Reviewerly/exHarmony huggingface dataset:
On Key Reinstallation Attacks over 4G LTE Control-Plane: Feasibility and Negative Impact

research-article Open Access Share on On Key Reinstallation Attacks over 4G LTE Control-Plane: Feasibility and Negative Impact Authors: Muhammad Taqi Raza The University of Arizona, United States of America The University of Arizona, United States of AmericaView Profile , Yunqi Guo UCLA UCLAView Profile , Songwu Lu UCLA UCLAView Profile , Fatima Muhammad Anwar UMASS Amherst UMASS AmherstView Profile Authors Info & Claims ACSAC '21: Annual Computer Security Applications ConferenceDecember 2021 Pages 877–886https://doi.org/10.1145/3485832.3485833Published:06 December 2021Publication History 0citation1,513DownloadsMetricsTotal Citations0Total Downloads1,513Last 12 Months1,513Last 6 weeks65 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
The elephant in the (class)room: Emergency Remote Teaching in an ecological perspective

ABSTRACT Most teachers are working from home and using digital tools to mediate their classes as a response to a demand for transitioning face-to-face to online classes due to the COVID-19 pandemic. Drawing on the ecological approach, this paper reports a qualitative study that aims to understand how digital technologies are integrated in the teaching practices during the Emergency Remote Teaching. Seventy-six language teachers answered a quasi-structured questionnaire about their experiences. The findings show how the network created with their peers scaffolded this experience and played a crucial role in their appropriation of those technologies. Moreover, the findings suggest that this network may have contributed to the “normalisation” of digital technology use.
Hindi Text Encryption using Double Transposition and Elgamal

With the advancement of technology, the chances of stealing the data are also increasing day by day. So, it’s important to protect the data from the attackers. To complete this task, we use a technique, called Cryptography. Cryptography is the technique of transforming the meaningful data into the unintelligible data so that attacker cannot get the idea about the original data from the unintelligible data. In India, today most people communicate in Hindi Language and also keep their confidential information in Hindi. So, it’s important to have a cryptographic technique which works on Hindi. In this paper, we have proposed a hybrid technique to encrypt or decrypt the data which is in Hindi Language. This hybrid technique uses two different cryptographic techniques: Double Transposition and Elgamal.
<scp>Stit</scp>-LOGIC FOR IMAGINATION EPISODES WITH VOLUNTARY INPUT

Abstract Francesco Berto proposed a logic for imaginative episodes. The logic establishes certain (in)validities concerning episodic imagination. They are not all equally plausible as principles of episodic imagination. The logic also does not model that the initial input of an imaginative episode is deliberately chosen. Stit -imagination logic models the imagining agent’s deliberate choice of the content of their imagining. However, the logic does not model the episodic nature of imagination. The present paper combines the two logics, thereby modelling imaginative episodes with deliberately chosen initial input. We use a combination of stit -imagination logic and a content-sensitive variably strict conditional à la Berto, for which we give a Chellas–Segerberg semantics. The proposed semantics has the following advantages over Berto’s: (i) we model the deliberate choice of initial input of imaginative episodes (in a multi-agent setting), (ii) we show frame correspondences for axiomatic analogues of Berto’s validities, which (iii) allows the possibility to disregard axioms that might be considered not plausible as principles concerning imaginative episodes. We do not take a definite stance on whether these should be disregarded but give reasons for why one might want to disregard them. Finally, we compare our semantics briefly with recent work, which aims to model voluntary imagination, and argue that our semantics models different aspects.
Empirical Guide to Use of Persistent Memory for Large-Scale In-Memory Graph Analysis

We investigate runtime environment characteristics and explore the challenges of conventional in-memory graph processing. This system-level analysis includes empirical results and observations, which are opposite to the existing expectations of graph application users. Specifically, since raw graph data are not the same as the in-memory graph data, processing a billion-scale graph exhausts all system resources and makes the target system unavailable due to out-of-memory at runtime.To address a lack of memory space problem for big-scale graph analysis, we configure real persistent memory devices (PMEMs) with different operation modes and system software frameworks. In this work, we introduce PMEM to a representative in-memory graph system, Ligra, and perform an in-depth analysis uncovering the performance behaviors of different PMEM-applied in-memory graph systems. Based on our observations, we modify Ligra to improve the graph processing performance with a solid level of data persistence. Our evaluation results reveal that Ligra, with our simple modification, exhibits 4.41× and 3.01× better performance than the original Ligra running on a virtual memory expansion and conventional persistent memory, respectively.
The Role Weights Play in Catastrophic Forgetting

Catastrophic forgetting is the sudden loss of performance when a neural network is trained on a new task or when experiencing unbalanced training. It often limits the ability of neural networks to learn new tasks. Previous work focused on the training data by changing the training regime, balancing the data, or replaying previous training episodes. Other methods used selective training to either allocate portions of the network to individual tasks or otherwise preserve prior task expertise. However, those approaches assume that network attractors are finely tuned, and even small changes to the weights cause misclassification. This fine-tuning is also believed to happen during overfitting and can be addressed with regularization. This paper introduces a method that quantifies how individual weights contribute to different tasks independent of weight strengths or previous training gradients. Applying this method reveals that backpropagation recruits all weights to contribute to a new task and that single weights may be somewhat more robust to noise than previously assumed.
Research on Full Link Intelligent Control Platform for Substation Maintenance through Edge-Aware

Abstract In view of the imperfect process control, safety control and data control in substation maintenance operation, this paper proposes a full link intelligent control platform for substation maintenance operation based on edge-aware. The platform is composed of on-site edge-aware system and big data decision service system. The on-site edge-aware system uses various intelligent tools to collect data, carries out real-time safety behavior early warning, maintenance quality control and maintenance process supervision through edge computing technology, and uploads the analyzed and processed information to the big data decision service system. The big data decision service system provides support for online decision-making of substation equipment maintenance according to the panoramic multi-source information of the maintenance site provided by the site edge perception system. The full link intelligent control platform for substation maintenance operation has built a control system for real-time control of on-site maintenance operation process, on-site maintenance information and on-site maintenance safety information, which improves the management efficiency of substation maintenance on-site operation and enhances the safety guarantee of maintenance operation.
Application of MobileNet-SSD Deep Neural Network for Real-Time Object Detection and Lane Tracking on an Autonomous Vehicle

Recent development in perception of autonomous vehicle is mainly originated by deep learning. To apply in real-time application, the object detector of learning model is required to run on a high-end GPU platform. Therefore, there is always a great challenge to process deep learning algorithm on an embedded system. In this study, we present an application of recent lightweight network, namely MobileNet – SSD, to detect and classify objects of an autonomous vehicle. The vehicle is self-piloted along a lane using an image processing algorithm. We employ the neural network on small power consumption NVIDIA Jetson Nano Development Kit platform. The accuracy of detection is up to 96% at the 42 fps, while the model can archive 26.7 ms timing latency and 74% mAP (mean average precision) via input dataset of 800 Full-HD resolution pictures. The prototype vehicle is capable of upgrading to large-scale autonomous vehicles which are required to work in special conditions such as quarantined or lockdown areas, poisonous and dangerous area, warehouse, factory, airport, etc.
Cost Effective Real-time System for cognitive computing using Personalized Eye Blink Detection from Camera

Eye blink is indicative of various mental states. Generally, vision based approaches are used for detecting eye blinks. However, performance of such approaches varies across participants. Standard eye tracker or eye glasses used for detecting blinks, are very costly. Here, we are proposing a personalized vision based eye blink detector system. Proposed approach is ubiquitous and unobtrusive in nature and can be implemented using standard webcams/mobile camera, making it deployable for real world scenarios. Our approach has been validated on a set of data collected from our lab and on an open data set. Results show that in both cases, our system performs well for various conditions like natural/artificial light, with or without spectacles. We achieved a Fscore of 0.98 for own collected data and 0.91 for open dataset, which outperform state of the art approaches.
A practical query selection framework for real-time Bayesian preference elicitation

Bayesian Preference Elicitation (PE) is an active learning technique aimed at discovering users’ preferences through a suitable sequence of queries. One of its prominent applications consists in providing personalized product recommendations in e-Commerce scenarios. In these settings, where catalogs may contain hundreds or thousands of different products, it becomes crucial to find a good trade-off between recommendation quality and computational cost.In this paper we introduce QUEST, a highly configurable PE framework supporting arbitrary value functions on multi-attribute product domains. We use QUEST to compare established and novel query selection methodologies to achieve real-time user interaction, while preserving recommendation quality. In particular, the experimental assessment shows that a novel uncertainty-based query selection strategy outperforms the largely used value-of-information (VOI) both in accuracy and execution time.
Sentiment Analysis Of Online Lecture Opinions On Twitter Social Media Using Naive Bayes Classifier

Sentiment analysis is a depiction of polarity in a text or word. Sentiment analysis is a form of expression of an individual or group in a particular problem. Sentiment analysis basically works to group the text in a sentence or document into positive and negative forms. The Naïve Bayes Classifier is a simple probabilistic classification method that calculates a set of probabilities by adding up the frequencies and combination of values from a given dataset. In this study, the classification process is divided into two classes, namely positive and negative. From a dataset of 1004, the results obtained accuracy of 62% with 2 scenarios, namely 70:30 and 80:20.
CLAP-Bot: a framework for automatic optimization of high-performance elastic applications on the Clouds

The computational cloud has become notorious due to its business model, where the user only pays to use the system, with no acquisition or maintenance costs. However, cloud providers such as AWS EC2 and Google Computing Engine offer several virtual machine types making it difficult to choose which of them is most suitable to the user’s application and objective.In this work, we present CLAP-Bot, a system that automatically monitors and adjusts the computing infrastructure based on some recipe. CLAP-Bot is built over CLAP, allowing creating and managing computational clusters in different cloud providers. The recipe is a component that can read application metrics and execute a set of actions on the infrastructure. The application monitor is decoupled from the recipe, allowing it to be used transparently with different applications. We show how CLAP-Bot works by implementing three dynamic provisioning policies as recipes and evaluating them. Besides that, together with CLAP-Bot we also present CLAP-Bot-Sim, a discrete event simulator that allows modeling the use of a given recipe without the need to instantiate any virtual machine. CLAP-Bot-Sim also allows modeling dynamic events, such as virtual machine interruptions and instance price oscillation over time. We show that CLAP-Bot-Sim can accurately simulate the effects of recipes on the computing infrastructure and can easily be interchanged with CLAP-Bot.
Discreet-PARA: Rowhammer Defense with Low Cost and High Efficiency

DRAM rowhammer attack is a severe security concern on computer systems using DRAM memories. A number of defense mechanisms have been proposed, but all with short-coming in either performance overhead, storage requirement, or defense strength. In this paper, we present a novel design called discreet-PARA. It creatively integrates two new components, namely Disturbance Bin Counting (DBC) and PARA-cache, into the existing PARA (Probabilistic Adjacent Row Activation) defense. The two components only require small counter and cache storages but can eliminate or significantly reduce the performance overhead of PARA. Our evaluation using SPEC CPU2017 workloads confirms that discreet-PARA can achieve very high defense strength with a performance overhead much lower than the original PARA.
Six DOF Robotic Arm Prototype Modelling By Matlab

Abstract The study in this paper allows us to control the manipulator and achieve any desired position and orientation. The Forward Kinematics was done using Denavait Hartenberg (DH) parameters, also the forward kinematics equations and homogenous transformation matrix was validated using MATLAB Toolbox. The modeling was carried out using the Peter Corke robotics toolbox. Finally, the forward kinematic study and the robot arm’s movement equations were compared with practical measurements to make sure it fulfilled the desired purpose and that it could point to the desired coordinates with a precision of ±0.5 cm.
Analysis of variability and degree of non-linearity of HC-128

HC-128 is a strong candidate of eSTREAM Project. The resistance of this algorithm against cryptanalytic attacks has been analyzed by several researchers. We analyze its security in terms of its variability and non-linearity.A strong crypto-algorithm should have full variability. By ‘variability of an algorithm’ we mean the number of distinct sequences, the algorithm can produce. Full variability means that the algorithm produces as many distinct sequences as there are key-IV pairs. There are research papers which indicate that the algorithm may have the full variability. However, no concrete proof is given. In this paper we present a deterministic proof of full variability of HC-128.Non-linearity plays an important role in determining the security of a crypto-algorithm. Because of the complicated nature of the algorithm, determining the exact degree of the HC- 128 function is quite difficult. In this paper, we demonstrate that the degree of non-linearity of key and IV setup function of HC-128 is very high and it is computationally hard to invert the cipher with the knowledge of key stream only.
Texture-based Intraoperative Image Guidance for Tumor Localization in Minimally Invasive Surgery

Intraoperative tumor localization in a deflated lung in minimally invasive surgery (MIS) is challenging as the lung cannot be manually palpated through small incisions. To do so remotely, an articulated multisensory imaging device combining tactile and ultrasound sensors was developed. It visualizes the surface tactile map and the depth of the tissue. However, with little maneuverability in MIS, localizing tumors using instrumented palpation is both tedious and inefficient. In this paper, a texture- based image guidance system that classifies tactile-guided ultrasound texture regions and provides beliefs on their types is proposed. The resulting interactive feedback allows directed palpation in MIS. A k-means classifier is used to first cluster gray-level co-occurrence matrix (GLCM)-based texture features of the ultrasound regions, followed by hidden Markov model-based belief propagation to establish confidence about the clustered features observing repeated patterns. When the beliefs converge, the system autonomously detects tumor and nontumor textures. The approach was tested on 20 ex vivo soft tissue specimens in a staged MIS. The results showed that with guidance, tumors in MIS could be localized with 98% accuracy, 99% sensitivity, and 97% specificity.Clinical Relevance- Texture-based image guidance adds efficiency and control to instrumented palpation in MIS. It renders fluidity and accuracy in image acquisition using a hand-held device where fatigue from prolonged handling affects imaging quality.
Dynamic Graph Neural Network for Super-Pixel Image Classification

Convolutional Neural Networks (CNN) have achieved a huge success in computer vision tasks. In spite of the fact that some CNN models can out-perform human in many specific tasks, the traditional CNN models can only handle images with fixed dimensions, i.e. width and height. Current state-of-the-art CNN models overcome the issue by resizing input images to maintain the consistency of dimensions among inputs. This approach has the main drawback of over up-sampling or over down-sampling with images whose dimensions are far too different to the standard of the models. Moreover, most of CNN models are built over-parameterized, which means most of them are heavier than necessary. In this paper, we aim to make use of graph neural networks to broaden the very new research field of applying the networks on visual tasks. We propose a position-aware dynamic graph propagation scheme to handle super-pixel images created by popular super-pixel segmentation algorithms. Although our model did not out-perform state-of-the-art traditional CNN models on all datasets due to the inevitable information loss of segmentation step, we achieved a huge improvement in accuracy compared to existing graph neural network methods and achieved state-of-the-art accuracy on Superpixel MNIST-75 dataset by 23% error rate drop; we also reduced the number of parameters by 99% compared to VGG16 model.
BORDE: Boundary and Sub-Region Denormalization for Semantic Brain Image Synthesis

Medical images are often expensive to acquire and offer limited use due to legal issues besides the lack of consistency and availability of image annotations. Thus, the use of medical datasets can be restrictive for training deep learning models. The generation of synthetic images along with their corresponding annotations can therefore aid to solve this issue. In this paper, we propose a novel Generative Adversarial Network (GAN) generator for multimodal semantic image synthesis of brain images based on a novel denormalization block named BOundary and sub-Region DEnormalization (BORDE). The new architecture consists of a decoder generator that allows: (i) an effectively sequential propagation of a-priori semantic information through the generator, (ii) noise injection at different scales to avoid mode-collapse, and (iii) the generation of rich and diverse multimodal synthetic samples along with their contours. Our model generates very realistic and plausible synthetic images that when combined with real data helps to improve the accuracy in brain segmentation tasks. Quantitative and qualitative results on challenging multimodal brain imaging datasets (BraTS 2020 [1] and ISLES 2018 [2]) demonstrate the advantages of our model over existing image-agnostic state-of-the-art techniques, improving segmentation and semantic image synthesis tasks. This allows us to prove the need for more domain-specific techniques in GANs models.
Graph Attention Network with Dependency Parsing for Aspect-level Sentiment Classification

The purpose of Aspect-level Sentiment Classification (ASC) is to judge the sentiment polarity of words or phrases with subjective emotions in a sentence. The existing mainstram method is to extract the context semantics of aspect targets words based on attention mechanism, but the attention mechanism is not sensitive to location information and not good at capture syntactic dependency information. To solve the existing problems, this paper proposes an Aspect-Specific Graph Attention Network (ASGAT) model to make up for the shortcoming of attention mechanism. Firstly, the dependency syntax tree of a sentence is obtained by dependency parsing, and then the adjacency matrix with local location information is generated by combining the local location relationship of aspect target words in the context. Then the context semantics of aspect target words is extracted by multi-layer Graph Attention Network (GAT), which makes the semantic features of extracted aspect targets more abundant. The model can capture the long-distance syntactic dependency and the local information of aspect words better. The proposed model has achieved better results on five benchmark datasets. The effectiveness of the model is verified by model comparision and experimental analysis.
Smartphone Authentication with Hand Gesture Recognition (HGR) Using LiDAR

The increase of a smartphone's storage capacity means the increase of important data saved and exchanged within the smartphone as well, thus ensuring secure authentication in smartphones is crucial. Nowadays text passwords and physical biometrics are becoming easier to be hacked due to their predictable nature and can be bypassed with brute force. On the other hand, Hand-Gesture Recognition requires the user to perform a hand gesture that is known and identical by themselves only. This work is carried out to gain a better understanding of Gesture Recognition Authentication especially in the field of Computer-based Gesture Recognition more specifically in the field of Hand Tracking. There is still room for improvement to achieve better authentication in smartphones, one of the techniques is to employ Hand-Tracking Gesture Recognition (HT-GR). Hand-Tracking Gesture Recognition (HT-GR) utilizes the “Vision” framework from Apple to gain better hand-gesture recognition. To be able to perform well in harsh environments, the LiDAR sensor is applied to assist the tracking process. The significance of this paper is to overcome the current limitation in authentication systems for smartphones. Some other techniques for Gesture Recognition did not work well in a harsh environment, such as a dark environment. Unlike the current approach, the LiDAR sensor in this study can improve the current weakness.