Reviewerly/exHarmony huggingface dataset:
Fast Correlation Attacks on K2 Stream Cipher

K2 is an LFSR-based dynamic feedback stream cipher and has been standardized by ISO/IEC 18033-4. The fast correlation attack (FCA) is a well-known cryptanalysis tool for LFSR-based stream ciphers. In this paper, we propose a guess-and-determine FCA on a dynamic feedback stream ciphers model. Moreover, we give a fast calculation method to calculate the correlation of the function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F(x,y,z)=x\boxplus _{n} S(y)\boxminus _{n} z$ </tex-math></inline-formula> by directly characterizing subtraction modulo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}$ </tex-math></inline-formula> . Then we propose a kind of mask structure of the linear approximations of the function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F(x,y,z)$ </tex-math></inline-formula> with high correlations. The structural characteristics of the kind of masks reduce both the time complexity of the fast calculation and the memory complexity of connection matrices, which enables us to efficiently search for linear approximations with high correlations. Based on the structural characteristics and the analysis of the number of active S-boxes of the linear approximations of K2, we present an effective search strategy, where the number of active S-boxes is 4. The best absolute correlation we found is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{-24.21}$ </tex-math></inline-formula> . Finally, we study the resistance of K2 against the FCA. For any of the four variants of K2, we give the best key recovery attack so far. The time/data/memory complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(2^{190.06})/O(2^{189.80})/O(2^{188.80})$ </tex-math></inline-formula> , respectively. The results indicate that the four variants of K2 cannot guarantee the claimed 192-bit and 256-bit security if we ignore the design constraint that the maximum keystream length for a single pair of key and IV is limited to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{64}$ </tex-math></inline-formula> . For the full version of K2, we present the first FCA, which is also the best attack result yet. And the time/data/memory complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(2^{313.57})/O(2^{149.02})/O(2^{148.02})$ </tex-math></inline-formula> , respectively. The large security redundancy indicates that the dynamic feedback structure provides higher security.
Application of Artificial Intelligence in the Construction of Intelligent Knowledge Base

In order to explore the construction of intelligent knowledge base, an application based on artificial intelligence in the construction of intelligent knowledge base is put forward. Methods Through the key technical problems and solutions of information recommendation based on artificial intelligence, the research on the construction of intelligent knowledge base was explored. The research shows that the application of artificial intelligence in the construction of intelligent knowledge base is about 13% more efficient than traditional methods. Under the action of artificial intelligence technology, it can better meet people’s needs, promote the improvement of knowledge base construction, provide people with sufficient information, and guide the stable development of network intelligent knowledge base system.
ProMiSE: A High-Performance Programmable Hardware Monitor for High Security Enforcement of Software Execution

In recent years, to prevent computer systems from software attacks, hardware monitors are proposed as a type of efficient security enforcement scheme, which can detect software attacks at runtime. However, due to the limited flexibility of dedicated hardware monitors, one monitor can be only applied to a few targeted application scenarios, and is hard to defend against unconsidered attacks. This leads to high cost for redesigning monitors for new scenarios. Although recent studies propose flexible hardware monitors, the scope and security of the reconfigurable monitoring policies are still limited. To further improve the flexibility and security, this work proposes a monitor instruction set and multiple security-assisting designs for supporting general operations needed by various attack detection schemes. Based on the above efforts, an efficient programmable hardware monitor named ProMiSE is designed. After implemented on the RocketChip RISC-V processor, ProMiSE can be programmed to realize a wider range of monitoring policies with higher security and similar hardware resource overhead, compared with stateof-the-art flexible hardware monitors. With these advantages, ProMiSE still has the detection latency as low as 18-59 CPU cycles. The performance overhead ranges from 0%-23.4%, which is also reasonable compared with the dedicated hardware monitors of corresponding policies.
Research on the Construction of Innovation Literacy Index System of Higher Vocational Students Based on Big Data

In order to accelerate the cultivation of high-quality skilled talents in our province, vocational education should strengthen students’ professional innovation literacy and cultivation. This paper focuses on the research object, and on the basis of full research, uses the correlation analysis of big data data analysis technology, calculates the covariance and correlation coefficient matrix through the cov() function and corrcoef0 in the numpy database, and determines the composition and weight of the primary and secondary indicators, The index system of innovation literacy of higher vocational students is constructed, which lays a foundation for how to cultivate the core quality of vocational innovation of higher vocational students.
IMM-Net: Integrated Multi-stream Memory Network for real-time semantic segmentation

Real-time semantic segmentation is a challenging topic in computer vision. The popular BiSeNet utilizes twobranch approach for spatial information and context information independently. However, we find that its additional, coarser downsampling path to encode spatial information may cause structure redundancy. In this paper, an efficient and effective Integrated Multi-stream Memory module is designed and seamlessly integrated into the whole network architecture (termed IMM-Net) to obtain an extensive range of receptive fields and efficiently integrate multi-scale contextual information. Considering spatial location information is essential to preserving object boundaries, a specific Feature Aggregation Guidance Module is proposed to encode both interpixel semantic relationships and long-range location relationships. Comprehensive experiments on Cityscapes and CamVid datasets indicate our IMM-Net outperforms a few state-of-the-art real-time semantic segmentation methods, achieving a good speed-accuracy trade-off.
Security Analysis and Improvement of Proxy Signature Scheme with Certificateless and Blind Properties

Proxy signature scheme with certificateless and blind properties is called certificateless proxy blind signature (CT-PBS) scheme which holds many security properties such as unforgeability and delegation. This paper makes a security analysis on an improved CT-PBS scheme proposed recently and shows that the improved CT-PBS scheme yet does not solve the security drawbacks existing in original scheme. Four types of forgery attacks are presented: 1) The attacker (acts as a malicious Key Generation Center (KGC)) can imitate the selected original signer to authorize a selected proxy signer to get the signer right of the original signer without being known by the original signer; 2) The malicious user can forge a proxy blind signature on any message by replacing the public key of the selected original signer and the public key the selected proxy signer; 3) The attacker can authorize a selected proxy signer to get the signer right of the original signer A by replacing A’s public key; 4) The malicious original signer can forge proxy blind signature on any message by replacing the public key of the selected proxy signer. We also give an improved method on these security problems and a security analysis.
Reveal Your Images: Gradient Leakage Attack Against Unbiased Sampling-Based Secure Aggregation

Recently, some Unbiased Gradient Sampling-based (UGS) methods have been proposed to enhance the security and efficiency of federated learning through crafted unbiased random transformation and sampling, such as MinMax Sampling in SIGMOD ’22. In this paper, we propose a novel attack, GLAUS, to show that UGS is not as secure as claimed in these works and is still vulnerable to the gradient leakage attack (GLA). Specifically, we demonstrate an idea to approximately infer the gradient for GLA in the context of the UGS scenario where the real gradient is not available. Once the gradient is approximately obtained, the security of the UGS frameworks is downgraded to that of the original federated learning. The approximate gradient is refined by the following steps: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">narrow the gradient searching range</i> to the finite set; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">obtain the magnitude</i> of each gradient value approximately; 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">revise the gradient signs</i> . Versus the failure of existing attacks, extensive experiments on six datasets show that our attack is effective in reconstructing private datapoints with pixel-wise accuracy on four network sizes and three image resolutions. Finally, we show how to defend against GLAUS while maintaining the high efficiency of UGS and only introducing an additional step to hide the sampled gradient indices.
Research on Computerized Automatic Word Segmentation of Chinese Stylistic Words

Natural language processing involves various aspects such as phonology, lexicon, syntax and semantics. In the case of Chinese, words are the smallest linguistic units that can be used independently, and they are also the basic units of Chinese natural language processing. In order to promote the computer’s mining and utilization of the relevant data in the form of natural language text in different stylistic, and improve the recognition and classification ability of the word segmentation system for specific function or domain words, this paper investigates the Chinese stylistic words based on computerized automatic word segmentation. According to the sampling method, a certain number of language materials are selected from different types of modern Chinese, and five word segmentation models of NLPIR- ICTCLAS, Jieba, LTP, THULAC and PKUSeg are used to divide these texts, and then the segmentation model which best fits the characteristics of stylistic words is selected. Then, based on the word segmentation results of the model and the theories related to linguistics, a certain number of stylistic words are extracted. This research can promote the machine’s mastery of natural language rules, and play an important role in the classification of language materials, recognition and retrieval of subject information, and contextual semantic analysis. And besides, it can improve the efficiency and accuracy of computer’s recognition of unregistered words of specific text and the accuracy of machine translation, which are of great value in both Chinese natural language understanding and natural language generation.
Advanced Process Monitoring through Fault Detection and Classification for the Process Development of Tantalum Nitride Thin Film Resistors

This paper discusses the optimization of an advanced process monitoring scheme with interdicting fault detection and classification (FDC) capabilities that improved the control over the process development of Tantalum Nitride thin film resistors (TaN TFR). Its implementation in a high-volume manufacturing environment resulted in a reduction of misprocessed wafers, shorter equipment downtime, higher throughput, enhanced process visibility, and yield improvement. Along with optimizing FDC capabilities, implementing a short loop sampling plan reduced the turnaround time for early electrical characterization by a factor of four; this allowed for timely inline adjustments within the fabrication process to tighten the statistical process control (SPC) over the distribution of the process control monitor (PCM) TaN resistor value and improve thin film uniformity.
Progressive Hard Negative Masking: From Global Uniformity to Local Tolerance

Unsupervised contrastive learning has recently become increasingly popular due to its amazing performance without the need for costly annotations. However, indiscriminate sampling of negative pairs is accompanied by the uniformity-tolerance dilemma, which is especially serious in node-level graph contrastive learning due to the smoothing property of graph convolutional operators. Previous negative mining strategies that either overly emphasize hard negatives or rely on precise distribution estimation can make minor improvements or even degrade the performance in such a case. In this paper, we investigate the role of hard negatives in the uniformity-tolerance dilemma and propose a novel contrastive objective with a progressive hard negative masking scheme. The proposed objective, as an asymptotically-tightened lower bound of mutual information, is theoretically and empirically demonstrated to be capable of allowing higher local tolerance and stronger contrastive effects, thus leading to higher-quality embedding distributions and considerable performance improvement in downstream node classification tasks.
Filter Transfer Learning Algorithm for Missing Data Imputation in Wastewater Treatment Process

Missing data imputation is a critical data processing procedure in wastewater treatment process. However, the existing imputation methods cannot stand the missing data with high proportions that frequently happens due to unmaintained instruments or detection failures. Transfer Learning aims to learn much reliable information for the target domain with previous learned knowledge from a source domain, which provides a framework for solving such problem. This paper proposes a filter transfer learning algorithm (FTLA) for missing data imputation with high proportions. First, a knowledge acquisition strategy is developed to extract the source knowledge, including independent knowledge from historical datasets and parallel knowledge in terms of related datasets. The missing data is then interpreted through source knowledge comprehensively. Second, a filter transfer learning algorithm is designed to achieve target knowledge that mimics the tendency of the missing data. This algorithm can avoid serious negative transfer by using the extended Kalman filter to filtrate source knowledge. Third, a knowledge rolling mechanism is established to perform the imputation online with target knowledge, which can maintain the reliable imputation for missing data with high proportions. Finally, several comparative experiments of wastewater data are provided to demonstrate the merits of missing data imputation with FTLA.
Agile Project Management Methodology: Exploring Factors Which Foster Successful Introduction and Institutionalization of the Framework

This study explores the agile project management methodology as a key tool that significantly contributes to a project success. It presents the challenges and reasons of implementing an agile approach. The focus is on the analysis of international studies conducted from 2002 to 2022. In addition, the quantitative and qualitative data was obtained from the companies, which operate in Georgia. Despite the fact that the research showed the effectiveness of agile project management and positive correlation between this methodology and project success, the presented findings only describe the current situation and they cannot be generalized on the whole population. The recommendation for the future researchers is to conduct qualitative research, such as observation and interviews, to get more in-depth analysis behind the use of the agile method. The problem is further complicated due to the fact that there is a lack of scientific literature and reliable market research in Georgia. Therefore, this paper has even more value and significance.
Recognizing Face Using the Combination of Singular Value Decomposition and Hidden Markov Model Algorithms

Face recognition as a biometric system is a development of an authentication system based on the face’s natural characteristics. Face recognition needs to be further researched because it has been applied to various fields. After all, this system does not require direct physical contact between humans and computer input sensors. However, problems in face recognition systems are pretty dynamic and complex. This study proposed a face recognition system using Singular Value Decomposition (SVD) for feature extraction and Hidden Markov Model (HMM) for classification. It is expected that this research may contribute to developing the method of face recognition. The recognition process is based on a frontal face image which is divided into seven areas, and each area is assigned to a state in a one-dimensional HMM. 100 images are used for database creation. Image acquisition is made 10 times for each of 10 different people with different expressions. Half of the images are used for training and the rest for the testing process. In this study, the moving probability from one state to the state itself and the next state is not the same. Still, it depends on the average value of the height of the area covered by a particular state, and the result shows that it can increase the recognition rate from 94 to 96%.
Hand Gesture Recognition Using FMCW Radar and Deep Learning for Understanding Deaf Sign Language

People have difficulty in understanding sign language of deaf people therefore a technology is needed to translate sign language to help deaf people. In Indonesia, society lacks fluency in national sign languages like Indonesian Sign Language (SIBI), which can affect their self-confidence and social interaction skills with others. Previous studies generally used a camera in dataset retrieval in sign language recognition. There is a weakness in using the camera because it has to pay attention to environmental conditions, such as being influenced by light intensity. Radar can correct this deficiency because it uses electromagnetic waves. In this study proposed, the use of uRAD radar by Antheral based on radar Frequency Modulated Continuous Wave (FMCW) to detect and then collect words of SIBI for raw data that is IQ signals. The proposed technique applies the preprocessing methods for the frequency-domain IQ signals of radar echoes as input to a convolutional neural network (CNN). The system designed is able to classify five words of Indonesian Sign Language into five classes. Thereafter compare three self-constructed architectures to find the best model with differences in the number of convolution and pooling layers in each architecture. The results of the proposed architectural classification show the movement classification accuracy exceeds more than 98%. This study has high accuracy because of the proposed preprocessing method and compares several CNN architectures whose accuracy is higher than previous studies which also used radar. Based on the results, the proposed system can be helpful in understanding deaf sign language with SIBI.
A Survey and a State-of-the-Art Related to Consensus Mechanisms in Blockchain Technology

Consensus is an essential element of Blockchain technology, as it allows distributed networks to achieve agreement on the state of their shared ledger without the need for a central authority. This is crucial for the security and reliability of the network since it ensures that all nodes have the same copy of the ledger and that the ledger is tamper-proof. Hence, the goal of this paper is to explore and review the most known consensus mechanisms in the wild. In this direction, we start by identifying the types of blockchains and discussing criteria for performing the comparison and the analysis of the studied consensus mechanisms while recalling the Problem of Byzantine Generals. The consensus mechanisms discussed in this paper are mainly those used in public and permissionless blockchains and those used in private and permissioned ones. Finally, we propose a comparison and an analysis of the studied consensus mechanisms, based on the speed and efficiency, validation process and the CAP theorem, to select the best choice for newly designed blockchain platforms.
Classification of Alphabets and Numbers Indonesian Sign System Using uRAD Radar Based on FMCW Radar and Deep Learning Techniques

The difficulty of society, especially in Indonesia, for interacting with people who are deaf and speech impaired is the Indonesian Language Sign System (SIBI). Technology is needed to translate the SIBI language to make it easier for Indonesian people to interact with deaf and speech-impaired people. This research proposal uses uRAD radar based on FMCW radar to detect the SIBI language used to collect datasets with the addition of Deep Learning Convolutional Neural Network (CNN) techniques for classification algorithms. The designed system can classify Alphabet Letters and Numbers into five classes, namely Letters C, F, and T, and Numbers 3 and 6. The classification results using six layers of Convolutional Neural Network (CNN), and ReLu activation obtained an accuracy of more than 92%. The proposed system’s results can help translate and understand the SIBI Alphabet and Numbers.
The Impact of Blockchain Technology and Business Intelligence on the Supply Chain Performance-Based Tracking Process

The holy grail of business intelligence is to gather and produce just-in-time data, combine it with current data assets, channel it through on-demand modeling to identify critical operational and strategic activities, and immediately distribute valuable information across the organization. Since blockchain provides users with a mechanism to track the data that are stored in its encrypted ledger-based database, which is already more secure than any centralized database now in use, business intelligence can tremendously benefit from it. Not only does Blockchain improves the traceability, transparency, and trust of data shared throughout a shared network. However, it also brings cost savings through new efficiencies. In this article, we illustrate how integrating blockchain with business intelligence may enhance the performance of the supply chain, which requires gathering various sorts of data from various resources for their process by making real-time data analytics available. On the other hand, we discuss how customers can profit from tracking information to ensure that their items have not been tampered with.
A Color Image Encryption Method Designed Using Chaotic Confusion, Enhanced CBC-MAC Mode and Pixel Shuffling

In this paper, a new encryption method is designed using two improved algorithms appointed P-PWLCM and CBC-MAC. The proposed method is conceptually divided into 3 steps, a first for confusion and a second for diffusion and this to satisfy the shannon constraints. The proposed method is also completed by a pixels shuffling to achieve a maximum-security level. An experimental study is made to evaluate the proposed method capabilities to reduce the various risks of cryptanalytic attacks to zero.