Reviewerly/exHarmony huggingface dataset:
A study on Generative Adversarial Text to Image Synthesis

Image captioning is one of the most prevalent and difficult challenges in Natural Language Processing and Computer Vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. Images and text are two separate "languages" to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions.
M-Party: A Secure Dynamic Cache Partitioning by More Than Two Parties

Last-level cache (LLC) is shared by processes in multi-core system. A number of cache-based side channel attacks were proposed to exploit the vulnerability of LLC to leak confidential information. To tackle security issues, we present M-party, a dynamic cache partitioning that activates a Dummy application to obfuscate the patterns of cache allocations. The evaluation results show that M-party achieves 4.3% improvement over static cache partitioning when the Dummy application is deactivated and performs 3.3% better than SecDCP [1] when the Dummy application is activated.
A Multi-stage Bias Reduction Framework for Eye Gaze Detection

Eye gaze detection is an important component of many multimedia processing algorithms, including user authentication, online education, and medical diagnostics. Hence, it is important to ensure that gaze detection works equally well for different sections of society. For instance, if such algorithms work well for men and not women, this would amplify existing societal biases to provide more security, education, and medical functionalities to men than women. Here, we audit one of the state-of-the-art gaze detection algorithms for gender bias. Audit results suggest that the algorithm performs better for the male group than the female group, indicating a gender bias. To tackle this challenge, we propose a multi-stage bias reduction framework that considers multiple subtasks performed at different stages during the course of the gaze detection algorithm. Like many multimedia algorithms, the decisions made at each stage can impact the performance of the next algorithm stage. Hence, we have designed a framework that finds optimal algorithmic parameters to support high fairness and accuracy by holistically considering multiple stages. The results suggest that the proposed approach yields promising results in terms of fairness and accuracy, thus yielding a path toward accurate and fair eye gaze detection.
Detection of Ethereum Smart Contracts Vulnerabilities Over Blockchain

Smart contracts are a widely researched topic due to their crucial role in blockchain technology. Ethereum, one of the most popular blockchain platforms, utilizes Solidity code for creating secure and transparent transactions through smart contracts. Etherscan provides an extensive collection of Solidity code for Ethereum smart contracts. However, smart contract vulnerabilities can lead to significant financial losses for contract holders. To address this issue, a comprehensive dataset containing numerous defects was collected from etherscan and smartbug-wild. This paper is Proposed a Mathematical model to effectively detect and analyze all possible vulnerabilities with high accuracy, providing an efficient solution for improving smart contract security and ensuring the integrity and reliability of blockchain transactions.
Case Study: Database Schema Design for Improved Performance in MongoDB

Database schema design is much more challenging in case of NoSQL databases compared to relational ones, as there is no straightforward recipe that can be easily applied. Every NoSQL database management system has its own schema design general guidelines, which implies that the process of database schema design is more tedious compared to the traditional schema design process used for relational databases. In order to make sure that the best database schema was chosen for a given use case, one can develop several database schema variants and run a benchmarking experiment, so it becomes clear which variant is the best choice. This paper presents a case study regarding database schema design for MongoDB. For an intercity transportation use case, two database schema variants were designed, implemented and evaluated.
Visual Passage Score Aggregation for Image Retrieval

This paper proposes an effective image retrieval method. Recent image retrieval approaches attempt to construct a single global feature, including local features of an image. In contrast, this paper proposes multiple features for each image. The basic idea is that a target object in a query image is not necessarily in a major part of a database image; therefore, its single feature may include noisy information from the surroundings of the target object. To deal with this, this paper proposes a Visual Passage Score Aggregation framework (VPSA). VPSA first decomposes an image into several pieces of images, called Visual Passages. Based on visual passages, VPSA aggregates relevance scores of visual passages for ranking. VPSA is efficient in the retrieval phase because an ordinary nearest neighbor search is used. The experiment revealed that VPSA showed superior or comparable performance to the state-of-the-art methods, and it takes a shorter time in the retrieval phase.
Persuasive Application for Sex Education

Young adults are curious, and due to a lack of proper knowledge of sexual health, they often tend to make risky sexual choices. Thus, sex education is important, as it increases individuals' knowledge of sexual health, teaches about safe sex practices and healthy sexual behavior, and enables one to make informed decisions. However, in some countries, like India, sex education is highly neglected due to the societal stigma around the topic of sex. Persuasive interventions have shown success in increasing awareness and promoting desired behaviour. However, most of these interventions consider developed countries. This work aims at investigating persuasive intervention for sex education specifically catered to the Indian audience. Particularly, this work discusses using a persuasive mobile app to help individuals learn about sexual health. As a first step to achieving our goal, we designed a prototype for an app called ‘Sex-Educated’ with persuasive features that aim to promote safe sexual practices and increase awareness about risky sexual behaviour among Indian adults. To evaluate the effectiveness of our app with persuasive design and features, we conducted a user study with 57 participants. Our results show that Indian adults are motivated to use a gamified persuasive app for sexual health education. Our app with persuasive features is much more effective to encourage learning, create awareness, and bring about positive attitude and behavioral changes towards sexual health.
Clustering-based Approach for Characterization of Patients with Preeclampsia using a Non-Redundant Feature Selection

Preeclampsia is one of the most frequent causes of maternal death during pregnancy. Knowing the characteristics of the patients can help to better distribute clinical care within the hospital. Thus, in this manuscript we present the application of clustering to characterize patients with preeclampsia. The dataset used in this work belongs to the patients with preeclampsia who were treated at the "IESS Los Ceibos" hospital in the city of Guayaquil. The feature subset selection (FSS) task has been considered for removing redundant features. However, according to some studies, the selection of non-redundant features is not assured when using only a filter or a wrapper FSS approach. As a result, we applied a methodology for training classification models that uses a combination of filter and wrapper FSS approaches to ensure that non-redundant attributes are selected during the data pre-processing phase. To carry out the clustering, the K-Means and EM algorithms were chosen. Based on the comparison of the 6 applied models, the three-cluster model with K-Means was chosen since it is the one that shows the greatest differences between them.
Investigating digital examinations through forensic analysis

During the Covid-19 pandemic, academic institutions rapidly transitioned to online assessments, which largely operated under an unsupervised model that relied on students to uphold academic integrity. However, this trust-based approach proved ineffective, as academic institutions experienced a significant rise in cheating incidents. This paper is an experience report presenting our first-hand observations of cheating behaviors and describes a tool we developed to detect potential cheating in online assessments.
Toward Multi-Level Automatic Updates of Deep Learning Models Using New Micro, Meso, and Macro Stability Metrics

Training deep learning models is a reasonably complex task. This complexity may drastically increase, particularly within the context of complicated industrial ecosystems. However, far beyond this complexity, major challenges continue to concern the maintenance of deep learning models. More precisely, it is extremely complex to maintain a model reliable throughout its life cycle. Indeed, frequent updates may not necessarily result into better performance, compared to older versions of the model. Therefore, it is of paramount importance to possess a solution that can suggest the right updates and the right time in a given deep learning model. To meet this goal, we propose in this paper a new approach for an automatic updating of deep learning models. Our approach includes eight new metrics that assess the stability of the models in terms of the disparity between predictions and/or versions. The metrics will ultimately allow for multi-level updates of the learning models. The right updates will, therefore, be implemented at the right time. In order to demonstrate the performance of our solution, we conducted several experiments on Convolutional Neural network architectures using the MedM-NIST dataset. Our current simulations are showing promising results in the specific field of predicting survival from colorectal cancer histology slides.
Message Passing Neural Network based Light Field Image Compression

Light Field possesses the ability to capture comprehensive information about light distribution in space, making it an appealing technology for immersive media applications. However, due to the vast amount of data generated by the Light Field, there has been significant interest in developing methods for its compression. The use of a graph data structure that outlines the relationship between viewpoints within the Light Field has been found to be a suitable way to process this data. Additionally, the graph convolution network (GCN), which merges graph and neural network approaches, has shown great promise in effectively handling graph data. However, since GCN doesn't directly support edge features, it is unable to weigh the contribution of source key view features, aggregating at the target non-key view, in the graph representation of a Light Field data. In this work, a Light Field compression scheme based on Message Passing Neural Network (MPNN) is proposed to account for the edge features during the training and reconstruction of non-key views, which is found to perform well on a sparse down-sampling pattern of key views. Experimental results show a gain of about 1 dB in PSNR, compared with the previous GCN-based approach, and our proposal also outperformed other baselines at low bitrates, using an adaptive quantization parameters (QP) selection.
Applied Anomaly Detection: a Bayesian Approach to Improve Robustness

The main drivers of the development and production of new energy devices are energy device efficiency and machinery maintenance strategies. The former to minimize pollutant emissions with a view to future carbon neutrality. Condition-Based Maintenance (CBM), on the other hand, can help improve machinery reliability and reduce downtime by monitoring equipment conditions and addressing potential problems before they become serious. It can also save companies money by reducing the number of unnecessary repairs, minimizing the need for spare parts, and optimizing maintenance schedules. In this paper, the authors propose a deep learning methodology to automatically detect anomalies on a real Combined Heat and Power (CHP) unit supplying a school in Germany. The core of the work is a convolutional autoencoder trained on the normal behavior of the energy generator. The autoencoder is enhanced with a Bayesian technique, the Monte Carlo dropout, used to add a stochastic component to the model to quantify the uncertainty degree of the detection. This information is crucial to determine if or when action is actually needed, optimizing the service and maintenance strategy. The proposed approach was applied to a real case study and was found to be effective, heat exchanger fouling was detected 5 weeks before the standard detection system. The algorithm returns high confidence in system anomalies and low detection confidence for minor alterations in behavior, less risky for the machine.
Content Matching for City Improvement

Developing new services or improving existing ones is becoming more accessible with the evolution of NLP techniques. Chatbots are a known example of an NLP-based service; they can interact with humans using text messages or natural language. NLP grants, however, the development of other types of services based on natural languages, such as machine translation, email spam detection, information extraction, content summarization, and question answering. A current need for employers and job seekers is a system that can match content (text) from a project offers description with the job seeker’s report to promote discovery between demand and supply by finding common patterns in different textual descriptions. This paper presents an implementation of an automated tool with AI and NLP to match needs and concrete ideas for innovation with the skills and offers of the business sector, including start-ups and entrepreneurs.
Safeguarding Inter-Device Communications for Connected Systems: A Holistic Security Framework

Embedded devices are a critical part of modern interconnected systems, from industrial control systems to consumer electronics. These devices are designed to perform specific functions and are often connected to networks or other devices, enabling them to exchange data and commands. However, as these devices become more connected, they also become more vulnerable to security threats.One area of particular concern is the security of embedded communication protocols. These protocols play a critical role in enabling devices to exchange data and commands with each other, but they are also vulnerable to various attack vectors that can be exploited by attackers to gain unauthorized access, modify data, or disrupt communication. To mitigate these security risks, the National Institute of Standards and Technology (NIST) has provided guidelines for securing communication protocols. These guidelines emphasize the importance of implementing secure design practices, such as using encryption, authentication, and authorization mechanisms, and limiting communication to only what is necessary.To address embedded devices’ security needs and ensure information security while abstracting away the use of particular communication protocols, this paper aims to introduce a security-enhanced communication framework over standard communication protocols used in embedded devices that are deployed in many mission-critical or safety-critical domains. The work also presents an implementation of the framework over standard I2C communication protocol as a reference implementation.
Clustering of Risk Factors for Coronary Heart Disease Using The k-prototypes Algorithm

Coronary heart disease is a disease that plays a major role as a cause of death in the world. Data from the World Health Organization (WHO) states that an average of 17.9 million people die each year, which is around 31 percent of the total deaths globally. The Institute for Health Metrics and Evaluation (IHME) also reports that 14.4 percent of the causes of death in Indonesia are coronary heart disease. The magnitude of the influence of coronary heart disease on death results in the need for earlier knowledge about the characteristics of each patient with coronary heart disease. Each patient has different behavior and characteristics towards the cause of coronary heart disease. To find out each characteristic for people with coronary heart disease, need a data to be analyzed. Currently, the amount of data is getting bigger and many are of mixed type, including data on coronary heart disease patients. . The aim of this research is to classify coronary heart disease patient data using the k-prototypes algorithm. The best cluster result is k=2, with a silhouette coefficient value of 0.808. Cluster 1 of 493 patients, most of whom were male, had smoking habits with lower averages for the variables age, glucose, body mass index (BMI), diastolic, and heart rate, and had no history of hypertension, blood pressure medication (BPMeds), stroke or. For cluster 2, only 18 female patients had a history of hypertension, diabetes, and all of the numerical variables had a higher average than cluster 1.
A Deep Learning-Based Approach for Detecting Bangla Spam Emails

The field of wireless communications is expanding quickly in this 21st century. One of the most important communication tools available today is Email. Billions of emails are sent everyday throughout the world, that make people more vulnerable to dangers. Spam emails can be used for phishing, blackmail, and extortion from our digital equipment. Phishing emails are those that beg recipients for personal or financial information through different advertisements and offers from any e-commerce websites. Such spam emails are quickly identified by spam filters, which must work harder to block emails with no links or with few links. Despite multiple studies, some valid emails are still labeled as phishing and vice versa by spam filters. For recognizing spam emails in key languages including English, there are a variety of techniques. Nevertheless there aren’t many of these spam email detection tools accessible in Bangla. This study aims to categorize emails in Bangla language using deep learning approaches. In this research, a number of algorithms are investigated, additionally, we have developed a dataset and it is eventually concluded that the Bi-LSTM (Bidirectional Long Short-Term Memory) method has the greatest accuracy (97%) for the identification of Bangla phishing mail.
Variational auto-encoding generative model for speech to singing voice conversion

Voice conversion (VC) is a technique that involves replacing the identity of a person’s voice with another person’s voice without changing the speech content. This paper proposes speech to singing voice conversion using variational auto-encoding Wasserstein GAN (VAWGAN). The proposed method can separate the lyrical content and singer identity from the reference singing templates and later incorporate the target speaker identity in place of reference singer identity. In this way, this can generate a singing voice in the voice of the target speaker using reference singing templates. The performance can be assessed through subjective and objective evaluation experiments. In all the experiments the proposed VAWGAN method outperforms the baseline method. This can be performed well with limited non-parallel training data.
Energy Consumption and Production Forecasting Using Artificial Neural Network for Optimal Energy Cost Savings

With the goal of reducing their carbon footprint, Instituto Superior de Engenharia de Lisboa (ISEL) has put in place several measures in the past few years. One has been to install a solar photovoltaic (PV) system for self-consumption. Load forecasting and PV production models were developed, based on measured data, resorting to Artificial Neural Networks (ANN), with the optimization of ISEL’s energetic needs in mind. To test the forecasting models, an optimization system using linear programming was also developed, focusing on minimizing the cost of energy consumption from the grid, resorting to the management of an Energy Storage System (ESS). The aim is to understand the impact on the optimization system using as entries the measured and the forecasted data by the ANN. The results obtained validate the ANN forecast model and demonstrate its robustness and predictive capacity.
Survey of security, performance, and profitability of Monero: a browser-based cryptocurrency

Information is the key to success in every domain; this statement also applies to cryptocurrencies and blockchain domains. Beginning with Bitcoin back in 2009, which the anonymous Japanese Satoshi Nakamoto launched, blockchain technology emerged. This technology has revolutionized and continues to do so in digital transactions. In addition, this technology led to the further development of other cryptocurrencies that are active at these moments on the market. One class of cryptocurrencies obtained differently from classical bitcoin is those obtained due to web-based mining. The most known coins of this type are Monero, Dash, or Electroneum. This paper aims to analyze the web-based mining process of Monero and the whole process’s security. In addition, we want to explore the performance of the mining processes used in the user’s web browser. Moreover, finally, we aim to research the profitability of coins obtained due to web-browser mining, having as an object of study the cryptocurrency Monero.
Adversarial Attacks on Traffic Sign Recognition: A Survey

Traffic sign recognition is an essential component of perception in autonomous vehicles, which is currently performed almost exclusively with deep neural networks (DNNs). However, DNNs are known to be vulnerable to adversarial attacks. Several previous works have demonstrated the feasibility of adversarial attacks on traffic sign recognition models. Traffic signs are particularly promising for adversarial attack research due to the ease of performing real-world attacks using printed signs or stickers. In this work, we survey existing works performing either digital or real-world attacks on traffic sign detection and classification models. We provide an overview of the latest advancements and highlight the existing research areas that require further investigation.