Reviewerly/exHarmony huggingface dataset:
Active Learning for Individual Data via Minimal Stochastic Complexity

Modern machine learning systems require massive amounts of labeled training data in order to achieve high accuracy rates. Active learning uses feedback to label the most informative data points and significantly reduces the labeling effort. Many heuristics for selecting data points have been developed in recent years which are usually tailored to a specific task and a general unified framework is lacking. In this work, the individual data setting is considered and an active learning criterion is proposed. In this setting the features and labels, both in the training and the test, are specific individual, deterministic quantities. Motivated by connections between source coding and minimax learning, the proposed criterion attempts to find data points which minimize the average Predictive Normalized Maximum Likelihood (pNML) on the unlabeled test set. It is shown using a real data set that the proposed criterion performs better than other active learning criteria.
Decentralized Anomaly Detection via Deep Multi-Agent Reinforcement Learning

We consider a decentralized anomaly detection problem, where multiple agents collaborate to localize a single anomalous process among a finite number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> of processes. At each time, a subset of the processes can be observed by each agent, and the observations from each chosen process follow two different distributions, depending on whether the process is normal or abnormal. The communication channel between agents is rate-limited. The objective is a sequential search strategy that minimizes the Bayes risk, consisting of the sampling cost, and the joint terminal cost among the agents. This problem generalizes previous studies that considered anomaly detection by a single detector. We develop a novel algorithm based on deep multi-agent reinforcement learning optimization to minimize the Bayes risk. Numerical experiments demonstrate the ability of the algorithm to learn good policies in this challenging problem, and improve the single-agent performance by applying the proposed multi-agent collaborative learning method.
A parallel pipeline connected-component labeling method for on-orbit space target monitoring

The paper designs a peripheral maximum gray difference (PMGD) image segmentation method, a connected-component labeling (CCL) algorithm based on dynamic run length (DRL), and a real-time implementation streaming processor for DRL-CCL. And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft (TZ-3). The PMGD image segmentation method can segment the image into highly discrete and simple point targets quickly, which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL. Through parallel pipeline design, the storage of the streaming processor is optimized by 55% with no need for external memory, the logic is optimized by 60%, and the energy efficiency ratio is 12 times than that of the graphics processing unit, 62 times than that of the digital signal proccessing, and 147 times than that of personal computers. Analyzing the results of 8756 images completed on-orbit, the speed is up to 5.88 FPS and the target detection rate is 100%. Our algorithm and implementation method meet the requirements of lightweight, high real-time, strong robustness, full-time, and stable operation in space irradiation environment.
AbitBridge: A cross-chain protocol based on main-sub-chain architecture

At present, with the iteration of existing technologies, the importance of blockchain is constantly being recognized by people, and cross-chain technology is also becoming more and more important with the development of blockchain. Existing cross-chain solutions often have various problems and challenges. Given the challenges faced by the existing cross-chain solutions, this paper proposes a cross-chain protocol technical solution based on the main-sub-chain architecture. This paper redesigns the relay chain model and changes the cross-chain mechanism of the cross-chain protocol. This scheme combined with the core idea of the notary scheme and the relay chain scheme, the relay chain model is designed through the realization idea of smart contract and relay chain, and 3 chain modules are proposed, which effectively reduces the development difficulty of the general relay chain model. We also propose a Reliable Verify Call module in the relay chain model to solve the difficult problem of invoking smart contracts in the general notary scheme and ensure the security of cross-chain data transmission. Based on the designed relay chain model, this paper proposes a cross-chain protocol based on the main-sub-chain architecture, which effectively combines the characteristics of the main-sub-chain architecture, improves the security of the relay chain scheme, and improves the main-sub-chain architecture's throughput performance.
ADAS: <u>A</u> High Computational Utilization <u>D</u> ynamic Reconfigurable Hardware <u>A</u> ccelerator for <u>S</u> uper Resolution

Super-resolution (SR) based on deep learning has obtained superior performance in image reconstruction. Recently, various algorithm efforts have been committed to improving image reconstruction quality and speed. However, the inference of SR contains huge amounts of computation and data access, leading to low hardware implementation efficiency. For instance, the up-sampling with the deconvolution process requires considerable computation resources. In addition, the sizes of output feature maps of several middle layers are extraordinarily large, which is challenging to optimize, causing serious data access issues. In this work, we present an all-on-chip hardware architecture based on the deconvolution scheme and feature map segmentation strategy, namely ADAS, where all the generated data by the middle layers are buffered on-chip to avoid large data movements between on- and off-chip. In ADAS, we develop a hardware-friendly and efficient deconvolution scheme to accelerate the computation. Also, the dynamic reconfigurable process element (PE) combined with efficient mapping is proposed to enhance PE utilization up to nearly 100% and support multiple scaling factors. Based on our experimental results, ADAS demonstrates real-time image SR and better image reconstruction quality with PSNR (37.15 dB ) and SSIM (0.9587). Compared to baseline and validated with the FPGA platform, ADAS can support scaling factors of 2, 3, and 4, achieving 2.68 ×, 5.02 ×, and 8.28 × speedup.
A color image encryption based on chaotic system and PSO-BP neural network and DNA mutation

Abstract A color image encryption based on the chaotic system, PSO-BP neural network and DNA mutation is proposed in this paper. Firstly, chaotic characteristics of the non-autonomous laser system are analyzed by phase diagram, Lyapunov exponent, and bifurcation diagram. Secondly, the hash value calculated by SHA-256 algorithm is used to change the initial conditions of chaotic system and generate chaotic sequence. Then, the color image is compressed by the back-propagation neural network based on particle swarm optimization. Based on dynamic encoding and decoding rules, image confusion and diffusion and DNA mutation are designed. Finally, experiments verify that the scheme can compress and encrypt color images, save transmission cost and improve the security performance, which is beneficial to the efficient transmission.
CBAM-YOLOv5 for infrared image object detection

Identifying an object of interest in thermal images plays a vital role in several military and civilian applications. The deep learning approach has shown its superiority in object detection in various RGB datasets. However, regarding to thermal images, their low resolution and shortage of detail properties impose a huge challenge that hinders the accuracy. In this paper, we propose an improved version of YOLOv5 model to tackle this problem. Convolution Block Attention Module (CBAM) is integrated into traditional YOLOv5 for better representation of objects by focusing on important features and neglecting unnecessary ones. The Selective Kernel Network(SENet) is added to maximize the shallow features usage. Furthermore, the multiscale detection mechanism is utilized to improve small object detection accuracy. We train our model on the mixed visible-thermal images collected from LSOTB-TIR, LLVIP, and COCO datasets. We evaluate the performance of our method on 8 classes of objects: person, bicycle, airplane, helicopter, car, motorbike, boat, and tank. Experiment results show that our approach can achieve mAP up to 90.2%, which outperforms the original YOLOv5 and other popular methods.
Sign Language Digit Detection with MediaPipe and Machine Learning Algorithm

A major challenge when developing Machine Learning (ML) sign language recognition using wearable is how to efficiently translate the gestures based on the acquired sensors data. Conventional method utilizes data fusion based on the obtained sensors' information by producing mapping/lookup table for creating classification model of gestures corresponding sensor value. Although this method is effective, it increases programming complexity. Therefore, emerging technology that can improve the simplicity and provide accuracy of gestures' data processing is needed. This work experiments the artificial intelligence approach of the development of American Sign Language (ASL) detection using MediaPipe, a ready-to-use cross-platform machine learning framework for computer vision works and Google Teachable Machine a free web tool of machine learning model creation.
ReaLSE: Reconfigurable Lightweight Security Engines for Trusted Edge Devices

Security has become a serious threat for IoT devices running on the edge. While IoT chips are usually subject to numerous constraints such as power, form factor, cost and more. The budget for on-chip security engines is very limited. A worldwide competition was launched by NIST to define lightweight cryptographic (LWC) primitives for securing tiny devices. So far, the final round of the LWC competition consists of ten candidates’ submissions that can be categorized as substitution box (Sbox)-based and add-rotate-xor (ARX)-based. The former heavily involves memory accessibility, while the latter involves heavy computations such as additions and rotations. While one can design customized accelerator for each LWC cipher, it lacks generality and involves huge design effort. In the meanwhile, a full reconfigurable system will incur hardware overheads in terms of area and power. Inspired by domain-specific architecture, we propose a series of compact security engine architectures called Reconfigurable Lightweight Security Engines (ReaLSE), that are coupled with some levels of reconfigurability such as different word sizes, different encryption/decryption processes or even different security levels. By striking a balance between flexibility and hardware cost, our proposed designs were able to integrate micro-architectural level optimizations that are specific to certain LWC ciphers while supporting various modes. This new design family increases the availability and adaptability of embedded LWC engines for IoT. We will present various such reconfigurable designs including both ARX and S-boxed based ciphers. Architecture details along with hardware implementation results will also be discussed.
De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning (MCR)

Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection.The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process.In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images.Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method.Our proposed MCR method is to eliminate salt & pepper and gaussian noises as much as possible from the brain MRI images.The MCR method has been trained and tested on the noise quantity levels 2% to 20% for both salt & pepper and gaussian noise.The experiments have been done on publically available brain MRI image datasets, which can easily be accessible in the experiments and result section.The Structure Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculate the similarity score between the denoised images by the proposed MCR method and the original clean images.Also, the Mean Squared Error (MSE) measures the error or difference between generated denoised and the original images.The proposed MCR denoising method has a 0.9763 SSIM score, 84.3182 PSNR, and 0.0004 MSE for salt & pepper noise; similarly, 0.7402 SSIM score, 72.7601 PSNR, and 0.0041 MSE for Gaussian noise at the highest level of 20% noise.In the end, we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.
Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech.Online provocation, abuses, and attacks are widely termed cyberbullying (CB).The massive quantity of user generated content makes it difficult to recognize CB.Current advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP) tools enable to detect and classify CB in social networks.In this view, this study introduces a spotted hyena optimizer with deep learning driven cybersecurity (SHODLCS) model for OSN.The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN.For achieving this, the SHODLCS model involves data pre-processing and TF-IDF based feature extraction.In addition, the cascaded recurrent neural network (CRNN) model is applied for the identification and classification of CB.Finally, the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance.The experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.
Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Over the past era, subgraph mining from a large collection of graph database is a crucial problem.In addition, scalability is another big problem due to insufficient storage.There are several security challenges associated with subgraph mining in today's on-demand system.To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG).The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing).A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture.To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture.Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain.The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain.Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets.Finally, the graph index refinement process is undertaken to improve the query results.Concerning query error, fuzzy logic is used to refine the index of the graph dynamically.The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings.
A Secure Framework for Blockchain Transactions Protection

One of the most extensively used technologies for improving the security of IoT devices is blockchain technology.It is a new technology that can be utilized to boost the security.It is a decentralized peer-to-peer network with no central authority.Multiple nodes on the network mine or verify the data recorded on the Blockchain.It is a distributed ledger that may be used to keep track of transactions between several parties.No one can tamper with the data on the blockchain since it is unchangeable.Because the blocks are connected by hashes, the transaction data is safe.It is managed by a system that is based on the consensus of network users rather than a central authority.The immutability and tamper-proof nature of blockchain security is based on asymmetric cryptography and hashing.Furthermore, Blockchain has an immutable and tamper-proof smart contract, which is a logic that enforces the Blockchain's laws.There is a conflict between the privacy protection needs of cyber-security threat intelligent (CTI) sharing and the necessity to establish a comprehensive attack chain during blockchain transactions.This paper presents a blockchain-based data sharing paradigm that protects the privacy of CTI sharing parties while also preventing unlawful sharing and ensuring the benefit of legitimate sharing parties.It builds a full attack chain using encrypted threat intelligence and exploits the blockchain's backtracking capacity to finish the decryption of the threat source in the attack chain.Smart contracts are also used to send automatic early warning replies to possible attack targets.Simulation tests are used to verify the feasibility and efficacy of the suggested model.
Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

Since transactions in blockchain are based on public ledger verification, this raises security concerns about privacy protection.And it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification, when the whole transaction on the chain is verified.In order to improve the efficiency and privacy protection of block data verification, this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof (ZKP), which not only protects the privacy of users but also improves the speed of data block verification.There is no need to put the whole transaction on the chain when verifying block data.It just needs to generate the ZKP and root hash with the transaction information, then save them to the smart contract for verification.Moreover, the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the block.When the data is validated, the buffer accepts the complete transaction, updates the transaction status in the cloud database, and packages up the chain.So, the ZKP strengthens the privacy protection ability of blockchain, and the smart contracts save the time cost of block verification.
A Machine Learning Based Funding Project Evaluation Decision Prediction

Traditional linear statistical methods cannot provide effective prediction results due to the complexity of human mind.In this paper, we apply machine learning to the field of funding allocation decision making, and try to explore whether personal characteristics of evaluators help predict the outcome of the evaluation decision?and how to improve the accuracy rate of machine learning methods on the imbalanced dataset of grant funding?Since funding data is characterized by imbalanced data distribution, we propose a slacked weighted entropy decision tree (SWE-DT).We assign weight to each class with the help of slacked factor.The experimental results show that the SWE decision tree performs well with sensitivity of 0.87, specificity of 0.85 and average accuracy of 0.75.It also provides a satisfied classification accuracy with Area Under Curve (AUC) = 0.87.This implies that the proposed method accurately classified minority class instances and suitable to imbalanced datasets.By adding evaluator factors into the model, sensitivity is improved by over 9%, specificity improved by nearly 8% and the average accuracy also increased by 7%.It proves the feasibility of using evaluators' characteristics as predictors.And by innovatively using machine learning method to predict evaluation decisions based on the personal characteristics of evaluators, it enriches the literature in the field of decision making and machine learning field.
Aesthetic and Ethical Significance of Work of Art in Educational Environment

The article is devoted to the study of the role of fiction in the field of education and upbringing. The most important functions of literature that are essential for education (more broadly – the educational environment) are indicated. The works of Chuvash writers, which are included in textbooks-readers on native (Chuvash) literature of basic general and secondary general education, are selectively analyzed. In this article, fiction is considered not so much as a subject of study of literary criticism and criticism, but rather as a socio-cultural phenomenon that has the ability to directly influence the emotional and sensory perception of a person. Fundamental to this scientific research is the descriptive method, which includes techniques of interpretation, comparison, generalization. As a result of the conducted research, it was summed up that verbal creativity always reflects the worldview and feelings of the writer and his people. The following article concludes about a work of art, its representation through the prism of a certain aesthetic microcosm, with its own rules and laws. That is why the work of literature is considered fiction and fantasy of the writer. But on the other hand, in his works the author recreates a certain life situation.
Demeter

Energy consumption in cloud data centers has become an increasingly important contributor to greenhouse gas emissions and operation costs. To reduce energy-related costs and improve environmental sustainability, most modern data centers consolidate Virtual Machine (VM) workloads belonging to different application classes, some being latency-critical (LC) and others being more tolerant to performance changes, known as best-effort (BE). However, in public cloud scenarios, the real classes of applications are often opaque to data center operators. The heterogeneous applications from different cloud tenants are usually consolidated onto the same hosts to improve energy efficiency, but it is not trivial to guarantee decent performance isolation among colocated workloads. We tackle the above challenges by introducing Demeter, a QoS-aware power management controller for heterogeneous black-box workloads in public clouds. Demeter is designed to work without offline profiling or prior knowledge about black-box workloads. Through the correlation analysis between network throughput and CPU resource utilization, Demeter automatically classifies black-box workloads as either LC or BE. By provisioning differentiated CPU management strategies (including dynamic core allocation and frequency scaling) to LC and BE workloads, Demeter achieves considerable power savings together with a minimum impact on the performance of all workloads. We discuss the design and implementation of Demeter in this work, and conduct extensive experimental evaluations to reveal its effectiveness. Our results show that Demeter not only meets the performance demand of all workloads, but also responds quickly to dynamic load changes in our cloud environment. In addition, Demeter saves an average of 10.6% power consumption than state of the art mechanisms.