Reviewerly/exHarmony huggingface dataset:
No One Drinks From the Firehose: How Organizations Filter and Prioritize Vulnerability Information

The number of published software vulnerabilities is increasing every year. How do organizations stay in control of their attack surface despite their limited staff resources? Prior work has analyzed the overall software vulnerability ecosystem as well as patching processes within organizations, but not how these two are connected.We investigate this missing link through semi-structured interviews with 22 organizations in critical infrastructure and government services. We analyze where in these organizations the responsibility is allocated to collect and triage information about software vulnerabilities, and find that none of our respondents is acquiring such information comprehensively, not even in a reduced and aggregated form like the National Vulnerability Database (NVD). This means that information on known vulnerabilities will be missed, even in critical infrastructure organizations. We observe that organizations apply implicit and explicit coping mechanisms to reduce their intake of vulnerability information, and identify three trade-offs in these strategies: independence, pro-activeness and formalization.Although our respondents’ behavior is in conflict with the widely accepted security advice to collect comprehensive vulnerability information about active systems, no respondents recall having experienced a security incident that was associated with missing information on a known software vulnerability. This suggests that, given scarce resources, reducing the intake of vulnerability information by up to 95% can be considered a rational strategy. Our findings raise questions about the allocation of responsibility and accountability for finding vulnerable systems, as well as suggest changing expectations around collecting vulnerability information.
DevIOus: Device-Driven Side-Channel Attacks on the IOMMU

Modern computer systems take advantage of Input/Output Memory Management Unit (IOMMU) to protect memory from DMA attacks, or to achieve strong isolation in virtualization. Despite its promising benefits, the IOMMU could be a new source of security threats. Like the MMU, the IOMMU also has Translation Lookaside Buffer (TLB) named IOTLB, an address translation cache that keeps the recent translations. Accordingly, the IOTLB can be a target of a timing side-channel attack, revealing victim’s secret. In this paper, we present DevIOus, a novel device-driven side-channel attack exploiting the IOTLB. DevIOus employs DMA-capable PCIe devices, such as GPU and RDMA-enabled NIC (RNIC), to deliver the attack. Thus, our attack has no influence on CPU caches or TLB in a victim’s machine. Implementing DevIOus is not trivial as microarchitectural internals of the IOTLB of Intel processors are hidden. We overcome this by reverse-engineering the IOTLB and disclose its hidden architectural properties. Based on this, we construct two IOTLB-based timing attack primitives using a GPU and an RNIC. Then, we demonstrate practical attacks that target co-located VMs under hardware-assisted isolation, and remote machines connected over the RDMA network. We also discuss possible mitigations against the proposed side-channel attack.
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking

Object detectors, which are widely deployed in security-critical systems such as autonomous vehicles, have been found vulnerable to patch hiding attacks. An attacker can use a single physically-realizable adversarial patch to make the object detector miss the detection of victim objects and undermine the functionality of object detection applications. In this paper, we propose ObjectSeeker for certifiably robust object detection against patch hiding attacks. The key insight in ObjectSeeker is patch-agnostic masking: we aim to mask out the entire adversarial patch without knowing the shape, size, and location of the patch. This masking operation neutralizes the adversarial effect and allows any vanilla object detector to safely detect objects on the masked images. Remarkably, we can evaluate ObjectSeeker’s robustness in a certifiable manner: we develop a certification procedure to formally determine if ObjectSeeker can detect certain objects against any white-box adaptive attack within the threat model, achieving certifiable robustness. Our experiments demonstrate a significant (~10%-40% absolute and ~2-6× relative) improvement in certifiable robustness over the prior work, as well as high clean performance (∼1% drop compared with undefended models). <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Fast RNS Processor for Moduli Set {2n+1, 2n, 2n-1}

Residue Number System (RNS) can perform subtraction, addition independently with borrow-free and carry-free propagation. To convert binary to residues and residues to binary, forward conversion and reverse conversion are used. In this paper new mod adder structures were proposed for the mod values (2n - 1) and (2n + 1), these mod adders provide outputs for all cases. A new forward converter has been proposed with the help of newly proposed mod adders to reduce the power, delay, and area than existing design. At the end of this paper deals with designing of RNS processor for error detection and correction in communication systems. These designs have been simulated using NC Launch - Encounter tool in Cadence.
StoryGenAI: An Automatic Genre-Keyword Based Story Generation

Story Generation through Deep Learning is a fascinating area of research in Artificial Intelligence that aims to create computer systems that can produce original and compelling narratives and is an interesting concept that has flourished in the domain of Machine Learning applications starting from 2018. Most of the research carried out in this specific area has shown advances in Modelling and efficiency of story generation. However, some of the setbacks in Artificial Story Generation include little to no coherency with human generating pattern, tokens/words limitation, missing plot twists and direction of story. In this paper, we have performed a comparative study on Automatic Story Generation as well as the proposed scheme of this paper has main focus on generating a meaningful story with the help of conditional text generation using keywords upto five hundred words by optimizing hugging face generative pre trained model Version Two catering towards the problem of coherency in the text generated. As a result each sentence is semantically coherent and the first three sentences are indeed related to the title itself. The experimental results show a BLEU score of 0.704 averaging over ten genres.
Improving Vehicle Detection using Ghost Convolution and GhostBottleneck layers in YOLOv5s

In the view of large number of vehicles on the road, manually controlling vehicles become very difficult and automated traffic management system is a need. Vehicle detection is necessary for automatic traffic management to accurately monitor and control the flow of traffic. In this paper we propose an improved YOLOv5s model for vehicle detection. It uses GhostConv and Ghostbottleneck as part of the architecture. One of the main benefits of this model is that it reduces the number of parameters, which can lead to faster training and inference times. Adapting the GhostConv and GhostBottleneck in model, reduces the amount of calculation, which makes the model faster. For training of models we make a traffic vehicles dataset that consist of four classes: auto, bike, car and bus. The experimental results show that the new model is able to reduce the number of parameters generated by 61.6% which also reduces the computation time of model. This model is also able to perform better as compared to original YOLOV5s.
IoT Based AID And Health Monitoring for Physically Disabled Person

The advancement of the Internet of Things (IoT) has paved the way for numerous applications that can benefit society in various domains. One of the areas that can greatly benefit from IoT technology is healthcare, especially for physically disabled persons. This research paper proposes an IoT-based AID and health monitoring system for physically disabled persons. The system comprises of various sensors, microcontrollers, and cloud computing technology to monitor and track the health status of physically disabled persons. The system can detect any abnormal health condition and send an alert to the caregivers or family members to provide timely medical assistance. The system also provides an assistive device that helps the physically disabled person to move around with ease. The assistive device is equipped with various sensors and actuators that enable the user to control it using their voice or gestures. The proposed system can significantly improve the quality of life for physically disabled persons by providing them with real-time health monitoring and an assistive device to move around.
Evaluating Performance Of Convolution Neural Networks And Their Ensembles For Face Mask Classification

During the Pandemic CoVID-19 there was a need to monitor the public spaces for correct usage of Face Mask to control the spread of infection. There has been extremely good improvement in the field of Image Processing using the Machine Learning Methods like Neural Networks. But there is lack of datasets with High Quality images and separate set for incorrect usage of masks. So we used the Mask Superimposition technique to create a dataset of HQ face mask images. We also compared different Machine Learning Models on different metrics like accuracy, loss, F1-Score and Kappa Score. We also develop multiple ensemble models and compare their performance.
Face Mask Recognition Using OpenCV

The world is experiencing several issues because of the spread of coronavirus (COVID-19). Wearing a facemask is an effective approach for protection against coronavirus, according to WHO standards. Though it is impossible to keep track of whether or not everyone is wearing a mask, it is possible to keep an eye on them. Face mask detection will be achieved using a CNN (convolutional neural network) model employing Tensor flow, Kera's library, and OpenCV (to do real-time face detection) in this. The goal of this paper is to determine that the person in a picture or video stream wearing a mask or not. 97% approval precision was accomplished amid the training of the CNN demonstrate, when an individual is wearing a facemask at that point an exactness rate of 98% was achieved. The experimental outcomes indicate that the approach proposed in this research can identify face masks and enable efficient personnel surveillance.
Efficient Machine leaning algorithms for sentiment analysis in Car rental sevice

Opportunities in both business and vacation travel spell good news for the future of the global vehicle rental sector. The global car rental market is projected to grow from its current value of $90 billion in 2020 to an estimated $120 billion in 2025, a CAGR of 6.1%. A growing international tourist sector, more international air passengers, and rising worldwide incomes are the primary forces propelling this market. Enhancing the user experience through digitization, increasing the number of environmentally friendly rental cars, and the idea of self-driving cars as an alternative to traditional chauffeur services are all examples of emerging trends that are having a significant impact on the industry as a whole. Companies may better cater to client needs by offering vehicles that are a good fit based on real-time data analysis. In this work, the proposed algorithm facilitates the renting of cars according to the requirements of individual clients, such as pick-up time, car type, location, and even more specialized need, such as infant car seats and various sports equipment carrying racks. Changing customer-service practices at car rental agencies are helping them attract and retain regulars. However, AI is the most important component in developing trust with customers through individualized service and suggestions.
Language Identification using Multi-Layer Perceptron

Speech-based language identification is one of the most promising areas. Language identification is the strategy for perceiving a particular language from short speech data. The wording of Telugu, Hindi, and English falls within this work's purview of language identification. Accuracy is the primary language identification problem in the literature. This study proposes a multi-layer perceptron with a sequential model where every epoch uses the Adam Optimizer to lower the error rate and boost accuracy. Mel Frequency Cepstral Coefficient is also utilized to separate highlights from sounds. With 85% accuracy, the proposed language identification model surpassed competing models in the literature.
Optimized Approach for Video Summarization using Transfer Learning and LSTM

Video content has been increasing at a very high pace, so the need for summarizing videos is of urgent need. Video summarization emphasizes on quick go-through of video content. In the last few decades, the field of video summarization has inspired a lot of research. Many of the techniques have been proposed by different researchers, but most of them are not able to create summaries of general videos, i.e., they are generally suitable for a set of categories of videos. To overcome this problem, we have introduced a general framework for video summarization, where we have used a pre-trained VGG16 model for feature extraction, and based on the features, we found the frame level importance using a Long Short Term memory (LSTM) network. After that, we selected the frames with high frame-level scores and finally combined them to form the video summary. Experimenting with the different types of videos taken from the different datasets shows that the proposed methodology outperforms the existing state-of-the-art methods.
Effectiveness of Digital Marketing to Protect Customer's Through Artificial Intelligence

The development of technology is inseparable from the development of digital marketing. Businesses gather precise real-time customer insights and an outstanding customer experience based on artificial intelligence technology and interaction with virtual assistants. The selected articles for the review were from English-language research papers and journals on digital marketing and artificial intelligence. This study's findings demonstrate that the digital market and technology are unquestionably essential components in fostering a market environment, and that technology is consistently altering its appearance. We can assert that AI will have a vast future application that will benefit not only the digital marketing industry but also other industries. As a result, we can say that technologies have effectively impacted the digital market by facilitating smoothness, cost-effectiveness, time savings, and other benefits. Businesses can better target their customers and create campaigns that are more effective and have higher conversion rates. Increased sales and customer satisfaction can be achieved through the use of AI's prediction efficiency by marketers, who can quickly and easily identify potential customers and their purchasing habits. The speed with which artificial intelligence is being implemented across industries will undoubtedly bring about a revolution in business practices.
Boosting Solar Energy Production of a Building using New Architectural Design

Fossil fuels and air pollution are significant problems nowadays, causing many countries to increase their investments in renewable energy development. As the world's energy demand grows, governments are looking into and developing alternative energy sources like solar energy. Solar energy is one of the most promising solutions to address the above problem. In addition, the development of sustainable design in architecture has become a crucial factor in the construction industry as the world faces the challenges of climate change and diminishing fossil fuel reserves. Architects are now seeking innovative ways to incorporate sustainable and renewable energy sources into their designs. Solar energy, as a sustainable and clean energy source, has gained significant attention in recent years. It offers an opportunity to reduce the dependence on non-renewable energy sources and lower carbon emissions. Additionally, the use of solar technology in architectural designs can enhance the aesthetic appeal of buildings while providing energy efficiency. Building-integrated photovoltaic systems have emerged as a popular solution that allows for the integration of photovoltaic cells into the structure of buildings. In this paper, a new architectural design for a building in Tehran is proposed, which leads to the increase of generated solar energy. Needless to say, the designs must be beautiful to look at, functional, and good for the environment. Grasshopper and rhino are utilized to demonstrate the design. In addition, System Advisor Model (SAM) is implemented to study the economic analysis of the solar photovoltaic system planted on the building. The simulation result indicates that with the new design, the power production is increased from 21437 kWh to 69484 kWh. In addition, by using the proposed system in comparison with fossil fuel power plants, a huge amount of air pollution is will be removed from the air.
Enhancing the resolution of Brain MRI images using Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are an emerging technology in Artificial Intelligence (AI). They are an unsupervised learning framework that involves automatically learning patterns from input data to generate synthetic output that closely resembles the original input. GANs enable the creation of datasets by replicating copies of fake outputs based on given inputs. Using GANs, machines can generate unique content that is distinct from human-created data. GANs are widely used in various image-related areas, such as image-text conversion, photo-to-emoji conversion, face aging, new human face generation, super-resolution image conversion, and more. This paper focuses on enhancing the resolution of magnetic resonance images (MRI), which is a common method used in medical imaging. However, obtaining high-quality MRI scans often requires long waiting times and the use of lab equipment with restrictions. Additionally, there are health hazards associated with MRI radiation, resulting in lower-quality images. Super-resolution GANs (SRGANs) can potentially address this issue. Super-resolution is a method that can generate high-resolution images from lower-resolution ones. Previously, technologies such as bi-cubic interpolation, linear interpolation, and nearest neighbor interpolation were used to achieve higher resolution. However, the results obtained using the proposed SRGAN model outperform these conventional methods in terms of performance
People Counting in Public Spaces using Deep Learning-based Object Detection and Tracking Techniques

Recent advancements in deep learning and machine learning have enabled exact people counting in various applications including crowd management, security, and retail analytics. Deep learning algorithms have proven tremendous promise for accurate and efficient people counting in difficult contexts. This paper offers a technique for counting people that utilises deep learning with MobileNet SSD, centroid tracking, and trackable object script. Gathering and preparing a labelled dataset, training a MobileNet SSD model, implementing centroid tracking and the trackable object script, increasing system performance, testing it on real-world scenarios, and deploying it in a production environment are all part of the approach. The recommended approach provides a wide framework for creating and deploying a deep learning-based people-counting system that can be customized and tuned to match a number of applications and purposes. Additionally, we have added alerts on maximum capacity, timely scheduling and input feed from the internet.
A Compreshensive review on Digital Image Watermarking Techniques

With the increasing prevalence of digital media, it is crucial to develop effective methods for transferring hidden data, establishing ownership of digital content, and safeguarding the rights of creators. In this article, we present a comprehensive analysis of watermarking strategies specifically employed in machine learning systems. Our investigation begins with an overview of the foundational principles of digital watermarking, encompassing both traditional practices and advancements facilitated by machine learning. Subsequently, we delve into a discussion of the most widely utilized digital watermarking techniques based on deep learning models. We conduct a comparative analysis of these techniques, examining their strengths and weaknesses. Furthermore, we provide a summary and analysis of the latest contributions to the field, thereby capturing the current state of research. By exploring the realm of watermarking strategies within machine learning, our study aims to shed light on advancements in this field and offer valuable insights into the most effective approaches. Protecting digital content and ensuring the integrity of shared information are critical objectives. Understanding the strengths and limitations of different watermarking techniques can pave the way for the development of more resilient and efficient solutions.
Detection of Faulty node with Hybrid Machine Learning using SVM model

The huge volume of data generated these days from a wide variety of sources has contributed to the widespread use of distributed data processing methods. In today's sophisticated huge computing systems, occupations are broken down into multiple smaller tasks that are carried out in parallel in order to improve work learning results and minimise consumption. Nevertheless, responding with straggler processes, which are processes that run slowly and contribute to an increase in the overall response time, is a common performance problem in such platforms. In this research, we suggested utilizing a hybrid machine learning approach to find struggler nodes in a large distributed environment. These nodes would be identified by their inability to successfully complete tasks. In this paper, we proposed a hybrid machine learning model for detecting faulty nodes in large distributed machines with collaboration of reinforcement and supervised machine learning. The large Virtual Machine (VM's) log data has been collected from the distributed environment and proceeded with reinforcement learning algorithm for module training and supervised machine learning for module testing. According to extracted features, reinforcement learning encompasses an activation function that generates the label for the respective node, whether healthy or weak. In the testing phase, the natural world VM's log data has been collected and evaluated with supervised machine learning classifiers. Several machine learning classification algorithms have evaluated and acquired the results. The SVM provides higher accuracy over the other machine learning classifiers with our reinforcement learning model.