these problems, we need an algorithm with features that can support adjustments to the difficulties that arise in multiclass unbalanced datasets. One of the algorithms that have features for adjustment is the ensemble algorithm, namely Xtreme Gradient Boosting. Based on the research, our proposed method with Xtreme Gradient Boosting showed better results than the other classification and ensemble algorithms on eight datasets with five evaluation metrics indicators such as balanced accuracy, the geometric-mean, multiclass area under the curve, true positive rate, and true negative rate. In future research, we suggest combining methods at the data level and Xtreme Gradient Boosting. With the performance increase in Xtreme Gradient Boosting, it can be a solution and reference in the case of handling multiclass imbalanced problems. Besides, we also recommended testing with datasets in the form of categorical and continuous data.
Voice-Authentication Model Based on Deep Learning for Cloud Environment

Cloud computing is becoming an essential technology for many organizations that are dynamically scalable and employ virtualized resources as a service done over the Internet. The security and privacy of the data stored in the cloud is cloud providers' main target. Every person wants to keep his data safe and store it in a secure place. The user considers cloud storage the best option to keep his data confidential without losing it. Authentication in the trusted cloud environment allows making knowledgeable authorization decisions for access to the protected individual's data. Voice authentication, also known as voice biometrics, depends on an individual's unique voice patterns for identification to access personal and sensitive data. The essential principle for voice authentication is that every person's voice differs in tone, pitch, and volume, which is adequate to make it uniquely distinguishable. This paper uses voice metric as an identifier to determine the authorized customers that can access the data in a cloud environment without risk. The Convolution Neural Network (CNN) architecture is proposed for identifying and classifying authorized and unauthorized people based on voice features. In addition, the 3DES algorithm is used to protect the voice features during the transfer between the client and cloud sides. In the testing, the experimental results of the proposed model achieve a high level of accuracy, reaching about 98%, and encryption efficiency metrics prove the proposed model's robustness against intended attacks to obtain the data.
Closer Look at Image Classification for Indonesian Sign Language with Few-Shot Learning Using Matching Network Approach

Huge datasets are important to build powerful pipelines and ground well to new images. In Computer Vision, the most basic problem is image classification. The classification of images may be a tedious job, especially when there are a lot of amounts. But CNN is known to be data-hungry while gathering. How can we build some models without much data? For example, in the case of Sign Language Recognition (SLR). One type of Sign Language Recognition system is vision-based. In Indonesian Sign Language dataset has a relatively small sample image. This research aims to classify sign language images using Computer Vision for Sign Language Recognition systems. We used a small dataset, Indonesian Sign Language. Our dataset is listed in 26 classes of alphabet, A-Z. It has loaded 12 images for each class. The methodology in this research is few-shot learning. Based on our experiment, the best accuracy for few-shot learning is Mnasnet1_0 (85.75%) convolutional network model for Matching Networks, and loss estimation is about 0,43. And the experiment indicates that the accuracy will be increased by increasing the number of shots. We can inform you that this model's matching network framework is unsuitable for the Inception V3 model because the kernel size cannot be greater than the actual input size. We can choose the best algorithm based on this research for the Indonesian Sign Language application we will develop further.
Preliminary study: Readiness of WLAN Infrastructure at Malaysian Higher Education Institutes to support Smart Campus Initiative

Smart campus initiative enables higher education to enhance services, decision-making, and campus sustainability. The initiatives are being actively implemented globally by higher education, including in Malaysia. The recent COVID-19 pandemic has underscored the need for the education sector to explore a digital revolution. The adaptation of digital technologies has improved many aspects, including the teaching and learning experiences and administration tasks, which results in more efficient task handling. This study investigates the readiness of the WLAN infrastructure at Malaysian Public Higher Education Institutes (HEIs) in implementing smart campus initiatives and measures readiness based on the availability of WLAN Infrastructure, WLAN logical architecture and WLAN populated coverage area. This study administered a questionnaire to 19 respondents, all of whom are IT personnel from Malaysian public HEIs to gather preliminary data on the readiness of WLAN infrastructure at Malaysian Public HEI to support the adaptation of smart campus initiatives in their teaching and learning activities. This study is a preliminary study concerning the readiness of WLAN infrastructure at Malaysian Public HEI in adapting smart campus initiatives. The findings show that, even though WLAN service is available at all Malaysian Public HEI, it is essential to enhance the adopted logical architecture and WLAN coverage to prepare HEI to become smart campuses. The findings of this study can provide the fundamental guidelines for the Ministry of Higher Education in determining the baseline of WLAN infrastructure required by Malaysian HEI to support smart campusÂ initiatives.