Reviewerly/exHarmony huggingface dataset:
FedCSR: A new cluster sampling based on rotation mechanism in horizontal federated learning

Horizontal federated learning (HFL) is a distributed framework that can be used when datasets from various participants have similar feature spaces but different sample spaces. HFL employs clustering algorithms to facilitate information sharing among cluster clients and model aggregation. However, this approach is not suitable for data with heterogeneity, and its stability cannot be guaranteed. Differing from all the current methods to address these issues, we propose a new HFL framework by employing a cluster sampling method based on a rotation mechanism. In our method, we calculate the magnitude of differences in client data by using Gaussian Model Mixture (GMM), and adaptively adjust the probability of intra-cluster clients being selected by the server as well as the number of clusters. Our numerical experiments show that the new framework can handle different data scenarios and outperforms other baselines in terms of convergence speed and accuracy.
Glitch in the matrix: A large scale benchmark for content driven audio–visual forgery detection and localization

Most deepfake detection methods focus on detecting spatial and/or spatio-temporal changes in facial attributes and are centered around the binary classification task of detecting whether a video is real or fake. This is because available benchmark datasets contain mostly visual-only modifications present in the entirety of the video. However, a sophisticated deepfake may include small segments of audio or audio-visual manipulations that can completely change the meaning of the video content. To addresses this gap, we propose and benchmark a new dataset, Localized Audio Visual DeepFake (LAV-DF), consisting of strategic content-driven audio, visual and audio-visual manipulations. The proposed baseline method, Boundary Aware Temporal Forgery Detection (BA-TFD), is a 3D Convolutional Neural Network-based architecture which effectively captures multimodal manipulations. We further improve (i.e. BA-TFD+) the baseline method by replacing the backbone with a Multiscale Vision Transformer and guide the training process with contrastive, frame classification, boundary matching and multimodal boundary matching loss functions. The quantitative analysis demonstrates the superiority of BA-TFD+ on temporal forgery localization and deepfake detection tasks using several benchmark datasets including our newly proposed dataset. The dataset, models and code are available at https://github.com/ControlNet/LAV-DF.
Pri-EMO: A universal perturbation method for privacy preserving facial emotion recognition

Facial emotion have great significance in human-computer interaction, virtual reality and people’s communication. Existing methods for facial emotion privacy mainly concentrate on the perturbation of facial emotion images. However, cryptography-based perturbation algorithms are highly computationally expensive, and transformation-based perturbation algorithms only target specific recognition models. In this paper, we propose a universal feature vector-based privacy-preserving perturbation algorithm for facial emotion. Our method implements privacy-preserving facial emotion images on the feature space by computing tiny perturbations and adding them to the original images. In addition, the proposed algorithm can also enable expression images to be recognized as specific labels. Experiments show that the protection success rate of our method is above 95% and the image quality evaluation degrades no more than 0.003. The quantitative and qualitative results show that our proposed method has a balance between privacy and usability.
Development of word count data corpus for Hindi and Marathi literature

India has a huge diversity of languages, and Hindi and Marathi are the most spoken languages in the northern and western parts of India. Hindi and Marathi have more than 528 million and 83 million speakers, respectively. The paper describes the development of the Hindi Word Corpus (Hindi WordCorp) and the Marathi Word Corpus (Marathi WordCorp), reporting the frequency of single words (1-gram) used in written texts of the respective languages using the bag-of-words model (BoW). The word frequencies are provided for eleven decades (pre-1920, 1920 to 2020). These texts include books (fiction, non-fiction, history, autobiographies, etc.) and magazines. Academic and reference books were not used. The Hindi WordCorp and Marathi WordCorp used 640 and 712 texts, respectively. An analysis was employed to check whether the texts used were enough to stabilize the rank-order of the total frequencies of the words. Zipf's and Heaps’ law coefficients indicated the sufficiency of the texts. Researchers in various areas like linguistics, social sciences, text mining, machine learning, etc., can use the dataset to answer research questions about language and culture. Some demonstrative examples are provided for using the datasets in the two languages. The dataset is made available on an open data repository. The paper is an account of dataset creation for Hindi and Marathi WordCorp. Hence, no empirical results or conclusions are made based on the data created. A WebApp named Indian Languages Word Corpus (ILWC) has been developed for users. Future directions for text mining and language models are discussed.
Nuclei Segmentation Using UNet on Breast Hematoxylin and Eosin Stained Histopathology Images

Pathology experts usually analyze digital versions of biopsy samples captured using digital microscope. Histopathological images contain adequate phenotypic information. Therefore, these images play an essential role in diagnosing and treating breast cancer. Pathologists perform a microscopic examination of tissue stained with Hematoxylin and Eosin stains. Nonetheless, the manual evaluation of histopathological images is a time-consuming job. With recent advancements in digital imaging, computer-aided analysis of histopathological slides has become essential. In order to perform image analysis using image processing, nuclei segmentation categorized as crucial initial stage. However, there are several challenges in segmenting nuclei images, including variations in color intensity, the presence of occluded objects, the wide distribution of cell clusters and the lack availability of appropriate annotated datasets makes it challenging to produce sufficient segmentation. This study present nuclei segmentation on histopathology images utilizing U-Net. From several tests conducted, the model shows promising result performance in cell segmentation with accuracy 92.70%, precision 87.10%, recall 84.07%, f1 score 85.24% and IoU 74.54%.
Design of an Interactive Learning System Using a Wearable Sensor Glove to Help Special Needs Student

Children who are suffering from mental disabilities struggles to keep up with their studies, this then result in them being unable to learn the proper studies and left them in a disadvantageous position. Studies have shown that students with mental disabilities such as autism might still be able to follow basic studies more slowly. Some studies have indicated that children have far more greater responds to studies which include bright colors. The reason for this is because special needs children have low attention span. For this reason, an interactive learning system needs to be developed. This study will try to make an interactive learning system with a flex sensor. The interactive learning system will be made as a mobile application. This application will be made by using a software named MIT App Inventor. A multiple-choice question will be used in this study with interesting photos. To answer the multiple-choice question, a flex sensor which has been attached to glove, will be used to select the multiple-choice answer. The subject must bend their finger to pick the multiple-choice answer. Testing this assistive device with special needs children has yielded a satisfactory result, of the 12 questions displayed in the application, 7 multiple choice questions have been successfully answered correctly for the first subject and 8 multiple choice question have been successfully answered correctly for the second subject.
Blockchain Technology for Implementation of Vehicle Tax Payment Digital Receipt Authentication Using IPFS and Hyperledger Fabric

Counterfeiting proof of payment is one of the problems in finance. In response to this, the regional apparatus organization in charge of motor vehicle tax has created an Online Motor Vehicle Tax Payment Verification System. The current payment authorization system uses centralized storage, causing the system vulnerable to SQL Injection that compromises data. In addition, the system’s transparency can be misused by irresponsible parties. This study aims to introduce a distributed blockchain-based system to present more transparent and validated data. In addition, blockchain technology enables data to be immutable (permanent). This system uses the InterPlanetary File System (IPFS) by combining Hyperledger Fabric to store data in a distributed manner. This system also uses the blockchain smart contract to keep the hash file of motor vehicle tax payments. We conducted Quality of Service (QoS) experiments by observing throughput, packet loss, and delay parameters. We also analyzed the use of the Central Processing Unit (CPU) and Random Access Memory (RAM) during the operations.
Semantic Role Labeling for Information Extraction on Indonesian Texts: A Literature Review

The information extraction process includes Semantic Role Labeling (SRL) as one of its sub-tasks. SRL aims to determine the semantic role of each entity within a sentence by examining the meaning of the predicate. This helps construct the sentence structure by identifying the relationships between predicates and their corresponding arguments. SRL development is less common than Named Entity Recognition (NER) for information extraction because SRL annotation process is complicated, and labeling results are sometimes ambiguous. In event extraction problem, the use of NER alone is insufficient. Identifying location entities generated by NER is still inaccurate because geographic coordinates indicate locations irrelevant to actual events. On the other hand, SRL can detect locations precisely and in depth according to actual events. Even though the annotation process is complicated, the SRL can be adjusted according to the required domain and its ontology so that SRL can extract location entities down to the event level.. This research aims to offer a comprehensive analysis concerning the advancement of Semantic Role Labeling (SRL) for extracting information from Indonesian texts. Indonesian is a low-resource language with a different character from English and only has very little literature, so it is interesting to study. The papers used for the review process came from IEEE, Science Direct, and Google Scholar from 2013 to 2023, and 15 papers were found that matched the research objectives. The study results show that most papers use Indonesian-language news articles as their dataset because they use formal language, which usually has a good language structure. The methods used in SRLs are mostly rule-based. A weakness of the rule-based development method is that the rules are very dependent on a particular language or problem domain. Thus, further work can use a transformer-based deep learning approach to perform SRL on Indonesian-language texts.
Implementation of Faster R-CNN Inception ResNet V2 Algorithm for Human Body Pieces Detection

Human detection is one of technology that could be implemented in many ways such as security, crime prevention, accidents, absenteeism, victims of natural disaster discovery, and many more. Humans have different shapes and sizes influenced by genetics and life patterns, so human detection technology is considered attractive to be implemented with various existing methods. In its development, human detection has been carried out in several different methods ranging from traditional to the most effective. This study uses the Faster R-CNN algorithm method with Inception ResNet v2 network architecture. Based on the tests that have been carried out performances that the improved network can effectively improve the efficiency of network operations, after testing 7 times ranging from 3000 steps to 13 steps, the accuracy of recognition of human body cut objects reached the highest 81.30% at 9000 steps Testing with a loss of 0.13671. In this way, it shows satisfactory results in agreeing with pieces of the human body and can be developed with better results.
Source Code Statement Classification using ANTLR and Random Forest

In software development, source code analysis is essential to maintain software. One of the metrics used to assess software quality is software complexity, which can be calculated by analyzing source code statements like declaration, expression, and control. However, analyzing source code statements using an existing popular technique, like NLP, requires another structured form of data, which can be obtained by converting the source code into a token stream using a programming language processor. This study aims to get the best source code statement classification from six observed models and two source code forms. The models are Decision Tree, Naïve Bayes, SVM, kNN, Rocchio Algorithm and Random Forest. The source code forms are the raw source code and token stream object. The dataset collection used 562 rows of source code statements from an open-source Java project in a public repository. Next, we use ANTLR to transform the raw source code and get the token stream object. Later, the dataset was modeled using TF-IDF as an NLP technique to get the source code features. Then, six machine learning models were built and evaluated as a comparison for the classification process. As a result, Random Forest became the best model with the highest accuracy values among other machine learning models. Moreover, the token stream became the best object over source code as a contribution from the ANTLR. The model successfully predicted source code statements with a 96.1% accuracy score using the Random Forest model and ANTLR tools. In advance, this multiclass classification was also evaluated and gave a result. No declaration statement was predicted as a control statement, and vice versa (100% accuracy), but some miss prediction was observed in declaration-expression and expression-control pairs. Nevertheless, the Random Forest model’s high accuracy and precision make it suitable for classifying source code statements.
Ball Detection Based on Color and Shape Features Captured by Omni-Directional Camera

The ability to detect the ball on a soccer robot is the key to victory in a match. However, in reality, there are several things that can become a distraction in the detection process. These include the color and shape of the ball which often change. This study aims to create a system that can detect balls based on the color and shape of the ball by comparing the level of accuracy and computation time process. The research process was carried out by capturing real-time video on an Omni-Directional camera and through image processing, including image segmentation by taking HSV colors at a certain range, noise image restoration, feature extraction using contours with optimal roundness, and analysis of detection results. From the application of this method to video images of random balls, an accuracy value of 90,61 percent is obtained. This is obtained with an average computing time of 0,025 seconds.
Electric Wheelchair Performance Testing With Joystick Control Command for Cerebral Palsy Subject

A physical disorder called cerebral palsy is defined by the loss of independence and limb loss brought on by brain injury. Cerebral palsy has unique characteristics, so in terms of assistive technology needs, special services are needed. The assistive technology that helps children with cerebral palsy is a wheelchair. In this study, participants with cerebral palsy were used to examine the effectiveness and confirm the system compatibility of an electric wheelchair with joystick control. There were three testing sessions: one with a traditional wheelchair, one with a joystick, and one with a joystick wheelchair. Out of the six participants, only two had success percentages greater than 50%, according to data analysis and test technique review. Testing using a traditional wheelchair has an average success rate of 70.8%, whereas testing with a joystick wheelchair has an average success rate of 15.5%.
Comparing Classification Algorithms to Analyze Twitter Sentiment on Public Opinion on Fuel Oil

Twitter is currently used as one of the applications that is often used to convey opinions or unrest by the public such as the current issue that is currently rife, namely regarding the increase in fuel oil prices (BBM) in Indonesia. With so much data scattered in a tweet that only consists of a few fragments of words, there must be valuable information or meaning that Twitter users want to convey. Text mining is a very appropriate method to do the analysis because in text mining there is a method that focuses on an opinion which will be concluded into positive and negative opinions. There are several algorithms that are most often used in data mining, namely K-Nearest Neighbor, Decision Tree, and Naive Bayes. The three algorithms used in this study were also compared with the use of feature selection Particle Swarm Optimization (PSO) which is well known for improving the performance of algorithms. There are 2165 negative opinion data and 2835 positive sentiment data. The data will be tried based on distinctive sums of data namely 1000 data, 3000 data, and 5000 data. The classic K-Nearest Neighbor algorithm obtained an accuracy result of 75.60% and made it the best when tested with 1000 data. The use of Particle Swarm Optimization (PSO) is proven to improve accuracy as when tested using 3000 data with an accuracy value of 78.21% on the K-Nearest Neighbor algorithm. In the mean time, in testing 5000 Data, the classic K-Nearest Neighbor once more gotten the most elevated exactness with a esteem of 74.02%.
OpenStack Implementation using Multinode Deployment Method for Private Cloud Computing Infrastructure

The worldwide market for cloud computing alone is expected to reach $\mathrm{US}{\$}947.3$ billion by 2026. OpenStack technology creates opportunities to build private clouds. Department of Information Technology ITS creates its own private cloud using OpenStack and employs the multinode deployment mechanism developed by Kolla Ansible. The OpenStack implementation's results were evaluated by using the Phoronix Test Suite, Ethr, and Rally-Openstack tools to identify the correlation between instance computing performance and the number of running instances, as well as OpenStack service performance while generating instances and changing flavors. The results show that the CPU performance of each instance will decrease with an average decrease of 0.68 GFLOPS or 1.41% as the number of instances increases. The memory performance of each instance will decrease with an average reduction of 0.37 GBps or 4.46%. The duration of the instance creation is unaffected by the number of concurrent requests, with an increase of only 0.3 seconds. The number of simultaneous requests also affects the time of flavor changes. The duration of flavor changes can be up to 2 times (in 4 concurrent requests) from one concurrent request but experiences instability in 5 or more concurrent requests. The KYPO Cyber Range Platform successfully implemented a linear training demo by allocating one sandbox pool to run the training using Openstack resources of 10 vCPUs and 14.3 GB of memory.
Implementation of Fuzzy C-Means and Self-Organizing Map for Data Clustering of Palm Oil

Palm oil is the most significant revenue-generating agricultural industry in Indonesia. PT. Perkebunan Nusantara V, or PTPN V is one of the state-owned companies managing Indonesia’s palm oil business in Riau Province. Related parties still cannot provide harvest planning due to the lack of mapping or grouping of fruit locations that have better potential to be produced. Therefore, this research will apply the FCM and SOM algorithms to cluster oil palm plantations in Riau Province based on data on Extensive, Production Realization, Production Plan, Bunches, Productivity Realization and Productivity Plan. It is known that the results of statistical tests comparing the FCM algorithm with SOM based on the DBI value of the 2020 FCM data are 0.465, while SOM is 0.91S. In 2021 FCM is 0.910 while SOM is 0.583. In 2022 FCM is 0.62S while SOM is 0.862. It is known that the DBI value of FCM is more significant and dominant in the last three years of data compared to SOM. In the last three years, 14 farms have the highest potential for cluster category oil palm production. Farms that have the lowest cluster category oil palm production potential in the last three years amounted to 4 farms.
Combination of DCT &amp; DWT techniques for image compression

The compression of images is one of the most important processes in image processing. The compression reduces the storage space of the images, which is used to increase the performance of storage or transmission of images. For good compression techniques, not only the size of the compressed images is important, but also the quality of the decompressed images. In this paper, two classical techniques of compression are combined to develop a new compression, the first one is the Discrete Cosine Transform (DCT), and the second one is the Discrete Wavelet Transform (DWT). The results of the proposed compression are compared with the classical techniques by using several tests such as Mean square error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural similarity index measurement (SSIM).
19 Knowledge Management Strategies in Clinical Settings, Southwest Nigeria

Clinical settings focus on delivering quality health care to patients in hospital and other settings. The creation and sharing of insights, ideas and exper­tise are crucial to effective care and treatment. This chapter reports on a study conducted in 2017 which investigated knowledge management (KM) strategies used by members of therapy teams including doctors, nurses, nutritionists, clin­ical psychologists, social workers and occupational therapists in two psychiatric hospitals in south-west Nigeria. Quantitative and qualitative data were collected with a self-administered questionnaire and interviews to understand what knowl­edge was shared and how, and to investigate whether workers were rewarded for activities undertaken in carrying out research and in sharing knowledge. A key question to answer was what mechanisms could be put in place to improve patient care through encouraging members of therapy teams to be more innovative and productive.
In-memory Photonic Tensor Core Accelerator for Neural Networks-based Applications

The growth of AI applications has pushed the need for new energy-efficient high-performance accelerators for computing Neural Networks at high speed. Here, we present our Photonic Tensor Core accelerator, which leverages photonics to compute MAC operation at high speed, and Phase Change Materials for reducing energy consumption. The resulting PTC is the key component for the next generation of AI systems.
Machine learning for photonics: from computing to communication

Neural networks are effective tools for learning direct and inverse models. Here, we review two specific applications of neural networks to photonics: (i) learning accurate direct models for optical matrix multipliers and (ii) inverse modeling for short-reach fiber communication systems, enabling signal equalization.