Reviewerly/exHarmony huggingface dataset:
Semantic Communication for Efficient Image Transmission Tasks based on Masked Autoencoders

Semantic communication, a promising candidate for 6G technology, has become a research hot spot. However, existing studies tend to focus more on image reconstruction rather than accurately transmitting semantic information at the pixel level. This paper introduces a novel approach using codec-based Masked AutoEncoders (MAE) for efficient image transmission. The proposed system compresses local information into low-dimensional latent vectors, improving system efficiency. We also design a selective module for enhanced image reconstruction and implement Noise Adversarial Training (NAT) to increase the system’s resilience to channel noise. Experimental results show that our method effectively improves downstream tasks while preserving image quality.
Deep Learning Based Coded Over-the-Air Computation for Personalized Federated Learning

Federated learning (FL) is an edge learning framework that has received significant attention recently. However, the cost of communication has become a major challenge for FL as the number of edge devices grows and the complexity of training models increases. Besides, data samples across all edge devices are usually not independent and identically distributed (non-IID), posing additional challenges to the convergence and model accuracy of FL. Therefore, we propose a novel personalized FL framework based on deep coded over-the-air computation, named DipFL. In this framework, we design a deep AirComp aggregation (DACA) module for n-to-1 information aggregation. Besides, a joint source-channel coding (JSCC) module is designed based on the variational auto-encoder (VAE) model, which not only encodes the transmitted data, but also reduces the bias of local samples by introducing certain regularisation terms. In addition, we propose a personalized mix module that allows local models to be more personalized by mixing the global model and the local models. Simulation results confirm that the proposed DipFL framework is able to significantly reduce the amount of transmitted data, while improving FL performance especially at low signal-to-noise regimes.
Batch(Offline) Reinforcement Learning for Recommender System

The explosive spread of the Internet in recent years has increased the types and amounts of big data, making it difficult for users to search for the data they need. With the continued growth of business on the Internet, e-learning, increased communication and sharing among users, and the advent of social networking, there is an undeniable need to design and implement systems that make it easier for people to search. A recommender system provides the ability to provide the most appropriate and accurate suggestions to users by checking user-related information from relevant datasets. In other words, it extracts user preferences and interests from data and makes suggestions. In this paper, we study the problem of learning recommendation systems with large datasets. We propose an offline RL framework for recommender systems to achieve high accuracy and perform recommendations quickly. Specifically, we propose a framework called Ofrec that first transforms the problem into a Markov Decision Process (MDP) and provides highly accurate and time-saving recommendations. We conduct extensive experiments on a large dataset of CIKM 2019 EComm AI and show that the proposed approach outperforms supervised learning and reinforcement learning algorithms.
Natural Language Processing for Diagnosis and Risk Assessment of Cardiovascular Disease

Even with better tools for diagnosis and prevention, heart disease continues to be a major killer. Diagnosis and prevention of heart disease require detection and awareness of risk factors. Disease progression modeling and clinical decision-making can both benefit from the automatic identification of these risk markers from clinical records. Many studies have tried, without success, to uncover the characteristics that put people at risk for cardiovascular disease. Many hybrid systems have been suggested that attempt to integrate knowledge-driven and data-driven approaches, however due to the usage of Machine Learning, rules, and dictionaries, methods, they frequently require substantial human input. Natural language processing and deep learning methods can be used to extract useful information from clinical tales. By using state-of-the-art methods involving stacked word embeddings to the 2014 i2b2 challenge, this article hopes to contribute to the state of the art in this area. The suggested model makes use of a stacking method that combines different embeddings (CHARACTER-BERT Embedding) to better understand the data.The application of this method to the i2b2 cardiovascular risk factors challenges dataset resulted in significant enhancement of our model’s performance. An F1 score of 92.65% is an excellent result. When compared to competing models and systems created for the 2014 i2b2 challenge, our proposed model fares exceptionally well.
Securing Indonesian Hoax News Dataset with Blockchain, IPFS, and Voting Mechanism

The spread of fake news and hoaxes on the internet has become a serious issue, with inaccurate information being presented as fact and affecting public opinion. Machine learning has been used to combat this issue, but the lack of open datasets for hoaxes in Indonesia poses a challenge to researchers. Building a dataset for hoax news dataset in Indonesia presents challenges such as the speed of hoax dissemination, the need for validation, and the difficulty in accessing and tracking dispersed datasets. Therefore, the paper proposes the solution of securing the dataset with blockchain, and voting mechanisms that can provide a transparent and democratic process for validating hoaxes. To ensure the integrity and security of the dataset, the paper proposes using a combination of blockchain technology and the use of InterPlanetary File System (IPFS). The blockchain technology can be used to create an immutable record of the dataset, ensuring that any changes to the data are transparent and traceable. IPFS can be used to store the dataset in a distributed manner, making it more resilient to attacks or data loss. The voting mechanism can be used to validate the authenticity of the data and ensure that only valid and trustworthy data is included. The proposed solution addresses the challenges of hoaxes and misinformation, which often evolve with the changing trends in society and make it up to date. The use of blockchain technology and IPFS ensures that the dataset is secure and trustworthy, while the voting mechanism ensures that only valid data is included.
A Novel Approach for Face Recognition: YOLO-Based Face Detection and Facenet

Advances in information technology encourage digitization of all aspects of human life, including government. The Indonesian government also uses the Electronic Based Government System (SPBE) to transform governance by utilizing information and communication technology to provide services to SPBE users. However, with this transformation, there are weaknesses and loopholes that irresponsible people, such as attacks on systems, theft of identity and personal data, and falsification of electronic documents, can exploit. Therefore, a system is needed to verify the user's identity. To meet these needs, we developed a face recognition system that uses faces to recognize and verify a person's identity. We develop a face recognition system by utilizing an existing pre-trained model, the YOLO (You Only Look Once) deep learning model, as a face detection system with various weights and different versions. Furthermore, the face detection system will be integrated with Facenet to perform feature extraction and face classification. The system will be tested and rated based on the average time it takes to process images and their accuracy. After testing the dataset consisting of 100 identities and 200 student facial images, the best results obtained are the YOLOv5 model and yolov5s weights with a processing time of 183 ms and an accuracy of 99.5 %.
Comparison of UI/UX Development Using Design Thinking vs Lean UX : A Comparative Study

The User Experience (UX) development method plays an important role in ensuring the successful design of the user experience of an application that is effective, satisfying and meets user needs. This study compares two popular methods that are often used in developing UX, namely Design Thinking and Lean UX. The purpose of this study is to evaluate the advantages, disadvantages and focus of each method in developing UX designs. The results of this study indicate that the Design Thinking method focuses more on understanding the user or is more user-centered, while Lean UX focuses more on flexibility and fast iteration as well as continuous experimentation or testing. Based on this, it is interesting to further investigate how a team determines which method to use in their UX design project, considering that both methods have their own strengths and weaknesses, and there are various factors that influence the UX design development stage, such as resources, time, project scale, and others.
Unified Deep Neural Demodulation Network Design for QAM Signal Recovery

In this paper, we focus on designing an unified deep neural demodulation network for recovering multiple QAM signals, which can adapt to the adaptive QAM modulation signal. We specifically introduce the convolution block, identity block, self-attention block to extract the input complex signal feature, such that the unified demodulation network can jointly decide the modulation type and symbol constellation index. In the meanwhile, our proposed demodulation network can compensate the time and frequency offset induced by the channel and receiver oscillator. We evaluate the symbol error rate (SER) of our proposed demodulation network for BPSK, QPSK, and 8QAM signals in AWGN, Nakagami and Rician channels under different constellation mapping schemes. Simulation results show that, even when the constellation graph overlaps with each other for BPSK, QPSK, and 8QAM signals, for AWGN channel, the SER of our proposed unified demodulation network can approach the theoretical bound. While for the Nakagami and Rician channels, our proposed demodulation network can also provide a competitive SER performance.
Recommendations for Fixing of Step Performed and Sequence Diagram Based on Text Mining on SRS Digibrary

Only some applications that are built are successful once the client uses them. This problem occurs because there is an inconsistency between applications built with the client's needs, especially in the Software Requirements Specification (SRS) document as the reference data for software development. These losses can be avoided by implementing consistency in the contents of the SRS document. Therefore, before application development, the SRS document consistency process is crucial to match the client's needs and the developer's understanding of developing software. This study aims to propose recommendations for fixing artifacts based on the lowest value of consistency between Step Performed and Sequence Diagrams through a text-mining approach. The results of the validation and reliability tests using Gwet's AC1 formula through the Python programming language produced a Kappa Score of 0.500000, while through expert questionnaires of 0.159998. Based on the comparison of artifact consistency calculations, recommendations for fixing artifacts were made for the lowest consistency measurement values in the comparison of SP09 (d9) with SD09 (d23), which had a value of 0.840124.
Joint Rendering Offloading and Resource Allocation Scheme for MEC-Assisted RS VR Systems

With the increasing demand of virtual reality (VR) applications, wireless systems need to provide ultra-high data rate to support VR streaming for multiple users simultaneously. In this paper, we propose a mobile edge computing-assisted rate splitting (RS) VR streaming transmission scheme to pursue better quality of experience (QoE) and alleviate the computing burden of VR users (VUs). We formulate an optimization problem to minimize the weighted energy consumption while ensuring the required QoE. The quantization parameters selection, rendering offloading decision, transmit precoding, rate allocation, and computing resource allocation are optimized and a joint Wrendering offloading and resource allocation algorithm is proposed. Simulation results validate the efficiency of the proposed algorithm and reveal the performance gain obtained from RS.
Over-the-Air Computation Empowered Federated Learning: A Joint Uplink-Downlink Design

In this paper, we investigate the communication designs of over-the-air computation (AirComp) empowered federated learning (FL) systems considering uplink model aggregation and downlink model dissemination jointly. We first derive an upper bound on the expected difference between the training loss and the optimal loss, which reveals that optimizing the FL performance is equivalent to minimizing the distortion in the received global gradient vector at each edge node. As such, we jointly optimize each edge node transmit and receive equalization coefficients along with the edge server forwarding matrix to minimize the maximum gradient distortion across all edge nodes. We further utilize the MNIST dataset to evaluate the performance of the considered FL system in the context of the handwritten digit recognition task. Experiment results show that deploying multiple antennas at the edge server significantly reduces the distortion in the received global gradient vector, leading to a notable improvement in recognition accuracy compared to the single antenna case.
Improved Attention U-Net Combined with Conditional Random Field for Ischemic Lesion Segmentation from Magnetic Resonance Images

Stroke Lesion segmentation from magnetic resonance images is of great research interest due to its capability in providing appropriate clinical information for the effective treatment of stroke. Deep learning methods have demonstrated promising results in medical image segmentation and U-Net is one of the most effective models. Nevertheless, these algorithms in the area of ischemic stroke lesion segmentation are in the first stages of development and they lack performance compared to other problems for instance brain tumor segmentation. In this paper, we improved the U-Net algorithm by applying blocks consisting of depth-wise separable convolutions with skip connections instead of normal convolution layers and new attention blocks. Based on these improvements, the new architecture has better performance and accuracy with fewer parameters which would need simpler equipment for implementation. We utilized a 3D fully connected Conditional Random Field (CRF) as post-processing to improve the model prediction. Experimental results showed that the proposed end-to-end deep encoder-decoder model has a significant improvement compared to existing deep learning methods on the publicly available Anatomical Tracings of Lesion After Stroke (ATLAS) dataset.
Semi-Supervised Deep Reinforcement Learning in Decentralized Multi-Agent Collision Avoidance and Path Planning in a Complex Environment

The problem of path planning and collision avoidance in complex and natural environments is one of the basic requirements of the robotic world, enabling robots to enter social environments. This paper aims to provide a decentralized path planning and collision avoidance method in multi-agent environments. In this method, each agent is a decision-making unit that decides independently from other agents and based on what is in its field of view. In the present paper, classical methods have been used to generate data for training purposes. Models were trained offline by imitating classical methods then semi-supervised methods were used for feature extraction. The results obtained from this method were compared with the Optimal Reciprocal Collision Avoidance (ORCA) method in three environments with different densities and three different indices. The proposed method performed relatively optimally and successfully increased the interaction index while decreasing the computation time. On the other hand, due to the scalable potential of this method, the number of agents could be increased without affecting the computation time.
Poker Face Defense: Countering Passive Circuit Fingerprinting Adversaries in Tor Hidden Services

Tor is a widely used tool that offers anonymity and privacy to its users while handling general internet traffic and providing hidden services (HS) for secure content access. Volunteer-operated virtual tunnels in a multi-hop connectivity model enable Tor’s hidden services to ensure anonymity for users, content providers, and servers. However, recent studies have shown that patterns in the connection process of Tor HS can compromise user anonymity and expose content despite encryption through website fingerprinting. This level of exposure can be dangerous for Tor users, given the sensitivity of the content it handles. Therefore, this study proposes a new approach to address the patterns that threaten Tor user anonymity. The proposed method, the Poker Face Algorithm, modifies Tor’s hidden service connectivity to eliminate distinguishable characteristics and make the connection process between the user and the hidden server indistinguishable. By effectively altering the way clients and hidden services connect to Tor, the Poker Face Algorithm successfully removes identifiable patterns that can compromise identity by leaving a connectivity pattern through tracing packets exchanged during connectivity.
Privacy-Preserving Learning using Autoencoder-Based Structure

The need for privacy makes data centers not provide their datasets to inference centers. On the other hand, inference centers need more data to train learning algorithms and provide suitable and acceptable services. Therefore, the existence of a structure that can keep the data confidential while maintaining its usefulness for utility providers is of great importance. In this paper, by modifying the structure of the auto encoder, a method is presented that manages the trade-off between utility and privacy. Moreover, the performance of the proposed method has been evaluated by simulation.
Detecting Cyberbullying using Machine Learning Approaches

Social media platforms serve as the main conduit for information and communication in today’s society. Social media platforms have ingrained themselves into our daily lives, and as access is expanded to more distant regions, their user bases are rapidly growing. Roman Urdu is the primary form of communication on social media among the 71.70 million users in Pakistan. As a result of these advancements and the rise in users, cyberbullying—also known as digital bullying—has increased. This study emphasizes on social media users who interact using Roman Urdu, an Urdu dialect written using the English alphabet. In this study, we investigated the issue of online bullying behavior on the data (Roman Urdu) that was collected from Kaggle. This is one of the rare research projects that, as far as we are aware, addresses Roman Urdu cyberbullying behavior. The purpose of the research we propose is to find a suitable model for Roman Urdu cyberbullying behavior classification. The data was in raw form in a way that the contents and data annotations were in the different lists inside different dictionaries of the root dictionary. After labeling the data, the dataset has then undergone pre-processing to remove duplications, stop words, punctuation, and other sources of noise. Then, based on supervised learning, a set of different learning algorithms, including Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, and Logistic Regression were applied to both types of extracted features. The features are extracted using two separate approaches, Count-Vectorizer and TF-IDF (term frequency-inverse document frequency) Vectorizer. With performance rates of 89.9% when applied to TF-IDF features and 88.3 percent when applied to CV features, Random Forest (RF) outperformed the other developed algorithms by both combinations. The suggested approach might assist online social apps and chat rooms create stronger bully word filters and make the internet a safer place for users.
E-Voting Using Blockchain: A Systematic Literature Review

The emergence of information technology has made many traditional systems start implementing technology, specifically voting systems. In addition, there is a lack of transparency and security that have been experienced in carrying out traditional voting. To support a transparent electronic-based voting system, decentralized blockchain technology is used. Therefore, transparency and security could be effectively implemented. This paper was created to provide insight to readers regarding the implementation of blockchain in e-voting systems. Based on 32 papers carried out by conducting a Systematic Literature Review (SLR) that have been collected and reviewed, this paper will discuss e-voting, blockchain, and blockchain implementation in e-voting systems. Performed SLR make it to identify the process of blockchain implementation in e-voting applications.
A Comparative Study of the Accuracy and Efficiency of Wideband Delphi and Planning Poker Software Effort Estimation Techniques

Estimating the effort required to develop a software project in an accurate and efficient manner is vital for software project managers. This research focuses on two well-known group-based effort estimation techniques i.e. Wideband Delphi and Planning Poker. A controlled experiment is designed and conducted to compare the estimation accuracy and efficiency of these two techniques. Two comparable teams of 5 practitioners each are formed for this purpose. One team is trained to use the Wideband Delphi process for estimation while the other is trained to follow the Planning Poker process. Both teams are asked to estimate the development effort of two different types of 10 real life software projects – 5 web apps and 5 mobile apps. Two well-known estimation accuracy metrics - MMRE and PRED(x) – are used to measure the estimation accuracy while the efficiency is measured in terms of the person hours (PH) consumed to complete the estimation process. Results of the comparison reveal that Wideband Delphi has a better estimation accuracy as compared to Planning Poker for both web and mobile apps. Planning Poker, however, is found to be more efficient for both types of apps.
A Comparative Analysis of Popular Software Effort Estimation Techniques

Software effort and time estimation has always been one of the biggest challenges for the software project managers around the world. Accurate effort estimation plays a significant role in the success of a project. The aim of this study is to identify the best software effort estimation technique among the popular software estimation techniques being used in Pakistani software houses employing agile methodologies. An online survey performed in this study has shown that the two most widely-used effort estimation techniques in Pakistan are Expert Judgment and Planning Poker. To determine which of these two effort estimation techniques gives better estimates, a controlled experiment involving two groups of professionals was performed. In one group, individual estimators used Expert Judgment while in the other group a team of estimators used Planning Poker to estimate the effort of real-life projects. Results of the comparison of these effort estimates with actual effort values have shown that the estimates obtained through Planning Poker appear to be more accurate than the estimates obtained through Expert Judgement. This finding is expected to be beneficial for professionals involved in managing software projects.