Reviewerly/exHarmony huggingface dataset:
Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding

Auto-completing code enables developers to speed up coding significantly. Recent advances in transformer-based large language model (LLM) technologies have been applied to code synthesis. However, studies show that many of such synthesized codes contain vulnerabilities. We propose a novel vulnerability-constrained decoding approach to reduce the amount of vulnerable code generated by such models. Using a small dataset of labeled vulnerable lines of code, we fine-tune an LLM to include vulnerability labels when generating code, acting as an embedded classifier. Then, during decoding, we deny the model to generate these labels to avoid generating vulnerable code. To evaluate the method, we chose to automatically complete Ethereum Blockchain smart contracts (SCs) as the case study due to the strict requirements of SC security. We first fine-tuned the 6-billion-parameter GPT-J model using 186,397 Ethereum SCs after removing the duplication from 2,217,692 SCs. The fine-tuning took more than one week using ten GPUs. The results showed that our fine-tuned model could synthesize SCs with an average BLEU (BiLingual Evaluation Understudy) score of 0.557. However, many codes in the auto-completed SCs were vulnerable. Using the code before the vulnerable line of 176 SCs containing different types of vulnerabilities to auto-complete the code, we found that more than 70% of the auto-completed codes were insecure. Thus, we further fine-tuned the model on other 941 vulnerable SCs containing the same types of vulnerabilities and applied vulnerability-constrained decoding. The fine-tuning took only one hour with four GPUs. We then auto-completed the 176 SCs again and found that our approach could identify 62% of the code to be generated as vulnerable and avoid generating 67% of them, indicating the approach could efficiently and effectively avoid vulnerabilities in the auto-completed code.
Illegal Broadcasting: A Way for Protecting Revenue

Digital Rights Management (DRM) is adopted by Over-the-Top (OTT) providers, intended to prevent unauthorized access and modification of content. However, DRM technology suffers from tracing content redistribution. Watermarking is a way to address this problem. The majority of watermarking is performing some actions in order to make possible unique request patterns per client for each content. The motivation behind this study is to achieve secure video streaming and reclaim lost revenue by tracing content redistribution without compromising the Quality of Experience (QoE). In this regard, we considered the best applicable watermarking technology (server-side vs client-side) as well as the limitations caused by the terminal processing power of legacy devices during the implementation. As a result, we implemented "Irdeto" server-side watermarking technology and run it in Digiturk live streaming platform. The results indicate that implemented solution can protect content redistribution without significantly compromising QoE.
On the performance of screening invalid jobs using the policy gradient method in deadline-aware data transfer scheduling

When transferring large amounts of data between data centers using residual bandwidth, some transfers must be completed before the deadline. Such data transfers are called deadline-aware jobs. Scheduling deadline-aware jobs is not easy, and existing methods such as Earliest Deadline First (EDF) are not optimal because they do not consider jobs that will arrive in the future. In this paper, we apply a policy-gradient deep reinforcement learning algorithm to schedule the deadline-aware job scheduling. We set conditions under which EDF cannot achieve optimal job scheduling, and show the proposed policy-gradient deep reinforcement learning algorithm outperforms the EDF method in this scenario. By varying the number of jobs and job size, we increased the job difficulty and investigated how the proposed method behaved. The results show that as the job difficulty is increased, the proposed method performs worse than EDF, although it still performs better. There is still room for improvement when applying reinforcement learning to this task.
By Your Command: Extracting the User Actions that Create Network Flows in Android

Given the complexity of modern systems, it can be difficult for device defenders to pinpoint the user action that precipitates a network connection. Mobile devices, such as smartphones, further complicate analysis since they may have diverse and ephemeral network connectivity and support users in both personal and professional capacities. There are multiple stakeholders associated with mobile devices, such as the end-user, device owner, and each organization whose assets are accessed via the device; however, none may be able to fully manage, troubleshoot, or defend the device on their own.In this work, we explore a set of techniques to determine the root cause of each new network flow, such the button press or gesture for user-initiated flows, associated with a mobile device. We fuse the User Interface (UI) context with network flow data to enhance network profiling on the Android operating system. In doing so, we find that we can improve network profiling by clearly linking user actions with network behavior. When exploring effectiveness, the system enables allow-lists to reach over 99% accuracy, even when user-specified destinations are used.
SQCC: Stable Queue Congestion Control

The advent of high-speed networks has revolutionized data center capabilities by providing low latency and high bandwidth for applications. However, traditional TCP congestion control algorithms are no longer adequate for data center networks. RTT-based congestion control leverages advanced NIC hardware to identify accumulated queuing delay of the end-to-end path. It is simple, effective, and adaptable to different environments. Nevertheless, RTT-based congestion control faces challenges related to unstable queue length and oscillation caused by RTT feedback delays. With the increase of queue length, the oscillation range also amplifies. To address these issues, we propose SQCC, which introduces two key enhancements. Firstly, it employs a novel error function to regulate the queue length within a controlled range that is proportional to the number of incast flows. Secondly, it incorporates self-adjustable parameters for rate increment and RTT threshold, effectively managing queue oscillation and ensuring a non-empty link. We evaluate the algorithm’s effectiveness through NS3 network simulations, and the results demonstrate that SQCC achieves an 80% reduction in queue size upon convergence and exhibits a significantly low oscillation range (27% to 57%) in large-scale incast scenarios.
CODEC: Cost-Effective Duration Prediction System for Deadline Scheduling in the Cloud

Modern cloud platforms allow customers to flexibly allocate or release computing resources. One crucial scenario is how to drive existing VMs to a specific state by a given deadline in a reliable and cost-effective manner. These state transition requests could involve starting or reallocating VMs. Performing millions of these requests per day can be challenging because the throughput of cloud platforms is not consistently deterministic. To meet customer service level agreements, cloud providers often trade-off cost for reliability and use oversized estimates to ensure that they initiate state transitions against VMs well ahead of time. In this paper, we propose a COst-effective Duration prEdiCtion system (CODEC) that solves the deadline scheduling problem for cloud providers by building intelligent automation to discover and execute optimal strategies when performing concurrent requests. In the CODEC, the core is to categorize durations of requests into buckets in real-time and the buffer time of each bucket is then predicted by extreme value theory. Extensive experiments show the proposed approach can guarantee the success rate of requests, while significantly saving cost with 38.71% and 86.97% compared to the baseline approach and the static buffer time, respectively. The results obtained show that CODEC can effectively and efficiently solve the deadline scheduling problem, and lead a worldwide cloud provider to deploy its prototype.
Preserving the Integrity of Hyperspectral Satellite Imagery Using a Secure and Efficient Reversible Watermarking Technique for Ownership Protection

Hyperspectral data plays a crucial role in various fields, including remote sensing, environmental monitoring and medical imaging. With the increasing importance of hyperspectral data, the need for secure transmission and storage has become imperative. Protecting the ownership and integrity of hyperspectral data is essential to prevent unauthorized access and ensure its reliability. This paper presents a novel approach to address these challenges by introducing a secure and efficient reversible watermarking method for ownership protection. The proposed method embeds ownership information into the hyperspectral data without affecting the spectral information. The technique involves three steps: embedding, extraction and restoration, and enables reversible watermark removal. The performance of the method is evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Normalized Correlation (NC) and shows high ownership protection while maintaining visual quality and fidelity. The experimental results confirm the effectiveness of the proposed technique and show its robustness to various attacks and computational efficiency. This research helps preserve the integrity of hyperspectral data and provides a reliable solution for property protection in critical decisionmaking applications.
A groupwise approach to the birthday paradox

A key quantity of the birthday paradox is the probability that all K individuals have distinct birthdays across N calendar days. The permutation approach computes this probability directly, using permutations of all individuals. The pairwise approach sequentially computes the conditional probability for each pair of individuals to have distinct birthdays given that all previous pairs do. We propose a groupwise approach, in which all possible groups of size J are handled sequentially. The groupwise approach contains the permutation and pairwise approaches as special cases. The conditional groupwise probability of no birthday collisions takes various shapes such as decreasing, increasing, and hump-shaped curves. We characterize these patterns with (N, K, J), and derive a number of new insights on the birthday paradox.
Characteristics of physical reality, virtual reality, and interface in interactive digital communication experience

Communication methods in fashion design have changed with the emergence of various devices and media in the digital environment, and combining fashion design with AI, IoT, and AR has increased in the wake of the 4th Industrial Revolution. This causes an expansion of fashion design, and interactive digital communication fashion design extends bodily experience from physical reality to virtual space. This study aimed to identify the experiential characteristics of physical reality, virtual reality, and interface for interactive digital communication and develop a measurement scale for each space. It used a Delphi method collecting opinions from industry, academic, and government experts through a questionnaire. The research findings offer a tool for measuring the characteristics of interactive digital communication fashion design experiences that allow a better understanding of the significance of the communication type in each space.
Blockchain storage optimization model based on remainder system

Storage scalability has become one of the bottlenecks of blockchain development. This paper proposes a blockchain storage scaling model based on redundant residual system (RRNS). The model divides the blockchain into high security blocks and low security blocks, and adopts different storage strategies for them. Among them, low security blocks are stored in the form of whole network storage (all nodes need to be saved), and high security blocks are partitioned by RRNS based segmentation algorithm and stored in a distributed form. In addition, the redundant residue system(RRNS)is used to detect and correct errors to prevent malicious node attacks, thereby improving data stability and integrity. The experimental results and security analysis show that the proposed model not only has security and fault tolerance, but also guarantees the integrity of data, effectively reduces the storage consumption of nodes, and enhances the storage of the blockchain system Scalability.
Study on a simple summative attention mechanism used in BERT model for topic classification and sentiment analysis of car reviews

In the automobile industry, consumers' focus and satisfaction with automobiles are important reference directions for product optimization. This key information can be obtained by analyzing automobile evaluations. At present, end-to-end model training has problems such as large demand for data and long training time. This study proposes a summative attention mechanism to improve the Bert model, and transfers human prior knowledge to the model by summarizing vocabulary. The results show that the summative attention mechanism can make the model converge faster, significantly improve the classification accuracy of reviews, and has a good application value in the topic classification of car reviews.
Optical Measurement and 3-D Reconstruction of Blade Profiles With Attention-Guided Deep Point Cloud Registration Network

The optical measurement and precise 3-D reconstruction of the blade are crucial in ensuring its processing quality. However, the complex and highly reflective free-form surfaces of blades may lead to inadequate overlaps and less prominent overlap-area features among multiple scanning data. The spatial sampling variance of optical measurement instruments can result in different point cloud densities. These make it challenging to achieve accurate point cloud registration of multiple scanning data, which is currently a critical step in the 3-D reconstruction of blade profiles. To address these issues and achieve the precise 3-D reconstruction of blade profiles, a learning-based method called attention-guided deep point cloud registration network (AGDnet) is proposed in this article, which mainly consists of a feature extraction backbone module, a cross-attention mechanism module, a self-attention mechanism module, and a transformation estimation module. Specifically, the feature extraction backbone incorporates subsampling to address density variations and noise contamination in point clouds. The cross-attention mechanism is proposed to facilitate critical information exchange between point clouds, while the self-attention mechanism integrates significant global features from individual point clouds to further enhance the feature representation. The transformation estimation module is designed to optimize dynamically the matching matrix, enabling the recovery of the underlying correspondences between point clouds. The experimental results on three representative blades and comparison with six point cloud registration methods prove the feasibility and accuracy of the suggested method.
Learning Invariant Representations Under General Interventions on the Response

It has become increasingly common nowadays to collect observations of feature and response pairs from different environments. As a consequence, one has to apply learned predictors to data with a different distribution due to distribution shifts. One principled approach is to adopt the structural causal models to describe training and test models, following the invariance principle which says that the conditional distribution of the response given its predictors remains the same across environments. However, this principle might be violated in practical settings when the response is intervened. A natural question is whether it is still possible to identify other forms of invariance to facilitate prediction in unseen environments. To shed light on this challenging scenario, we focus on linear structural causal models (SCMs) and introduce invariant matching property (IMP), an explicit relation to capture interventions through an additional feature, leading to an alternative form of invariance that enables a unified treatment of general interventions on the response as well as the predictors. We analyze the asymptotic generalization errors of our method under both the discrete and continuous environment settings, where the continuous case is handled by relating it to the semiparametric varying coefficient models. We present algorithms that show competitive performance compared to existing methods over various experimental settings including a COVID dataset.
Optimization of the Random Forest Method Using Principal Component Analysis to Predict House Prices

Investment is an interesting thing, especially property investment. The developer must also be careful in determining the price of the property. It should be noted that every year, both short-term and long-term, property prices increase and rarely go down. In determining the price, it is often also based on the features of the house such as the concept, location, bedrooms, etc. To predict house prices based on their features, the random forest has a good performance for predicting house prices. However, the random forest method has the disadvantage that if you use too many variables, the training process will take longer and feature selection tends to select features that are not informative. One way to reduce features without removing other features is to use Principal Component Analysis. In this research, the method used is Principal Component Analysis (PCA) and Random Forest. From the results of model training, it can be concluded that the use of model evaluation results using PCA has a smaller error rate and more consistent values, with an average of 0.018. While the results of the evaluation without PCA and using only Random Forest have a higher error value with an average of 0.03125. The training time using the PCA model has a faster time, with an average of 7918 milliseconds, while those using only random forest without PCA have an average time of 8975 milliseconds.
GIS partial discharge data enhancement method based on self attention mechanism VAE-GAN

The reliability of geographic information system (GIS) partial discharge fault diagnosis is crucial for the safe and stable operation of power grids. This study proposed a data enhancement method based on a self-attention mechanism to optimize the VAE-GAN method and solve the problem of the lack of partial discharge samples and the unbalanced distribution between different defects. First, the non-subsampled contourlet transform (NSCT) algorithm was used to fuse the UHF and optical partial discharge signals to obtain a photoelectric fusion phase resolved partial discharge (PRPD) spectrum with richer information. Subsequently, the VAE structure was introduced into the traditional GAN, and the excellent hidden layer feature extraction ability of the VAE was used to guide the generation of the GAN. Then, the self-attention mechanism was integrated into the VAE-GAN, and the Wasserstein distance and gradient penalty mechanisms were used to optimize the network loss function and expand the sample sets to an equilibrium state. Finally, the KAZE and polar coordinate distribution entropy methods were used to extract the expanded samples. The eigenvectors of the sets were substituted into the long short-term memory (LSTM) network for partial discharge fault diagnosis. The experimental results show that the sample generation quality and fault diagnosis results of this method were significantly better than the traditional data enhancement method. The structure similarity index measure (SSIM) index is increased by 4.5% and 21.7%, respectively, and the average accuracy of fault diagnosis is increased by 22.9%, 9%, 5.7%, and 6.5%, respectively. The data enhancement method proposed in this study can provide a reference for GIS partial discharge fault diagnosis.
Inner-Product Matchmaking Encryption: Bilateral Access Control and Beyond Equality

We present an inner-product matchmaking encryption (IP-ME) scheme achieving weak privacy and authenticity in prime-order groups under symmetric external Diffie–Hellman (SXDH) assumption in the standard model. We further present an IP-ME with Monotone Span Program Authenticity (IP-ME with MSP Auth) scheme, where the chosen sender policy is upgraded to MSP, and the scheme also achieves weak privacy and authenticity in prime-order groups under SXDH assumption in the standard model. Both of the schemes have more expressive functionalities than identity-based matchmaking encryption (IB-ME) scheme, and are simpler than Ateniese et al.’s modular ME scheme (Crypto’ 19). But our schemes only achieve a very limited flavor of security, which is reflected in the privacy.
Technical, forensic and organisational aspects of work with Monero cryptocurrency

The forensic, organisational and technical features of law enforcement agencies' work with the Monero cryptocurrency in the context of pre-trial investigation and operational search activities are analysed. The development of the Monero system is described. The reasons and trends of Monero use by offenders are identified, and the scheme of operation of this payment system, which ensures its increased confidentiality, is shown. Examples of criminal offences in which Monero is used are presented. The functionality of OpenAlias to facilitate the work with Monero addresses is disclosed. The possibility of identifying participants in Monero transactions is studied. It is stated that there are currently no effective ways of such identification without knowledge of the public address and the corresponding keys, especially if users use additional security mechanisms such as connection to the TOR network.&#x0D; The features of forensic investigation of computer equipment used to work with Monero are revealed. It is established that the most effective is the study of traces of work with Monero, which are removed from the relevant computer equipment of the person of interest. Useful information can be stored in RAM, on a disc, and partially in network traffic. The article identifies artefacts that should be taken into account during inspection and search. Atomic Swaps of XMR are modelled to determine the trace pattern and identify artefacts of increased attention during forensic procedures. The fact that an atomic swap was carried out to obfuscate traces may be evidenced by the presence of specific software files on the disc used for this purpose.&#x0D; The algorithm for XMR withdrawal using multisig addresses has been proposed, from which funds can be withdrawn only when digital signatures of several persons are superimposed. The work of this algorithm in the test network Stagenet is modelled. It has been concluded that law enforcement agencies should focus on classical investigative measures to identify Monero users of interest. At the same time, there are effective mechanisms for documenting traces of work with the Monero payment system and proven methods for extracting passphrases to crypto-wallets and other sensitive information on the movement of funds in the Monero system from computer equipment.