Reviewerly/exHarmony huggingface dataset:
Self-Supervised Depth Completion Based on Multi-Modal Spatio-Temporal Consistency

Due to the low cost and easy deployment, self-supervised depth completion has been widely studied in recent years. In this work, a self-supervised depth completion method is designed based on multi-modal spatio-temporal consistency (MSC). The self-supervised depth completion nowadays faces other problems: moving objects, occluded/dark light/low texture parts, long-distance completion, and cross-modal fusion. In the face of these problems, the most critical novelty of this work lies in that the self-supervised mechanism is designed to train the depth completion network by MSC constraint. It not only makes better use of depth-temporal data, but also plays the advantage of photometric-temporal constraint. With the self-supervised mechanism of MSC constraint, the overall system outperforms many other self-supervised networks, even exceeding partially supervised networks.
HeatGoggles: Enabling Ubiquitous Touch Input through Head-Mounted Devices using Thermal Imaging

Displays are a ubiquitous part of our everyday life. Digital, as well as paper-based displays, are used on various occasions to present us with different information. While users cannot interact with many of these displays yet, they could benefit from making them interactive. Research shows that thermal cameras can be exploited to make surfaces interactive by sensing touchpoints based on the heat signature and residue. However, most of the research done uses a stationary setup. This work proposes HeatGoggles, a wearable gadget detecting touch input on arbitrary surfaces using thermal imaging. We report on its design, prototypical implementation, and first evaluation. We show that it can be used on non-touch-enabled displays as well as other traditional information displays.
Modeling University Faculty Member’s Digital Image

Introduction. The discussion of the shaping of a digital image of university faculty member is driven by the need for effective educational interaction between students and faculty in the context of digitalization. However, there are practically no studies of the faculty’s image as a system, as well as technologies for modeling the faculty’s digital image. The objective of this study is to model faculty’s digital image for purposes of decision-making in the process of managing digital image in educational interactions. Materials and Methods. Secondary data were received through methods of content analysis and systemic and structural-functional evaluation of information. Primary data were obtained through comparative marketing study of faculty’s digital image on a sample of 400 students and 92 faculty members of a Moscow-based and a regional university and through an expert survey. Methods of multivariate scaling and analysis of statistically significant differences were also applied. Results. Novelty of the study lies in incorporating basic and instrumental core components in the model of university faculty’s digital image due to a dual role of “digital competence” element. The image modeling technique is also developed. It is noticed that faculty’s digital competence is not only an element in the structure of faculty’s image, but also a tool that transmits the image to external environment. Based on results of the study, recommendations on implementation of technique of faculty member’s digital image modeling are proposed. Revealed differences in opinions of students and faculty of two universities can be attributed to unequal access to digital technologies in different regions. Discussion and Conclusion. Conclusions made by authors contribute to development of scientific thought on shaping and promotion of university faculty’s digital image. Results of the comparative study will enable the Moscow-based and regional universities faculty to differentiate and precisely approach issues of shaping and promoting personal digital image.
Challenges of Real-time Processing with Embedded Vision for IoT Applications

Recent advances in both Artificial Intelligent (AI) and the Internet of Things (loT) make it possible to implement surveillance systems that can detect and recognize objects in an automatic manner. It is still challenging to set up the embedded visions in resource-limited devices. This study investigates how the system configuration and the memory size affect the performance of embedded vision applications. For this proj ect, we set up a vision sensor accessible wirelessly from a single board computer (SBC). The designed system utilizes Raspberry Pi (SBC) as a computing server, and Python programming language to receive the captured images from the vision sensor using an open-source computer vision and machine learning software (OpenCV) library, which can detect objects and allow for easy accommodation with high performance. We also use a set of Python classes (Image ZM Q) that can transport live video streams from one controller to another for a distributed image processing network. We evaluate the performance with frame rate, frame transfer delay, and frame processing time. The proposed system is cost-efficient and suitable for home security, industrial wireless control, and other loT applications.
Compression and Privacy of Hyperspectral Images

This paper presents a unified approach to the compression and privacy of Hyperspectral Images presenting a lossless compression method based on linear prediction and the application of watermarks to a Region of Interest (ROI) of an image. The proposed methods are experimentally evaluated.
Pedestrian Detection Based on Improved SSD Object Detection Algorithm

Pedestrian detection is an important application of object detection. SSD is one of the target detection algorithms based on deep learning with better performance. The weak detection ability of SSD for small objects, and there will still be false detections and missed detections in the detection situation of the complex environment. In order to improve the detection accuracy of SSD for pedestrians, we propose an improved SSD object detection algorithm based on DenseNet and multi-scale feature fusion. Based on the SSD algorithm, we design the DenseNet-66 module to enhance the feature extraction and utilization capabilities of the model. In the target detection part, a fusion mechanism of multi-scale feature layers is introduced, and an attention feature fusion module is added to further improve the detection performance of the model for small target pedestrians. After training on PASCAL VOC, INRIA, ETH, TUD, CoCo datasets, the experimental results show that our improved SSD model has 300 × 300 input to achieve PASCAL VOC 2007, VOC 2012, INRIA, ETH, TUD, CoCo datasets Up 89.50% mAP, 84.76% mAP, 78.49% mAP, 69.50% mAP, 78.58% mAP and 57.35% mAP. Compared with SSD, the improved SSD detection accuracy increases by 3.75%, 1.77%, 3.06%, 3.66%, 1.90% and 1.87%, respectively.
Unsupervised Learning with Restricted Boltzmann Machines and Autoencoders

Unsupervised learning is a branch of machine learning that tries to find hidden structures within unlabeled data and derive insights from it. Clustering, data dimensionality-reduction techniques, noise reduction, segmentation, anomaly detection, fraud detection, and other rich methods rely on unsupervised learning to drive analytics. Today, with so much data around us, it is impossible to label all data for supervised learning. This makes unsupervised learning even more critical. Restricted Boltzmann machines and autoencoders are unsupervised methods that are based on artificial neural networks. They have a wide range of uses in data compression and dimensionality reduction, noise reduction from data, anomaly detection, generative modeling, collaborative filtering, and initialization of deep neural networks, among other things. We will go through these topics in detail and then touch upon a couple of unsupervised preprocessing techniques for images, namely, PCA (principal component analysis) whitening and ZCA whitening. Also, since restricted Boltzmann machines use sampling techniques during training, I have briefly touched upon Bayesian inference and Markov chain Monte Carlo sampling for reader’s benefit.
Full Round Zero-Sum Distinguishers on TinyJAMBU-128 and TinyJAMBU-192 Keyed-Permutation in the Known-Key Setting

TinyJAMBU is one of the finalists in the NIST lightweight standardization competition. This paper presents full round practical zero-sum distinguishers on the keyed permutation used in TinyJAMBU. We propose a full round zero-sum distinguisher on the 128- and 192-bit key variants and a reduced round zero-sum distinguisher for the 256-bit key variant in the known-key settings. Our best known-key distinguisher works with $$2^{16}$$ data/time complexity on the full 128-bit version and with $$2^{23}$$ data/time complexity on the full 192-bit version. For the 256-bit version, we can distinguish 1152 rounds (out of 1280 rounds) in the known-key settings. In addition, we present the best zero-sum distinguishers in the secret-key settings: with complexity $$2^{23}$$ we can distinguish 544 rounds in the forward direction or 576 rounds in the backward direction. For finding the zero-sum distinguisher, we bound the algebraic degree of the TinyJAMBU permutation using the monomial prediction technique proposed by Hu et al. at ASIACRYPT 2020. We model the monomial prediction rule on TinyJAMBU in MILP and find upper bounds on the degree by computing the parity of the number of solutions.
Feature Selection Based on Machine Learning Algorithms: A weighted Score Feature Importance Approach for Facial Authentication

The emergence of biometric-based face detection and recognition technology has attracted the attention of all industries. Facial recognition allows you to recognize people by their facial features. This technology has been a part of our daily lives and has been utilized in security, forensic investigation, and check-ins at airports. Feature selection allows for finding the most distinct facial biometric traits from the first feature set. In addition to identifying the most important features, feature selection also aids in lowering the feature size dimension. It is an essential step in the process since biometric authentication occurs in real-time. This paper presents a novel feature selection method based on the fusion feature importance scores of machine learning models. A weighted score level fusion method based on the Genetic Algorithm (GA) is used to combine the scores collected from various machine learning models. The objective function, which represents the accuracy of the biometrics system, is optimized by the GA, which is utilized as an optimizer to choose the optimal weights. The Fetch Olivetti Faces database has been used to test the proposed method. According to the stated results, the proposed approach increased accuracy from 93.5 percent to 95.62 percent while enabling a decrease in feature size from 4096 to 964.
On the Effectiveness of Intersection Attacks in Anonymous Microblogging

Intersection attacks, which are popular traffic analysis attacks, have been extensively studied in anonymous point-to-point communication scenarios. These attacks are also known to be challenging threats to anonymous group communication, e.g., microblogging. However, it remains unclear how powerful these attacks can be, especially when considering realistic user communication behavior. In this paper, we study the effectiveness of intersection attacks on anonymous microblogging systems utilizing Twitter and Reddit datasets. Our findings show that the attacks are effective regardless of whether users post their messages under pseudonyms or publish them to topics without attaching identifiers. Additionally, we observed that attacks are feasible under certain settings despite increasing userbase size, communication rounds’ length, cover traffic, or traffic delays.
SemiPainter: Learning to Draw Semi-realistic Paintings from the Manga Line Drawings and Flat Shadow

Semi-realistic paintings are widely used in game concept art, film posters and animation, etc. However, none of existing work can reproduce this type of art. We propose the SemiPainter to generate semi-realistic paintings from line drawings based on a deep learning network. We divide the complex work into two parts, generating the shadow from a line drawing and a simple flat shadow image at first, then generating flat color from line drawings. We merge these two paintings and get the final result. We use two networks with U-Net structure to realize these two stages of work. In order to ensure the global consistency of picture illumination, we add the lighting direction to the shadow network. The color of each part of the painting is uncertain, so color hints from users is provided when coloring, so that the network can realize controllable colorization. To the best of our knowledge, this is the first framework for reproducing semi-realistic paintings.
$${\textsf {ISAP}{+}}$$: $$\textsf {ISAP} $$ with Fast Authentication

This paper analyses the lightweight, sponge-based NAEAD mode $$\textsf {ISAP} $$ , one of the finalists of the NIST Lightweight Cryptography (LWC) standardisation project, that achieves high-throughput with inherent protection against differential power analysis (DPA). We observe that $$\textsf {ISAP} $$ requires 256-bit capacity in the authentication module to satisfy the NIST LWC security criteria. In this paper, we study the analysis carefully and observe that this is primarily due to the collision in the associated data part of the hash function which can be used in the forgery of the mode. However, the same is not applicable to the ciphertext part of the hash function because a collision in the ciphertext part does not always lead to a forgery. In this context, we define a new security notion, named $${\textsf {2PI{+}}}$$ security, which is a strictly stronger notion than the collision security, and show that the security of a class of encrypt-then-hash based MAC type of authenticated encryptions, that includes $$\textsf {ISAP} $$ , reduces to the $${\textsf {2PI{+}}}$$ security of the underlying hash function used in the authentication module. Next we investigate and observe that a feed-forward variant of the generic sponge hash achieves better $${\textsf {2PI{+}}}$$ security as compared to the generic sponge hash. We use this fact to present a close variant of $$\textsf {ISAP} $$ , named $${\textsf {ISAP}{+}}$$ , which is structurally similar to $$\textsf {ISAP} $$ , except that it uses the feed-forward variant of the generic sponge hash in the authentication module. This improves the overall security of the mode, and hence we can set the capacity of the ciphertext part to 192 bits (to achieve a higher throughput) and yet satisfy the NIST LWC security criteria.
Revisiting the Efficiency of Perfectly Secure Asynchronous Multi-party Computation Against General Adversaries

In this paper, we present a perfectly-secure multi-party computation (MPC) protocol in the asynchronous communication setting with optimal resilience. Our protocol is secure against a computationally-unbounded malicious adversary characterized by an adversary structure $$\mathcal {Z}$$ , which enumerates all possible subsets of potentially corrupt parties. The protocol incurs an amortized communication of $$\mathcal {O}(|\mathcal {Z}|^2)$$ bits per multiplication. This improves upon the previous best protocol of Choudhury and Pappu (INDOCRYPT 2020), which requires an amortized communication of $$\mathcal {O}(|\mathcal {Z}|^3)$$ bits per multiplication. Previously, perfectly-secure MPC with amortized communication of $$\mathcal {O}(|\mathcal {Z}|^2)$$ bits per multiplication was known only in the relatively simpler synchronous communication setting (Hirt and Tschudi, ASIACRYPT 2013).
Weighted Competitive-Collaborative Representation Based Classifier for Imbalanced Data Classification

Competitive-collaborative representation based classification (CCRC) has been widely used in pattern recognition and machine learning due to its simplicity, effectiveness, and low complexity. However, its performance is highly dependent on the data distribution. When addressing imbalanced classification issue, its classification results usually tend towards the majority classes. To solve this deficiency, a class weight learning algorithm is introduced into the framework of CCRC for imbalanced classification. The weight of each class is adaptively generated according to the representation ability of each class of training samples, in which the minority classes can be given larger weights. Our proposed model is solved with a closed-form solution and inherits the efficiency property of CCRC. Extensive experimental results show that our model outperforms the commonly used imbalanced classification methods.
Quantum AI: Achievements and Challenges in the Interplay of Quantum Computing and Artificial Intelligence

In recent times, Quantum Computing (QC) is receiving growing attention, thanks to the enormous advances in the construction of operational quantum computers, quantum materials and quantum cryptography. Given the advances in the physical construction and the scaling up of quantum computers, it is now necessary to foster the creation of quantum algorithms and methods that are adapted to such computers and that make the most of their intrinsic computational and communication capabilities. In the era of Big Data, some of the most computationally demanding tasks fall into the field of Artificial Intelligence (AI), including tasks that are currently computationally intractable due to physical limitations. The intrinsic parallelism, computational efficiency, and representational power offered by QC make for an excellent alternative to binary computers, holding the promise of enhanced AI models. This novel Quantum Artificial Intelligence (QAI) concept will result in the detection of patterns that classical AI algorithms are unable to identify, and in the time reduction of several orders of magnitude. In this review, we describe the scientific progresses in the confluence of AI and QC. We start by presenting both areas, basic concepts and the timeline of most significant advances in the history of AI and QC, to then focus on existing research made in the bidirectional approaches of QC benefiting from AI and AI benefiting from QC. Finally, we describe future avenues of research for the QAI incipient scientific area, and conclude.
Lightweight Authentication Encryption to Improve DTLS, Quark Combined with Overhearing to Prevent DoS and MITM on Low-Resource IoT Devices

This research has provided an overview of the Internet of Things (IoT) and performed an overall analysis of security protocols and available mechanisms to protect the security of IoT systems, showing that weaknesses still exist in the information security system, and are also the main reasons that limit the development, powerful application of IoT. We presented a solution to prevent denial of service attacks with an improved method of Over-hearing combined with Quark to improve DTLS (Datagram Transport Layer Security), limiting damage from active and passive attacks by building the solution using lightweight authentication encryption for weak resource IoT devices. Our research improved security protocols such as DTLS. We integrated Quark into DTLS with Over-hearing to prevent denial of service attacks, active attacks as well as passive attacks along with the man-in-the-middle attacks. Our results have proven to have an effective application on low-resource IoT devices. The research has achieved certain new results and proposed further research directions in the future. Using our proposed research solution, we can overcome it to a great extent.
Evaluation of Spaceport Site Selection Criteria Based on Hesitant Z-Fuzzy Linguistic Terms: A Case for Türkiye

The location of the spaceport is a significant factor in increasing the success of operations and reducing costs. To choose a suitable site, critical features need to be defined and evaluated. In this context, critical criteria have been collected and prioritized to determine the location of Türkiye’s spaceport. As space technologies are a new subject in Türkiye, expert evaluations include some uncertainty. To overcome this problem, a hybrid methodology based on hesitant fuzzy set theory has been developed by integrating two fuzzy multicriteria decision-making methods. By using hesitant fuzzy Z-DEMATEL, the relationships between the criteria were revealed, and the weights of the criteria were calculated by hesitant fuzzy Z-cognitive mapping method. According to the results, the most important main criteria are technical requirements, infrastructure, and cost and economy, while the most important sub-criteria are technical factors, flight safety/cruise route, and main/supporting infrastructure. To show the validity of the proposed methodology, the spherical fuzzy AHP method was employed for the same case. The comparative analysis has determined the same criteria as significant with the proposed methodology. The research would help policymakers and businesses better understand the critical criteria to spaceport site selection to catch the space race worldwide.
eBPF-based Extensible Paravirtualization

High performance applications usually need to give many hints to the OS kernel regarding their needs. For example, CPU affinity is commonly used to pin processes to cores and avoid the cost of CPU migration, isolate performance critical tasks, bring related tasks together, and so on. However, when running inside a Virtual Machine, the (guest) OS kernel can only assign virtual resources, e.g., pinning a guest process to a virtual CPU thread (vCPU); the vCPU thread, however, is still freely scheduled by the host hypervisor, which is unaware of the guest application requests. This semantic gap is usually overcome by statically allocating virtual resources to their hardware counterparts, which is costly and inflexible, or via paravirtualization, i.e., by modifying the guest kernel to pass the hints to the host, which is cumbersome and difficult to extend. We propose to use host-injected eBPF programs as a way for the host to obtain this kind of information from the guest in an extensible way, without modifying the guest (Linux) kernel, and without statically allocating resources. We apply this idea to the example of CPU affinity and run some experiments to show its effect on several microbenchmarks. Finally, we discuss the implications for confidential computing.