Reviewerly/exHarmony huggingface dataset:
Measurement of Fatigue Crack Growth in Compact Tension Specimen Using an Optical Extensometer

Abstract In this investigation, fatigue crack growth (FCG) tests are performed on transformation induced plasticity steels to characterize the relevance of stress intensity and crack growth rates with standard compact tension (CT) specimens. An optical extensometer that can achieve real-time displacement measurement is developed based on digital image correlation. Instead of a displacement gauge, the optical extensometer is adopted in FCG tests to measure CT specimen crack opening displacement (COD). Compared with stress intensity, it is difficult to calculate crack growth rates with COD. Therefore, the compliance equation and incremental polynomial method are programmed with C++ languages to obtain the steady crack growth curve directly. The proposed method is verified with experimental results obtained by the displacement extensometer. In addition, the precision of the displacement measurement is assessed, and the error induced by displacement output rates is discussed.
Altered Region Identification in Image Files using Copy-Move Forgery Detection Technique

Nowadays, digital images and videos have high importance because they have become the primary carriers of information. However, the easy availability of powerful image editing software has made it possible to manipulate and edit digital images and videos, leading to a loss of trust in their authenticity. It is possible to add or remove important features from an image without leaving any obvious traces of tampering. To detect this such type of forgeries, the proposed method involves dividing the image into overlapping blocks of the same size, extracting a feature for each block, and representing it as a vector. The vectors are then sorted using radix sort, and the difference in the positions of adjacent feature vectors in the sorted list is computed to obtain a shift vector. This method can even identify tampered parts of an image that have been enhanced or retouched to merge with the background or saved in a lossy format like JPEG. Several forged images were tested to demonstrate the effectiveness of this proposed method. Its application has significant implications in fields like forensics, journalism, and medical imaging to ensure the authenticity of digital images and videos.
Serverless Blockchain-based AI-Powered Financial Transaction Management System on Cloud

The Finance Manager Application is used to automate the existing finance tracking system and to offer the user to have a more engaging and aesthetic user experience with the bill tracking feature. The existing systems are used to record several financial transactions and events. They are not giving an overview of the amount spent and in which category the user has transacted more amount and hence it does not support expense management. Mainly the existing finance tracking systems are not using AI-based authentication which is more convenient for the user. Our web application is proposed to give transaction charts and graphs that give more clarity of the income, expenses, and transfers. In addition, we can analyse the transactions in category-wise, and we can track the history. Along with form-data authentication, we are providing OAuth and Zero-effort authentication that is more user friendly. Our SPA provides the facility to add multiple transactions at a time and it is to show the balance in the wallet on the future according to the planned transactions. Also, it can automate the data entry for recurrent transactions.
A Comprehensive Analysis of Deep Learning Frameworks to Mitigate the Impact of Varied Lighting and Weather Conditions

Object detection is an extensively researched field in computer vision and deep learning-related use cases. Popular frameworks such as YOLO, RCNN, SSD, FPN, RetinaNet, etc., and other cutting-edge detection systems are extensively used in various applications, such as autonomous vehicle driving scenarios and surveillance systems. However, in practical applications, the performance of the generic object detection systems suffers when subject to images with insufficient or poor lighting due to adverse weather conditions and various external factors. Several frameworks utilizing different deep learning and image processing-based approaches have been presented to enhance object detection considering recent advancements in this field. This paper provides a comprehensive overview of several contemporary techniques under various challenging lighting and weather situations. This study also talks about several public benchmark datasets and applications where varying illumination, or adverse weather conditions play a crucial role in estimating the efficacy of detection. Finally, the study summarizes the future scope and research direction.
Review of Deep Reinforcement Learning-Based Recommender Systems

Recommender systems (RS) are an indispensable technology that helps to address the issue of information explosion in the digital age. It can assist the customers by proposing personalised items and providers by increasing traffic to their website. Recommender systems can be applied to variety of business use cases including but not limited to electronic commerce applications and media recommendation (e.g., news, motion pictures, music, and videos). Deep reinforcement learning (DRL)-based RS has recently gained popularity as a research topic. Because of its interactive approach and autonomous learning ability, it frequently outperforms classic recommendation approaches, including deep learning-based methods. The objective of this work is to present a thorough overview of the state of the art for DRL implementations in recommendation systems. This paper starts with the background technologies involved in applying DRL in RS. Then, this paper examines recent advancements in DRL-based RS and discusses open issues. This review serves as an introduction to the topic of DRL in RS and its various facets. It identifies potential areas of research and provides valuable feedback to readers from academic and industry.
The Impact of Two Mediums Based on Virtual Reality Technology in the Education of Practical Courses

Virtual reality (VR) technology has great potential in practical courses. This study aims to explain the benefits of virtual reality (VR) in the transformation of practical courses learning and student experience. This study will take physics courses as an example. Two independent studies were conducted, and college students were divided into two groups, using a computer all-in-one machine and a head-mounted display (HMD) device. Students using head-mounted display (HMD) reported higher levels of presence, engagement and empathy, compared to computer all-in-one, and had better mastery of knowledge than students using computer all-in-one. These findings suggest that virtual reality (VR) has a positive effect on student emotions in practical courses like physics. Moreover, head-mounted display (HMD) virtual reality (VR) devices have the potential to provide students with new immersive experiences, with cognitive and emotional benefits.
A Machine Vision-Based Person Detection Under Low-Illuminance Conditions Using High Dynamic Range Imagery for Visual Surveillance System

The most difficult component of any computer vision application objects recognition and tracking. Video surveillance is a major study subject in computer vision in a dynamic environment, especially for security purposes. Video surveillance technology is critical in preventing crime, terrorism, and other threats. Several existing technologies can accurately monitor a person's mobility in interior environments. As well as regulated outdoor settings, object recognition and tracking at night, on the other hand, remain significant challenges for visual surveillance. The objects are usually farther away, inconspicuous, and have low brightness against the background. In this paper, detecting objects specifically people under low lighting conditions with the help of deep learning algorithms was used. For applications like in-car cameras and surveillance systems that operate in low light, these algorithms might improve picture identification performance.
Traffic Violation Detection via Depth and Gradient Angle Change

The paper aims to develop an effective traffic violation detection system to detect traffic violations automatically from videos reported by the public, especially for case footage recorded by dashcams facing forward mounted on vehicles or helmets. We aim to address two types of traffic violations: (1) running a red light and (2) turning on a red light. The proposed traffic violation detection system includes two main parts: Violation Target Tracking (VTT) and Target Action Analysis (TAA). First, we detect red traffic lights and vehicles and obtain their locations and depths in VTT. Next, we model the vehicles’ depth and gradient angle changes to catch traffic violations. The experimental results show that our violation detection system can achieve 76.1% true accuracy and 81.9% conditional accuracy on average for all the violation cases.
Aspect Level Sentiment Analysis Aided Supportive System for Interactive Platforms

Artificial intelligence should be used to make decisions based on sentiment analysis using message history. This paper proposes an added feature that learns and finds sentiment in a text messaging application, using PyScript to evaluate accuracy, aspect level sentiment analysis and decision making ability.
High-Sensity Pedestrian Detection from Aerial View

High-density pedestrian detection from aerial view is a novel and challenging work in both the field of computer vision and pedestrian flow research. Although many pedestrian detection algorithms have emerged, their performance are not good enough, especially in high-density situations. Therefore, we modified the original YOLOv5 model, and make it more suitable for this job. Based on the video data recorded by unmanned aerial vehicle (UAV), we build the pedestrian dataset, and use the modified YOLOv5 model to train the heads and shoulders of pedestrians in the dataset. The new detection results show that our proposed model can finish the job of high-density pedestrian detection from aerial view very well.
Prediction and Its Impact on Its Attributes While Biasing MachineLearning Training Data

Machine learning models are built utilizing biased training data that comes from human experience. The data gathered reflects the cognitive bias that human's display in their actions and thought processes. The most effective machine learning models are considered to resemble human cognition; as a result, these models are biased. For improved explainable models, bias detection and evaluation are crucial. This study identifies bias in learning models in relation to cognitive bias in humans and suggests a cutting-edge method for identifying and evaluating machine learning bias. It also tries to identify bias in the dataset. The potentially skewed qualities are seen in the deployed dataset. Prior to employing the notion of alternation function to swap the values of PBAs and analyze the influence on prediction using KL Divergence, we first choose a few common biased characteristics. Compare the KL divergence values from the various models used to train the dataset and forecast the output in this section.
Design of Power Data Encryption Speed Improvement Scheme Based on SM4

Electric power data contains rich private information, and encryption technology is often used to protect its information security. Due to its huge data volume, considering the lightweight and high-efficiency encryption algorithm, based on the encryption algorithm SM4 with independent intellectual property rights in my country, combined with the big data technology Hadoop, a parallel encryption speed-up scheme based on the national encryption algorithm SM4 is designed to realize the data encryption speed. Encryption and decryption protection and acceleration of computation. Finally, an experimental platform based on Hadoop is built, and the data is encrypted in blocks with the MapReduce computing framework, and the feasibility test of the scheme is carried out. The experimental results show that the algorithm scheme can improve the encryption speed of unstructured data files. The optimal number of Maps in the established cluster is 8, and the speedup ratio between serial encryption and parallel encryption is more than 2. Compared with serial encryption, it can save 50% of computing overhead time.
Integration of Virtual Reality Technology in the Primary School: Students' Creativity and Learning Engagement

During the last few years, technology has had a good impact on teaching content, teaching methods, and teaching effectiveness in primary schools with the integration of virtual reality (VR) and making the effects of VR more challenging. In the following study, “teaching with VR Technology environment” and “traditional teaching” were designed to verify and implement a series of teaching activities to compare two primary school student groups (Experiment group “EG” and Control group “CG”). The results of the experiment found how easier to integrate VR technology in the primary school classroom and how it is positively correlated with students learning engagement compared to the traditional classroom. VR technology helps and allows primary school students to give full play to their creativity during the learning process.
Traffic Sign Recognition Algorithm Based on Improved YOLOv5

Due to the low detection accuracy of the existing YOLOv5s recognition algorithm for traffic sign recognition, it cannot meet the application requirements of actual scenarios. This paper makes some improvements on the basis of the original YOLOv5s model. First, a detection layer is added to the original three detection layers to obtain more location information of the target in the underlying feature map. After that, a convolution extraction module with CBAM attention mechanism is designed in the backbone of the model, so that the network pays more attention to the feature information of traffic signs. Then, in the Neck part of the model, a lightweight neural network C3Ghost is designed to replace the original convolutional network, which reduces the network model parameters, and finally integrates more semantic features by adding a cross-layer connection structure. The experiment uses the traffic sign dataset jointly released by Tsinghua and Tencent, and trains 45 types of traffic signs with a large number. The test results show that the optimized network model reduces parameters, and the detection accuracy is improved by 5.3% compared with the original network, which meets the needs of actual scene detection accuracy.
Optical Flow-Guided Deep Convolutional Neural Networks for UAV Detection in Infrared Videos

Early warning of small civilian UAVs is a critical issue in the field of public security and is a challenging task in object detection. Video object detection methods based on deep convolution neural networks have attracted increasing attention over recent years. The object detection algorithm designed for static images can hardly be used directly in video object detection, due to motion blur, out-of-focus, etc. Traditional methods mainly apply temporal information to address this problem. In this paper, we proposed a method based on optical flow for UAV video object detection, employing optical flow information and the object appearance visual features in different stages. We establish a motion enhancement module to utilize motion features of moving objects while suppressing complex backgrounds. In addition, direction information is used to associate detection boxes of adjacent frames, improving the stability of the detection. The results of the experiments on the Anti-UAV2021 Challenge dataset show that the average precision of our model is 89.8%, significantly higher than that of FGFA, SELSA, and YOLOv5 models. The experimental results also demonstrate that the motion enhancement module and direction post-processing module can improve the recall rate of small UAVs under complex backgrounds.
Keyed Sum of Permutations: A Simpler RP-Based PRF

Idealized constructions in cryptography prove the security of a primitive based on the security of another primitive. The challenge of building a pseudorandom function (PRF) from a random permutation (RP) has only been recently tackled by Chen, Lambooij and Mennink [CRYPTO 2019] who proposed Sum of Even-Mansour (SoEM) with a provable beyond-birthday-bound security. In this work, we revisit the challenge of building a PRF from an RP. On the one hand, we describe Keyed Sum of Permutations (KSoP) that achieves the same provable security as SoEM while being strictly simpler since it avoids a key addition but still requires two independent keys and permutations. On the other hand, we show that it is impossible to further simplify the scheme by deriving the two keys with a simple linear key schedule as it allows a non-trivial birthday-bound key recovery attack. The birthday-bound attack is mostly information-theoretic, but it can be optimized to run faster than a brute-force attack.
Multiparty Noninteractive Key Exchange from Ring Key-Homomorphic Weak PRFs

A weak pseudorandom function $$F: \mathcal {K} \times \mathcal {X} \rightarrow \mathcal {Y}$$ is said to be ring key-homomorphic if, given $$F \left( k_{1}, x \right) $$ and $$F \left( k_{2}, x \right) $$ , there are efficient algorithms to compute $$F \left( k_{1} \oplus k_{2}, x \right) $$ and $$F \left( k_{1} \otimes k_{2}, x \right) $$ where $$\oplus $$ and $$\otimes $$ are the addition and multiplication operations in the ring $$\mathcal {K}$$ , respectively. In this work, we initiate the study of ring key-homomorphic weak PRFs (RKHwPRFs). As our main result, we show that any RKHwPRF implies multiparty noninteractive key exchange (NIKE) for an arbitrary number of parties in the standard model. Our analysis of RKHwPRFs in a sense takes a major step towards the goal of building cryptographic primitives from Minicrypt primitives with structure, which has been studied in a recent line of works. With our result, most of the well-known asymmetric cryptographic primitives can be built from a weak PRF with either a group or ring homomorphism over either the input space or the key space.
Putting the Online Phase on a Diet: Covert Security from Short MACs

An important research direction in secure multi-party computation (MPC) is to improve the efficiency of the protocol. One idea that has recently received attention is to consider a slightly weaker security model than full malicious security – the so-called setting of covert security. In covert security, the adversary may cheat but only is detected with certain probability. Several works in covert security consider the offline/online approach, where during a costly offline phase correlated randomness is computed, which is consumed in a fast online phase. State-of-the-art protocols focus on improving the efficiency by using a covert offline phase, but ignore the online phase. In particular, the online phase is usually assumed to guarantee security against malicious adversaries. In this work, we take a fresh look at the offline/online paradigm in the covert security setting. Our main insight is that by weakening the security of the online phase from malicious to covert, we can gain significant efficiency improvements during the offline phase. Concretely, we demonstrate our technique by applying it to the online phase of the well-known TinyOT protocol (Nielsen et al., CRYPTO ’12). The main observation is that by reducing the MAC length in the online phase of TinyOT to t bits, we can guarantee covert security with a detection probability of $$1- \frac{1}{2^t}$$ . Since the computation carried out by the offline phase depends on the MAC length, shorter MACs result in a more efficient offline phase and thus speed up the overall computation. Our evaluation shows that our approach reduces the communication complexity of the offline protocol by at least 35% for a detection rate up to $$\frac{7}{8}$$ . In addition, we present a new generic composition result for analyzing the security of online/offline protocols in terms of concrete security.
NNBits: Bit Profiling with a Deep Learning Ensemble Based Distinguisher

We introduce a deep learning ensemble (NNBits) as a tool for bit-profiling and evaluation of cryptographic (pseudo) random bit sequences. On the one hand, we show how to use NNBits ensemble to explain parts of the seminal work of Gohr [16]: Gohr’s depth-1 neural distinguisher reaches a test accuracy of 78.3% in round 6 for SPECK32/64 [3]. Using the bit-level information provided by NNBits we can partially explain the accuracy obtained by Gohr (78.1% vs. 78.3%). This is achieved by constructing a distinguisher which only uses the information about correct or incorrect predictions on the single bit level and which achieves 78.1% accuracy. We also generalize two heuristic aspects in the construction of Gohr’s network: i) the particular input structure, which reflects expert knowledge of SPECK32/64, as well as ii) the cyclic learning rate. On the other hand, we extend Gohr’s work as a statistical test on avalanche datasets of SPECK32/64, SPECK64/128, SPECK96/144, SPECK128/128, and AES-128. In combination with NNBits ensemble we use the extended version of Gohr’s neural network to draw a comparison with the NIST Statistical Test Suite (NIST STS) on the previously mentioned avalanche datasets. We compare NNBits in conjunction with Gohr’s generalized network to the NIST STS and conclude that the NNBits ensemble performs either as good as the NIST STS or better. Furthermore, we demonstrate cryptanalytic insights that result from bit-level profiling with NNBits, for example, we show how to infer the strong input difference $$(0x0040, 0x0000)$$ for SPECK32/64 or infer a signature of the multiplication in the Galois field of AES-128.
On-Line/Off-Line DCR-Based Homomorphic Encryption and Applications

On-line/off-line encryption schemes enable the fast encryption of a message from a pre-computed coupon. The paradigm was put forward in the case of digital signatures. This work introduces a compact public-key additively homomorphic encryption scheme. The scheme is semantically secure under the decisional composite residuosity (DCR) assumption. Compared to Paillier cryptosystem, it merely requires one or two integer additions in the on-line phase and no increase in the ciphertext size. This work also introduces a compact on-line/off-line trapdoor commitment scheme featuring the same fast on-line phase. Finally, applications to chameleon signatures are presented.
Flexible Password-Based Encryption: Securing Cloud Storage and Provably Resisting Partitioning-Oracle Attacks

We introduce flexible password-based encryption (FPBE), an extension of traditional password-based encryption designed to meet the operational and security needs of contemporary applications like end-to-end secure cloud storage. Operationally, FPBE supports nonces, associated data and salt reuse. Security-wise, it strengthens the usual privacy requirement, and, most importantly, adds an authenticity requirement, crucial because end-to-end security must protect against a malicious server. We give an FPBE scheme called DtE that is not only proven secure, but with good bounds. The challenge, with regard to the latter, is in circumventing partitioning-oracle attacks, which is done by leveraging key-robust (also called key-committing) encryption and a notion of authenticity with corruptions. DtE can be instantiated to yield an efficient and practical FPBE scheme for the target applications.