Reviewerly/exHarmony huggingface dataset:
Segmentasi Warna Kulit Menggunakan Ruang Warna YCBCR Untuk Deteksi Wajah Manusia

Image processing is a system where the process is done by entering in the form of imagery and the result is also in the form of imagery. At first this image processing was done to improve the quality of the image, but with the development of the world of computing which is characterized by increasing the capacity and speed of computer processes. In this research image processing will be used for skin color detection which is the initial process in image processing. Success in detecting pixel imagery can be a category of skin or not skin seen from the results of processing processes such as the process of detecting human faces. Skin detection is also a process of finding skin pixels of color in the area of the image or video, this process is usually used as a preprocessing step to find areas that could potentially have a human face. In this study by applying the color space YcbCr we were able to detect faces in humans with a high percentage accuracy rate with a precession percentage of 77%, Recal 87% and an accuracy rate of 72% from the results of 10 image samples.
Adaptive-and-Resolvable Fractional Repetition Codes Based on Hypergraph

Fractional repetition (FR) codes can achieve exact uncoded repair for multiple failed nodes, with lower computational complexity and bandwidth overhead, and effectively improve repair performance in distributed storage systems (DSS).The actual distributed storage system is dynamic, that is, the parameters such as node storage overhead and number of storage nodes will change randomly and dynamically.Considering that traditional FR codes cannot be flexibly applied to dynamic distributed storage systems, a new construction scheme of adaptive-and-resolvable FR codes based on hypergraph coloring is proposed in this paper.Specifically, the linear uniform regular hypergraph can be constructed based on the heuristic algorithm of hypergraph coloring proposed in this paper.Then edges and vertices in hypergraph correspond to nodes and coded packets of FR codes respectively, further, FR codes is constructed.According to hypergraph coloring, the FR codes can achieve rapid repair for multiple failed nodes.Further, FR codes based on hypergraph coloring can be generalized to heterogeneous distributed storage systems.Compared with Reed-Solomon (RS) codes, simple regenerating codes (SRC) and locally repairable codes (LRC), adaptive-and-resolvable FR codes have significant advantages over repair locality, repair bandwidth overhead, computational complexity and time overhead during repairing failed nodes.
Icensor : Unwanted Image Detection and Censoring

The globe today, practically everyone uses the internet, which is filled with a vast amount of information and content, the majority of which includes pornographic and violent photos and movies. Social media has grown in importance in today's society as a result of the expansion of the internet. This has increased the risk of privacy invasion, which includes the release of private photos that should not be shared because they violate the privacy of some people. Today, even a young child can easily access these materials. Recent image leaks from prominent social media applications and the use of private photos by clever algorithms have caused the public to re-evaluate the need for individual privacy when uploading images on social media. The process of sharing photos on social networking sites is complex in and of itself, and the measures in place to safeguard privacy in daily life are labor-intensive and fall short of providing tailored, precise, and adaptable privacy protection. We have found that techniques like "privacy intelligence" solutions, which concentrate on current privacy issues related to online social networking image sharing, are effective. An optical character recognition-based system that filters photographs with sensitive text, in addition, a visual algorithm that filters out pictures that aesthetically resemble those on an image blacklist is used. These measures can help stop the spread of any delicate content on social media.
A Knowledge Representation Method for Hierarchical Diagnosis Decision-Making Under Uncertain Conditions

In order to effectively solve the problems existing in hierarchical diagnosis decision-making knowledge representation under uncertain conditions, the paper lists several key characteristics of complex systems diagnosis decision-making knowledge, combines with hierarchical diagnosis decision-making knowledge classification, makes ontology modeling of related diagnosis decision-making knowledge based on ontology theory, and constructs the semantic hierarchical analysis and its relationship between global ontology and each core domain ontology. In this way, the paper realizes semantic hierarchical analysis and knowledge association. Through the case study of a certain modular aircraft support system, the knowledge representation of diagnosis decision-making under uncertain conditions is realized.
Robust Color Texture Descriptors for Color Face Recognition

Extraction of the local color texture has been one of the most desired and challenging research areas in face image recognition due to the ever-increasing use of color face images in numerous applications including biometric pattern matching, surveillance, and security. In this paper, we propose two novel color local texture descriptors called the local binary pattern of correlation of adjacent pixels (LBPC_A) and the local binary pattern of correlation of neighborhood pixels with the center pixel (LBPC_C). These two operators provide different local characteristics of an image. The operator LBPC_A provides the low-order texture characteristics while the operator LBPC_C provides the high-order texture characteristics. When these two operators are fused at the feature level, they provide high recognition rates with a small feature size for the face images. The resulting approach is referred to as LBPC_A + LBPC_C. The performance of the proposed operators has been compared with the state-of-the-art LBP-like operators on three color face databases-FERET, CMU-PIE, and AR. Experiments conducted on FERET, CMP-PIE, and AR demonstrate that the proposed descriptors outperform the state-of-the-art color texture descriptors.
Multi-Model Federated Learning with Provable Guarantees

Federated Learning (FL) is a variant of distributed learning where edge devices collaborate to learn a model without sharing their data with the central server or each other. We refer to the process of training multiple independent models simultaneously in a federated setting using a common pool of clients as multi-model FL. In this work, we propose two variants of the popular FedAvg algorithm for multi-model FL, with provable convergence guarantees. We further show that for the same amount of computation, multi-model FL can have better performance than training each model separately. We supplement our theoretical results with experiments in strongly convex, convex, and non-convex settings.
AI BASED – ASSISTED SEARCH FOR MISSING PERSON

Face recognition is a biometric-based technology that mathematically maps a particular person’s or individual’s facial features and stores all that data as a face print. By using this technique, the information on the face of a person is saved mathematically or in the format of graphs in the database, which is used for detecting that particular face.The face recognition model in our system will find a match of that person in the database. If a match is found, it will be notified to the police and the guardian of that person. In this paper, we will use the ideas of the AWS facial recognition algorithm which is based on Artificial Intelligence (AI) and will detect faces with the maximum accuracy to find the missing person.
CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

Fast and accurate prohibited object detection in X-ray images is great challenging. Based on YOLOv6 object detection framework, in this paper, Channel-Target Attention Feature Pyramid Network (CTA-FPN) is proposed for prohibited object detection in X-ray images. It includes two key components: TAAM (Target Aware Attention Module) and CAM (Channel Attention Module). TAAM is to generate the target attention map to enhance the features of prohibited object regions and suppress those of the background regions, so as to solve the problems of object occlusion and cluttered background in X-ray images. CAM is to highlight the feature channels important to the detection tasks, and suppress the irrelevant ones. The target-wise and channel-wise feature enhancement can effectively strengthen the feature representation capability of the network. The proposed CTA-FPN is incorporated into S, M and L models of YOLOv6 respectively, obtaining three X-ray prohibited object detection models. The experimental results on two publicly available benchmark datasets of SIXray and CLCXray show that, CTA-FPN can effectively improve the detection performance of YOLOv6. Especially, YOLOv6-CTA-FPN-L can achieve the state-of-the-arts detection accuracy.
Survey of Investigators About Sharing Human Research Data in the Neurosciences

In the Research Article “Survey of Investigators About Sharing Human Research Data in the Neurosciences” by Hendriks et al.,1 the copyright line should be as follows:
Theoretical investigation of pure-state single-photon and large-bandwidth-correlation biphoton generation from micro/nanofiber

Pure-state single photons and large-bandwidth-correlation biphotons are fundamental resources for quantum information processing. The dispersion properties of micro/nanofiber (MNF) can be tailored by carefully choosing its diameter, resulting in a flexibly tailored biphoton spectrum. We theoretically investigate pure-state single photons and large-bandwidth-correlation biphotons produced by degenerate spontaneous four-wave mixing in MNF. In our simulation, a single-photon state with a purity of 99% will be theoretically attained by choosing the appropriate pump bandwidth and center wavelength with respect to the diameter and length of the MNF. Further, when an appropriate diameter with a negligible curvature at the zero dispersion wavelength is chosen, even a narrow pump bandwidth is capable of motivating remarkably broadband correlation biphotons, e.g., for a MNF diameter of 0.7 μm, the theoretical full width at half maximum is 473 nm. In practice, the application of a MNF-based quantum light source is dependent on the technologies that precisely control and measure the diameter. Our theoretical investigation will guide the experimental realization of high-quality quantum light sources based on MNF.
Next Word Prediction in Bodhi Language Using LSTM-based Approach

Bodhi language is one of the rare languages which is still spoken in the Leh neighborhood, Ladakh and many Tibetan regions.There is not much linguistic research done in this language.Even google translate does not work on this language.There are various types of other linguistic researches and model available on language like English and some other regional languages like Hindi, Bangla, Ukrainian etc.But there are almost negligible research and models available on Bodhi Language.In this paper, we proposed a Language Modelling Technique using Long Short Term Memory network (LSTM) which is based on Recurrent Neural Network (RNN), using this machine learning technique we have made a model to predict the next word in bodhi language, when the user will input anything, the model will predict the next word according to the previous word(s).This model is already made for the English language but we are making the model or basically programing the model to predict the next word in the Ladakhi language which is also called as Bodhi language.This language is more complex than English language.We have tried to make the model as accurate as possible while predicting the next word in Ladakhi language.To prepare the model we have collected dataset as a large collection of Bodhi words.In this model, we have trained the model in 500 iterations (Epochs).we used the TensorFlow, keras, dictionaries, pandas, NumPy packages.For the coding purpose we used the platform called Google Colab which is provided by google for machine learning enthusiasts.
ENHANCING STUDENTS’ READING COMPREHENSION THROUGH KAHOOT APPLICATION

Technological innovations can be used to enhance students’ reading skills. Implementing an online tool using Kahoot could be an alternative way to improve students’ reading comprehension. The objectives of the research were to find out (1) whether there was a significant improvement in the Informatics Engineering students’ reading skills by applying Kahoot or not and (2) whether there was a significant difference in the Informatics Engineering students’ reading skills at Politeknik Sekayu between those who were taught and those who were not or not. The research method was a quasi-experimental design, which involved 50 Informatics Engineering students at Politeknik Sekayu as the sample selected by using purposive sampling. Reading test was used as the technique for collecting the data while paired sample t-test and independent sample t-test were used for analyzing the data. The results imply that the value of t-obtained was 4.987. The significance level was 0.000 with the degree of freedom at 48. Because the t-obtained, 4.987, was higher than t-table 1.667, and the significance level was lower than the alpha value, 0.05. Therefore, there was a significant difference in Informatics Engineering students’ reading skills at Politeknik Sekayu through Kahoot between those who were taught and those who were not. Kahoot significantly improved students’ reading skills, especially in vocabulary, inference, facts and opinions, reference, scanning for detail, and main idea.
The Nature-Related Influence of College Students English-Speaking Anxiety in Indonesia

The nature influence of speaking anxiety on foreign language (FL) learning settings has been established. In addition, research acknowledges the relevance of speaking performance in affecting individual predisposition to experience anxious feelings. However, few studies examine the Indonesian college students' anxiety influencing factors, mainly when speaking English. Data strategies employed a parallel design combining quantitative and qualitative methods mixing a closed-ended Likert-scale questionnaire with an in-depth interview. Twenty-eight college students responded online via Google Form to the questionnaire, while in addition, the in-depth interviews lasted 45-60 minutes with six college students. The results indicate that learning situations in classroom activities revolve around how teachers teach, college students' self-confidence, belief in language learning experiences, and excessive learning material are nature-related prominence factors of college students' anxiety in speaking English. In contrast, a lack of college students' preparation has no effect. Examined are the means through which this research contributes to the advancement of understanding and comprehension of previously unidentified components that influence college students' speaking anxiety.
Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue

There is growing concern that male reproduction is affected by environmental chemicals. One way to determine the adverse effect of environmental pollutants is to use wild animals as monitors and evaluate testicular toxicity using histopathology. We propose an automated method to process histology images of testicular tissue.Testicular tissue consists of seminiferous tubules. Segmenting the epithelial layer of the seminiferous tubule is a prerequisite for developing automated methods to detect abnormalities in tissue. We suggest an encoder-decoder fully connected convolutional neural network model to segment the epithelial layer of the seminiferous tubules in histological images. The ResNet-34 is used in the feature encoder module, and the squeeze and excitation attention block is integrated into the encoding module improving the segmentation and localization of epithelium.We applied the proposed method for the two-class problem, where the epithelial layer of the tubule is the target class. The F -score and Intersection over Union of the proposed method are 0.85 and 0.92. Although the proposed method is trained on a limited training set, it performs well on an independent dataset and outperforms other state-of-the-art methods.The pretrained ResNet-34 in the encoder and attention block suggested in the decoder result in better segmentation and generalization. The proposed method can be applied to testicular tissue images from any mammalian species and can be used as the first part of a fully automated testicular tissue processing pipeline. The dataset and codes are publicly available on GitHub.
Barriers of Online Reading Learning Faced by College Students

In the online reading classroom, the learning activity is different from offline reading learning. It engages the internet and technology to read, comprehend, and learn new information. However, those skills perceive some barriers in the process of learning in students during online reading learning. This qualitative study examined the descriptions of the barriers in reading online learning in a college from students’ perspectives. This study was conducted in one of the private universities in West Java, Indonesia. The participants of this study were 7 college students and one reading lecturer. This study applied the interview and observation as the data collection. The finding of this study has resulted 5 barriers of online reading learning with some description for each barrier. Those barriers are; epistemological barriers, infrastructure barriers, attitudinal barriers, technical barriers, and financial barriers.
Claude Ake On Depsychologising Socio-Economic Problems of Africa And Its Public Services

Modern public services in Africa were the creation of European colonialism.They were established at first to assist in the exploitation of the continent's resources.From this small beginning they later expanded to cover many services catering for the lives of African colonial people.When African nationalists took over governance and the public services, they decided to use them to engineer great political, economic, and social development.At first, the public services recorded some amazing successes, but as a result of many factors, not least poor leadership Africa's public services since the 1960s have been bedeviled by many problems, including inability to lift many Africans from abject poverty.These problems have led some scholars, commentators and outsiders to hastily conclude that these are indeed congenital and inherent failures of Africans.This is "psychologising" the problems of Africa in its people.Relying on Claude Ake's theoretical formulation on the state in Africa, this essay debunks this redundant thinking of uninformed analysts by locating the real socio-economic problems of Africa and its public services in the inherited colonial state, which lacks autonomy and is thus the booty of dominant political faction in power at a given time.The resultant battle by other factions with the dominant class in what is truly an intra class conflict leads to over-politicization of the public services.This latter process according to Ake is indeed the depsychologising of Africa's problem.Ake does not provide any direct and specific solution to the problems identified; one of the key omissions made by Ake, especially with regards to Africa's public bureaucracies.As a solution to the conundrum, the state in Africa and its public services have found themselves the essay concludes that the mixed economy system as in the Scandinavian countries should serve as a model for Africa.This will help to reduce if not eradicate poverty and assist in the rebuild of Africa's public services.
Social–financial approach for analyzing financial transitions

Abstract This study proposes a social-financial approach (SFA) to fill the methodological research gap in strategic policy design for managing financial transitions during social changes. The SFA seeks to characterize inclusive transitions in response to innovation and analyze financial management in social changes. Using a multilevel perspective, we combine evolutionary finance and inclusive growth analytics into this framework. We contend that the interaction between the different levels can be summarized as spontaneous adjustments and the alignment of financial elements with the indicators. Actors who attempt to achieve their goals based on past performance evaluations and other forms of bounded rationality strive to cope with adjustments and further trigger a reorientation of the existing regime. We also developed a new configuration tool called the three-axis description to describe the evolution of financial transitions at different stages. These methods allow us to analyze the evolution of financial transition and efficiency, and we argue that market efficiency evolves in stages with the financial transition. Finally, to demonstrate the capability of SFA to identify diverse financial transition pathways, we examined an example case: the establishment of the Bretton Woods System.
Explanation of an innovative programming approach to create a progressive two-dimensional data file of three inter-dependant variables in a fully automated data acquisition system

This technical document explains a programming model for recording data of a special case in a comma-separated (.csv) file. Generally, in any data acquisition system storing of data increments in one direction; that is in rows. In this article a different programming concept has been illustrated for an automated application to store data in two dimensions; that not only expands in rows but also in columns during the progress of next all successive data acquisition loops for the same set of parameters with a new updated value of the third variable. The effectiveness of the programme has been verified based on a practical proto type test made by monitoring and recording several sets of resultant values of DC current at different values of applied DC voltage and had performed these tests consecutively at different values of temperature. All these acquired data sets were saved in a single comma separated (.csv) data file. The full process can be completed in a single test run and most importantly, without any intervention from a human being. By following the logic and method demonstrated, a similar application could be developed also by other text-based programming languages like Java or Python.