Reviewerly/exHarmony huggingface dataset:
FPGA implementation of novel hybrid hash function SHѦES for digital signatures

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Manne Muddu Sravani, Ananiah Durai Sundarajan, Nabihah Ahmad; FPGA implementation of novel hybrid hash function SHѦES for digital signatures. AIP Conf. Proc. 26 October 2023; 2564 (1): 030004. https://doi.org/10.1063/5.0123048 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A Fast Affine Motion Estimation Algorithm Based on Motion Vector Difference

In Versatile Video Coding (VVC), Affine Motion Estimation (AME) mode has been introduced for inter frame motion estimation, to improve the accuracy in predicting complex motion. Meanwhile, it brings high computational complexity in deciding AME mode. In this paper, a fast affine motion estimation algorithm based on Motion Vector Difference (MVD) is proposed to solve this problem. Through the statistical analysis, it is observed that there exists notable correlation between the MVD value of the current CU and the skipping affine mode. Therefore, one metric based on MVD of TME (Translational Motion Estimation) is defined to guide the decision of skipping affine mode, which is also used to help SKIP type coding block to skip affine mode, where SKIP type coding block means that its parent CU or neighboring CU is Skip mode in this algorithm. The other metric based on CPMVD (Control Point Motion Vector Difference) of four parameter affine model is defined to skip only six parameter affine mode. The experimental results show that compared with VTM10.0, the total time complexity of the proposed algorithm in Random Access (RA) mode is reduced by 9.77%, the total affine motion estimation time complexity is reduced by 40.65%, and the BDBR coding loss is only 0.07%. In Low Delay B (LDB) mode, the total coding time is reduced by 18.64%, the total affine motion estimation time is reduced by 51.54%, and the BD-BR coding loss is only 0.22%.
Classification guided thick fog removal network for drone imaging: ClassifyCycle

The foggy images captured by drones are nonuniform due to inhomogeneous distribution of fog in higher altitude, leading to the obvious fog thickness differences in the images. This paper proposes a classification guided thick fog removal network for drone imaging, termed ClassifyCycle. The drone images are input into the proposed classification module (ICLFn) to enhance the reliability of follow-up learning network. The style migration module (ISMn) is introduced to reduce the image distortion, such as hue artifact and texture distort. The proposed network ClassifyCycle does not require paired foggy and corresponding fog-free datasets, avoiding the phenomena of overexposure, distortion, color deviation and fog residue after defogging. Extensive experimental results show that the proposed ClassifyCycle network surpasses the state-of-the-art algorithms on synthetic and realistic drone images captured in thick fog weather.
DeepLSH: A Deep Learning-based Locality Sensitive Hash Method

Approximate nearest neighbor (ANN) search plays a vital role in the database system, data retrieval, and any scenario where similar elements need to be queried in a fast manner. The traditional ANN tasks are typically completed by hash-style techniques, such as the locality-sensitive-hash (LSH). However, while the amount of data is consistently rising in modern real applications, an ANN system is required to scale to the larger data size as well as the increasing data dimension. Considering that the hash functions struggle with precision and efficiency on such a dataset, the hash-based ANN systems are becoming unsuitable for big data environments. By contrast, the learning-based methods provide a promising way for handling high-dimensional data, which also reach a good balance between precision and efficiency in ANN tasks. Starting from this, we advocate that a pleasing way to improve the ANN system is to leverage both the deep learning and the distribution information. We propose a practical deep learning-based hash framework, which is termed as deep learning-based locality-sensitive-hash (DeepLSH). This framework optimizes the binary hash code by building up an encoder-decoder deep neural network which can be trained in an end-to-end manner. Experimental results show that our method satisfies the generalized LSH and outperforms the existing hashing algorithms on challenging datasets.
Cybersmish: A Proactive Approach for Smishing Detection and Prevention using Machine Learning

Social engineering attacks pose a significant risk to user data confidentiality, including credit card details and social network passwords. To combat the rise of smishing attacks targeting digital devices, this research proposes Cybersmish, which utilizes machine learning and NLP Transformer-based models. Cybersmish’s three main components include a message content analyzer, URL analyzer and HTML content analyzer. Leveraging pre-trained models such as MobileBERT, DistilBERT, XGBoost and TF-BERT, Cybersmish achieves accuracy levels exceeding 90% in detecting and analyzing SMSs, classifying URLs, and evaluating HTML web application authenticity. This research underscores the effectiveness of deploying advanced technologies to enhance mobile device security, protecting users from social engineering attacks. It addresses the need for robust solutions against evolving cyber threats, emphasizing the critical role of technology in safeguarding sensitive information in the digital age.
Image Tampering Detection Method Based on Swin Transformer and Dense Upsampling Convolution

In this paper, we propose a deep neural structure for detecting image copy and paste tampering, including basic copy-move types and copy-move types that overlay post-processing operations. Based on multi-scale Swin Transformer and dense upsampling convolution, we can effectively detect tampered images in small areas. By introducing multi-scale Swin Transformer feature extraction network and integrating global features and local features, the network can adapt to various shapes and sizes of tampered areas, especially in small areas. The effect of tampering is significantly improved. At the same time, the dense up-sampling convolution is used, and the multi-channel filter is used to amplify the down-sampling feature map to restore the input size. The experimental results show that on the public image tamper detection benchmark, this method has significantly improved compared with the comparison method. Compared with BusterNet2019, the accuracy rate, recall rate and value have increased by 16.74 percentage points, 16.48 percentage points and 16.68 percentage points respectively, and the effect has been improved more significantly in small area tampering.
An efficient high dynamic range 3D shape reconstruction method based on double phase-shifting profilometry

Abstract Phase-shifting profilometry is the most widely used wrapped phase extraction method due to the advantages of high accuracy and resolution, robust to noise. However, due to the local specular reflection of the measured surfaces and the limited camera dynamic range, some pixels of the captured fringes become intensity saturated, thus introducing wrapped phase error. In this work, we simulate the Fourier spectrum of the saturated fringe and represent the saturated fringe approximately by a third-order Fourier series. Subsequently, we establish the saturation-induced wrapped phase error model. According to this model, it can be found that for the N -step phase-shifting method, the spatial frequency of the saturation-induced wrapped phase error is N times of that of the ideal wrapped phase. We phase shift the N -step phase-shifting fringe sequence π / N to generate the additional phase-shifting fringe sequence, and thus generate the opposite saturation-induced wrapped phase error. Finally, the saturation-induced wrapped phase error can be greatly reduced by fusing the wrapped phases of the two sets of phase-shifting fringe sequences. Compared with the traditional method and the adaptive projection fringe method, the phase error of the double N -step phase-shifting method is reduced by 67.63% and 65.2%, respectively.
Linearly convergent adjoint free solution of least squares problems by random descent

Abstract We consider the problem of solving linear least squares problems in a framework where only evaluations of the linear map are possible. We derive randomized methods that do not need any other matrix operations than forward evaluations, especially no evaluation of the adjoint map is needed. Our method is motivated by the simple observation that one can get an unbiased estimate of the application of the adjoint. We show convergence of the method and then derive a more efficient method that uses an exact linesearch. This method, called random descent, resembles known methods in other context and has the randomized coordinate descent method as special case. We provide convergence analysis of the random descent method emphasizing the dependence on the underlying distribution of the random vectors. Furthermore we investigate the applicability of the method in the context of ill-posed inverse problems and show that the method can have beneficial properties when the unknown solution is rough. We illustrate the theoretical findings in numerical examples. One particular result is that the random descent method actually outperforms established transposed-free methods (TFQMR and CGS) in examples.
Mitigating Phase Error Accumulation in Programming MZI-Based Optical Processors

The practical implementation of reconfigurable interferometric-based optical processors requires complicated and time-consuming calibration and programming schemes to address errors induced by fabrication process variations. Among the various prevalent mesh topologies, the presence of a diagonal path inherently provides independent access to calibrate, monitor, and program each phase shifter, regardless of any existing dynamic errors in the bias of other blocks. In other words, the phase error of MZIs do not accumulate along a diagonal path. This attribute of the diagonal path has been experimentally validated in a 4×4 Bokun mesh topology by introducing phase distortion in the previous Mach-Zehnder interferometer block along the same diagonal path. Measurements demonstrate that the same phase distortion in a mesh topology lacking this attribute can lead to accumulating calibration and programming errors. The Bokun mesh topology, with its inherent beneficial attributes, supports the realization of significantly superior performance in the case of on-chip weight optimization, referred to as in-situ programming, due to enabling an error-free, faster, and easier programming scheme.
FPGA Acceleration of Rotation in Homomorphic Encryption Using Dynamic Data Layout

Homomorphic Encryption (HE) is a promising technique to guarantee the security and privacy of Machine Learning (ML) applications in the cloud. Rotation is a key operation in HE ML; however, the high computational complexity and memory bandwidth requirements severely limit its performance. This work proposes a low-latency HE rotation accelerator targeting HBM-enabled FPGAs. First, we identify memory inefficiencies due to the access patterns of various sub-routines in rotation. We propose a dynamic data layout technique that converts large stride memory accesses to unit stride accesses to improve the bandwidth utilization. We leverage this technique to develop an FPGA accelerator that supports rotation for various HE parameter settings. The accelerator utilizes an optimized dataflow and an architecture specially designed to perform the dynamic data layout. We evaluate the accelerator using AMD U280 FPGA. Our design achieves up to 2.1 x speedup compared with two commonly used static layout approaches and up to 1.47x speedup compared with state-of-the-art GPU implementation across various rotation benchmarks.
Contact tracing: Characteristics of COVID-19 cases that provided contacts

This cross-sectional study evaluated COVID-19 contact tracing efforts to identify variations in contact tracing outcomes in different population subgroups. Contact tracing was a critical tool to slow the COVID-19 epidemic. A literature gap evaluating contact tracing elicitation exits, particularly on prioritized groups. We analyzed data from COVID-19 cases linking statewide case management, immunization, laboratory testing, and hospitalization databases in New York State (NYS) outside of New York City from February 1 to November 30, 2021. Focus was cases in home-based residential settings (excluding congregate care) and prioritized groups (educational institutions, large households, close quarters, higher-risk persons, hospitalized). The primary outcome was completed interviews that provided a contact. Of the 550,850 cases interviewed during the study period, 316,645 (57.5%) provided at least one contact. Adults aged 18 to 49 years were most likely to provide contacts than those aged 65 years and older (adjusted odds ratio [aOR], 1.42; 95% confidence interval [CI], 1.39-1.45). Compared to unvaccinated cases, boosted individuals (aOR, 1.61; 95% CI, 1.50-1.73) were most likely to provide contacts, followed by persons with only a primary vaccine series (aOR, 1.3; 95%CI, 1.28-1.33) and partially vaccinated (aOR, 1.21; 95%CI, 1.18-1.24). Repeat cases (aOR, 1.07; 95%CI, 1.01-1.14), pregnant persons (aOR, 1.26; 95% CI, 1,19-1.34), those with underlying conditions (aOR 1.22; 95%CI, 1.20-1.23), and those in K-12 settings (aOR 1.55; 95%CI, 1.50-1.61) were more likely to provide contacts. There was no clear association between hospitalized, while zip code level income may (aOR, 1.006; 95%CI, 1.003, 1.009). Persons from larger households were more likely to provide contacts: aOR for two or more persons vs. one person households ranged from 2.49 to 4.7 (95%CI, 2.20-4.78). Our findings indicate success in eliciting contacts from prioritized groups and identify variable contact elicitation outcomes from different population groups. These results may serve as a tool for future contact tracing efforts.
Network Device Identification Scheme Based on Network Traffic Analysis

Network device identification is the basis of building network topology, which is the premise of preventing malicious attacks. It is of great significance to propose an efficient network device identification scheme. Existing physical device identification technologies are mainly oriented to Internet of Things devices and wireless devices. They collect network element information through active detection, which consumes extra network traffic and increases the risk of detection behavior identification. Identifying devices by protocol analysis or fingerprint matching has become the mainstream, but most of these solutions are based on a certain protocol, which is difficult to apply to the complex network of multi-protocol cloud data center. At present, there is no network device identification scheme for cloud data center network traffic analysis. Therefore, we propose a network device identification scheme based on network traffic analysis, which collects network traffic passively, selects network traffic characteristics automatically, and uses decision tree algorithm to realize network device identification. Finally, the accuracy of the proposed scheme is verified by the simulation, and the results show that the accuracy of the scheme is to 96%.
Knowledge distillation of multi-scale dense prediction transformer for self-supervised depth estimation

Abstract Depth estimation is an inverse projection problem that estimates pixel-level distances from a single image. Although, supervised methods have shown promising results, it has intrinsic limitations in requiring ground truth depth from an external sensor. On the other hand, self-supervised depth estimation relieves the burden for collecting calibrated training data, while there is still a large performance gap between supervised and self-supervised methods. The objective of this study is to reduce the performance gap between the supervised and self-supervised approaches. The loss function of previous self-supervised methods is mainly based on a photometric error, which is indirectly computed from synthesized images using depth and pose estimates. In this paper, we argue that direct depth cue is more effective to train a depth estimation network. To obtain the direct depth cue, we employed a knowledge distillation technique, which is a teacher-student learning framework. The teacher network was trained in a self-supervised manner based on a photometric error, and its predictions were utilized to train a student network. We constructed a multi-scale dense prediction transformer with Monte Carlo dropout, and multi-scale distillation loss was proposed to train the student network based on the ensemble of stochastic estimates. Experiments were conducted on the KITTI and Make3D datasets, and our proposed method achieved the state-of-the-art accuracy in self-supervised depth estimation. Our code is publicly available at https://github.com/ji-min-song/KD-of-MS-DPT .
IXR '23: 2nd International Workshop on Interactive eXtended Reality

Despite remarkable advances, current Extended Reality (XR) applications are in their majority local and individual experiences. A plethora of interactive applications, such as teleconferencing, telesurgery, interconnection in new buildings project chain, Cultural Heritage, and Museum contents communication, are well on their way to integrating immersive technologies. However, interconnected, and interactive XR, where participants can virtually interact across vast distances, remains a distant dream. In fact, three great barriers stand between current technology and remote immersive interactive life-like experiences, namely (i) content realism, (ii) motion-to-photon latency, and accurate (iii) human-centric quality assessment and control. Overcoming these barriers will require novel solutions at all elements of the end-to-end transmission chain. This workshop focuses on the challenges, applications, and major advancements in multimedia, networks, and end-user infrastructures to enable the next generation of interactive XR applications and services.
Dark-Channel Soft-Constrained and Object-Perception-Enhanced Deep Dehazing Networks Used for Road Inspection Images

Haze seriously affects the visual quality of road inspection images and contaminates the discrimination of key road objects, which thus hinders the execution of road inspection work. The basic assumptions of the classical dark-channel prior are not suitable for road images containing light-colored lane lines and vehicles, while typical deep dehazing networks lack physical model interpretability, and they focus on global dehazing effects, neglecting the preservation of object features. For this reason, this paper proposes a Dark-Channel Soft-Constrained and Object-Perception-Enhanced Deep Dehazing Network (DCSC-OPE-Net) for the information recovery of road inspection images. The network is divided into two modules: a dark-channel soft-constrained dehazing module and a near-view object-perception-enhanced module. Unlike the traditional dark-channel algorithms that impose strong constraints on dark pixels, a dark-channel soft-constrained loss function is constructed to ensure that the features of light-colored vehicles and lane lines are effectively maintained. To avoid resolution loss due to patch-based dark-channel processing for image dehazing, a resolution enhancement module is used to strengthen the contrast of the dehazed image. To autonomously perceive and enhance key road features to support road inspection, edge enhancement loss combined with a transmission map is embedded into the network to autonomously discover near-view objects and enhance their key features. The experiments utilize public datasets and real road inspection datasets to validate the performance of the proposed DCSC-OPE-Net compared with typical networks using dehazing evaluation metrics and road object recognition metrics. The experimental results demonstrate that the proposed DCSC-OPE-Net can obtain the best dehazing performance, with an NIQE score of 4.5 and a BRISQUE score of 18.67, and obtain the best road object recognition results (i.e., 83.67%) among the comparison methods.
Construction of Wu Di Music Culture Education Inheritance Innovation System Based on Clustering Algorithm in the Internet Background

Abstract This paper constructs a blended teaching strategy based on Internet information technology and gives the teaching design of Wu Di music culture education. Behavioral science theory and the K-means clustering algorithm of rough set optimization were used as the guide to construct the behavioral analysis model of students’ learning Wu Di music culture education. The effectiveness of the blended teaching model in the inheritance and innovation of Wu Di music culture education was verified through empirical analysis. The results show that blended teaching can further enhance students’ absorption of Wudi music culture, and the percentage of the experimental class who are very interested in Wudi music culture education is 12.03% higher than that of the control class. The blended teaching mode can enhance students' interest in Wudi music and culture education and contribute to the inheritance and innovation of Wudi music and culture education.
Rancang Bangun Sistem Pemberi Minum Lalat BSF (Black Soldier Fly) Otomatis Berbasis Mikrokontroler

The BSF fly (Black Soldier Fly) commonly known as the black soldier fly is an insect whose larvae are widely used as animal feed or commonly known as a maggot. BSF flies only need to drink, and do not eat. BSF fly feeding should be given regularly based on the ambient temperature around the cage. At this time, BSF fly breeders tend to have a busy and congested schedule so the cage spraying schedule is neglected. In addition, human negligence factors such as laziness and forgetting also make the spraying schedule irregular. Spraying of the cage was done manually using a plant sprayer. Therefore, research was conducted on the design of a microcontroller-based BSF fly drinking system. This system can perform automatic spraying of BSF flies based on the high and low temperatures around the cage, as well as provide notifications to the telegram of the BSF fly owner. Based on the study's results, this system detects the temperature around the cage using a DHT22 sensor and then sprays drinking BSF flies with the help of a DC pump. This system can be connected to the internet to send notifications that the cage has been sprayed via the telegram application of the cage owner.
On Proofs of Properties of Semirecursive Sets

In this paper, we present proofs of properties of semirecursive sets based directly on the definition of these sets and on the recursiveness of Kleene predicates. These proofs are shorter and clearer than traditional proofs of similar statements for recursively enumerable sets.
A Brief Introduction to Gurukul Education

This paper presents gurukul system of education as the main concern in the quality of human beings. Education is the only means of civilizing ourselves to maintain high qualities of integrity, devotion, sincerity and an ethical and moral purpose in our national life. The goal of our national education should be to supply a constant stream of knowledge, character, fearless open minded, truthful, and patriotic people, who will become the basic material, for building up the nation. Gurukul Style of Education system is a 5000-year old Hindu tradition of transmitting higher knowledge and enlightenment to the students by yogic powers by the guru with an aim to develop the latent yogic powers of the child and build him up into a moral and spiritual power.