Reviewerly/exHarmony huggingface dataset:
Reconstructing with Less: Leakage Abuse Attacks in Two Dimensions

Access and search pattern leakage from range queries are detrimental to the security of encrypted databases, as evidenced by a large body of work on attacks that reconstruct one-dimensional databases. Recently, the first attack from 2D range queries showed that higher-dimensional databases are also in danger (Falzon et al. CCS 2020). Their attack requires the access and search pattern of all possible queries. We present an order reconstruction attack that only depends on access pattern leakage, and empirically show that the order allows the attacker to infer the geometry of the underlying data. Notably, this attack also achieves full database reconstruction when the 1D horizontal and vertical projections of the points are dense. We also give an approximate database reconstruction attack that is distribution-agnostic and works with any sample of queries, given the search pattern and access pattern leakage of those queries, and the order of the database records. Finally, we show how to improve the reconstruction given knowledge of auxiliary information (e.g., the centroid of a related dataset). We support our results with formal analysis and experiments on real-world databases with queries drawn from various distributions.
UAV Behavior-Intention Estimation Method Based on 4-D Flight-Trajectory Prediction

Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted dynamically by a genetic algorithm, which can predict UAV trajectory and the time of entering the protection zone instantly and accurately. Then, according to the number of areas where the tangent line of the current trajectory point intersects with the collision area, alarm area, alert area, and the time of entering the protection zone, the UAV’s behavior intention can be estimated. The simulation experiments verify the dangerous behaviors of UAV under different danger levels, which provides reference for the subsequent maneuvering strategies.
Local Affine Preservation With Motion Consistency for Feature Matching of Remote Sensing Images

As a fundamental and essential task in the field of remote sensing and photogrammetry, feature matching endeavors to establish reliable correspondences between two sets of feature points extracted from an image pair of the same scene. In this article, we propose an efficient and general algorithm, which is called local affine preservation (LAP) matching, for robust feature matching of remote sensing images. We start by constructing the putative point correspondences according to the similarity of well-designed feature descriptors and then focus on removing false matches from the putative set. The key idea of LAP is to search motion-consistent neighborhoods and maintain the local neighborhood topological structures of the true putative matches. To this end, we present a local geometric constraint, which exploits the property of affine invariance to measure the preservation degree of neighborhood topology, since the property is still held under both rigid and complex nonrigid transformations for a minimum topological unit. Moreover, in order to avoid the random distribution of outliers to destroy the neighborhood structure preservation of inliers, a neighbor mining strategy is introduced to search motion-consistent neighbors for each correspondence. We formulate the problem into an optimization model and derive a closed-form solution with linearithmic time complexity. Extensive experimental results on remote sensing images demonstrate that our LAP is able to achieve better performance over the current state-of-the-art approaches.
A Gaussian process‐based approach toward credit risk modeling using stationary activations

Abstract The task of predicting the risk of defaulting of a lender using tools in the domain of AI is an emerging one and in growing demand, given the revolutionary potential of AI. Various attributes like income, properties acquired, educational status, and many other socioeconomic factors can be used to train a model to predict the possibilities of nonrepayment of a loan or its chances. Most of the techniques and algorithms used in this regard previously do not submit any attention to the uncertainty in predictions for out of distribution (OOD) in a dataset, which contributes to overfitting, leading to relatively lower accuracy for predicting these data points. Specifically, for credit risk classification, this is a serious concern, given the structure of the available datasets and the trend they follow. With a focus on this issue, we propose a robust and better methodology that uses a recent and efficient family of nonlinear neural network activation functions, which mimics the properties induced by the widely‐used Matérn family of kernels in Gaussian process (GP) models. We tested the classification performance metrics on three openly available datasets after prior preprocessing. We achieved a high mean classification accuracy of 87.4% and a lower mean negative log predictive density loss of 0.405.
Post-quantum cryptography for automotive systems

Automotive systems have become powerful computing platforms with an increasing demand for secure communication. The hyperconnectivity of the Vehicle-to-Everything (V2X) environment drastically increases the attack surface and the need for crypto-agility. In addition, the long lifecycle of automotive products demands that not only current but also future attacks are considered. Thus, empowering automotive devices with efficient, robust, and long-term security solutions is challenging. The foreseeable breakthrough of quantum computers and their threat to traditional cryptography requires that automotive devices are able to efficiently implement quantum secure cryptographic mechanisms, also known as Post-Quantum Cryptography (PQC). One of the most promising PQC approaches is lattice-based cryptography. Among the seven finalists of the NIST third round post-quantum standardization process, five algorithms (three KEM/encryption and two signature algorithms) belong to the category of lattice-based cryptography. While lattice-based cryptography has been previously integrated in general-purpose microcontrollers, their impact on automotive environments has been neglected. To this end, this work presents two contributions. As a first contribution, we provide a performance exploration of four lattice-based KEM/encryption algorithms implemented on the automotive microcontroller AURIX. The exploration includes the three finalists CRYSTALS-KYBER, NTRU, and Saber, together with ThreeBears. Despite ThreeBears was not selected as a PQC finalist, NIST recommended further investigations in this direction due to interesting security and performance characteristics of the algorithm. Our analysis has shown that all of these algorithms can be implemented on the AURIX microcontroller while achieving a competitive performance. As a second contribution, we explore the improvement of the security level of ThreeBears by extending its error correction capability.
Improving Hierarchy Storage for Video Streaming in Cloud

Frequently accessed video streams are pre-transcoded into several formats to satisfy the characteristics of all display devices. Storing several video stream formats imposes a high cost on video stream providers using the old classical way. Alternatively, cloud providers offer a high flexibility of using their services and at a low cost relatively. Therefore, video stream companies adopted cloud technology to store their video streams. Generally, having all video streams stored in one type of cloud storage, the cost rises gradually. More importantly, the variation of the access pattern to frequently accessed video streams impacts negatively the storage cost and increases it significantly. To optimize storage usage and lower its cost, we propose a method that manages the cloud hierarchy storage. Particularly, we develop an algorithm that operates on parts of different videos that are frequently accessed and stores them in their suitable storage type cloud. Experiments came up with promising results on reducing the cost of using cloud storage by 18.75 %.
Fake Instagram Profile Identification and Classification using Machine Learning

With the increase in Internet usage, Instagram is now considered a very important platform for advertising marketing and social interaction. It is used by millions of users but, some users tend to misuse the platform by creating false identities. In recent years though Internet is a boon, online social networks are susceptible to threats by cyber criminals and spammers. Moreover, the popularity of social media users is determined by followers and hence users resort to different wrong means to promote increased profile followers. Researchers has offered a lot of feasible solutions for social media applications. In this paper, the automatic detection of fake profiles has been proposed to identify fake Instagram profiles so that the social life of Instagram users is secure. The prediction of fake Instagram profiles is facilitated using supervised learning machine algorithms. Upon classification, fake profile IDs are stored in a data dictionary to further help the concerned authorities to take necessary actions against fraudulent social media profiles. Experimentation has been done to compare the classification algorithms used to train the dataset.
Blockchain based Decentralized Vehicle Booking Service

Online vehicle booking service became a growing need now-a-days as it offers an efficient and cheaper alternative. One can directly call a taxi to any location at any time they want without the need for a lengthy delay. There are various platforms implementing online vehicle booking service but the major issue with them is having a centralized authority which are pocketing huge profits. A fixed percentage of money paid by the user is taken by the centralized authority. These centralized systems also tend to misuse their user’s data and are also prone to several attacks. One of the best solutions to this problem is to use BlockChain. Our objective is to avoid this centralized authority by creating a decentralized application. Not just removing the central authority, BlockChain also provides several features like immutability, better transparency, enhanced security and traceability. Platforms like Ethereum and Hyperledger allow one to develop decentralized applications and deploy them in the blockchain. Smart Contracts can be developed using Solidity to achieve decentralization. This maintains transparency, immutability and removes a central authority which may misuse its user's information.
Investigating the Experience of Social Engineering Victims: Exploratory and User Testing Study

The advent of mobile technologies and social network applications has led to an increase in malicious scams and social engineering (SE) attacks which are causing loss of money and breaches of personal information. Understanding how SE attacks spread can provide useful information in curbing them. Artificial Intelligence (AI) has demonstrated efficacy in detecting SE attacks, but the acceptability of such a detection approach is yet to be investigated across users with different levels of SE awareness. This paper conducted two studies: (1) exploratory study where qualitative data were collected from 20 victims of SE attacks to inform the development of an AI-based tool for detecting fraudulent messages; and (2) a user testing study with 48 participants with different occupations to determine the detection tool acceptability. Overall, six major themes emerged from the victims’ actions “experiences: reasons for falling for attacks; attack methods; advice on preventing attacks; detection methods; attack context and victims”. The user testing study showed that the AI-based tool was accepted by all users irrespective of their occupation. The categories of users’ occupations can be attributed to the level of SE awareness. Information security awareness should not be limited to organizational levels but extend to social media platforms as public information.
Detection of HOG Features on Tuberculosis X-Ray Results Using SVM and KNN

Image processing is one of the sciences in image processing which can involve several other techniques such as data mining techniques, in this case the detection of an image. Images are generally carried out classification which results in accurate detection wherein the detection of an image is carried out by extracting the features so that the image can be recognized by computation. One of the extract features that are superior and easy to apply in computational techniques is HOG (Histogram OF Oriented Gradients). The HOG feature can be useful in helping detect images in the form of Tuberculosis xray. After extracting the features, then the classification is carried out using 2 methods that are good for learning levels such as KNN (K-Nearest Neighbor) and SVM (Support Vector Machine). The results of this paper in the detection of HOG Tuberculosis X-ray with KNN for positive images got an accuracy of 77.95% while the negative ones got an accuracy of 78.65%. The results of HOG detection on Tuberculosis X-ray results with SVM on images that were positive got an accuracy of 65.75% while those who were negative were 79.39%.
A Mixed-Reality System to Promote Child Engagement in Remote Intergenerational Storytelling

We present a mixed reality (MR) storytelling system designed specifically for multi-generational collaboration with child engagement as a key focus. Our "Let’s Make a Story" system comprises a two-sided experience that brings together a remote adult and child to tell a story collaboratively. The child has a mixed reality phone-based application with an augmented manipulative that controls the story’s main character. The remote adult participates through a web-based interface. The adult reads the story to the child and helps the child play the story game by providing them with items they need to clear the scenes.In this paper, we detail the implementation of our system and the results of a user study. Eight remote adult-child pairs experienced both the MR and a traditional paper-based storytelling system. To measure engagement, we used questionnaire analysis, engagement time with the story activity, and the word count of the child’s description of how the story should end. We found that children uniformly preferred the MR system, spent more time engaged with the MR system, and used more words to describe how the story should end incorporating details from the game.
On Pless symmetry codes, ternary QR codes, and related Hadamard matrices and designs

It is proved that a code L(q) which is monomially equivalent to the Pless symmetry code C(q) of length $$2q+2$$ contains the (0,1)-incidence matrix of a Hadamard 3- $$(2q+2,q+1,(q-1)/2)$$ design D(q) associated with a Paley–Hadamard matrix of type II. Similarly, any ternary extended quadratic residue code contains the incidence matrix of a Hadamard 3-design associated with a Paley–Hadamard matrix of type I. If $$q=5, 11, 17, 23$$ , then the full permutation automorphism group of L(q) coincides with the full automorphism group of D(q), and a similar result holds for the ternary extended quadratic residue codes of lengths 24 and 48. All Hadamard matrices of order 36 formed by codewords of the Pless symmetry code C(17) are enumerated and classified up to equivalence. There are two equivalence classes of such matrices: the Paley–Hadamard matrix H of type I with a full automorphism group of order 19584, and a second regular Hadamard matrix $$H'$$ such that the symmetric 2-(36, 15, 6) design D associated with $$H'$$ has trivial full automorphism group, and the incidence matrix of D spans a ternary code equivalent to C(17).
Comparative Analysis of Information Retrieval Models on Quran Dataset in Cross-Language Information Retrieval Systems

English is an international language used for communication worldwide but still many cannot read, write, understand, or communicate in English. On the other hand, the World Wide Web has unlimited resources of information in different languages which English native find challenging to understand. To avoid such barriers, Cross-Language Information Retrieval (CLIR) systems are proposed, which refers to document retrieval tasks across different languages. This work focuses on the performance evaluation of different Information Retrieval (IR) models in CLIR system using Quran dataset. Furthermore, this work also investigated the length of query and query expansion models for effective retrieval. The results show that different length of queries has an impact on the performance of the retrieval methods in terms of effectiveness. Hence, after comprehensive experiments, an appropriate length of query for Arabic CLIR system is suggested along with the best query expansion and retrieval model.
DNS Cache Poisoning Attack: Resurrections with Side Channels

DNS is one of the fundamental and ancient protocols on the Internet that supports many network applications and services. Unfortunately, DNS was designed without security in mind and is subject to a variety of serious attacks, one of which is the well-known DNS cache poisoning attack. Over the decades of evolution, it has proven extraordinarily challenging to retrofit strong security features into it. To date, only weaker versions of defenses based on the principle of randomization have been widely deployed, e.g., the randomization of UDP ephemeral port number, making it hard for an off-path attacker to guess the secret. However, as it has been shown recently, such randomness is subject to clever network side channel attacks, which can effectively derandomize the ephemeral port number.
Public Transportation Operational Health Assessment Based on Multi-Source Data

In order to solve the problem of inefficient long-term operation of urban public transport vehicles and the difficulty of finding the cause of the disease, a new analysis idea was designed using machine learning methods. This study aimed to provide a rapid, accurate, and convenient solution model and algorithm to address the drawbacks of traditional analysis tools that are incapable of handling multiple sources of public transport data. Based on a full process analysis of the bus operation status, the influencing factors and calculation methods were defined. Afterwards, the calculation results were used to construct a training set with a Random Forest regression model to obtain the weight ranking of different influencing factors. The results of the simulation validation proved that the model can use the basic data of bus operation to quickly find out the primary factors affecting the operation condition and pinpoint to the bottleneck interval. The method has high accuracy and feasibility. It can be universally applied to the analysis of regular bus scenarios to provide strong decision support for the operation level.
Vision Paper

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.
A strategy for on-machine springback measurement in rotary draw bending using digital image-based laser tracking

Abstract Rotary draw bending is a commonly used metal forming technique for profile bending. Due to the elastic recovery of the material, springback compensation to control the bent product quality is one of the critical manufacturing issues. The realized bend angle has to be measured for springback compensation before removing the profile from the machine, and the bending process can follow an iterative approach until the product quality is satisfied. However, this trial-and-error is costly for batch production in manufacturing. An on-machine measurement technique is therefore developed to measure springback in rotary draw bending with an affordable laser and a webcam. An image processing technology is integrated with the manufacturing process to track the deformation and measure springback angle in real time, eliminating the need for the workpiece to be transferred to a measurement device. In this paper, bending experiments were conducted with AA6082 rectangular hollow profiles bent at 30°, 45°, 60°, and 90° angles, and springback angles from the conventional manual measurement were compared to that from the on-machine measurement. Since the difference in springback measurement between the proposed and the conventional methods was within 0.15° on average, it is demonstrated that the laser tracking, on-machine measurement is a feasible real-time springback measurement technique for Industry 4.0.
On Architecture Selection for Linear Inverse Problems with Untrained Neural Networks

In recent years, neural network based image priors have been shown to be highly effective for linear inverse problems, often significantly outperforming conventional methods that are based on sparsity and related notions. While pre-trained generative models are perhaps the most common, it has additionally been shown that even untrained neural networks can serve as excellent priors in various imaging applications. In this paper, we seek to broaden the applicability and understanding of untrained neural network priors by investigating the interaction between architecture selection, measurement models (e.g., inpainting vs. denoising vs. compressive sensing), and signal types (e.g., smooth vs. erratic). We motivate the problem via statistical learning theory, and provide two practical algorithms for tuning architectural hyperparameters. Using experimental evaluations, we demonstrate that the optimal hyperparameters may vary significantly between tasks and can exhibit large performance gaps when tuned for the wrong task. In addition, we investigate which hyperparameters tend to be more important, and which are robust to deviations from the optimum.