Reviewerly/exHarmony huggingface dataset:
Reversible data hiding based on high fidelity prediction scheme for reducing the number of invalid modifications

Prediction-error expansion (PEE) has been extensively investigated among different approaches of reversible data hiding (RDH). Most PEE-based RDH approaches, e.g., various extensions of adaptive embedding and optimal expansion-bins selection, utilize local complexity of pixels to manage the expansion manner of prediction errors aiming to decrease the number of shifted pixels. However, this fact is not taken into account that prediction errors are not necessarily proportionate to the local complexity of the pixels. In this paper, we focus on forming a prediction-error histogram in the prediction step that causes fewer invalid modifications and results in the reduction of embedding distortion. Hence, an RDH algorithm based on the PEE is presented that provides a general framework for reducing the number of invalid modifications (NIM). To this mean, a high fidelity prediction scheme is proposed using weibull generalized exponential distribution (WGED) to generate two asymmetric prediction-error histograms (APEH). Then, a two-layer embedding procedure is performed through PEE using each APEH. By utilizing WGED, the proposed predictor changes adaptively based on the desired payload and the given cover image to reduce NIM and embedding distortion. The results of comprehensive experiments using three databases confirm the superiority of the presented approach over some existing methods.
Enhanced SBIR based Re-Ranking and Relevance Feedback

Day by day need for a continuous assessment on effectiveness and its accuracy of the recovery algorithm increased. Several sketch-based recovery algorithms exist in the world, but they are not optimal. In the existing work, file structures are applied to enormous databases and data warehouses to acknowledge the recovery process. The process can be sensible and may get affected by quantization blunders. However, the ambiguousness of client models exhibits inappropriate information when using customary picture recovery strategies. So the proposed method, the Sketch-based picture recovery (SBIR) approach, works with recoding and testing. Our methodology utilizes the semantics in inquiry outlines and the top positioned pictures of the essential outcomes. The proposed work applied criticism to find progressively significant information from the sketch-based image. The efficiency of the proposed method is evaluated on QMUL-Shoe dataset and Saavedra dataset. Results show that proposed algorithm improves the accuracy of the sketch-based recovery algorithm.
An Efficient Blockchain PBFT Consensus Protocol in Energy Constrained IoT Applications

While the Internet of Things (IoT) can support various smart applications, data privacy and security still have been the problems due to a great diversity of IoT devices. Blockchain technology, with distributed, traceable and non-tamperable characteristics, offers opportunities to solve these problems. However, due to the limited energy and computing capability of IoT devices, it is hard to conduct expensive consensus in blockchain systems, especially in PoW based blockchains. In this paper, we propose a high energy efficiency PBFT consensus protocol designed for energy-constrained IoT-blockchain applications. In our proposed PBFT protocol, we design the energy effective consensus node selection mechanism and use the VRF to ensure the security of the leader. Besides, we investigate the node authority by extending the degree of centrality to choose relay nodes in the case of multi-hop neighbor nodes. The simulation results validate that our proposed consensus protocol can cut down the energy consumption as well as can attain high performance.
A TOU-IBT Pricing Strategy to Manage the Cryptocurrency Micro-Miners

Cryptocurrency mining device (CMD) is a plug-and-play device installed by any low-voltage customer to convert electrical energy to digital money. Recently, the significant increase in the price of digital currencies has persuaded residential customers in many countries to employ CMDs to obtain quick and easy profit. However, these rapidly evolving loads can cause a substantial increase in the coincident peak load of distribution networks. Therefore, it can threaten the safe and reliable operation of the network. A straightforward way to restrict the penetration rate of these home-based miners is to increase the electricity price such that this business becomes unprofitable; however, this solution affects the national regulations and is not practical in many countries. This paper proposes a practical electricity pricing strategy to manage the operation of cryptocurrency mining loads appropriately. This pricing strategy can change the miner’ threat to an opportunity to sell more electrical energy during the off-peak hours to these loads and to maximize the profit of the utility. The proposed strategy is studied using the load profile dataset obtained from the measurements of Tehran Electricity Distribution Company, and its effectiveness is demonstrated.
When do neural networks outperform kernel methods?*

Abstract For a certain scaling of the initialization of stochastic gradient descent (SGD), wide neural networks (NN) have been shown to be well approximated by reproducing kernel Hilbert space (RKHS) methods. Recent empirical work showed that, for some classification tasks, RKHS methods can replace NNs without a large loss in performance. On the other hand, two-layers NNs are known to encode richer smoothness classes than RKHS and we know of special examples for which SGD-trained NN provably outperform RKHS. This is true even in the wide network limit, for a different scaling of the initialization. How can we reconcile the above claims? For which tasks do NNs outperform RKHS? If covariates are nearly isotropic, RKHS methods suffer from the curse of dimensionality, while NNs can overcome it by learning the best low-dimensional representation. Here we show that this curse of dimensionality becomes milder if the covariates display the same low-dimensional structure as the target function, and we precisely characterize this tradeoff. Building on these results, we present the spiked covariates model that can capture in a unified framework both behaviors observed in earlier work. We hypothesize that such a latent low-dimensional structure is present in image classification. We test numerically this hypothesis by showing that specific perturbations of the training distribution degrade the performances of RKHS methods much more significantly than NNs.
A Unified Approach to Kinship Verification

In this work, we propose a deep learning-based approach for kin verification using a unified multi-task learning scheme where all kinship classes are jointly learned. This allows us to better utilize small training sets that are typical of kin verification. We introduce a novel approach for fusing the embeddings of kin images, to avoid overfitting, which is a common issue in training such networks. An adaptive sampling scheme is derived for the training set images, to resolve the inherent imbalance in kin verification datasets. A thorough ablation study exemplifies the effectivity of our approach, which is experimentally shown to outperform contemporary state-of-the-art kin verification results when applied to the Families In the Wild, FG2018, and FG2020 datasets.
MSKNP: Multistage Key Negotiation Protocol for IoT-Blockchain Environment

With the development of the Internet, the technical framework that integrates Internet of Things (IoT) and blockchain has gradually developed. However, how to ensure the secure with balanced performance of devices becomes a key issue. In this article, we design a new multistage session key negotiation protocols for the Internet of Things-blockchain environment by using “Agents.” We transfer bilinear operations to Agents, which has strong computing capabilities. Based on this, the resource of IoT devices in the key negotiation process is reduced without simplifying bilinear pairing compute and making them more secure. The simplified BPR model and the random oracle model ID-BJM are used to test the security of requirements in different stages of the scheme, furthermore, theoretically analyze its performance overhead. This protocol can reduce the computing overhead of IoT devices in the key negotiation process under the premise of ensuring security.
Automating User Notice Generation for Smart Contract Functions

Smart contracts have obtained much attention and are crucial for automatic financial and business transactions. For end-users who have never seen the source code, they can read the user notice shown in end-user client to understand what a transaction does of a smart contract function. However, due to time constraints or lack of motivation, user notice is often missing during the development of smart contracts. For end-users who lack the information of the user notices, there is no easy way for them to check the code semantics of the smart contracts. Thus, in this paper, we propose a new approach SMARTDOC to generate user notice for smart contract functions automatically. Our tool can help end-users better understand the smart contract and aware of the financial risks, improving the users’ confidence on the reliability of the smart contracts. SMARTDOC exploits the Transformer to learn the representation of source code and generates natural language descriptions from the learned representation. We also integrate the Pointer mechanism to copy words from the input source code instead of generating words during the prediction process. We extract 7,878 〈function, notice〉 pairs from 54,739 smart contracts written in Solidity. Due to the limited amount of collected smart contract functions (i.e., 7,878 functions), we exploit a transfer learning technique to utilize the learned knowledge to improve the performance of SMARTDOC. The learned knowledge obtained by the pre-training on a corpus of Java code, that has similar characteristics as Solidity code. The experimental results show that our approach can effectively generate user notice given the source code and significantly outperform the state-of-the-art approaches. To investigate human perspectives on our generated user notice, we also conduct a human evaluation and ask participants to score user notice generated by different approaches. Results show that SMARTDOC outperforms baselines from three aspects, naturalness, informativeness, and similarity.
A Transformer Based Approach for Open Set Specific Emitter Identification

Specific emitter identification (SEI) can verify the identity of emitters and plays an important role in a wide range of military and civilian fields. Most recently, there has been great interests in applying deep learning techniques for SEI. However, the vast majority of existing work limits SEI to closed-set classification, where deep networks only perform classification in a limited set of known emitters. These algorithms are not capable of handling transmitters that appear outside the closed set, compromising the security of the communication system once such emitters within the communication range. In this paper, we propose a novel deep learning method for SEI based on the open-set recognition. By using a combination of an improved Transformer and the modified intra-class splitting (ICS) method, our proposed method can identify unknown class of signals while maintaining a high accuracy of known classes. Numerical experiments demonstrate its ability to reject signals from unknown emitters with high accuracy on USRP-generated datasets, and its classification performance exceeds that of various existing open-set classification algorithms.
Designing an intelligent tutoring system for computer programing in the Pacific

Intelligent Tutoring Systems (ITSs) are educational systems that reflect knowledge using artificial intelligence implements. In this paper, we give an outline of the Programming-Tutor architectural design with the core implements on user interaction. This pilot proposal is for designing a model domain of a subset in the computer programming language. The completed project would be adequate to show the idea of a completely developed computing Intelligent Tutoring System in online programming courses to offer benefits to students in the Pacific. This proposed concept would also provide students with an immersive learning experience in an online course to assist in a formative assessment to enhance student learning. A smart tutoring system can provide prompt input of high quality which not only conveys to students about the consistency of the solution but also provides them with information on the precision of the key concerning their existing solutions expertise. This Intelligent Tutoring System (ITS) is proposed to be designed using intelligent algorithms such as optimized ant colony to be able to support the online tutoring system that can initiate the complex learning principles in computing science courses. It is also hypothesized that, based on the performance of other Intelligent Tutoring Systems, students would be able to learn to program more easily in regional campuses and acquire experiences more rapidly and efficiently than students who are taught using conventional methods in an online mode.
End-User Perceptions on Information Security

Information is a vital asset needed by many organizations to function effectively. However, this asset can easily be compromised thus its protection is crucial to the efficacy of an organization. A common information security breach used is social engineering. Social engineering is the use of manipulative and deceptive techniques against the inherent nature of human beings to access sensitive and confidential information to achieve an illicit action or omission of action. Through a qualitative inquiry, this article investigated the perceptions of employees concerning social engineering in the workplace to extract practical lessons from local businesses located in Gauteng Province, South Africa.The findings confirm that human beings should be at the forefront of defense against social engineering attacks and advocates for a multi-inter-trans-disciplinary social engineering protection model to practically assist organizations in developing a healthy and effective information security culture.
Energy-Aware Virtual Machine Allocation in DVFS-Enabled Cloud Data Centers

Energy management is considered the major concern in cloud computing, which supports the rapid growth of data centers and computing centers; therefore, energy and load balancing have become crucial issues in cloud data centers.To address this issue, the present paper proposed a two-phase energyaware load balancing (EALB) scheduling algorithm using the virtual machine migration through the Particle Swarm Optimization (PSO) algorithm to be applicable to dynamic voltage frequency scaling-enabled cloud data centers, which is called EALBPSO.In the first phase, an objective function was employed to deactivate a large number of physical machines in order to reduce energy consumption.The main idea of the algorithm was to maximize load balancing in the second phase, in which the remaining virtual and physical machines were used as the PSO inputs, and an objective function was also defined to distribute the load appropriately among the physical machines.In addition, a dataset was developed to test different parameters and scenarios with the aim of assessing the effectiveness of the proposed EALBPSO algorithm in comparison with other algorithms already proposed in the literature for similar purposes.The experimental results demonstrated that the proposed algorithm was capable of saving up to 0.896%, 9.716%, and 10.8% energy compared with the MDPSO algorithm, Kumar et al.'s algorithm, and Dahsti and Rahmani algorithm, respectively, and also it showed 5.91%, 16%, and 16.267% improvements for the number of virtual machines migrations, and 3.867%, 8.623%, and 6.953% improvements for the deviation of processors, all compared with their competitors stated above, respectively.
Trade-Off Between Memory and Model-Based Collaborative Filtering Recommender System

In this paper, we address both memory- and model-based collaborative filtering (CF) recommendation system. CF is the most popular and widely used technique in the recommendation environment. A CF technique is more beneficial for a personalization recommendation system than a content-based recommender system, as the former approach receives explicit ratings directly from the user. In this paper, we investigate the trade-off between model- and memory-based CF techniques in terms of their performance on different datasets. The experimental results indicate that the model-based technique has produced low prediction error than the memory-based, hence performing better compared to the memory-based. Also, model-based approaches handle sparse data more accurately than memory-based techniques. Further, we found that the model-based approach takes less time to recommend a product than the memory-based CF recommender system.
YOLO-Green: A Real-Time Classification and Object Detection Model Optimized for Waste Management

Deep neural networks (DNNs) play an important role in our daily lives, from aiding us in menial tasks to solving world issues such as cancer cell detection. However, few pieces of research have been conducted using DNNs and deep learning models as a medium to help classify and detect trash, in efforts to solve our global waste crisis. This is because current DNNs struggle to be both efficient and accurate while detecting indistinct objects such as waste. To address this issue, this work focuses on YOLO-Green, a novel real-time object detection model designed specifically for trash detection. The model is trained on a dataset gathered from real-world trash divided into seven of the most common types of solid waste. With only 100 epochs of training, YOLO-Green achieves an outstanding mAP of 78.04%, FPS of 2.72, while retaining a model size of only 117 MB. Based on the original object detection of YOLOv4, YOLO-Green exceeds YOLOv4 and other popular deep learning models in both its accuracy and efficiency, while maintaining a relatively small model size. Ultimately, this study sheds a positive light on the potential of using deep learning models as an alternative to manual waste management.
An Empirical Study of Bugs in WebAssembly Compilers

WebAssembly is the newest programming language for the Web. It defines a portable bytecode format for use as a compilation target for programs developed in high-level languages such as C, C++, and Rust. As a result, WebAssembly binaries are generally created by WebAssembly compilers rather than being written manually. To port native code to the Web, WebAssembly compilers need to address the differences between the source and target languages and dissimilarities in their execution environments. A deep understanding of the bugs in WebAssembly compilers can help compiler developers determine where to focus development and testing efforts. In this paper, we conduct two empirical studies to understand the characteristics of the bugs found in WebAssembly compilers. First, we perform a qualitative analysis of bugs in Emscripten, the most widely-used WebAssembly compiler. We investigate 146 bug reports in Emscripten related to the unique challenges WebAssembly compilers encounter compared with traditional compilers. Second, we provide a quantitative analysis of 1,054 bugs in three open-source WebAssembly compilers, AssemblyScript, Emscripten, and Rustc/Wasm-Bindgen. We analyze these bugs along three dimensions: lifecycle, impact, and sizes of bug-inducing inputs and bug fixes. These studies deepen our understanding of WebAssembly compiler bugs. We hope that the findings of our study will shed light on opportunities to design practical tools for testing and debugging WebAssembly compilers.
Time‐to‐event prediction using survival analysis methods for Alzheimer's disease progression

Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time.We simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017.The experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval.The proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.
Nonmyopic Informative Path Planning Based on Global Kriging Variance Minimization

Remote, hazardous, and extreme exploration missions require robots to be equipped with on-board sensors for rich and heterogeneous information during deployment. In such tasks, path planning can directly affect the quality and quantity of the observations obtained under temporal and energetic constraints. While most informative path planners can only plan for a short horizon ahead of time (referred as to myopic planners), we propose a novel planner that is capable of planning global paths in which the nonmyopic exploration efficiency is optimized. To achieve this, a novel sampling algorithm named MPE is proposed to adaptively sample landmarks that are associated with high information capacity, in order to minimize the global Kriging variance. The traverse path for the landmarks is then obtained by the IPP-MPE algorithm for minimizing the overall traveling cost. The algorithm is flexible enough to be applied to various information acquisition tasks. The tractable computational cost allows the horizon to be long enough for scene coverage. The algorithm was deployed on a real robot for accomplishing tactile based object searching tasks, which shows superior efficiency compared to the myopic planner baseline. Last, the complexity and other theoretical analysis of the algorithm is provided.