Reviewerly/exHarmony huggingface dataset:
Meningkatkan Kemampuan Pemahaman Konsep Matematis Siswa Melalui Pembelajaran Kalaboratif MURDER

This research aims to determine the improvement of students' conceptual understanding skills with MURDER collaborative learning and student responses to the application of MURDER collaborative learning. This research is a research experiment (quasi experimental design) with the design of non-equivalent control group design. The population is all students of grade VII SMP Negeri 1 Lalolae as much as 54 people. Sampling with a totally sampling technique obtained the VIIA class as the experimental class and the VIIB class as the control class. The hypothesis test indicates that at the level of the significance α = 0.05, the value of tcount = 3,863 &gt; ttable = 2,022. That means that H0 rejected and H1 accepted. So it can be concluded that MURDER collaborative learning can improve students' understanding of mathematical concepts. based on the results of the student response questionnaire analysis, the percentage of the average score for student responses was 84.68% and was included in the very good category.
A New Correlation Cube Attack Based on Division Property

Correlation cube attacks were proposed by Liu et al. at EUROCRYPT 2018, which targeted a modern symmetric-key cryptosystem based on nonlinear feedback shift registers. The main idea of correlation cube attacks lies in recovering the secret key by exploiting conditional correlation properties between the superpoly of a cube and a specific set of low-degree polynomials called a basis. In this paper, we propose a new correlation cube attack based on the division property. The new attack expresses a given superpoly p as $$ fg\oplus h $$ and calculates correlation probabilities for all possible f, where f only involves key variables. This novel idea breaks the restriction on the basis used in the original attack and provides more choices to find good correlation probabilities, thus making the correlation cube attack much more powerful. Besides, the first application of the division property to correlation cube attacks is given, which aided by MILP modeling techniques facilitates the search for desirable cubes. As illustrations, we apply the new correlation cube attack to Trivium. For 844-round Trivium, we can recover about 4-bit key information on average with the time complexity $$2^{45}$$ , which could be fully verified by experiments. This is so far the best key recovery attack for 844-round Trivium.
Caos: A Reusable Scala Web Animator of Operational Semantics

This tool paper presents : a methodology and a programming framework for computer-aided design of structural operational semantics for formal models. This framework includes a set of Scala libraries and a workflow to produce visual and interactive diagrams that animate and provide insights over the structure and the semantics of a given abstract model with operational rules. follows an approach in which theoretical foundations and a practical tool are built together, as an alternative to foundations-first design (“tool justifies theory”) or tool-first design (“foundations justify practice”). The advantage of is that the tool-under-development can immediately be used to automatically run numerous and sizeable examples in order to identify subtle mistakes, unexpected outcomes, and unforeseen limitations in the foundations-under-development, as early as possible. We share two success stories of methodology and framework in our own teaching and research context, where we analyse a simple while-language and a choreographic language, including their operational rules and the concurrent composition of such rules. We further discuss how others can include in their own analysis and Scala tools.
Quantum-Access Security of Hash-Based Signature Schemes

In post-quantum cryptography, hash-based signature schemes are attractive choices because of the weak assumptions. Most existing hash-based signature schemes are proven secure against post-quantum chosen message attacks (CMAs), where the adversaries are able to execute quantum computations and classically query to the signing oracle. In some cases, the signing oracle is also considered quantum-accessible, meaning that the adversaries are able to send queries with superpositions to the signing oracle. Considering this, Boneh and Zhandry propose a stronger security notion called existential unforgeability under quantum chosen message attacks (EUF-qCMA). We call it quantum-access security (or Q2 security in some literature). The quantum-access security of practical signature schemes is lacking in research, especially of the hash-based ones. In this paper, we analyze the quantum-access security of hash-based signature schemes in two directions. First, we show concrete quantum chosen message attacks (or superposition attacks) on existing hash-based signature schemes, such as SPHINCS and SPHINCS+. The complexity of the attacks is obviously lower than that of optimal classical chosen message attacks, implying that quantum chosen message attacks are more threatening than classical ones to these schemes. Second, we propose a simple variant of SPHINCS+ and give security proof against quantum chosen message attacks. As far as we know, it is the first practical hash-based stateless signature scheme against quantum chosen message attacks with concrete provable security.
Reusable, Instant and Private Payment Guarantees for Cryptocurrencies

Despite offering numerous advantages, public decentralized cryptocurrencies such as Bitcoin suffer from scalability issues such as high transaction latency and low throughput. The vast array of so-called Layer-2 solutions tackling the scalability problem focus on throughput, and consider latency as a secondary objective. However, in the context of retail payments, instant finality of transactions is arguably a more pressing concern, besides the overarching concern for privacy. In this paper, we provide an overlay network that allows privacy-friendly low latency payments in a retail market. Our approach follows that of a recent work called Snappy, which achieved low latency but exposed identities of customers and their transaction histories. Our construction ensures this data is kept private, while providing merchants with protection against double-spending attacks. Although our system is still based upon customers registering with a collateral, crucially this collateral is reusable over time. The technical novelty of our work comes from randomness-reusable threshold encryption (RRTE), a cryptographic primitive we designed specifically for the following features: our construction provably guarantees payments to merchants, preserves the secret identity of honest customers and prevents their transactions from being linked. We also present an implementation of our construction, showing its capacity for fast global payments in a retail setting with a delay of less than 1 s.
A Novel Model for Ship Trajectory Anomaly Detection Based on Gaussian Mixture Variational Autoencoder

The use of traditional methods in anomaly detection of multi-class ship trajectories showed some limitations in terms of robustness and learning ability of trajectory features. In view of this, an anomaly detection model for ship trajectory data based on Gaussian Mixture Variational Autoencoder (GMVAE) is proposed in this study using an unsupervised classification method. The proposed model modifies Variational Autoencoder (VAE) by changing the inferential distribution of prior distribution and approximate posterior to the Gaussian mixture model. A high-dimensional hidden space is constructed to learn the features of multi-class trajectory data, and the Dynamic Time Warping (DTW) method is applied to measure the error between the reconstructed trajectory and the original trajectory in order to judge whether the ship trajectory is abnormal. The Automatic Identification System (AIS) data from the US coastal areas are used to verify the proposed model, and the results are compared with other commonly used models in a manually labeled dataset. The research results indicate that the detection rate of the proposed model is 91.26%, and the false alarm rate is 0.68%, which performs the best. Using the Gaussian mixture model to describe the distribution of hidden space can improve the learning ability of multi-class trajectories of VAE, thus increasing the robustness of the model. This research can provide technical support for ship trajectory data analysis and risk management of maritime transportation.
PWLU: Learning Specialized Activation Functions with the Piecewise Linear Unit

The choice of activation functions is crucial to deep neural networks. ReLU is a popular hand-designed activation function. Swish, the automatically searched activation function, outperforms ReLU on many challenging datasets. However, the search method has two main drawbacks. First, the tree-based search space is highly discrete and restricted, which is difficult to search. Second, the sample-based search method is inefficient in finding specialized activation functions for each dataset or neural architecture. To overcome these drawbacks, we propose a new activation function called Piecewise Linear Unit (PWLU), incorporating a carefully designed formulation and learning method. PWLU can learn specialized activation functions for different models, layers, or channels. Besides, we propose a non-uniform version of PWLU, which maintains sufficient flexibility but requires fewer intervals and parameters. Additionally, we generalize PWLU to three-dimensional space to define a piecewise linear surface named 2D-PWLU, which can be treated as a non-linear binary operator. Experimental results show that PWLU achieves SOTA performance on various tasks and models, and 2D-PWLU is better than element-wise addition when aggregating features from different branches. The proposed PWLU and its variation are easy to implement and efficient for inference, which can be widely applied in real-world applications.
The Difficult Balance Between Modern Hardware and Conventional CPUs

Research has demonstrated the potential of accelerators in a wide range of use cases. However, there is a growing imbalance between modern hardware and the CPUs that submit the workload. Recent studies of GPUs on real systems have shown that many servers are often needed per accelerator to generate a high enough load so the computing power is leveraged. This fact is often ignored in research, although it often determines the actual feasibility and overall efficiency of a deployment. In this paper, we conduct a detailed study of the possible configurations and overall cost efficiency of deploying an FPGA-based accelerator on a commercial search engine. First, we show that there are many possible configurations balancing the upstream system and the way the accelerator is configured. Of these configurations, not all of them are suitable in practice, even if they provide some of the highest throughput. Second, we analyse the cost of a deployment capable of sustaining the required workload of the commercial search engine. We examine deployments both on-premises and in the cloud with and without FPGAs and with different board models. The results show that, while FPGAs have the potential to significantly improve overall performance, the performance imbalance between their host CPUs and the FPGAs can make the deployments economically unattractive. These findings are intended to inform the development and deployment of accelerators by showing what is needed on the CPU side to make them effective and also to provide important insights into their end-to-end integration within existing systems.
An Efficient Approach for Sending Identification Bit With the Help of Blowfish-RC6

This chapter used an efficient mechanism based on blowfish and the RC6 (BRC6) algorithm for better security against cross-site scripting (XSS) attacks. The data types covered in this are text, images, web files (scripts and web pages), Word documents, and PDFs. In this process, data is requested from the server, and the server prepares the file so that only legitimate users can access the data by using BRC-6 with the send identification bit (SIB) mechanism. Various comparisons were made based on statics. Results are also compared based on the number of messages (NOM) and number of random numbers (NRN), which includes seven traditional and proposed methods. Then a comparison based on the number of data encryption (NDE), number of data decryption (NDD), and key length (KL) is done. The overall comparison results clearly show that BRC6 outperforms all other methods, and the approach outperforms traditional methods in all criteria.
Strategic Latency Reduction in Blockchain Peer-to-Peer Networks

Most permissionless blockchain networks run on peer-to-peer (P2P) networks, which offer flexibility and decentralization at the expense of performance (e.g., network latency). Historically, this tradeoff has not been a bottleneck for most blockchains. However, an emerging host of blockchain-based applications (e.g., decentralized finance) are increasingly sensitive to latency; users who can reduce their network latency relative to other users can accrue (sometimes significant) financial gains. In this work, we initiate the study of strategic latency reduction in blockchain P2P networks. We first define two classes of latency that are of interest in blockchain applications. We then show empirically that a strategic agent who controls only their local peering decisions can manipulate both types of latency, achieving 60% of the global latency gains provided by the centralized, paid service bloXroute, or, in targeted scenarios, comparable gains. Finally, we show that our results are not due to the poor design of existing P2P networks. Under a simple network model, we theoretically prove that an adversary can always manipulate the P2P network's latency to their advantage, provided the network experiences sufficient peer churn and transaction activity.
Ensuring security in edge computing through effective blockchain node detection

Abstract The rapid development of blockchain technology has garnered increasing attention, particularly in the field of edge computing. It has become a significant subject of research in this area due to its ability to protect the privacy of data. Despite the advantages that blockchain technology offers, there are also security threats that must be addressed. Attackers may manipulate certain nodes in the blockchain network, which can result in tampering with transaction records or other malicious activities. Moreover, the creation of a large number of false nodes can be utilized to gain control and manipulate transaction records of the blockchain network, which can compromise the reliability and security of edge computing. This paper proposes a blockchain node detection method named $$T^2A2vec$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>T</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mi>A</mml:mi> <mml:mn>2</mml:mn> <mml:mi>v</mml:mi> <mml:mi>e</mml:mi> <mml:mi>c</mml:mi> </mml:mrow> </mml:math> that provides a more secure, credible, and reliable solution to address these challenges. In order to achieve $$T^2A2vec$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>T</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mi>A</mml:mi> <mml:mn>2</mml:mn> <mml:mi>v</mml:mi> <mml:mi>e</mml:mi> <mml:mi>c</mml:mi> </mml:mrow> </mml:math> , a transaction dataset that is evenly distributed in both space and time was collected. The transaction dataset is constructed as a transaction graph, where nodes represent accounts and edges describe transactions. BP neural network is used to extract account features, and a random walk strategy based on transaction time, type, and amount is used to extract transaction features. The obtained account features and transaction features are fused to obtain account representation. Finally, the obtained node representation is fed into different classifiers to identify malicious nodes.
Pre-Computing Batch Normalisation Parameters for Edge Devices on a Binarized Neural Network

Binarized Neural Network (BNN) is a quantized Convolutional Neural Network (CNN), reducing the precision of network parameters for a much smaller model size. In BNNs, the Batch Normalisation (BN) layer is essential. When running BN on edge devices, floating point instructions take up a significant number of cycles to perform. This work leverages the fixed nature of a model during inference, to reduce the full-precision memory footprint by half. This was achieved by pre-computing the BN parameters prior to quantization. The proposed BNN was validated through modeling the network on the MNIST dataset. Compared to the traditional method of computation, the proposed BNN reduced the memory utilization by 63% at 860-bytes without any significant impact on accuracy. By pre-computing portions of the BN layer, the number of cycles required to compute is reduced to two cycles on an edge device.
Pedestrian Flows Characterization and Estimation with Computer Vision Techniques

This work describes a straightforward implementation of detecting and tracking pedestrian walking across a public square using computer vision. The methodology consists of the use of the well-known YOLOv3 algorithm over videos recorded during different days of the week. The chosen location was the Piazza Duca d’Aosta in the city of Milan, Italy, in front of the main Centrale railway station, an access point for the subway. Several analyses have been carried out to investigate macroscopic parameters of pedestrian dynamics such as densities, speeds, and main directions followed by pedestrians, as well as testing strengths and weaknesses of computer-vision algorithms for pedestrian detection. The developed system was able to represent spatial densities and speeds of pedestrians along temporal profiles. Considering the whole observation period, the mean value of the Voronoi density was about 0.035 person/m2 with a standard deviation of about 0.014 person/m2. On the other hand, two main speed clusters were identified during morning/evening hours. The largest number of pedestrians with an average speed of about 0.77 m/s was observed along the exit direction of the subway entrances during both morning and evening hours. The second relevant group of pedestrians was observed walking in the opposite direction with an average speed of about 0.65 m/s. The analyses generated initial insights into the future development of a decision-support system to help with the management and control of pedestrian dynamics.
ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion

To enhance ship detection accuracy in the presence of complex scenes and significant variations in object scales, this study introduces three enhancements to ReDet, resulting in a more powerful ship detection model called rotation-equivariant bidirectional feature fusion detector (ReBiDet). Firstly, the feature pyramid network (FPN) structure in ReDet is substituted with a rotation-equivariant bidirectional feature fusion feature pyramid network (ReBiFPN) to effectively capture and enrich multiscale feature information. Secondly, K-means clustering is utilized to group the aspect ratios of ground truth boxes in the dataset and adjust the anchor size settings accordingly. Lastly, the difficult positive reinforcement learning (DPRL) sampler is employed instead of the random sampler to address the scale imbalance issue between objects and backgrounds in the dataset, enabling the model to prioritize challenging positive examples. Through numerous experiments conducted on the HRSC2016 and DOTA remote sensing image datasets, the effectiveness of the proposed improvements in handling complex environments and small object detection tasks is validated. The ReBiDet model demonstrates state-of-the-art performance in remote sensing object detection tasks. Compared to the ReDet model and other advanced models, our ReBiDet achieves mAP improvements of 3.20, 0.42, and 1.16 on HRSC2016, DOTA-v1.0, and DOTA-v1.5, respectively, with only a slight increase of 0.82 million computational parameters.
Unsupervised Low-Light Image Enhancement Based on Generative Adversarial Network

Low-light image enhancement aims to improve the perceptual quality of images captured under low-light conditions. This paper proposes a novel generative adversarial network to enhance low-light image quality. Firstly, it designs a generator consisting of residual modules with hybrid attention modules and parallel dilated convolution modules. The residual module is designed to prevent gradient explosion during training and to avoid feature information loss. The hybrid attention module is designed to make the network pay more attention to useful features. A parallel dilated convolution module is designed to increase the receptive field and capture multi-scale information. Additionally, a skip connection is utilized to fuse shallow features with deep features to extract more effective features. Secondly, a discriminator is designed to improve the discrimination ability. Finally, an improved loss function is proposed by incorporating pixel loss to effectively recover detailed information. The proposed method demonstrates superior performance in enhancing low-light images compared to seven other methods.
Kernel Code Integrity Protection at the Physical Address Level on RISC-V

An operating system kernel has the highest privilege in most computer systems, making its code integrity critical to the entire system’s security. Failure to protect the kernel code integrity allows an attacker to modify the kernel code pages directly or trick the kernel into executing instructions stored outside the kernel code pages. Existing prevention mechanisms rely on the memory management unit in which certain memory pages are marked as not-executable in supervisor mode to prevent such attacks. However, an attacker can bypass these existing mechanisms by directly manipulating the page table contents to mark the memory pages with malicious code as supervisor-executable. This paper shows that a small architectural extension enables a physical address-level mechanism to stop this threat without relying on page table integrity. PrivLock lets, at boot time, the kernel specifies the physical address ranges containing its code. At run time, PrivLock ensures that the content within the range is not manipulated and that only the instructions from those pages are executed while the processor runs in supervisor mode. Despite this protection, the kernel can still create new code pages ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., for loadable kernel modules) and make them executable with the help of PrivLock’s secure loader. The experimental results show that PrivLock incurs low performance (<0.5%), area (0.14–0.3%), and energy/power (0.053–2%) overhead.
An Overview of Object Detection and Tracking Algorithms

With the development of information technology, the vision-based detection and tracking of moving objects is gradually penetrating into all aspects of people’s lives, and its importance is becoming more prominent, attracting more and more scientists and research institutions at home and abroad to participate in research in this field. With in-depth research into vision-based object detection and tracking, various superior algorithms have appeared in recent years. In this article, we attempt to compare some of the classic algorithms in this area of detection and tracking that have appeared recently. This article examines and summarizes two areas: the detection and the tracking of moving objects. First, we divide object detection into one-stage algorithms and two-stage algorithms depending on whether a region proposal should be generated, and we accordingly outline some commonly used object detection algorithms. Second, we separate object tracking into the KCF and SORT algorithms according to the differences in the underlying algorithms.